



'''simple docstring'''





from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFDeiTForImageClassification,
           TFDeiTForImageClassificationWithTeacher,
           TFDeiTForMaskedImageModeling,
           TFDeiTModel,
       )
       from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import DeiTImageProcessor







class     UpperCAmelCase_       :
    """simple docstring"""







    def __init__(    self					:     Any ,     snake_case_					:     List[Any] ,     snake_case_					:     Any=13 ,     snake_case_					:     Dict=30 ,     snake_case_					:     Any=2 ,     snake_case_					:     Optional[Any]=3 ,     snake_case_					:     Dict=True ,     snake_case_					:     Optional[int]=True ,     snake_case_					:     Dict=32 ,     snake_case_					:     Optional[Any]=2 ,     snake_case_					:     Union[str, Any]=4 ,     snake_case_					:     List[Any]=37 ,     snake_case_					:     Optional[Any]="gelu" ,     snake_case_					:     Tuple=0.1 ,     snake_case_					:     Union[str, Any]=0.1 ,     snake_case_					:     Dict=10 ,     snake_case_					:     Dict=0.02 ,     snake_case_					:     str=3 ,     snake_case_					:     str=None ,     snake_case_					:     int=2 ,     ):
          snake_case__  :    Any    						=      parent
          snake_case__  :    Union[str, Any]    						=      batch_size
          snake_case__  :    str    						=      image_size
          snake_case__  :    str    						=      patch_size
          snake_case__  :    Optional[int]    						=      num_channels
          snake_case__  :    Union[str, Any]    						=      is_training
          snake_case__  :    Optional[Any]    						=      use_labels
          snake_case__  :    Union[str, Any]    						=      hidden_size
          snake_case__  :    Union[str, Any]    						=      num_hidden_layers
          snake_case__  :    Optional[int]    						=      num_attention_heads
          snake_case__  :    List[str]    						=      intermediate_size
          snake_case__  :    Union[str, Any]    						=      hidden_act
          snake_case__  :    Optional[Any]    						=      hidden_dropout_prob
          snake_case__  :    Dict    						=      attention_probs_dropout_prob
          snake_case__  :    str    						=      type_sequence_label_size
          snake_case__  :    Optional[Any]    						=      initializer_range
          snake_case__  :    Dict    						=      scope
          snake_case__  :    Tuple    						=      encoder_stride

          # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
          snake_case__  :    Tuple    						=      (image_size // patch_size) ** 2
          snake_case__  :    List[Any]    						=      num_patches + 2




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    str    						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

          snake_case__  :    Optional[int]    						=      None
          if self.use_labels:
                snake_case__  :    Any    						=      ids_tensor([self.batch_size] ,     self.type_sequence_label_size						)

          snake_case__  :    Tuple    						=      self.get_config()

          return config, pixel_values, labels




    def lowerCamelCase       (    self					:     Tuple						):
          return DeiTConfig(
              image_size=self.image_size ,     patch_size=self.patch_size ,     num_channels=self.num_channels ,     hidden_size=self.hidden_size ,     num_hidden_layers=self.num_hidden_layers ,     num_attention_heads=self.num_attention_heads ,     intermediate_size=self.intermediate_size ,     hidden_act=self.hidden_act ,     hidden_dropout_prob=self.hidden_dropout_prob ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob ,     is_decoder=snake_case_ ,     initializer_range=self.initializer_range ,     encoder_stride=self.encoder_stride ,     )




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[Any] ,     snake_case_					:     int ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    Dict    						=      TFDeiTModel(config=snake_case_						)
          snake_case__  :    Any    						=      model(snake_case_						)
          self.parent.assertEqual(result.last_hidden_state.shape ,     (self.batch_size, self.seq_length, self.hidden_size)						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     Tuple ,     snake_case_					:     List[str] ,     snake_case_					:     Tuple						):
          snake_case__  :    List[Any]    						=      TFDeiTForMaskedImageModeling(config=snake_case_						)
          snake_case__  :    List[str]    						=      model(snake_case_						)
          self.parent.assertEqual(
              result.reconstruction.shape ,     (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

