
import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCamelCase__	=							logging.get_logger(__name__)







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Tuple	=		["input_features"]

						def __init__(			self       ,							a=80       ,							a=1_60_00       ,							a=1_60       ,							a=30       ,							a=4_00       ,							a=0.0       ,							a=False       ,							**a       ,							)					->      List[Any]:


								'''simple docstring'''

								super().__init__(
								    feature_size=a       ,							sampling_rate=a       ,							padding_value=a       ,							return_attention_mask=a       ,							**a       ,							)
								_UpperCamelCase						    =	n_fft
								_UpperCamelCase						    =	hop_length
								_UpperCamelCase						    =	chunk_length
								_UpperCamelCase						    =	chunk_length * sampling_rate
								_UpperCamelCase						    =	self.n_samples // hop_length
								_UpperCamelCase						    =	sampling_rate
								_UpperCamelCase						    =	mel_filter_bank(
								    num_frequency_bins=1 + n_fft // 2       ,							num_mel_filters=a       ,							min_frequency=0.0       ,							max_frequency=8000.0       ,							sampling_rate=a       ,							norm="""slaney"""       ,							mel_scale="""slaney"""       ,							)

						def  A_				(			self       ,							a				)					->      np.ndarray:


								'''simple docstring'''

								_UpperCamelCase						    =	spectrogram(
								    a       ,							window_function(self.n_fft       ,							"""hann"""				)       ,							frame_length=self.n_fft       ,							hop_length=self.hop_length       ,							power=2.0       ,							mel_filters=self.mel_filters       ,							log_mel="""log10"""       ,							)
								_UpperCamelCase						    =	log_spec[:, :-1]
								_UpperCamelCase						    =	np.maximum(a       ,							log_spec.max() - 8.0				)
								_UpperCamelCase						    =	(log_spec + 4.0) / 4.0
								return log_spec

						@staticmethod
						# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
						def  A_				(			a       ,							a       ,							a = 0.0				)					->      List[np.ndarray]:


								'''simple docstring'''

								if attention_mask is not None:
										_UpperCamelCase						    =	np.array(a       ,							np.intaa				)
										_UpperCamelCase						    =	[]

										for vector, length in zip(a       ,							attention_mask.sum(-1				)				):
												_UpperCamelCase						    =	(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7				)
												if length < normed_slice.shape[0]:
														_UpperCamelCase						    =	padding_value

												normed_input_values.append(a				)
								else:
										_UpperCamelCase						    =	[(x - x.mean()) / np.sqrt(x.var() + 1e-7				) for x in input_values]

								return normed_input_values

						def __call__(			self       ,							a       ,							a = True       ,							a = None       ,							a = None       ,							a = None       ,							a = "max_length"       ,							a = None       ,							a = None       ,							a = None       ,							**a       ,							)					->      BatchFeature:


								'''simple docstring'''

								if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
												raise ValueError(
												    F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
												    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
												    F' was sampled with {self.sampling_rate} and not {sampling_rate}.'				)
								else:
										logger.warning(
										    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
										    """Failing to do so can result in silent errors that might be hard to debug."""				)

								_UpperCamelCase						    =	isinstance(a       ,							np.ndarray				) and len(raw_speech.shape				) > 1
								if is_batched_numpy and len(raw_speech.shape				) > 2:
										raise ValueError(F'Only mono-channel audio is supported for input to {self}'				)
								_UpperCamelCase						    =	is_batched_numpy or (
								    isinstance(a       ,							(list, tuple)				) and (isinstance(raw_speech[0]       ,							(np.ndarray, tuple, list)				))
								)

								if is_batched:
										_UpperCamelCase						    =	[np.asarray([speech]       ,							dtype=np.floataa				).T for speech in raw_speech]
								elif not is_batched and not isinstance(a       ,							np.ndarray				):
										_UpperCamelCase						    =	np.asarray(a       ,							dtype=np.floataa				)
								elif isinstance(a       ,							np.ndarray				) and raw_speech.dtype is np.dtype(np.floataa				):
										_UpperCamelCase						    =	raw_speech.astype(np.floataa				)

