"""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    __snake_case       (     __A				):
      a__        =		"""new-model"""


if is_tf_available():




 class    __snake_case       (     __A				):
       a__        =		NewModelConfig




@require_tf
class    __snake_case       (     unittest.TestCase				):



      @slow
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[int]										=	"bert-base-cased"
        a__:     Dict										=	AutoConfig.from_pretrained(lowerCAmelCase_)
        self.assertIsNotNone(lowerCAmelCase_)
        self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)

        a__:     Tuple										=	TFAutoModel.from_pretrained(lowerCAmelCase_)
        self.assertIsNotNone(lowerCAmelCase_)
        self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      @slow
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     str										=	"bert-base-cased"
        a__:     Any										=	AutoConfig.from_pretrained(lowerCAmelCase_)
        self.assertIsNotNone(lowerCAmelCase_)
        self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)

        a__:     Dict										=	TFAutoModelForPreTraining.from_pretrained(lowerCAmelCase_)
        self.assertIsNotNone(lowerCAmelCase_)
        self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      @slow
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     List[str]										=	AutoConfig.from_pretrained(lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)

          a__:     Optional[Any]										=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase_)
          a__:     Dict										=	TFAutoModelForCausalLM.from_pretrained(lowerCAmelCase_    ,			output_loading_info=lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      @slow
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:     Any										=	AutoConfig.from_pretrained(lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)

          a__:     List[Any]										=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      @slow
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





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

          a__:     Optional[int]										=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase_)
          a__:     Optional[int]										=	TFAutoModelForMaskedLM.from_pretrained(lowerCAmelCase_    ,			output_loading_info=lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      @slow
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





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

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



      @slow
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





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

          a__:     Union[str, Any]										=	TFAutoModelForSequenceClassification.from_pretrained(lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      @slow
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





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

          a__:     List[Any]										=	TFAutoModelForQuestionAnswering.from_pretrained(lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      @slow
      @require_tensorflow_probability
      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





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

          a__:     int										=	TFAutoModelForTableQuestionAnswering.from_pretrained(lowerCAmelCase_)
          a__:     List[Any]										=	TFAutoModelForTableQuestionAnswering.from_pretrained(
              lowerCAmelCase_    ,			output_loading_info=lowerCAmelCase_)
          self.assertIsNotNone(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     str										=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_)
        self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)
        self.assertEqual(model.num_parameters()    ,			1_44_10)
        self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_)    ,			1_44_10)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     str										=	TFAutoModelWithLMHead.from_pretrained(lowerCAmelCase_)
        self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)
        self.assertEqual(model.num_parameters()    ,			1_44_10)
        self.assertEqual(model.num_parameters(only_trainable=lowerCAmelCase_)    ,			1_44_10)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Union[str, Any]										=	TFAutoModel.from_pretrained('sgugger/funnel-random-tiny')
        self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)

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

        with tempfile.TemporaryDirectory() as tmp_dir:
          model.save_pretrained(lowerCAmelCase_)
          a__:     Any										=	TFAutoModel.from_pretrained(lowerCAmelCase_)
          self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





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

          a__:     Union[str, Any]										=	[
              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
              a__:     List[Any]										=	BertModelTester(self).get_config()
              a__:     Union[str, Any]										=	NewModelConfig(**tiny_config.to_dict())
              a__:     int										=	auto_class.from_config(lowerCAmelCase_)
              self.assertIsInstance(lowerCAmelCase_    ,			lowerCAmelCase_)

              with tempfile.TemporaryDirectory() as tmp_dir:
                model.save_pretrained(lowerCAmelCase_)
                a__:     Dict										=	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   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        with self.assertRaisesRegex(
            lowerCAmelCase_    ,			r'aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)'):
          a__:     Union[str, Any]										=	TFAutoModel.from_pretrained(lowerCAmelCase_    ,			revision='aaaaaa')



      def   lowerCamelCase_   (						self)	->			Union[str, Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





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



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[Any]										=	TFAutoModel.from_pretrained('hf-internal-testing/tiny-random-bert')
        with RequestCounter() as counter:
          a__:     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
        a__:     Dict										=	TFAutoModel.from_pretrained('ArthurZ/tiny-random-bert-sharded')
        with RequestCounter() as counter:
          a__:     int										=	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)










"""simple docstring"""
import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    '''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
    ''' StableDiffusionInpaintPipeline` instead.'''
)



