






import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				return params[F"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_="attention"							):






				'''simple docstring'''
				lowercase__  					=     lowercase__  					=     np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]							)
				lowercase__  					=     k_tmp.reshape(k_tmp.shape[0]						,  k_tmp.shape[1] * k_tmp.shape[2]							)
				lowercase__  					=     np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]							)
				lowercase__  					=     o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]						,  o_tmp.shape[2]							)
				lowercase__  					=     np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]							)
				lowercase__  					=     q_tmp.reshape(q_tmp.shape[0]						,  q_tmp.shape[1] * q_tmp.shape[2]							)
				lowercase__  					=     np.ascontiguousarray(params[F"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]							)
				lowercase__  					=     v_tmp.reshape(v_tmp.shape[0]						,  v_tmp.shape[1] * v_tmp.shape[2]							)
				return k, o, q, v






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_=False							):






				'''simple docstring'''
				if split_mlp_wi:
								lowercase__  					=     params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
								lowercase__  					=     params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
								lowercase__  					=     (wi_a, wi_a)
				else:
								lowercase__  					=     params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

				lowercase__  					=     params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
				return wi, wo






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]






def     a     (		lowerCamelCase_						,  *, lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_ = False							):






				'''simple docstring'''
				lowercase__  					=     traverse_util.flatten_dict(variables['''target''']							)
				lowercase__  					=     {'''/'''.join(lowerCamelCase_							): v for k, v in old.items()}

				# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
				lowercase__  					=     '''encoder/encoder/mlp/wi_0/kernel''' in old
				print('''Split MLP:'''						,  lowerCamelCase_							)

				lowercase__  					=     collections.OrderedDict()

				# Shared embeddings.
				lowercase__  					=     old['''token_embedder/embedding''']

				# Encoder.
				for i in range(lowerCamelCase_							):
								# Block i, layer 0 (Self Attention).
								lowercase__  					=     tax_layer_norm_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''encoder'''						,  '''pre_attention_layer_norm'''							)
								lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     tax_attention_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''encoder'''						,  '''attention'''							)
								lowercase__  					=     layer_norm
								lowercase__  					=     k.T
								lowercase__  					=     o.T
								lowercase__  					=     q.T
								lowercase__  					=     v.T

								# Block i, layer 1 (MLP).
								lowercase__  					=     tax_layer_norm_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''encoder'''						,  '''pre_mlp_layer_norm'''							)
								lowercase__					,		lowercase__  					=     tax_mlp_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''encoder'''						,  lowerCamelCase_							)
								lowercase__  					=     layer_norm
								if split_mlp_wi:
												lowercase__  					=     wi[0].T
												lowercase__  					=     wi[1].T
								else:
												lowercase__  					=     wi.T
								lowercase__  					=     wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												lowercase__  					=     tax_relpos_bias_lookup(
												    lowerCamelCase_						,  lowerCamelCase_						,  '''encoder'''							).T

				lowercase__  					=     old['''encoder/encoder_norm/scale''']

				if not scalable_attention:
								lowercase__  					=     tax_relpos_bias_lookup(
								    lowerCamelCase_						,  0						,  '''encoder'''							).T
								lowercase__  					=     tax_relpos_bias_lookup(
								    lowerCamelCase_						,  0						,  '''decoder'''							).T

				if not is_encoder_only:
								# Decoder.
								for i in range(lowerCamelCase_							):
												# Block i, layer 0 (Self Attention).
												lowercase__  					=     tax_layer_norm_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''decoder'''						,  '''pre_self_attention_layer_norm'''							)
												lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     tax_attention_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''decoder'''						,  '''self_attention'''							)
												lowercase__  					=     layer_norm
												lowercase__  					=     k.T
												lowercase__  					=     o.T
												lowercase__  					=     q.T
												lowercase__  					=     v.T

												# Block i, layer 1 (Cross Attention).
												lowercase__  					=     tax_layer_norm_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''decoder'''						,  '''pre_cross_attention_layer_norm'''							)
												lowercase__					,		lowercase__					,		lowercase__					,		lowercase__  					=     tax_attention_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''decoder'''						,  '''encoder_decoder_attention'''							)
												lowercase__  					=     layer_norm
												lowercase__  					=     k.T
												lowercase__  					=     o.T
												lowercase__  					=     q.T
												lowercase__  					=     v.T