								# always return batch
								if not is_batched:
										_UpperCamelCase						    =	[np.asarray([raw_speech]				).T]

								_UpperCamelCase						    =	BatchFeature({"""input_features""": raw_speech}				)

								# convert into correct format for padding

								_UpperCamelCase						    =	self.pad(
								    a       ,							padding=a       ,							max_length=max_length if max_length else self.n_samples       ,							truncation=a       ,							pad_to_multiple_of=a       ,							return_attention_mask=return_attention_mask or do_normalize       ,							)

								# zero-mean and unit-variance normalization
								if do_normalize:
										_UpperCamelCase						    =	self.zero_mean_unit_var_norm(
										    padded_inputs["""input_features"""]       ,							attention_mask=padded_inputs["""attention_mask"""]       ,							padding_value=self.padding_value       ,							)
										_UpperCamelCase						    =	np.stack(padded_inputs["""input_features"""]       ,							axis=0				)

								# make sure list is in array format
								_UpperCamelCase						    =	padded_inputs.get("""input_features"""				).transpose(2       ,							0       ,							1				)

								_UpperCamelCase						    =	[self._np_extract_fbank_features(a				) for waveform in input_features[0]]

								if isinstance(input_features[0]       ,							a				):
										_UpperCamelCase						    =	[np.asarray(a       ,							dtype=np.floataa				) for feature in input_features]
								else:
										_UpperCamelCase						    =	input_features

								if return_attention_mask:
										# rescale from sample (48000) to feature (3000)
										_UpperCamelCase						    =	padded_inputs["""attention_mask"""][:, :: self.hop_length]

								if return_tensors is not None:
										_UpperCamelCase						    =	padded_inputs.convert_to_tensors(a				)

								return padded_inputs







						def  A_				(			self				)					->      Dict[str, Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	copy.deepcopy(self.__dict__				)
								_UpperCamelCase						    =	self.__class__.__name__
								if "mel_filters" in output:
										del output["mel_filters"]
								return output





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()
lowerCamelCase__	=							logging.get_logger(__name__)


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	os.path.abspath(lowerCAmelCase )
		logger.info(F'Converting TensorFlow checkpoint from {tf_path}' )
		# Load weights from TF model
		_UpperCamelCase						    =	tf.train.list_variables(lowerCAmelCase )
		_UpperCamelCase						    =	[]
		_UpperCamelCase						    =	[]
		_UpperCamelCase						    =	[]
		for full_name, shape in init_vars:
				# logger.info(f"Loading TF weight {name} with shape {shape}")
				_UpperCamelCase						    =	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						    =	name[1:]
				# figure out how many levels deep the name is
				_UpperCamelCase						    =	0
				for _name in name:
						if _name.startswith("""layer_with_weights""" ):
								depth += 1
						else:
								break
				layer_depth.append(lowerCAmelCase )
				# read data
				_UpperCamelCase						    =	tf.train.load_variable(lowerCAmelCase						,  lowerCAmelCase )
				names.append("""/""".join(lowerCAmelCase ) )
				arrays.append(lowerCAmelCase )
		logger.info(F'Read a total of {len(lowerCAmelCase ):,} layers' )