          # test greyscale images
          snake_case__  :    int    						=      1
          snake_case__  :    Optional[int]    						=      TFDeiTForMaskedImageModeling(snake_case_						)

          snake_case__  :    Optional[int]    						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
          snake_case__  :    List[str]    						=      model(snake_case_						)
          self.parent.assertEqual(result.reconstruction.shape ,     (self.batch_size, 1, self.image_size, self.image_size)						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Union[str, Any] ,     snake_case_					:     str ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    Dict    						=      self.type_sequence_label_size
          snake_case__  :    Optional[Any]    						=      TFDeiTForImageClassification(snake_case_						)
          snake_case__  :    List[str]    						=      model(snake_case_ ,     labels=snake_case_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.type_sequence_label_size)						)

          # test greyscale images
          snake_case__  :    str    						=      1
          snake_case__  :    List[Any]    						=      TFDeiTForImageClassification(snake_case_						)

          snake_case__  :    Optional[int]    						=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
          snake_case__  :    Tuple    						=      model(snake_case_ ,     labels=snake_case_						)
          self.parent.assertEqual(result.logits.shape ,     (self.batch_size, self.type_sequence_label_size)						)




    def lowerCamelCase       (    self					:     int						):
          snake_case__  :    Optional[Any]    						=      self.prepare_config_and_inputs()
          snake_case__  :    Dict    						=      config_and_inputs
          snake_case__  :    Any    						=      {"""pixel_values""": pixel_values}
          return config, inputs_dict







@require_tf
class     UpperCAmelCase_       (       __SCREAMING_SNAKE_CASE		,		__SCREAMING_SNAKE_CASE		,		unittest.TestCase	):
    """simple docstring"""







    lowercase					=		(
        (
            TFDeiTModel,
            TFDeiTForImageClassification,
            TFDeiTForImageClassificationWithTeacher,
            TFDeiTForMaskedImageModeling,
        )
        if is_tf_available()
        else ()
    )
    lowercase					=		(
        {
            """feature-extraction""": TFDeiTModel,
            """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
        }
        if is_tf_available()
        else {}
    )

    lowercase					=		False
    lowercase					=		False
    lowercase					=		False
    lowercase					=		False




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    Tuple    						=      TFDeiTModelTester(self						)
          snake_case__  :    List[Any]    						=      ConfigTester(self ,     config_class=snake_case_ ,     has_text_modality=snake_case_ ,     hidden_size=37						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          self.config_tester.run_common_tests()




    @unittest.skip(reason="""DeiT does not use inputs_embeds"""						)
    def lowerCamelCase       (    self					:     Optional[int]						):
          pass




    def lowerCamelCase       (    self					:     str						):
          snake_case__  :    Optional[int]    						=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__  :    List[str]    						=      model_class(snake_case_						)
                self.assertIsInstance(model.get_input_embeddings() ,     (tf.keras.layers.Layer)						)
                snake_case__  :    Dict    						=      model.get_output_embeddings()
                self.assertTrue(x is None or isinstance(snake_case_ ,     tf.keras.layers.Dense						)						)




    def lowerCamelCase       (    self					:     Optional[Any]						):
          snake_case__  :    Tuple    						=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                snake_case__  :    Union[str, Any]    						=      model_class(snake_case_						)
                snake_case__  :    str    						=      inspect.signature(model.call						)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                snake_case__  :    List[Any]    						=      [*signature.parameters.keys()]

                snake_case__  :    Union[str, Any]    						=      ["""pixel_values"""]
                self.assertListEqual(arg_names[:1] ,     snake_case_						)




    def lowerCamelCase       (    self					:     List[str]						):
          snake_case__  :    List[Any]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case_						)




    def lowerCamelCase       (    self					:     Optional[int]						):
          snake_case__  :    Union[str, Any]    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_						)




    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    str    						=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case_						)




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     int ,     snake_case_					:     int ,     snake_case_					:     Optional[int]=False						):
          snake_case__  :    Union[str, Any]    						=      super()._prepare_for_class(snake_case_ ,     snake_case_ ,     return_labels=snake_case_						)

          if return_labels:
                if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call						).parameters:
                      del inputs_dict["labels"]

          return inputs_dict




    @slow
    def lowerCamelCase       (    self					:     List[Any]						):
          for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                snake_case__  :    str    						=      TFDeiTModel.from_pretrained(snake_case_						)
                self.assertIsNotNone(snake_case_						)





def 						__snake_case(				)		-> Union[str, Any]:
      snake_case__  :    Optional[int]    						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
      return image







