





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch





def 					lowerCAmelCase_			(	__a		)					->		Dict:  # picklable for multiprocessing







  """simple docstring"""





  return x.sum()





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:  # picklable for multiprocessing







  """simple docstring"""





  return i + 1



@dataclass
class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''
 lowercase_		=      42
 lowercase_		=      42



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        ={}
   lowerCamelCase__:				Dict        =[]
   lowerCamelCase__:				Any        =1
   lowerCamelCase__:				List[str]        =[1, 2]
   lowerCamelCase__:				List[str]        ={"a": 1, "b": 2}
   lowerCamelCase__:				Optional[Any]        ={"a": [1, 2], "b": [3, 4]}
   lowerCamelCase__:				Union[str, Any]        ={"a": {"1": 1}, "b": 2}
   lowerCamelCase__:				Tuple        ={"a": 1, "b": 2, "c": 3, "d": 4}
   lowerCamelCase__:				Any        ={}
   lowerCamelCase__:				Union[str, Any]        =[]
   lowerCamelCase__:				int        =2
   lowerCamelCase__:				Dict        =[2, 3]
   lowerCamelCase__:				List[str]        ={"a": 2, "b": 3}
   lowerCamelCase__:				str        ={"a": [2, 3], "b": [4, 5]}
   lowerCamelCase__:				List[str]        ={"a": {"1": 2}, "b": 3}
   lowerCamelCase__:				Union[str, Any]        ={"a": 2, "b": 3, "c": 4, "d": 5}
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_)	,			UpperCAmelCase_)

   lowerCamelCase__:				List[str]        =2
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)

   lowerCamelCase__:				int        ={"a": np.eye(2), "b": np.zeros(3), "c": np.ones(2)}
   lowerCamelCase__:				Dict        ={"a": 2, "b": 0, "c": 2}
   lowerCamelCase__:				Dict        ={
       "a": np.eye(2).astype(UpperCAmelCase_),
       "b": np.zeros(3).astype(UpperCAmelCase_),
       "c": np.ones(2).astype(UpperCAmelCase_),
   }
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			map_numpy=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(
       {k: v.tolist() for k, v in map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			map_numpy=UpperCAmelCase_).items()}	,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}	,			)
   self.assertEqual(map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			map_numpy=UpperCAmelCase_	,			num_proc=UpperCAmelCase_)	,			UpperCAmelCase_)
   self.assertEqual(
       {k: v.tolist() for k, v in map_nested(UpperCAmelCase_	,			UpperCAmelCase_	,			map_numpy=UpperCAmelCase_	,			num_proc=UpperCAmelCase_).items()}	,			{k: v.tolist() for k, v in expected_map_nested_sna_int.items()}	,			)
   with self.assertRaises(UpperCAmelCase_):  # can't pickle a local lambda
     map_nested(lambda UpperCAmelCase_: x + 1	,			UpperCAmelCase_	,			num_proc=UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->int:



   '''simple docstring'''



   lowerCamelCase__:				List[Any]        ={"a": 1, "b": 2}
   lowerCamelCase__:				int        ={"a": 3, "b": 4}
   lowerCamelCase__:				Union[str, Any]        ={"a": 5, "b": 6}
   lowerCamelCase__:				List[Any]        =sorted([("a", (1, 3, 5)), ("b", (2, 4, 6))])
   self.assertEqual(sorted(zip_dict(UpperCAmelCase_	,			UpperCAmelCase_	,			UpperCAmelCase_))	,			UpperCAmelCase_)







 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->str:



   '''simple docstring'''



   class        _SCREAMING_SNAKE_CASE						:







    '''simple docstring'''
    lowercase_		=      "bar"



   lowerCamelCase__:				Dict        =Foo()
   self.assertEqual(foo.my_attr	,			"bar")
   with temporary_assignment(UpperCAmelCase_	,			"my_attr"	,			"BAR"):
     self.assertEqual(foo.my_attr	,			"BAR")
   self.assertEqual(foo.my_attr	,			"bar")