		# Sanity check
		if len(set(lowerCAmelCase ) ) != 1:
				raise ValueError(F'Found layer names with different depths (layer depth {list(set(lowerCAmelCase ) )})' )
		_UpperCamelCase						    =	list(set(lowerCAmelCase ) )[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(lowerCAmelCase						,  lowerCAmelCase ):
				_UpperCamelCase						    =	full_name.split("""/""" )
				_UpperCamelCase						    =	model
				_UpperCamelCase						    =	[]
				for i, m_name in enumerate(lowerCAmelCase ):
						if m_name == ".ATTRIBUTES":
								# variable names end with .ATTRIBUTES/VARIABLE_VALUE
								break
						if m_name.startswith("""layer_with_weights""" ):
								_UpperCamelCase						    =	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						    =	getattr(lowerCAmelCase						,  """embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
								elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
										# encoder layers
										trace.extend(["""encoder""", """layer""", str(layer_num - 4 )] )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """encoder""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """layer""" )
										_UpperCamelCase						    =	pointer[layer_num - 4]
								elif layer_num == config.num_hidden_layers + 4:
										# pooler layer
										trace.extend(["""pooler""", """dense"""] )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """pooler""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "embeddings":
								trace.append("""embeddings""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """embeddings""" )
								if layer_num == 0:
										trace.append("""word_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """word_embeddings""" )
								elif layer_num == 1:
										trace.append("""position_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """position_embeddings""" )
								elif layer_num == 2:
										trace.append("""token_type_embeddings""" )
										_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """token_type_embeddings""" )
								else:
										raise ValueError(F'Unknown embedding layer with name {full_name}' )
								trace.append("""weight""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """weight""" )
						elif m_name == "_attention_layer":
								# self-attention layer
								trace.extend(["""attention""", """self"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """self""" )
						elif m_name == "_attention_layer_norm":
								# output attention norm
								trace.extend(["""attention""", """output""", """LayerNorm"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
						elif m_name == "_attention_output_dense":
								# output attention dense
								trace.extend(["""attention""", """output""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """attention""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_dense":
								# output dense
								trace.extend(["""output""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_layer_norm":
								# output dense
								trace.extend(["""output""", """LayerNorm"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """LayerNorm""" )
						elif m_name == "_key_dense":
								# attention key
								trace.append("""key""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """key""" )
						elif m_name == "_query_dense":
								# attention query
								trace.append("""query""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """query""" )
						elif m_name == "_value_dense":
								# attention value
								trace.append("""value""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """value""" )
						elif m_name == "_intermediate_dense":
								# attention intermediate dense
								trace.extend(["""intermediate""", """dense"""] )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """intermediate""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """dense""" )
						elif m_name == "_output_layer_norm":
								# output layer norm
								trace.append("""output""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """output""" )
						# weights & biases
						elif m_name in ["bias", "beta"]:
								trace.append("""bias""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """bias""" )
						elif m_name in ["kernel", "gamma"]:
								trace.append("""weight""" )
								_UpperCamelCase						    =	getattr(lowerCAmelCase						,  """weight""" )
						else:
								logger.warning(F'Ignored {m_name}' )
        # for certain layers reshape is necessary
				_UpperCamelCase						    =	""".""".join(lowerCAmelCase )
				if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""						,  lowerCAmelCase ) or re.match(
				    R"""(\S+)\.attention\.output\.dense\.weight"""						,  lowerCAmelCase ):
						_UpperCamelCase						    =	array.reshape(pointer.data.shape )
				if "kernel" in full_name:
						_UpperCamelCase						    =	array.transpose()
				if pointer.shape == array.shape:
						_UpperCamelCase						    =	torch.from_numpy(lowerCAmelCase )
				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(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		logger.info(F'Loading model based on config from {config_path}...' )
		_UpperCamelCase						    =	BertConfig.from_json_file(lowerCAmelCase )
		_UpperCamelCase						    =	BertModel(lowerCAmelCase )

		# Load weights from checkpoint
		logger.info(F'Loading weights from checkpoint {tf_checkpoint_path}...' )
		load_tfa_weights_in_bert(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )

		# Save pytorch-model
		logger.info(F'Saving PyTorch model to {pytorch_dump_path}...' )
		torch.save(model.state_dict()						,  lowerCAmelCase )


if __name__ == "__main__":
		lowerCamelCase__	=							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).",
		)
		lowerCamelCase__	=							parser.parse_args()
		convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



