
import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						str							,						__UpperCamelCase :						str       ) ->       int:

				"""simple docstring"""







				def get_masked_lm_array(__UpperCamelCase :						str       ):
								A__  :	Any         =							F"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
								A__  :	Union[str, Any]         =							tf.train.load_variable(__UpperCamelCase							,						__UpperCamelCase       )

								if "kernel" in name:
												A__  :	str         =							array.transpose()

								return torch.from_numpy(__UpperCamelCase       )

				def get_encoder_array(__UpperCamelCase :						str       ):
								A__  :	Dict         =							F"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
								A__  :	List[str]         =							tf.train.load_variable(__UpperCamelCase							,						__UpperCamelCase       )

								if "kernel" in name:
												A__  :	Any         =							array.transpose()

								return torch.from_numpy(__UpperCamelCase       )

				def get_encoder_layer_array(__UpperCamelCase :						int							,						__UpperCamelCase :						str       ):
								A__  :	Dict         =							F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
								A__  :	List[str]         =							tf.train.load_variable(__UpperCamelCase							,						__UpperCamelCase       )

								if "kernel" in name:
												A__  :	List[str]         =							array.transpose()

								return torch.from_numpy(__UpperCamelCase       )

				def get_encoder_attention_layer_array(__UpperCamelCase :						int							,						__UpperCamelCase :						str							,						__UpperCamelCase :						str       ):
								A__  :	Any         =							F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
								A__  :	List[Any]         =							tf.train.load_variable(__UpperCamelCase							,						__UpperCamelCase       )
								A__  :	Optional[int]         =							array.reshape(__UpperCamelCase       )

								if "kernel" in name:
												A__  :	Optional[Any]         =							array.transpose()

								return torch.from_numpy(__UpperCamelCase       )

				print(F"Loading model based on config from {config_path}..."       )
				A__  :	List[Any]         =							BertConfig.from_json_file(__UpperCamelCase       )
				A__  :	Tuple         =							BertForMaskedLM(__UpperCamelCase       )

				# Layers
				for layer_index in range(0							,						config.num_hidden_layers       ):
								A__  :	BertLayer         =							model.bert.encoder.layer[layer_index]

								# Self-attention
								A__  :	BertSelfAttention         =							layer.attention.self

								A__  :	str         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_query_dense/kernel'''							,						self_attn.query.weight.data.shape       )
								A__  :	Optional[Any]         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_query_dense/bias'''							,						self_attn.query.bias.data.shape       )
								A__  :	Dict         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_key_dense/kernel'''							,						self_attn.key.weight.data.shape       )
								A__  :	Optional[Any]         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_key_dense/bias'''							,						self_attn.key.bias.data.shape       )
								A__  :	Dict         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_value_dense/kernel'''							,						self_attn.value.weight.data.shape       )
								A__  :	int         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_value_dense/bias'''							,						self_attn.value.bias.data.shape       )

								# Self-attention Output
								A__  :	BertSelfOutput         =							layer.attention.output

								A__  :	Optional[int]         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_output_dense/kernel'''							,						self_output.dense.weight.data.shape       )
								A__  :	Tuple         =							get_encoder_attention_layer_array(
								    __UpperCamelCase							,						'''_output_dense/bias'''							,						self_output.dense.bias.data.shape       )

								A__  :	Tuple         =							get_encoder_layer_array(__UpperCamelCase							,						'''_attention_layer_norm/gamma'''       )
								A__  :	Union[str, Any]         =							get_encoder_layer_array(__UpperCamelCase							,						'''_attention_layer_norm/beta'''       )

								# Intermediate
								A__  :	BertIntermediate         =							layer.intermediate

								A__  :	Tuple         =							get_encoder_layer_array(__UpperCamelCase							,						'''_intermediate_dense/kernel'''       )
								A__  :	int         =							get_encoder_layer_array(__UpperCamelCase							,						'''_intermediate_dense/bias'''       )

