


"""simple docstring"""
def 		lowercase							(     _snake_case						:      int     )     ->list:
	"""simple docstring"""





	if bit_count < 0:
		raise ValueError('''The given input must be positive'''     )

	# get the generated string sequence
	__snake_case	:						int						=				gray_code_sequence_string(_snake_case     )
	#
	# convert them to integers
	for i in range(len(_snake_case     )     ):
		__snake_case	:						Dict						=				int(sequence[i]     ,   2     )

	return sequence






def 		lowercase							(     _snake_case						:      int     )     ->list:
	"""simple docstring"""





	if bit_count == 0:
		return ["0"]

	if bit_count == 1:
		return ["0", "1"]

	__snake_case	:						int						=				1 << bit_count  # defines the length of the sequence
	# 1<< n is equivalent to 2^n

	# recursive answer will generate answer for n-1 bits
	__snake_case	:						Optional[int]						=				gray_code_sequence_string(bit_count - 1     )

	__snake_case	:						Optional[int]						=				[]

	# append 0 to first half of the smaller sequence generated
	for i in range(seq_len // 2     ):
		__snake_case	:						Optional[int]						=				'''0''' + smaller_sequence[i]
		sequence.append(_snake_case     )

	# append 1 to second half ... start from the end of the list
	for i in reversed(range(seq_len // 2     )     ):
		__snake_case	:						Optional[Any]						=				'''1''' + smaller_sequence[i]
		sequence.append(_snake_case     )

	return sequence


if __name__ == "__main__":
			import doctest

			doctest.testmod()









"""simple docstring"""
from __future__ import annotations

import copy
import tempfile
import unittest

from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPTaConfig, TaConfig, TapasConfig, is_tf_available
from transformers.testing_utils import (
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tensorflow_probability,
    require_tf,
    slow,
)

from ..bert.test_modeling_bert import BertModelTester


if is_tf_available():
			from transformers import (
			    TFAutoModel,
			    TFAutoModelForCausalLM,
			    TFAutoModelForMaskedLM,
			    TFAutoModelForPreTraining,
			    TFAutoModelForQuestionAnswering,
			    TFAutoModelForSeqaSeqLM,
			    TFAutoModelForSequenceClassification,
			    TFAutoModelForTableQuestionAnswering,
			    TFAutoModelForTokenClassification,
			    TFAutoModelWithLMHead,
			    TFBertForMaskedLM,
			    TFBertForPreTraining,
			    TFBertForQuestionAnswering,
			    TFBertForSequenceClassification,
			    TFBertModel,
			    TFFunnelBaseModel,
			    TFFunnelModel,
			    TFGPTaLMHeadModel,
			    TFRobertaForMaskedLM,
			    TFTaForConditionalGeneration,
			    TFTapasForQuestionAnswering,
			)
			from transformers.models.auto.modeling_tf_auto import (
			    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
			    TF_MODEL_FOR_MASKED_LM_MAPPING,
			    TF_MODEL_FOR_PRETRAINING_MAPPING,
			    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
			    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
			    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
			    TF_MODEL_MAPPING,
			)
			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
			from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST



class 			_UpperCAmelCase (							__snake_case				):






		'''simple docstring'''

		lowerCamelCase__				='new-model'


if is_tf_available():



			class 			_UpperCAmelCase (							__snake_case				):






					'''simple docstring'''

					lowerCamelCase__				=NewModelConfig



@require_tf
class 			_UpperCAmelCase (							unittest.TestCase				):






		'''simple docstring'''

		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				'''bert-base-cased'''
			__snake_case	:						Dict						=				AutoConfig.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)

			__snake_case	:						int						=				TFAutoModel.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[int]						=				'''bert-base-cased'''
			__snake_case	:						Optional[Any]						=				AutoConfig.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)

			__snake_case	:						List[Any]						=				TFAutoModelForPreTraining.from_pretrained(a_					)
			self.assertIsNotNone(a_					)
			self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						int						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Dict						=				TFAutoModelForCausalLM.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						int						=				TFAutoModelForCausalLM.from_pretrained(a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						Tuple						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Tuple						=				TFAutoModelWithLMHead.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						List[str]						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Optional[Any]						=				TFAutoModelForMaskedLM.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						int						=				TFAutoModelForMaskedLM.from_pretrained(a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__snake_case	:						int						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						List[Any]						=				TFAutoModelForSeqaSeqLM.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						Union[str, Any]						=				TFAutoModelForSeqaSeqLM.from_pretrained(a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in ["bert-base-uncased"]:
				__snake_case	:						Any						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Dict						=				TFAutoModelForSequenceClassification.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in ["bert-base-uncased"]:
				__snake_case	:						Optional[int]						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						Optional[Any]						=				TFAutoModelForQuestionAnswering.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		@slow
		@require_tensorflow_probability
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
				__snake_case	:						Dict						=				AutoConfig.from_pretrained(a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)