@require_tf
@require_vision
class     UpperCAmelCase_       (       unittest.TestCase	):
    """simple docstring"""







    @cached_property
    def lowerCamelCase       (    self					:     Optional[int]						):
          return (
              DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""						)
              if is_vision_available()
              else None
          )




    @slow
    def lowerCamelCase       (    self					:     List[Any]						):
          snake_case__  :    str    						=      TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""						)

          snake_case__  :    Dict    						=      self.default_image_processor
          snake_case__  :    List[str]    						=      prepare_img()
          snake_case__  :    str    						=      image_processor(images=snake_case_ ,     return_tensors="""tf"""						)

          # forward pass
          snake_case__  :    Optional[Any]    						=      model(**snake_case_						)

          # verify the logits
          snake_case__  :    str    						=      tf.TensorShape((1, 1_000)						)
          self.assertEqual(outputs.logits.shape ,     snake_case_						)

          snake_case__  :    Optional[Any]    						=      tf.constant([-1.0266, 0.1912, -1.2861]						)

          self.assertTrue(np.allclose(outputs.logits[0, :3] ,     snake_case_ ,     atol=1E-4						)						)


import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test


sys.path.append(str(Path(__file__).parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402


_lowerCamelCase					   ={
    """return_dict""": False,
    """output_hidden_states""": True,
    """output_attentions""": True,
    """torchscript""": True,
    """torch_dtype""": """float16""",
    """use_bfloat16""": True,
    """tf_legacy_loss""": True,
    """pruned_heads""": {"""a""": 1},
    """tie_word_embeddings""": False,
    """is_decoder""": True,
    """cross_attention_hidden_size""": 1_2_8,
    """add_cross_attention""": True,
    """tie_encoder_decoder""": True,
    """max_length""": 5_0,
    """min_length""": 3,
    """do_sample""": True,
    """early_stopping""": True,
    """num_beams""": 3,
    """num_beam_groups""": 3,
    """diversity_penalty""": 0.5,
    """temperature""": 2.0,
    """top_k""": 1_0,
    """top_p""": 0.7,
    """typical_p""": 0.2,
    """repetition_penalty""": 0.8,
    """length_penalty""": 0.8,
    """no_repeat_ngram_size""": 5,
    """encoder_no_repeat_ngram_size""": 5,
    """bad_words_ids""": [1, 2, 3],
    """num_return_sequences""": 3,
    """chunk_size_feed_forward""": 5,
    """output_scores""": True,
    """return_dict_in_generate""": True,
    """forced_bos_token_id""": 2,
    """forced_eos_token_id""": 3,
    """remove_invalid_values""": True,
    """architectures""": ["""BertModel"""],
    """finetuning_task""": """translation""",
    """id2label""": {0: """label"""},
    """label2id""": {"""label""": """0"""},
    """tokenizer_class""": """BertTokenizerFast""",
    """prefix""": """prefix""",
    """bos_token_id""": 6,
    """pad_token_id""": 7,
    """eos_token_id""": 8,
    """sep_token_id""": 9,
    """decoder_start_token_id""": 1_0,
    """exponential_decay_length_penalty""": (5, 1.01),
    """suppress_tokens""": [0, 1],
    """begin_suppress_tokens""": 2,
    """task_specific_params""": {"""translation""": """some_params"""},
    """problem_type""": """regression""",
}





@is_staging_test
class 							A__		(	unittest.TestCase):
      @classmethod
      def 						UpperCamelCase__							(      cls      ):
       lowerCamelCase		:	int							=      TOKEN
       HfFolder.save_token(__magic_name__      )
      @classmethod
      def 						UpperCamelCase__							(      cls      ):
       try:
        delete_repo(token=cls._token    ,  repo_id="""test-config"""      )
       except HTTPError:
        pass

       try:
        delete_repo(token=cls._token    ,  repo_id="""valid_org/test-config-org"""      )
       except HTTPError:
        pass

       try:
        delete_repo(token=cls._token    ,  repo_id="""test-dynamic-config"""      )
       except HTTPError:
        pass
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      BertConfig(
           vocab_size=9_9    ,  hidden_size=3_2    ,  num_hidden_layers=5    ,  num_attention_heads=4    ,  intermediate_size=3_7      )
       config.push_to_hub("""test-config"""    ,  use_auth_token=self._token      )

       lowerCamelCase		:	Any							=      BertConfig.from_pretrained(F'''{USER}/test-config'''      )
       for k, v in config.to_dict().items():
        if k != "transformers_version":
         self.assertEqual(__magic_name__    ,  getattr(__magic_name__    ,  __magic_name__      )      )

