"""
 Copyright (C) 2018-2020 Intel Corporation

 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
 You may obtain a copy of the License at

      http://www.apache.org/licenses/LICENSE-2.0

 Unless required by applicable law or agreed to in writing, software
 distributed under the License is distributed on an "AS IS" BASIS,
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
"""

import logging as log

import numpy as np

from mo.front.extractor import get_new_placeholder_name
from mo.graph.graph import Node, Graph
from mo.utils.error import Error
from mo.utils.utils import refer_to_faq_msg


class packed_U1(np.generic):
    # packed U1 and U8 types of data are stored in numpy uint8 data type
    # to distinguish true uint8 and u1 data we introduce this class not to store,
    # but to have unique data type in SUPPORTED_DATA_TYPES map
    pass


SUPPORTED_DATA_TYPES = {
    'float': (np.float32, 'FP32', 'f32'),
    'half': (np.float16, 'FP16', 'f16'),
    'FP32': (np.float32, 'FP32', 'f32'),
    'FP64': (np.float64, 'FP64', 'f64'),
    'FP16': (np.float16, 'FP16', 'f16'),
    'I32': (np.int32, 'I32', 'i32'),
    'I64': (np.int64, 'I64', 'i64'),
    'uint8': (np.uint8, 'U8', 'u8'),
    'int32': (np.int32, 'I32', 'i32'),
    'int64': (np.int64, 'I64', 'i64'),
    'bool': (np.bool, 'BOOL', 'boolean'),
    'U1': (packed_U1, 'U1', 'u1'),
}


def data_type_str_to_np(data_type_str: str):
    return SUPPORTED_DATA_TYPES[data_type_str][0] if data_type_str in SUPPORTED_DATA_TYPES else None


def data_type_str_to_precision(data_type_str: str):
    return SUPPORTED_DATA_TYPES[data_type_str][1] if data_type_str in SUPPORTED_DATA_TYPES else None


def data_type_str_to_destination_type(data_type_str: str):
    return SUPPORTED_DATA_TYPES[data_type_str][2] if data_type_str in SUPPORTED_DATA_TYPES else None


def np_data_type_to_precision(np_data_type):
    for np_t, precision, _ in SUPPORTED_DATA_TYPES.values():
        if np_t == np_data_type:
            return precision
    raise Error('Data type "{}" is not supported'.format(np_data_type))


def np_data_type_to_destination_type(np_data_type):
    for np_t, _, destination_type in SUPPORTED_DATA_TYPES.values():
        if np_t == np_data_type:
            return destination_type
    raise Error('Data type "{}" is not supported'.format(np_data_type))


def destination_type_to_np_data_type(dst_type):
    for np_t, _, destination_type in SUPPORTED_DATA_TYPES.values():
        if destination_type == dst_type:
            return np_t
    raise Error('Destination type "{}" is not supported'.format(dst_type))


def precision_to_destination_type(data_type_str):
    for _, precision, destination_type in SUPPORTED_DATA_TYPES.values():
        if precision == data_type_str:
            return destination_type
    raise Error('Data type "{}" is not supported'.format(data_type_str))


def convert_blob(blob: np.ndarray, dst_type: type):
    if blob.dtype == dst_type:
        return blob, None, None

    converted_blob = blob.astype(dtype=dst_type, casting="unsafe")
    if dst_type in (np.int32, np.int64, np.uint8, np.int8) and not np.array_equal(blob, converted_blob):
        raise Error('The conversion of blob with value "{}" to dst_type "{}" results in rounding'.format(
            blob, dst_type))

    finite_match = (np.isfinite(blob) != np.isfinite(converted_blob))
    zero_match = ((blob == 0) != (converted_blob == 0))
    finite_match_count = np.count_nonzero(finite_match)
    zero_match_count = np.count_nonzero(zero_match)

    return converted_blob, finite_match_count, zero_match_count


def convert_node_blobs(graph: Graph, node: Node, data_type: type):
    out_edges = graph.out_edges(node.node, data=True)

    # if the data.value is used as binary weights
    if any('bin' in d for _, __, d in out_edges):
        blob = node.value
        if blob.dtype != data_type:
            new_blob, finite_match_count, zero_match_count = convert_blob(blob, data_type)
            consumers = [x.name if x.has_valid('name') else '<NO NAME>' for x in node.out_nodes()]
            log.debug(
                'Blob was converted to {} while dumping to the bin file. This blob is an input for {} nodes.'.format(
                    data_type, consumers))
            if finite_match_count:
                log.error(
                    ("{} elements of {} were clipped to infinity while converting a blob for node [{}] to {}. " +
                     refer_to_faq_msg(76)).format(finite_match_count, blob.size, consumers, data_type))
            if zero_match_count:
                log.warning(
                    ("{} elements of {} were clipped to zero while converting a blob for node [{}] to {}. " +
                     refer_to_faq_msg(77)).format(zero_match_count, blob.size, consumers, data_type))

            node.value = new_blob
            # for the constant node need to propagate the converted value to the node output because there is a fake
            # input data for the 'Const' nodes being generated in the CreateConstNodesReplacement
            if len(node.out_nodes()) == 1 and node.out_node(0).op == 'Const':
                const_node = node.out_node(0)
                const_node.value = new_blob
                const_node.infer(const_node)
                const_node.type_infer(const_node)


def convert_parameters_data_type(graph: Graph, data_type_str: str):
    inputs = graph.get_op_nodes(op='Parameter')
    data_type = data_type_str_to_np(data_type_str)
    user_defined_data_types = graph.graph['user_shapes'] if 'user_shapes' in graph.graph else None
    for input in inputs:
        user_defined_type = None
        name = input.soft_get('initial_node_name', input.id)

        # override data type for Parameter specified by the user. This is a workaround for the issue in the
        # extensions.middle.ChangePlaceholderTypes transformation which has an incorrect condition and always overrides
        # Parameter data type to np.float32. When the transformation is fixed the code below must be updated
        if user_defined_data_types is not None and name in user_defined_data_types:
            for desc in user_defined_data_types[name]:
                if 'port' in desc and desc['port'] is None:  # neither input nor output port specified
                    user_defined_type = desc.get('data_type', None)
                else:  # need to check the particular port the Parameter was created for
                    p_name = get_new_placeholder_name(name, 'out' in desc, desc['out'] if 'out' in desc else desc['in'])
                    if p_name == input.soft_get('name'):
                        user_defined_type = desc.get('data_type', None)
        if user_defined_type is not None:
            log.info('Overriding Parameter node {} data type to {}'.format(name, user_defined_type))
            input['data_type'] = user_defined_type
            input.out_port(0).set_data_type(user_defined_type, True)
        elif not input.has_valid('data_type') or input.data_type == np.float32:
            input['data_type'] = data_type
            input.out_port(0).set_data_type(data_type, True)
        else:
            log.info('Do not change data type for node {}'.format(input.soft_get('name')))


def convert_blobs(graph: Graph, data_type_str: str):
    for node in graph.get_data_nodes():
        if node.value is not None:
            try:
                if node.value.dtype in [np.float32, np.float64, np.float16] and not node.has_and_set('correct_data_type'):
                    convert_node_blobs(graph, node, data_type_str_to_np(data_type_str))
            except Exception as e:
                raise Error('Coudn\'t convert blob {}, details: {}', node.soft_get('name'), e) from e