@pytest.mark.parametrize(
    "iterable_length, num_proc, expected_num_proc"     ,   [
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]     ,   )
def 					lowerCAmelCase_			(	__a     ,   __a     ,   __a		)					->		List[Any]:







  """simple docstring"""





  with patch("datasets.utils.py_utils._single_map_nested"		) as mock_single_map_nested, patch(
      "datasets.parallel.parallel.Pool"		) as mock_multiprocessing_pool:
    lowerCamelCase__:				List[str]        ={F"""{i}""": i for i in range(__a		)}
    lowerCamelCase__:				str        =map_nested(lambda __a		: x + 10     ,   __a     ,   num_proc=__a     ,   parallel_min_length=16		)
    if expected_num_proc == 1:
      assert mock_single_map_nested.called
      assert not mock_multiprocessing_pool.called
    else:
      assert not mock_single_map_nested.called
      assert mock_multiprocessing_pool.called
      assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @require_tf
 def 							SCREAMING_SNAKE_CASE_ (self    :       Dict)	->int:



   '''simple docstring'''



   import tensorflow as tf
   from tensorflow.keras import layers

   lowerCamelCase__:				Optional[Any]        =layers.Dense(2)

   def gen_random_output():
     lowerCamelCase__:				Optional[int]        =tf.random.uniform((1, 3))
     return model(UpperCAmelCase_).numpy()

   with temp_seed(42	,			set_tensorflow=UpperCAmelCase_):
     lowerCamelCase__:				Optional[Any]        =gen_random_output()
   with temp_seed(42	,			set_tensorflow=UpperCAmelCase_):
     lowerCamelCase__:				Any        =gen_random_output()
   lowerCamelCase__:				Union[str, Any]        =gen_random_output()

   np.testing.assert_equal(UpperCAmelCase_	,			UpperCAmelCase_)
   self.assertGreater(np.abs(outa - outa).sum()	,			0)

 @require_torch
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->str:



   '''simple docstring'''



   import torch

   def gen_random_output():
     lowerCamelCase__:				Union[str, Any]        =torch.nn.Linear(3	,			2)
     lowerCamelCase__:				List[Any]        =torch.rand(1	,			3)
     return model(UpperCAmelCase_).detach().numpy()

   with temp_seed(42	,			set_pytorch=UpperCAmelCase_):
     lowerCamelCase__:				Tuple        =gen_random_output()
   with temp_seed(42	,			set_pytorch=UpperCAmelCase_):
     lowerCamelCase__:				Tuple        =gen_random_output()
   lowerCamelCase__:				Optional[Any]        =gen_random_output()

   np.testing.assert_equal(UpperCAmelCase_	,			UpperCAmelCase_)
   self.assertGreater(np.abs(outa - outa).sum()	,			0)







 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Union[str, Any]:



   '''simple docstring'''



   def gen_random_output():
     return np.random.rand(1	,			3)

   with temp_seed(42):
     lowerCamelCase__:				Optional[int]        =gen_random_output()
   with temp_seed(42):
     lowerCamelCase__:				Tuple        =gen_random_output()
   lowerCamelCase__:				Dict        =gen_random_output()

   np.testing.assert_equal(UpperCAmelCase_	,			UpperCAmelCase_)
   self.assertGreater(np.abs(outa - outa).sum()	,			0)





@pytest.mark.parametrize("input_data"     ,   [{}]		)
def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =NestedDataStructure(__a		).data
  assert output_data == input_data