								# Output
								A__  :	BertOutput         =							layer.output

								A__  :	Any         =							get_encoder_layer_array(__UpperCamelCase							,						'''_output_dense/kernel'''       )
								A__  :	Union[str, Any]         =							get_encoder_layer_array(__UpperCamelCase							,						'''_output_dense/bias'''       )

								A__  :	Optional[Any]         =							get_encoder_layer_array(__UpperCamelCase							,						'''_output_layer_norm/gamma'''       )
								A__  :	Union[str, Any]         =							get_encoder_layer_array(__UpperCamelCase							,						'''_output_layer_norm/beta'''       )

				# Embeddings
				A__  :	Dict         =							get_encoder_array('''_position_embedding_layer/embeddings'''       )
				A__  :	int         =							get_encoder_array('''_type_embedding_layer/embeddings'''       )
				A__  :	Any         =							get_encoder_array('''_embedding_norm_layer/gamma'''       )
				A__  :	Tuple         =							get_encoder_array('''_embedding_norm_layer/beta'''       )

				# LM Head
				A__  :	str         =							model.cls.predictions.transform

				A__  :	List[Any]         =							get_masked_lm_array('''dense/kernel'''       )
				A__  :	Tuple         =							get_masked_lm_array('''dense/bias'''       )

				A__  :	List[Any]         =							get_masked_lm_array('''layer_norm/gamma'''       )
				A__  :	str         =							get_masked_lm_array('''layer_norm/beta'''       )

				A__  :	Any         =							get_masked_lm_array('''embedding_table'''       )

				# Pooling
				A__  :	Optional[int]         =							BertPooler(config=__UpperCamelCase       )
				A__  :	BertPooler         =							get_encoder_array('''_pooler_layer/kernel'''       )
				A__  :	BertPooler         =							get_encoder_array('''_pooler_layer/bias'''       )

				# Export final model
				model.save_pretrained(__UpperCamelCase       )

				# Integration test - should load without any errors ;)
				A__  :	Any         =							BertForMaskedLM.from_pretrained(__UpperCamelCase       )
				print(new_model.eval()       )

				print('''Model conversion was done sucessfully!'''       )


if __name__ == "__main__":
					_SCREAMING_SNAKE_CASE    :      List[Any]          =			argparse.ArgumentParser()
					parser.add_argument(
					    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping 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.',
					)
					_SCREAMING_SNAKE_CASE    :      Tuple          =			parser.parse_args()
					convert_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  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				"microsoft/speecht5_tts"
	_lowerCAmelCase							       =				(
	    "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."
	)
	_lowerCAmelCase							       =				"text_reader"
	_lowerCAmelCase							       =				SpeechTaProcessor
	_lowerCAmelCase							       =				SpeechTaForTextToSpeech
	_lowerCAmelCase							       =				SpeechTaHifiGan

	_lowerCAmelCase							       =				["text"]
	_lowerCAmelCase							       =				["audio"]





	def   __snake_case				(			self		):
					if self.post_processor is None:
									A__  :	int         =							'''microsoft/speecht5_hifigan'''
					super().setup()





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__=None		):
					A__  :	List[Any]         =							self.pre_processor(text=UpperCamelCase__				,       return_tensors='''pt'''				,       truncation=UpperCamelCase__		)

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

									A__  :	List[Any]         =							load_dataset('''Matthijs/cmu-arctic-xvectors'''				,       split='''validation'''		)
									A__  :	Dict         =							torch.tensor(embeddings_dataset[7305]['''xvector''']		).unsqueeze(0		)

					return {"input_ids": inputs["input_ids"], "speaker_embeddings": speaker_embeddings}





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.model.generate_speech(**UpperCamelCase__		)





	def   __snake_case				(			self				,       UpperCamelCase__		):
					with torch.no_grad():
									return self.post_processor(UpperCamelCase__		).cpu().detach()