


"""simple docstring"""
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()
__SCREAMING_SNAKE_CASE							:    Dict		=    logging.get_logger(__name__)







def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Union[str, Any]:
							snake_case_           =		os.path.abspath(_SCREAMING_SNAKE_CASE				)
							logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""				)
							# Load weights from TF model
							snake_case_           =		tf.train.list_variables(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		[]
							snake_case_           =		[]
							snake_case_           =		[]
							for full_name, shape in init_vars:
														# logger.info(f"Loading TF weight {name} with shape {shape}")
														snake_case_           =		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'
																					snake_case_           =		name[1:]
														# figure out how many levels deep the name is
														snake_case_           =		0
														for _name in name:
																					if _name.startswith("""layer_with_weights"""				):
																												depth += 1
																					else:
																												break
														layer_depth.append(_SCREAMING_SNAKE_CASE				)
														# read data
														snake_case_           =		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				)				)})"""				)
							snake_case_           =		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				):
														snake_case_           =		full_name.split("""/"""				)
														snake_case_           =		model
														snake_case_           =		[]
														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"""				):
																												snake_case_           =		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"""]				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """embeddings"""				)
																																			snake_case_           =		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				)]				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """encoder"""				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """layer"""				)
																																			snake_case_           =		pointer[layer_num - 4]
																												elif layer_num == config.num_hidden_layers + 4:
																																			# pooler layer
																																			trace.extend(["""pooler""", """dense"""]				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """pooler"""				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """dense"""				)
																					elif m_name == "embeddings":
																												trace.append("""embeddings"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """embeddings"""				)
																												if layer_num == 0:
																																			trace.append("""word_embeddings"""				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """word_embeddings"""				)
																												elif layer_num == 1:
																																			trace.append("""position_embeddings"""				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """position_embeddings"""				)
																												elif layer_num == 2:
																																			trace.append("""token_type_embeddings"""				)
																																			snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """token_type_embeddings"""				)
																												else:
																																			raise ValueError(f"""Unknown embedding layer with name {full_name}"""				)
																												trace.append("""weight"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """weight"""				)
																					elif m_name == "_attention_layer":
																												# self-attention layer
																												trace.extend(["""attention""", """self"""]				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """attention"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """self"""				)
																					elif m_name == "_attention_layer_norm":
																												# output attention norm
																												trace.extend(["""attention""", """output""", """LayerNorm"""]				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """attention"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """output"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """LayerNorm"""				)
																					elif m_name == "_attention_output_dense":
																												# output attention dense
																												trace.extend(["""attention""", """output""", """dense"""]				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """attention"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """output"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """dense"""				)
																					elif m_name == "_output_dense":
																												# output dense
																												trace.extend(["""output""", """dense"""]				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """output"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """dense"""				)
																					elif m_name == "_output_layer_norm":
																												# output dense
																												trace.extend(["""output""", """LayerNorm"""]				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """output"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """LayerNorm"""				)
																					elif m_name == "_key_dense":
																												# attention key
																												trace.append("""key"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """key"""				)
																					elif m_name == "_query_dense":
																												# attention query
																												trace.append("""query"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """query"""				)
																					elif m_name == "_value_dense":
																												# attention value
																												trace.append("""value"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """value"""				)
																					elif m_name == "_intermediate_dense":
																												# attention intermediate dense
																												trace.extend(["""intermediate""", """dense"""]				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """intermediate"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """dense"""				)
																					elif m_name == "_output_layer_norm":
																												# output layer norm
																												trace.append("""output"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """output"""				)
																					# weights & biases
																					elif m_name in ["bias", "beta"]:
																												trace.append("""bias"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """bias"""				)
																					elif m_name in ["kernel", "gamma"]:
																												trace.append("""weight"""				)
																												snake_case_           =		getattr(_SCREAMING_SNAKE_CASE				, """weight"""				)
																					else:
																												logger.warning(f"""Ignored {m_name}"""				)
																												# for certain layers reshape is necessary
														snake_case_           =		""".""".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				):
																					snake_case_           =		array.reshape(pointer.data.shape				)
														if "kernel" in full_name:
																					snake_case_           =		array.transpose()
														if pointer.shape == array.shape:
																					snake_case_           =		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 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				Dict:
							# Instantiate model
							logger.info(f"""Loading model based on config from {config_path}..."""				)
							snake_case_           =		BertConfig.from_json_file(_SCREAMING_SNAKE_CASE				)
							snake_case_           =		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__":
				__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    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).',
				)
				__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    parser.parse_args()
				convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=snake_case__)
class      __A     (snake_case__):





							'''simple docstring'''

							__lowercase:		str							      =    field(default="""automatic-speech-recognition"""						,		metadata={"""include_in_asdict_even_if_is_default""": True})
							__lowercase:		ClassVar[Features]							      =    Features({"""audio""": Audio()})
							__lowercase:		ClassVar[Features]							      =    Features({"""transcription""": Value("""string""")})
							__lowercase:		str							      =    "audio"
							__lowercase:		str							      =    "transcription"



							def 		lowerCAmelCase       (				self							:      List[Any]		,       UpperCAmelCase_							:      Any       )					->int:



														"""simple docstring"""



														if self.audio_column not in features:
																					raise ValueError(F"""Column {self.audio_column} is not present in features."""       )
														if not isinstance(features[self.audio_column]		,       UpperCAmelCase_       ):
																					raise ValueError(F"""Column {self.audio_column} is not an Audio type."""       )
														snake_case_           =		copy.deepcopy(self       )
														snake_case_           =		self.input_schema.copy()
														snake_case_           =		features[self.audio_column]
														snake_case_           =		input_schema
														return task_template



							@property
							def 		lowerCAmelCase       (				self							:      List[str]       )					->Dict[str, str]:



														"""simple docstring"""



														return {self.audio_column: "audio", self.transcription_column: "transcription"}