@pytest.mark.parametrize(
    "data, expected_output"     ,   [
        ({}, []),
        ([], []),
        ("foo", ["foo"]),
        (["foo", "bar"], ["foo", "bar"]),
        ([["foo", "bar"]], ["foo", "bar"]),
        ([[["foo"], ["bar"]]], ["foo", "bar"]),
        ([[["foo"], "bar"]], ["foo", "bar"]),
        ({"a": 1, "b": 2}, [1, 2]),
        ({"a": [1, 2], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[1, 2]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [[3, 4]]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, 4]}, [1, 2, 3, 4]),
        ({"a": [[[1], [2]]], "b": [3, [4]]}, [1, 2, 3, 4]),
        ({"a": {"1": 1}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": 2}, [1, 2]),
        ({"a": {"1": [1]}, "b": [2]}, [1, 2]),
    ]     ,   )
def 					lowerCAmelCase_			(	__a     ,   __a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				List[str]        =NestedDataStructure(__a		).flatten()
  assert output == expected_output





def 					lowerCAmelCase_			(	)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				Optional[Any]        =A(x=1     ,   y="foobar"		)
  lowerCamelCase__:				Dict        ={"x": 1, "y": "foobar"}
  assert asdict(__a		) == expected_output

  lowerCamelCase__:				str        ={"a": {"b": A(x=10     ,   y="foo"		)}, "c": [A(x=20     ,   y="bar"		)]}
  lowerCamelCase__:				Optional[Any]        ={"a": {"b": {"x": 10, "y": "foo"}}, "c": [{"x": 20, "y": "bar"}]}
  assert asdict(__a		) == expected_output

  with pytest.raises(__a		):
    asdict([1, A(x=10     ,   y="foo"		)]		)





def 					lowerCAmelCase_			(	__a		)					->		List[Any]:







  """simple docstring"""





  return text.split()





def 					lowerCAmelCase_			(	__a		)					->		Optional[Any]:







  """simple docstring"""





  yield (time.time(), content)
  time.sleep(2		)
  yield (time.time(), content)






def 					lowerCAmelCase_			(	)					->		List[Any]:







  """simple docstring"""





  with Pool(2		) as pool:
    lowerCamelCase__:				Any        =list(iflatmap_unordered(__a     ,   _split_text     ,   kwargs_iterable=[{"text": "hello there"}] * 10		)		)
    assert out.count("hello"		) == 10
    assert out.count("there"		) == 10
    assert len(__a		) == 20

  # check multiprocess from pathos (uses dill for pickling)
  with multiprocess.Pool(2		) as pool:
    lowerCamelCase__:				Optional[int]        =list(iflatmap_unordered(__a     ,   _split_text     ,   kwargs_iterable=[{"text": "hello there"}] * 10		)		)
    assert out.count("hello"		) == 10
    assert out.count("there"		) == 10
    assert len(__a		) == 20

  # check that we get items as fast as possible
  with Pool(2		) as pool:
    lowerCamelCase__:				str        =[]
    for yield_time, content in iflatmap_unordered(
        __a     ,   _aseconds_generator_of_aitems_with_timing     ,   kwargs_iterable=[{"content": "a"}, {"content": "b"}]		):
      assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
      out.append(__a		)
    assert out.count("a"		) == 2
    assert out.count("b"		) == 2
    assert len(__a		) == 4








from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
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 TFViTForImageClassification, TFViTModel


if is_vision_available():
     from PIL import Image

     from transformers import ViTImageProcessor



class        _SCREAMING_SNAKE_CASE						:







 '''simple docstring'''

 def __init__(self    :       Optional[Any]	,			UpperCAmelCase_    :       Optional[int]	,			UpperCAmelCase_    :       List[Any]=13	,			UpperCAmelCase_    :       Dict=30	,			UpperCAmelCase_    :       str=2	,			UpperCAmelCase_    :       Union[str, Any]=3	,			UpperCAmelCase_    :       Union[str, Any]=True	,			UpperCAmelCase_    :       List[str]=True	,			UpperCAmelCase_    :       Dict=32	,			UpperCAmelCase_    :       List[Any]=2	,			UpperCAmelCase_    :       List[str]=4	,			UpperCAmelCase_    :       List[str]=37	,			UpperCAmelCase_    :       List[str]="gelu"	,			UpperCAmelCase_    :       List[Any]=0.1	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       str=10	,			UpperCAmelCase_    :       str=0.02	,			UpperCAmelCase_    :       Optional[int]=3	,			UpperCAmelCase_    :       Dict=None	,			)	->Union[str, Any]:



   '''simple docstring'''



   lowerCamelCase__:				int        =parent
   lowerCamelCase__:				Any        =batch_size
   lowerCamelCase__:				List[str]        =image_size
   lowerCamelCase__:				Tuple        =patch_size
   lowerCamelCase__:				Tuple        =num_channels
   lowerCamelCase__:				Tuple        =is_training
   lowerCamelCase__:				Optional[int]        =use_labels
   lowerCamelCase__:				Dict        =hidden_size
   lowerCamelCase__:				Dict        =num_hidden_layers
   lowerCamelCase__:				Optional[Any]        =num_attention_heads
   lowerCamelCase__:				Union[str, Any]        =intermediate_size
   lowerCamelCase__:				List[str]        =hidden_act
   lowerCamelCase__:				Optional[Any]        =hidden_dropout_prob
   lowerCamelCase__:				str        =attention_probs_dropout_prob
   lowerCamelCase__:				str        =type_sequence_label_size
   lowerCamelCase__:				Optional[Any]        =initializer_range
   lowerCamelCase__:				Optional[int]        =scope

   # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
   lowerCamelCase__:				List[str]        =(image_size // patch_size) ** 2
   lowerCamelCase__:				Optional[Any]        =num_patches + 1

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->List[str]:



   '''simple docstring'''



   lowerCamelCase__:				Tuple        =floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

   lowerCamelCase__:				List[str]        =None
   if self.use_labels:
     lowerCamelCase__:				Any        =ids_tensor([self.batch_size]	,			self.type_sequence_label_size)

   lowerCamelCase__:				List[str]        =self.get_config()

   return config, pixel_values, labels

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->List[str]:



   '''simple docstring'''



   return ViTConfig(
       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=UpperCAmelCase_	,			initializer_range=self.initializer_range	,			)

 def 							SCREAMING_SNAKE_CASE_ (self    :       int	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple)	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =TFViTModel(config=UpperCAmelCase_)
   lowerCamelCase__:				Tuple        =model(UpperCAmelCase_	,			training=UpperCAmelCase_)
   self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.seq_length, self.hidden_size))

   # Test with an image with different size than the one specified in config.
   lowerCamelCase__:				Dict        =self.image_size // 2
   lowerCamelCase__:				List[str]        =pixel_values[:, :, :image_size, :image_size]
   lowerCamelCase__:				List[str]        =model(UpperCAmelCase_	,			interpolate_pos_encoding=UpperCAmelCase_	,			training=UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =(image_size // self.patch_size) ** 2 + 1
   self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, seq_length, self.hidden_size))

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any]	,			UpperCAmelCase_    :       Tuple	,			UpperCAmelCase_    :       int	,			UpperCAmelCase_    :       Any)	->str:



   '''simple docstring'''



   lowerCamelCase__:				Any        =self.type_sequence_label_size
   lowerCamelCase__:				str        =TFViTForImageClassification(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =model(UpperCAmelCase_	,			labels=UpperCAmelCase_	,			training=UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size))

   # Test with an image with different size than the one specified in config.
   lowerCamelCase__:				Union[str, Any]        =self.image_size // 2
   lowerCamelCase__:				Dict        =pixel_values[:, :, :image_size, :image_size]
   lowerCamelCase__:				Tuple        =model(UpperCAmelCase_	,			interpolate_pos_encoding=UpperCAmelCase_	,			training=UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size))

   # test greyscale images
   lowerCamelCase__:				int        =1
   lowerCamelCase__:				List[str]        =TFViTForImageClassification(UpperCAmelCase_)
   lowerCamelCase__:				List[str]        =floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
   lowerCamelCase__:				Any        =model(UpperCAmelCase_)
   self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size))







