'''simple docstring'''




import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      List[Any]				)    ->   Tuple:
							# Initialise PyTorch model
							lowercase_      :    List[Any]       		= RemBertConfig.from_json_file(UpperCAmelCase__				)
							print("""Building PyTorch model from configuration: {}""".format(str(UpperCAmelCase__				)				)				)
							lowercase_      :    Any       		= RemBertModel(UpperCAmelCase__				)

							# Load weights from tf checkpoint
							load_tf_weights_in_rembert(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__				)

							# Save pytorch-model
							print("""Save PyTorch model to {}""".format(UpperCAmelCase__				)				)
							torch.save(model.state_dict()   ,   UpperCAmelCase__				)


if __name__ == "__main__":
						_lowercase		:				Optional[Any]	       =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
						)
						parser.add_argument(
						    "--rembert_config_file",
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        "The config json file corresponding to the pre-trained RemBERT model. \n"
						        "This specifies the model architecture."
						    ),
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						_lowercase		:				str	       =		parser.parse_args()
						convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






'''simple docstring'''




from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import tensorflow as tf

						from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM





@require_tf
@require_sentencepiece
@require_tokenizers
class 					__magic_name__		(					unittest.TestCase):







			@slow
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Any       		= TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""					)
										lowercase_      :    Dict       		= AutoTokenizer.from_pretrained("""google/mt5-small"""					)

										lowercase_      :    Union[str, Any]       		= tokenizer("""Hello there"""       ,    return_tensors="""tf"""					).input_ids
										lowercase_      :    List[str]       		= tokenizer("""Hi I am"""       ,    return_tensors="""tf"""					).input_ids

										lowercase_      :    Optional[Any]       		= model(lowercase_       ,    labels=lowercase_					).loss
										lowercase_      :    Optional[int]       		= -tf.math.reduce_mean(lowercase_					).numpy()

										lowercase_      :    Optional[int]       		= -21.22_81_68
										self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 2E-4					)





