



from __future__ import annotations

import unittest

from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow


if is_tf_available():
							from transformers import (
							    AutoConfig,
							    BertConfig,
							    GPTaConfig,
							    TaConfig,
							    TFAutoModel,
							    TFAutoModelForCausalLM,
							    TFAutoModelForMaskedLM,
							    TFAutoModelForPreTraining,
							    TFAutoModelForQuestionAnswering,
							    TFAutoModelForSeqaSeqLM,
							    TFAutoModelForSequenceClassification,
							    TFAutoModelWithLMHead,
							    TFBertForMaskedLM,
							    TFBertForPreTraining,
							    TFBertForQuestionAnswering,
							    TFBertForSequenceClassification,
							    TFBertModel,
							    TFGPTaLMHeadModel,
							    TFRobertaForMaskedLM,
							    TFTaForConditionalGeneration,
							)
							from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
							from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST

if is_torch_available():
							from transformers import (
							    AutoModel,
							    AutoModelForCausalLM,
							    AutoModelForMaskedLM,
							    AutoModelForPreTraining,
							    AutoModelForQuestionAnswering,
							    AutoModelForSeqaSeqLM,
							    AutoModelForSequenceClassification,
							    AutoModelWithLMHead,
							    BertForMaskedLM,
							    BertForPreTraining,
							    BertForQuestionAnswering,
							    BertForSequenceClassification,
							    BertModel,
							    GPTaLMHeadModel,
							    RobertaForMaskedLM,
							    TaForConditionalGeneration,
							)
@is_pt_tf_cross_test
class 			_snake_case     (   unittest.TestCase):




				@slow
				def 							A__ (  self   :  Dict  ):
							# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["bert-base-uncased"]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModel.from_pretrained(__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModel.from_pretrained(__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				@slow
				def 							A__ (  self   :  Optional[Any]  ):
							# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["bert-base-uncased"]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModelForPreTraining.from_pretrained(__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModelForPreTraining.from_pretrained(__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				@slow
				def 							A__ (  self   :  Optional[Any]  ):
							for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModelForCausalLM.from_pretrained(__lowercase,   from_pt=__lowercase  )
										lowercase__					,    lowercase__   					=			TFAutoModelForCausalLM.from_pretrained(
										    __lowercase,   output_loading_info=__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModelForCausalLM.from_pretrained(__lowercase,   from_tf=__lowercase  )
										lowercase__					,    lowercase__   					=			AutoModelForCausalLM.from_pretrained(
										    __lowercase,   output_loading_info=__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				@slow
				def 							A__ (  self   :  Dict  ):
							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModelWithLMHead.from_pretrained(__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModelWithLMHead.from_pretrained(__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				@slow
				def 							A__ (  self   :  List[Any]  ):
							for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModelForMaskedLM.from_pretrained(__lowercase,   from_pt=__lowercase  )
										lowercase__					,    lowercase__   					=			TFAutoModelForMaskedLM.from_pretrained(
										    __lowercase,   output_loading_info=__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModelForMaskedLM.from_pretrained(__lowercase,   from_tf=__lowercase  )
										lowercase__					,    lowercase__   					=			AutoModelForMaskedLM.from_pretrained(
										    __lowercase,   output_loading_info=__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				@slow
				def 							A__ (  self   :  Dict  ):
							for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModelForSeqaSeqLM.from_pretrained(__lowercase,   from_pt=__lowercase  )
										lowercase__					,    lowercase__   					=			TFAutoModelForSeqaSeqLM.from_pretrained(
										    __lowercase,   output_loading_info=__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModelForSeqaSeqLM.from_pretrained(__lowercase,   from_tf=__lowercase  )
										lowercase__					,    lowercase__   					=			AutoModelForSeqaSeqLM.from_pretrained(
										    __lowercase,   output_loading_info=__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				@slow
				def 							A__ (  self   :  Union[str, Any]  ):
							# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["bert-base-uncased"]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModelForSequenceClassification.from_pretrained(__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModelForSequenceClassification.from_pretrained(__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				@slow
				def 							A__ (  self   :  int  ):
							# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							for model_name in ["bert-base-uncased"]:
										lowercase__   					=			AutoConfig.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			TFAutoModelForQuestionAnswering.from_pretrained(__lowercase,   from_pt=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )

										lowercase__   					=			AutoModelForQuestionAnswering.from_pretrained(__lowercase,   from_tf=__lowercase  )
										self.assertIsNotNone(__lowercase  )
										self.assertIsInstance(__lowercase,   __lowercase  )




				def 							A__ (  self   :  str  ):
							lowercase__   					=			TFAutoModelWithLMHead.from_pretrained(__lowercase,   from_pt=__lowercase  )
							self.assertIsInstance(__lowercase,   __lowercase  )
							self.assertEqual(model.num_parameters(),   1_4410  )
							self.assertEqual(model.num_parameters(only_trainable=__lowercase  ),   1_4410  )

							lowercase__   					=			AutoModelWithLMHead.from_pretrained(__lowercase,   from_tf=__lowercase  )
							self.assertIsInstance(__lowercase,   __lowercase  )
							self.assertEqual(model.num_parameters(),   1_4410  )
							self.assertEqual(model.num_parameters(only_trainable=__lowercase  ),   1_4410  )




				def 							A__ (  self   :  Tuple  ):
							lowercase__   					=			TFAutoModelWithLMHead.from_pretrained(__lowercase,   from_pt=__lowercase  )
							self.assertIsInstance(__lowercase,   __lowercase  )
							self.assertEqual(model.num_parameters(),   1_4410  )
							self.assertEqual(model.num_parameters(only_trainable=__lowercase  ),   1_4410  )

							lowercase__   					=			AutoModelWithLMHead.from_pretrained(__lowercase,   from_tf=__lowercase  )
							self.assertIsInstance(__lowercase,   __lowercase  )
							self.assertEqual(model.num_parameters(),   1_4410  )
							self.assertEqual(model.num_parameters(only_trainable=__lowercase  ),   1_4410  )









from pathlib import Path

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


if __name__ == "__main__":
							fire.Fire(minify)