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



   '''simple docstring'''



   lowerCamelCase__:				Optional[Any]        =self.prepare_config_and_inputs()
   lowerCamelCase__  , lowerCamelCase__  , lowerCamelCase__:				Union[str, Any]        =config_and_inputs
   lowerCamelCase__:				Optional[Any]        ={"pixel_values": pixel_values}
   return config, inputs_dict



@require_tf
class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ,							__SCREAMING_SNAKE_CASE   ,							unittest.TestCase   ):







 '''simple docstring'''
 lowercase_		=      (TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
 lowercase_		=      (
     {"feature-extraction": TFViTModel, "image-classification": TFViTForImageClassification}
     if is_tf_available()
     else {}
 )

 lowercase_		=      False
 lowercase_		=      False
 lowercase_		=      False

 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Dict:



   '''simple docstring'''



   lowerCamelCase__:				int        =TFViTModelTester(self)
   lowerCamelCase__:				List[str]        =ConfigTester(self	,			config_class=UpperCAmelCase_	,			has_text_modality=UpperCAmelCase_	,			hidden_size=37)

 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->Tuple:



   '''simple docstring'''



   self.config_tester.run_common_tests()

 @unittest.skip(reason="ViT does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Any:



   '''simple docstring'''



   pass

 @unittest.skip(reason="ViT does not use inputs_embeds")
 def 							SCREAMING_SNAKE_CASE_ (self    :       int)	->str:



   '''simple docstring'''



   pass

 def 							SCREAMING_SNAKE_CASE_ (self    :       Tuple)	->Optional[int]:



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				int        =self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     lowerCamelCase__:				int        =model_class(UpperCAmelCase_)
     self.assertIsInstance(model.get_input_embeddings()	,			(tf.keras.layers.Layer))
     lowerCamelCase__:				Tuple        =model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(UpperCAmelCase_	,			tf.keras.layers.Layer))

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



   '''simple docstring'''



   lowerCamelCase__  , lowerCamelCase__:				Optional[Any]        =self.model_tester.prepare_config_and_inputs_for_common()

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

     lowerCamelCase__:				str        =["pixel_values"]
     self.assertListEqual(arg_names[:1]	,			UpperCAmelCase_)

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



   '''simple docstring'''



   lowerCamelCase__:				Optional[int]        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase_)

 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[Any])	->Any:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_)







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



   '''simple docstring'''



   lowerCamelCase__:				Any        =TFViTModel.from_pretrained("google/vit-base-patch16-224")
   self.assertIsNotNone(UpperCAmelCase_)





def 					lowerCAmelCase_			(	)					->		Tuple:







  """simple docstring"""





  lowerCamelCase__:				Any        =Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
  return image






@require_tf
@require_vision
class        _SCREAMING_SNAKE_CASE						(     unittest.TestCase   ):







 '''simple docstring'''

 @cached_property
 def 							SCREAMING_SNAKE_CASE_ (self    :       str)	->List[Any]:



   '''simple docstring'''



   return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224") if is_vision_available() else None







 @slow
 def 							SCREAMING_SNAKE_CASE_ (self    :       Optional[int])	->Tuple:



   '''simple docstring'''



   lowerCamelCase__:				List[str]        =TFViTForImageClassification.from_pretrained("google/vit-base-patch16-224")

   lowerCamelCase__:				List[str]        =self.default_image_processor
   lowerCamelCase__:				Union[str, Any]        =prepare_img()
   lowerCamelCase__:				List[Any]        =image_processor(images=UpperCAmelCase_	,			return_tensors="tf")

   # forward pass
   lowerCamelCase__:				Dict        =model(**UpperCAmelCase_)

   # verify the logits
   lowerCamelCase__:				List[Any]        =tf.TensorShape((1, 1_000))
   self.assertEqual(outputs.logits.shape	,			UpperCAmelCase_)

   lowerCamelCase__:				List[Any]        =tf.constant([-0.2744, 0.8215, -0.0836])

   tf.debugging.assert_near(outputs.logits[0, :3]	,			UpperCAmelCase_	,			atol=1E-4)