				__snake_case	:						List[str]						=				TFAutoModelForTableQuestionAnswering.from_pretrained(a_					)
				__snake_case	,							__snake_case	:						Dict						=				TFAutoModelForTableQuestionAnswering.from_pretrained(
				    a_				,       output_loading_info=a_					)
				self.assertIsNotNone(a_					)
				self.assertIsInstance(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				TFAutoModelWithLMHead.from_pretrained(a_					)
			self.assertIsInstance(a_				,       a_					)
			self.assertEqual(model.num_parameters()				,       1_44_10					)
			self.assertEqual(model.num_parameters(only_trainable=a_					)				,       1_44_10					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				TFAutoModelWithLMHead.from_pretrained(a_					)
			self.assertIsInstance(a_				,       a_					)
			self.assertEqual(model.num_parameters()				,       1_44_10					)
			self.assertEqual(model.num_parameters(only_trainable=a_					)				,       1_44_10					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny'''					)
			self.assertIsInstance(a_				,       a_					)

			__snake_case	:						Optional[Any]						=				copy.deepcopy(model.config					)
			__snake_case	:						int						=				['''FunnelBaseModel''']
			__snake_case	:						Any						=				TFAutoModel.from_config(a_					)
			self.assertIsInstance(a_				,       a_					)

			with tempfile.TemporaryDirectory() as tmp_dir:
				model.save_pretrained(a_					)
				__snake_case	:						Dict						=				TFAutoModel.from_pretrained(a_					)
				self.assertIsInstance(a_				,       a_					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			try:
				AutoConfig.register('''new-model'''				,       a_					)

				__snake_case	:						List[str]						=				[
				    TFAutoModel,
				    TFAutoModelForCausalLM,
				    TFAutoModelForMaskedLM,
				    TFAutoModelForPreTraining,
				    TFAutoModelForQuestionAnswering,
				    TFAutoModelForSequenceClassification,
				    TFAutoModelForTokenClassification,
				]

				for auto_class in auto_classes:
					with self.subTest(auto_class.__name__					):
						# Wrong config class will raise an error
						with self.assertRaises(a_					):
							auto_class.register(a_				,       a_					)
						auto_class.register(a_				,       a_					)
						# Trying to register something existing in the Transformers library will raise an error
						with self.assertRaises(a_					):
							auto_class.register(a_				,       a_					)

						# Now that the config is registered, it can be used as any other config with the auto-API
						__snake_case	:						Union[str, Any]						=				BertModelTester(self					).get_config()
						__snake_case	:						str						=				NewModelConfig(**tiny_config.to_dict()					)
						__snake_case	:						Optional[int]						=				auto_class.from_config(a_					)
						self.assertIsInstance(a_				,       a_					)

						with tempfile.TemporaryDirectory() as tmp_dir:
							model.save_pretrained(a_					)
							__snake_case	:						Optional[int]						=				auto_class.from_pretrained(a_					)
							self.assertIsInstance(a_				,       a_					)

			finally:
				if "new-model" in CONFIG_MAPPING._extra_content:
					del CONFIG_MAPPING._extra_content["new-model"]
				for mapping in (
				    TF_MODEL_MAPPING,
				    TF_MODEL_FOR_PRETRAINING_MAPPING,
				    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
				    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
				    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_MASKED_LM_MAPPING,
				):
					if NewModelConfig in mapping._extra_content:
						del mapping._extra_content[NewModelConfig]



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(
			    a_				,       '''bert-base is not a local folder and is not a valid model identifier'''					):
				__snake_case	:						Any						=				TFAutoModel.from_pretrained('''bert-base'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(
			    a_				,       R'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'''					):
				__snake_case	:						Dict						=				TFAutoModel.from_pretrained(a_				,       revision='''aaaaaa'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(
			    a_				,       '''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin'''				,       ):
				__snake_case	:						Any						=				TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			with self.assertRaisesRegex(a_				,       '''Use `from_pt=True` to load this model'''					):
				__snake_case	:						Dict						=				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only'''					)



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)
			with RequestCounter() as counter:
				__snake_case	:						List[Any]						=				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert'''					)
				self.assertEqual(counter.get_request_count				,       0					)
				self.assertEqual(counter.head_request_count				,       1					)
				self.assertEqual(counter.other_request_count				,       0					)

			# With a sharded checkpoint
			__snake_case	:						Dict						=				TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''					)
			with RequestCounter() as counter:
				__snake_case	:						Dict						=				TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded'''					)
				self.assertEqual(counter.get_request_count				,       0					)
				self.assertEqual(counter.head_request_count				,       1					)
				self.assertEqual(counter.other_request_count				,       0					)





