




'''simple docstring'''







import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def     lowerCamelCase_ (					SCREAMING_SNAKE_CASE__ :     str,     SCREAMING_SNAKE_CASE__ :     Any,     SCREAMING_SNAKE_CASE__ :     Optional[Any]				)	->		Dict:
						# Initialise PyTorch model
						UpperCAmelCase_     : List[str]         =					TaConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
						print(F"""Building PyTorch model from configuration: {config}"""				)
						UpperCAmelCase_     : List[str]         =					TaForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

						# Load weights from tf checkpoint
						load_tf_weights_in_ta(SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__,     SCREAMING_SNAKE_CASE__				)

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


if __name__ == "__main__":
					snake_case_       :    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(
					    "--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."
					)
					snake_case_       :    Union[str, Any]          =					parser.parse_args()
					convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)












'''simple docstring'''







from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest


@require_tokenizers
class    __a (lowerCamelCase       ):
							__a   :				Any       =    SqueezeBertTokenizer
							__a   :				Dict       =    SqueezeBertTokenizerFast
							__a   :				Any       =    True

							def       UpperCAmelCase__		(							self							:       Union[str, Any]    ,       **__magic_name__							:       Dict				)   ->    int:







													"""simple docstring"""
													return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname    ,       **__magic_name__				)



							@slow
							def       UpperCAmelCase__		(							self							:       Union[str, Any]				)   ->    int:







													"""simple docstring"""
													UpperCAmelCase_     : List[str]         =					SqueezeBertTokenizer.from_pretrained('''squeezebert/squeezebert-mnli-headless'''				)

													UpperCAmelCase_     : Any         =					tokenizer.encode('''sequence builders'''    ,       add_special_tokens=__magic_name__				)
													UpperCAmelCase_     : Optional[Any]         =					tokenizer.encode('''multi-sequence build'''    ,       add_special_tokens=__magic_name__				)

													UpperCAmelCase_     : List[Any]         =					tokenizer.build_inputs_with_special_tokens(__magic_name__				)
													UpperCAmelCase_     : Any         =					tokenizer.build_inputs_with_special_tokens(__magic_name__    ,       __magic_name__				)

													assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
													assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
													    tokenizer.sep_token_id
													]