												# Block i, layer 2 (MLP).
												lowercase__  					=     tax_layer_norm_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''decoder'''						,  '''pre_mlp_layer_norm'''							)
												lowercase__					,		lowercase__  					=     tax_mlp_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''decoder'''						,  lowerCamelCase_							)
												lowercase__  					=     layer_norm
												if split_mlp_wi:
																lowercase__  					=     wi[0].T
																lowercase__  					=     wi[1].T
												else:
																lowercase__  					=     wi.T
												lowercase__  					=     wo.T

												if scalable_attention:
																# convert the rel_embedding of each layer
																lowercase__  					=     tax_relpos_bias_lookup(lowerCamelCase_						,  lowerCamelCase_						,  '''decoder'''							).T

								lowercase__  					=     old['''decoder/decoder_norm/scale''']

								# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
								if "decoder/logits_dense/kernel" in old:
												lowercase__  					=     old['''decoder/logits_dense/kernel'''].T

				return new






def     a     (		lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     collections.OrderedDict([(k, torch.from_numpy(v.copy()							)) for (k, v) in converted_params.items()]							)

				# Add what is missing.
				if "encoder.embed_tokens.weight" not in state_dict:
								lowercase__  					=     state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												lowercase__  					=     state_dict['''shared.weight''']

								if "lm_head.weight" not in state_dict:  # For old 1.0 models.
												print('''Using shared word embeddings as lm_head.'''							)
												lowercase__  					=     state_dict['''shared.weight''']

				return state_dict






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     checkpoints.load_tax_checkpoint(lowerCamelCase_							)
				lowercase__  					=     convert_tax_to_pytorch(
				    lowerCamelCase_						,  num_layers=config.num_layers						,  is_encoder_only=lowerCamelCase_						,  scalable_attention=lowerCamelCase_							)
				lowercase__  					=     make_state_dict(lowerCamelCase_						,  lowerCamelCase_							)
				model.load_state_dict(lowerCamelCase_						,  strict=lowerCamelCase_							)






def     a     (		lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_ = False						,  lowerCamelCase_ = False						,  ):






				'''simple docstring'''
				lowercase__  					=     MTaConfig.from_json_file(lowerCamelCase_							)
				print(F"""Building PyTorch model from configuration: {config}"""							)
				# Non-v1.1 checkpoints could also use T5Model, but this works for all.
				# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
				if is_encoder_only:
								lowercase__  					=     UMTaEncoderModel(lowerCamelCase_							)
				else:
								lowercase__  					=     UMTaForConditionalGeneration(lowerCamelCase_							)

				# Load weights from tf checkpoint
				load_tax_weights_in_ta(lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_							)

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
				model.save_pretrained(lowerCamelCase_							)

				# Verify that we can load the checkpoint.
				model.from_pretrained(lowerCamelCase_							)
				print('''Done'''							)


if __name__ == "__main__":
							A__      :     Optional[int]       =					argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
							# Required parameters
							parser.add_argument(
							    '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
							)
							parser.add_argument(
							    '--config_file',
							    default=None,
							    type=str,
							    required=True,
							    help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
							)
							parser.add_argument(
							    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							parser.add_argument(
							    '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
							)
							parser.add_argument(
							    '--scalable_attention',
							    action='store_true',
							    help='Whether the model uses scaled attention (umt5 model)',
							    default=False,
							)
							A__      :     Tuple       =					parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)












from jiwer import compute_measures

import datasets


A__      :     Tuple       =					'\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'

A__      :     Optional[int]       =					'\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'

A__      :     Dict       =					'\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = ["this is the prediction", "there is an other sample"]\n    >>> references = ["this is the reference", "there is another one"]\n    >>> wer = datasets.load_metric("wer")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,_KWARGS_DESCRIPTION      )
class  _UpperCAmelCase   (			datasets.Metric      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								return datasets.MetricInfo(
								    description=_DESCRIPTION,    citation=_CITATION,    inputs_description=_KWARGS_DESCRIPTION,    features=datasets.Features(
								        {
								            '''predictions''': datasets.Value('''string''',    id='''sequence'''    ),
								            '''references''': datasets.Value('''string''',    id='''sequence'''    ),
								        }    ),    codebase_urls=['''https://github.com/jitsi/jiwer/'''],    reference_urls=[
								        '''https://en.wikipedia.org/wiki/Word_error_rate''',
								    ],    )




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       str=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       List[str]=False    ):

								'''simple docstring'''
								if concatenate_texts:
												return compute_measures(lowerCamelCase,    lowerCamelCase    )["wer"]
								else:
												lowercase__  					=     0
												lowercase__  					=     0
												for prediction, reference in zip(lowerCamelCase,    lowerCamelCase    ):
																lowercase__  					=     compute_measures(lowerCamelCase,    lowerCamelCase    )
																incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
																total += measures["substitutions"] + measures["deletions"] + measures["hits"]
												return incorrect / total