        # Reset repo
       delete_repo(token=self._token    ,  repo_id="""test-config"""      )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
        config.save_pretrained(__magic_name__    ,  repo_id="""test-config"""    ,  push_to_hub=__magic_name__    ,  use_auth_token=self._token      )

       lowerCamelCase		:	Optional[Any]							=      BertConfig.from_pretrained(F'''{USER}/test-config'''      )
       for k, v in config.to_dict().items():
        if k != "transformers_version":
         self.assertEqual(__magic_name__    ,  getattr(__magic_name__    ,  __magic_name__      )      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      BertConfig(
           vocab_size=9_9    ,  hidden_size=3_2    ,  num_hidden_layers=5    ,  num_attention_heads=4    ,  intermediate_size=3_7      )
       config.push_to_hub("""valid_org/test-config-org"""    ,  use_auth_token=self._token      )

       lowerCamelCase		:	Optional[int]							=      BertConfig.from_pretrained("""valid_org/test-config-org"""      )
       for k, v in config.to_dict().items():
        if k != "transformers_version":
         self.assertEqual(__magic_name__    ,  getattr(__magic_name__    ,  __magic_name__      )      )

        # Reset repo
       delete_repo(token=self._token    ,  repo_id="""valid_org/test-config-org"""      )

       # Push to hub via save_pretrained
       with tempfile.TemporaryDirectory() as tmp_dir:
        config.save_pretrained(
            __magic_name__    ,  repo_id="""valid_org/test-config-org"""    ,  push_to_hub=__magic_name__    ,  use_auth_token=self._token      )

       lowerCamelCase		:	List[str]							=      BertConfig.from_pretrained("""valid_org/test-config-org"""      )
       for k, v in config.to_dict().items():
        if k != "transformers_version":
         self.assertEqual(__magic_name__    ,  getattr(__magic_name__    ,  __magic_name__      )      )






      def 						UpperCamelCase__							(      self      ):
       CustomConfig.register_for_auto_class()
       lowerCamelCase		:	Optional[Any]							=      CustomConfig(attribute=4_2      )

       config.push_to_hub("""test-dynamic-config"""    ,  use_auth_token=self._token      )

       # This has added the proper auto_map field to the config
       self.assertDictEqual(config.auto_map    ,  {"""AutoConfig""": """custom_configuration.CustomConfig"""}      )

       lowerCamelCase		:	List[str]							=      AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config'''    ,  trust_remote_code=__magic_name__      )
       # Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
       self.assertEqual(new_config.__class__.__name__    ,  """CustomConfig"""      )
       self.assertEqual(new_config.attribute    ,  4_2      )





class 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      GPTaConfig()

       # attempt to modify each of int/float/bool/str config records and verify they were updated
       lowerCamelCase		:	Optional[int]							=      c.n_embd + 1  # int
       lowerCamelCase		:	Optional[int]							=      c.resid_pdrop + 1.0  # float
       lowerCamelCase		:	Tuple							=      not c.scale_attn_weights  # bool
       lowerCamelCase		:	Any							=      c.summary_type + """foo"""  # str
       c.update_from_string(
           F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}'''      )
       self.assertEqual(__magic_name__    ,  c.n_embd    ,  """mismatch for key: n_embd"""      )
       self.assertEqual(__magic_name__    ,  c.resid_pdrop    ,  """mismatch for key: resid_pdrop"""      )
       self.assertEqual(__magic_name__    ,  c.scale_attn_weights    ,  """mismatch for key: scale_attn_weights"""      )
       self.assertEqual(__magic_name__    ,  c.summary_type    ,  """mismatch for key: summary_type"""      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	str							=      PretrainedConfig()
       lowerCamelCase		:	int							=      [key for key in base_config.__dict__ if key not in config_common_kwargs]
       # If this part of the test fails, you have arguments to addin config_common_kwargs above.
       self.assertListEqual(
           __magic_name__    ,  ["""is_encoder_decoder""", """_name_or_path""", """_commit_hash""", """transformers_version"""]      )
       lowerCamelCase		:	List[str]							=      [key for key, value in config_common_kwargs.items() if value == getattr(__magic_name__    ,  __magic_name__      )]
       if len(__magic_name__      ) > 0:
        raise ValueError(
            """The following keys are set with the default values in"""
            """ `test_configuration_common.config_common_kwargs` pick another value for them:"""
            F''' {", ".join(__magic_name__      )}.'''      )
      def 						UpperCamelCase__							(      self      ):
       with self.assertRaises(__magic_name__      ):
        # config is in subfolder, the following should not work without specifying the subfolder
        lowerCamelCase		:	Dict							=      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""      )

