
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   __lowerCAmelCase		(		UpperCamelCase__					):



      """simple docstring"""







      A__		:	str         =			"""new-model"""


if is_tf_available():


 class   __lowerCAmelCase		(		UpperCamelCase__					):



       """simple docstring"""







       A__		:	Optional[int]         =			NewModelConfig


@require_tf
class   __lowerCAmelCase		(		unittest.TestCase					):



      """simple docstring"""







      @slow
      def 						snake_case_	(					self						:				Optional[Any] ):
          __lowercase						:	str					=				"bert-base-cased"
          __lowercase						:	Any					=				AutoConfig.from_pretrained(lowerCAmelCase__ )
          self.assertIsNotNone(lowerCAmelCase__ )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

          __lowercase						:	Optional[Any]					=				TFAutoModel.from_pretrained(lowerCAmelCase__ )
          self.assertIsNotNone(lowerCAmelCase__ )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      def 						snake_case_	(					self						:				Union[str, Any] ):
          __lowercase						:	List[Any]					=				"bert-base-cased"
          __lowercase						:	Dict					=				AutoConfig.from_pretrained(lowerCAmelCase__ )
          self.assertIsNotNone(lowerCAmelCase__ )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

          __lowercase						:	Tuple					=				TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase__ )
          self.assertIsNotNone(lowerCAmelCase__ )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      def 						snake_case_	(					self						:				Optional[int] ):




          for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase						:	Any					=				AutoConfig.from_pretrained(lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

              __lowercase						:	Optional[int]					=				TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__ )
              __lowercase						:	Union[str, Any]					=				TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase__ ,				output_loading_info=lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      def 						snake_case_	(					self						:				Dict ):




          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase						:	str					=				AutoConfig.from_pretrained(lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

              __lowercase						:	List[str]					=				TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      def 						snake_case_	(					self						:				Tuple ):




          for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase						:	Optional[int]					=				AutoConfig.from_pretrained(lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

              __lowercase						:	Optional[int]					=				TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__ )
              __lowercase						:	Dict					=				TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase__ ,				output_loading_info=lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      def 						snake_case_	(					self						:				List[str] ):




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

              __lowercase						:	Any					=				TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ )
              __lowercase						:	List[Any]					=				TFAutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__ ,				output_loading_info=lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      def 						snake_case_	(					self						:				str ):




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

              __lowercase						:	List[Any]					=				TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      def 						snake_case_	(					self						:				Union[str, Any] ):




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

              __lowercase						:	Dict					=				TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      @slow
      @require_tensorflow_probability
      def 						snake_case_	(					self						:				Union[str, Any] ):




          for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
              __lowercase						:	Any					=				AutoConfig.from_pretrained(lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

              __lowercase						:	Union[str, Any]					=				TFAutoModelForTableQuestionAnswering.from_pretrained(lowerCAmelCase__ )
              __lowercase						:	List[str]					=				TFAutoModelForTableQuestionAnswering.from_pretrained(
                  lowerCAmelCase__ ,				output_loading_info=lowerCAmelCase__ )
              self.assertIsNotNone(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	Optional[int]					=				TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__ )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )
          self.assertEqual(model.num_parameters() ,				1_4410 )
          self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__ ) ,				1_4410 )




      def 						snake_case_	(					self						:				Optional[Any] ):
          __lowercase						:	int					=				TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase__ )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )
          self.assertEqual(model.num_parameters() ,				1_4410 )
          self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase__ ) ,				1_4410 )




      def 						snake_case_	(					self						:				Tuple ):
          __lowercase						:	str					=				TFAutoModel.from_pretrained('''sgugger/funnel-random-tiny''' )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

          __lowercase						:	Union[str, Any]					=				copy.deepcopy(model.config )
          __lowercase						:	Union[str, Any]					=				["FunnelBaseModel"]
          __lowercase						:	Dict					=				TFAutoModel.from_config(lowerCAmelCase__ )
          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

          with tempfile.TemporaryDirectory() as tmp_dir:
              model.save_pretrained(lowerCAmelCase__ )
              __lowercase						:	List[str]					=				TFAutoModel.from_pretrained(lowerCAmelCase__ )
              self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )




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




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

              __lowercase						:	Tuple					=				[
                  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(lowerCAmelCase__ ):
                          auto_class.register(lowerCAmelCase__ ,				lowerCAmelCase__ )
                      auto_class.register(lowerCAmelCase__ ,				lowerCAmelCase__ )
                      # Trying to register something existing in the Transformers library will raise an error
                      with self.assertRaises(lowerCAmelCase__ ):
                          auto_class.register(lowerCAmelCase__ ,				lowerCAmelCase__ )

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

                      with tempfile.TemporaryDirectory() as tmp_dir:
                          model.save_pretrained(lowerCAmelCase__ )
                          __lowercase						:	int					=				auto_class.from_pretrained(lowerCAmelCase__ )
                          self.assertIsInstance(lowerCAmelCase__ ,				lowerCAmelCase__ )

          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 						snake_case_	(					self						:				Optional[Any] ):




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




      def 						snake_case_	(					self						:				str ):




          with self.assertRaisesRegex(
              lowerCAmelCase__ ,				r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
              __lowercase						:	int					=				TFAutoModel.from_pretrained(lowerCAmelCase__ ,				revision='''aaaaaa''' )




      def 						snake_case_	(					self						:				Tuple ):




          with self.assertRaisesRegex(
              lowerCAmelCase__ ,				'''hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin''' ,				):
              __lowercase						:	Optional[int]					=				TFAutoModel.from_pretrained('''hf-internal-testing/config-no-model''' )




      def 						snake_case_	(					self						:				Optional[Any] ):




          with self.assertRaisesRegex(lowerCAmelCase__ ,				'''Use `from_pt=True` to load this model''' ):
              __lowercase						:	Optional[Any]					=				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-bert-pt-only''' )




      def 						snake_case_	(					self						:				Dict ):
          __lowercase						:	Any					=				TFAutoModel.from_pretrained('''hf-internal-testing/tiny-random-bert''' )
          with RequestCounter() as counter:
              __lowercase						:	List[str]					=				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
          __lowercase						:	Any					=				TFAutoModel.from_pretrained('''ArthurZ/tiny-random-bert-sharded''' )
          with RequestCounter() as counter:
              __lowercase						:	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 )









from math import pi
def      _A       (		SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   int						):





  """simple docstring"""



  return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
     print(arc_length(90, 10))






