import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase							 =					logging.get_logger(__name__)



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :List[str]			  =		os.path.abspath(SCREAMING_SNAKE_CASE__	)
    logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""	)
    # Load weights from TF model
    __UpperCamelCase     :Optional[Any]			  =		tf.train.list_variables(SCREAMING_SNAKE_CASE__	)
    __UpperCamelCase     :Dict			  =		[]
    __UpperCamelCase     :List[Any]			  =		[]
    __UpperCamelCase     :str			  =		[]
    for full_name, shape in init_vars:
        # logger.info(f"Loading TF weight {name} with shape {shape}")
        __UpperCamelCase     :List[str]			  =		full_name.split('''/'''	)
        if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
            logger.info(f"""Skipping non-model layer {full_name}"""	)
            continue
        if "optimizer" in full_name:
            logger.info(f"""Skipping optimization layer {full_name}"""	)
            continue
        if name[0] == "model":
            # ignore initial 'model'
            __UpperCamelCase     :List[Any]			  =		name[1:]
        # figure out how many levels deep the name is
        __UpperCamelCase     :int			  =		0
        for _name in name:
            if _name.startswith('''layer_with_weights'''	):
                depth += 1
            else:
                break
        layer_depth.append(SCREAMING_SNAKE_CASE__	)
        # read data
        __UpperCamelCase     :List[Any]			  =		tf.train.load_variable(SCREAMING_SNAKE_CASE__  ,			SCREAMING_SNAKE_CASE__	)
        names.append('''/'''.join(SCREAMING_SNAKE_CASE__	)	)
        arrays.append(SCREAMING_SNAKE_CASE__	)
    logger.info(f"""Read a total of {len(SCREAMING_SNAKE_CASE__	):,} layers"""	)

    # Sanity check
    if len(set(SCREAMING_SNAKE_CASE__	)	) != 1:
        raise ValueError(f"""Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE__	)	)})"""	)
    __UpperCamelCase     :Union[str, Any]			  =		list(set(SCREAMING_SNAKE_CASE__	)	)[0]
    if layer_depth != 1:
        raise ValueError(
            '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
            ''' heads.'''	)

    # convert layers
    logger.info('''Converting weights...'''	)
    for full_name, array in zip(SCREAMING_SNAKE_CASE__  ,			SCREAMING_SNAKE_CASE__	):
        __UpperCamelCase     :List[str]			  =		full_name.split('''/'''	)
        __UpperCamelCase     :int			  =		model
        __UpperCamelCase     :List[Any]			  =		[]
        for i, m_name in enumerate(SCREAMING_SNAKE_CASE__	):
            if m_name == ".ATTRIBUTES":
                # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                break
            if m_name.startswith('''layer_with_weights'''	):
                __UpperCamelCase     :Tuple			  =		int(m_name.split('''-'''	)[-1]	)
                if layer_num <= 2:
                    # embedding layers
                    # layer_num 0: word_embeddings
                    # layer_num 1: position_embeddings
                    # layer_num 2: token_type_embeddings
                    continue
                elif layer_num == 3:
                    # embedding LayerNorm
                    trace.extend(['''embeddings''', '''LayerNorm''']	)
                    __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''embeddings'''	)
                    __UpperCamelCase     :List[str]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''LayerNorm'''	)
                elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                    # encoder layers
                    trace.extend(['''encoder''', '''layer''', str(layer_num - 4	)]	)
                    __UpperCamelCase     :Optional[Any]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''encoder'''	)
                    __UpperCamelCase     :List[Any]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''layer'''	)
                    __UpperCamelCase     :Dict			  =		pointer[layer_num - 4]
                elif layer_num == config.num_hidden_layers + 4:
                    # pooler layer
                    trace.extend(['''pooler''', '''dense''']	)
                    __UpperCamelCase     :Optional[int]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''pooler'''	)
                    __UpperCamelCase     :Dict			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''dense'''	)
            elif m_name == "embeddings":
                trace.append('''embeddings'''	)
                __UpperCamelCase     :int			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''embeddings'''	)
                if layer_num == 0:
                    trace.append('''word_embeddings'''	)
                    __UpperCamelCase     :Dict			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''word_embeddings'''	)
                elif layer_num == 1:
                    trace.append('''position_embeddings'''	)
                    __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''position_embeddings'''	)
                elif layer_num == 2:
                    trace.append('''token_type_embeddings'''	)
                    __UpperCamelCase     :Tuple			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''token_type_embeddings'''	)
                else:
                    raise ValueError(f"""Unknown embedding layer with name {full_name}"""	)
                trace.append('''weight'''	)
                __UpperCamelCase     :int			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''weight'''	)
            elif m_name == "_attention_layer":
                # self-attention layer
                trace.extend(['''attention''', '''self''']	)
                __UpperCamelCase     :Any			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''attention'''	)
                __UpperCamelCase     :Optional[Any]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''self'''	)
            elif m_name == "_attention_layer_norm":
                # output attention norm
                trace.extend(['''attention''', '''output''', '''LayerNorm''']	)
                __UpperCamelCase     :Tuple			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''attention'''	)
                __UpperCamelCase     :Dict			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''output'''	)
                __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''LayerNorm'''	)
            elif m_name == "_attention_output_dense":
                # output attention dense
                trace.extend(['''attention''', '''output''', '''dense''']	)
                __UpperCamelCase     :List[str]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''attention'''	)
                __UpperCamelCase     :Optional[Any]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''output'''	)
                __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''dense'''	)
            elif m_name == "_output_dense":
                # output dense
                trace.extend(['''output''', '''dense''']	)
                __UpperCamelCase     :Optional[int]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''output'''	)
                __UpperCamelCase     :Optional[Any]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''dense'''	)
            elif m_name == "_output_layer_norm":
                # output dense
                trace.extend(['''output''', '''LayerNorm''']	)
                __UpperCamelCase     :Union[str, Any]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''output'''	)
                __UpperCamelCase     :Optional[Any]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''LayerNorm'''	)
            elif m_name == "_key_dense":
                # attention key
                trace.append('''key'''	)
                __UpperCamelCase     :Optional[int]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''key'''	)
            elif m_name == "_query_dense":
                # attention query
                trace.append('''query'''	)
                __UpperCamelCase     :str			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''query'''	)
            elif m_name == "_value_dense":
                # attention value
                trace.append('''value'''	)
                __UpperCamelCase     :Optional[int]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''value'''	)
            elif m_name == "_intermediate_dense":
                # attention intermediate dense
                trace.extend(['''intermediate''', '''dense''']	)
                __UpperCamelCase     :int			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''intermediate'''	)
                __UpperCamelCase     :int			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''dense'''	)
            elif m_name == "_output_layer_norm":
                # output layer norm
                trace.append('''output'''	)
                __UpperCamelCase     :List[str]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''output'''	)
            # weights & biases
            elif m_name in ["bias", "beta"]:
                trace.append('''bias'''	)
                __UpperCamelCase     :Dict			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''bias'''	)
            elif m_name in ["kernel", "gamma"]:
                trace.append('''weight'''	)
                __UpperCamelCase     :List[str]			  =		getattr(SCREAMING_SNAKE_CASE__  ,			'''weight'''	)
            else:
                logger.warning(f"""Ignored {m_name}"""	)
        # for certain layers reshape is necessary
        __UpperCamelCase     :List[Any]			  =		""".""".join(SCREAMING_SNAKE_CASE__	)
        if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''  ,			SCREAMING_SNAKE_CASE__	) or re.match(
            R'''(\S+)\.attention\.output\.dense\.weight'''  ,			SCREAMING_SNAKE_CASE__	):
            __UpperCamelCase     :List[Any]			  =		array.reshape(pointer.data.shape	)
        if "kernel" in full_name:
            __UpperCamelCase     :str			  =		array.transpose()
        if pointer.shape == array.shape:
            __UpperCamelCase     :Tuple			  =		torch.from_numpy(SCREAMING_SNAKE_CASE__	)
        else:
            raise ValueError(
                f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
                f""" {array.shape}"""	)
        logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""	)
    return model



def lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    logger.info(f"""Loading model based on config from {config_path}..."""	)
    __UpperCamelCase     :Any			  =		BertConfig.from_json_file(SCREAMING_SNAKE_CASE__	)
    __UpperCamelCase     :Union[str, Any]			  =		BertModel(SCREAMING_SNAKE_CASE__	)

    # Load weights from checkpoint
    logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""	)
    load_tfa_weights_in_bert(SCREAMING_SNAKE_CASE__  ,			SCREAMING_SNAKE_CASE__  ,			SCREAMING_SNAKE_CASE__	)

    # Save pytorch-model
    logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""	)
    torch.save(model.state_dict()  ,			SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
    __lowercase							 =					argparse.ArgumentParser()
    parser.add_argument(
        '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
    )
    parser.add_argument(
        '''--bert_config_file''',
        type=str,
        required=True,
        help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''',
        type=str,
        required=True,
        help='''Path to the output PyTorch model (must include filename).''',
    )
    __lowercase							 =					parser.parse_args()
    convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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 torch

from ..models.speechta import SpeechTaForTextToSpeech, SpeechTaHifiGan, SpeechTaProcessor
from ..utils import is_datasets_available
from .base import PipelineTool


if is_datasets_available():
  from datasets import load_dataset

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"microsoft/speecht5_tts"
    _a		:						Tuple=				(
        "This is a tool that reads an English text out loud. It takes an input named `text` which should contain the "
        "text to read (in English) and returns a waveform object containing the sound."
    )
    _a		:						Dict=				"text_reader"
    _a		:						Optional[Any]=				SpeechTaProcessor
    _a		:						Tuple=				SpeechTaForTextToSpeech
    _a		:						Optional[int]=				SpeechTaHifiGan

    _a		:						Union[str, Any]=				["text"]
    _a		:						Optional[int]=				["audio"]
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           if self.post_processor is None:
                  lowercase							:     Any       	=      """microsoft/speecht5_hifigan"""
           super().setup()
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case		,snake_case=None      ):



           '''simple docstring'''







           lowercase							:     int       	=      self.pre_processor(text=snake_case		,return_tensors="""pt"""		,truncation=snake_case      )

           if speaker_embeddings is None:
                  if not is_datasets_available():
                         raise ImportError("""Datasets needs to be installed if not passing speaker embeddings."""      )

                  lowercase							:     Tuple       	=      load_dataset("""Matthijs/cmu-arctic-xvectors"""		,split="""validation"""      )
                  lowercase							:     List[str]       	=      torch.tensor(embeddings_dataset[7305]["""xvector"""]      ).unsqueeze(0      )

           return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.model.generate_speech(**snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           with torch.no_grad():
                  return self.post_processor(snake_case      ).cpu().detach()