       lowerCamelCase		:	str							=      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert-subfolder"""    ,  subfolder="""bert"""      )

       self.assertIsNotNone(__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       # A mock response for an HTTP head request to emulate server down
       lowerCamelCase		:	Dict							=      mock.Mock()
       lowerCamelCase		:	Optional[int]							=      5_0_0
       lowerCamelCase		:	List[Any]							=      {}
       lowerCamelCase		:	Tuple							=      HTTPError
       lowerCamelCase		:	Union[str, Any]							=      {}

       # Download this model to make sure it's in the cache.
       lowerCamelCase		:	List[str]							=      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert"""      )

       # Under the mock environment we get a 500 error when trying to reach the model.
       with mock.patch("""requests.Session.request"""    ,  return_value=__magic_name__      ) as mock_head:
        lowerCamelCase		:	Any							=      BertConfig.from_pretrained("""hf-internal-testing/tiny-random-bert"""      )
        # This check we did call the fake head request
        mock_head.assert_called()
      def 						UpperCamelCase__							(      self      ):
       # This test is for deprecated behavior and can be removed in v5
       lowerCamelCase		:	List[str]							=      BertConfig.from_pretrained(
           """https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json"""      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      AutoConfig.from_pretrained("""bert-base-cased"""      )
       lowerCamelCase		:	Optional[Any]							=      ["""config.4.0.0.json"""]

       with tempfile.TemporaryDirectory() as tmp_dir:
        configuration.save_pretrained(__magic_name__      )
        lowerCamelCase		:	str							=      2
        json.dump(configuration.to_dict()    ,  open(os.path.join(__magic_name__    ,  """config.4.0.0.json"""      )    ,  """w"""      )      )

        # This should pick the new configuration file as the version of Transformers is > 4.0.0
        lowerCamelCase		:	Union[str, Any]							=      AutoConfig.from_pretrained(__magic_name__      )
        self.assertEqual(new_configuration.hidden_size    ,  2      )

        # Will need to be adjusted if we reach v42 and this test is still here.
        # Should pick the old configuration file as the version of Transformers is < 4.42.0
        lowerCamelCase		:	Any							=      ["""config.42.0.0.json"""]
        lowerCamelCase		:	Optional[Any]							=      7_6_8
        configuration.save_pretrained(__magic_name__      )
        shutil.move(os.path.join(__magic_name__    ,  """config.4.0.0.json"""      )    ,  os.path.join(__magic_name__    ,  """config.42.0.0.json"""      )      )
        lowerCamelCase		:	int							=      AutoConfig.from_pretrained(__magic_name__      )
        self.assertEqual(new_configuration.hidden_size    ,  7_6_8      )






      def 						UpperCamelCase__							(      self      ):
       # This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
       lowerCamelCase		:	str							=      """hf-internal-testing/test-two-configs"""

       import transformers as new_transformers

       lowerCamelCase		:	Tuple							=      """v4.0.0"""
       lowerCamelCase			,    lowerCamelCase		:	Optional[int]							=      new_transformers.models.auto.AutoConfig.from_pretrained(
           __magic_name__    ,  return_unused_kwargs=__magic_name__      )
       self.assertEqual(new_configuration.hidden_size    ,  2      )
       # This checks `_configuration_file` ia not kept in the kwargs by mistake.
       self.assertDictEqual(__magic_name__    ,  {}      )

       # Testing an older version by monkey-patching the version in the module it's used.
       import transformers as old_transformers

       lowerCamelCase		:	Tuple							=      """v3.0.0"""
       lowerCamelCase		:	Any							=      old_transformers.models.auto.AutoConfig.from_pretrained(__magic_name__      )
       self.assertEqual(old_configuration.hidden_size    ,  7_6_8      )




