
"""simple docstring"""



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       lowerCAmelCase__				:







     def __init__(							self			:			Optional[int]    ,      _lowerCamelCase			:			List[str]    ,      _lowerCamelCase			:			Union[str, Any]=13    ,      _lowerCamelCase			:			List[Any]=30    ,      _lowerCamelCase			:			List[str]=2    ,      _lowerCamelCase			:			int=3    ,      _lowerCamelCase			:			Dict=True    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			Any=32    ,      _lowerCamelCase			:			Dict=2    ,      _lowerCamelCase			:			Tuple=4    ,      _lowerCamelCase			:			Union[str, Any]=37    ,      _lowerCamelCase			:			List[str]="gelu"    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Optional[int]=0.1    ,      _lowerCamelCase			:			Any=10    ,      _lowerCamelCase			:			Optional[int]=0.0_2    ,      _lowerCamelCase			:			str=3    ,      _lowerCamelCase			:			Optional[int]=None    ,      ):
       _snake_case					     =       parent
       _snake_case					     =       batch_size
       _snake_case					     =       image_size
       _snake_case					     =       patch_size
       _snake_case					     =       num_channels
       _snake_case					     =       is_training
       _snake_case					     =       use_labels
       _snake_case					     =       hidden_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       intermediate_size
       _snake_case					     =       hidden_act
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_probs_dropout_prob
       _snake_case					     =       type_sequence_label_size
       _snake_case					     =       initializer_range
       _snake_case					     =       scope

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







     def      lowercase	(							self			:			int			):
       _snake_case					     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

       _snake_case					     =       None
       if self.use_labels:
         _snake_case					     =       ids_tensor([self.batch_size]    ,      self.type_sequence_label_size			)

       _snake_case					     =       self.get_config()

       return config, pixel_values, labels







     def      lowercase	(							self			:			List[str]			):
       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      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Union[str, Any]			):
       _snake_case					     =       TFViTModel(config=__UpperCamelCase			)
       _snake_case					     =       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.
       _snake_case					     =       self.image_size // 2
       _snake_case					     =       pixel_values[:, :, :image_size, :image_size]
       _snake_case					     =       model(__UpperCamelCase    ,      interpolate_pos_encoding=__UpperCamelCase    ,      training=__UpperCamelCase			)
       _snake_case					     =       (image_size // self.patch_size) ** 2 + 1
       self.parent.assertEqual(result.last_hidden_state.shape    ,      (self.batch_size, seq_length, self.hidden_size)			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			List[str]			):
       _snake_case					     =       self.type_sequence_label_size
       _snake_case					     =       TFViTForImageClassification(__UpperCamelCase			)
       _snake_case					     =       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.
       _snake_case					     =       self.image_size // 2
       _snake_case					     =       pixel_values[:, :, :image_size, :image_size]
       _snake_case					     =       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
       _snake_case					     =       1
       _snake_case					     =       TFViTForImageClassification(__UpperCamelCase			)
       _snake_case					     =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
       _snake_case					     =       model(__UpperCamelCase			)
       self.parent.assertEqual(result.logits.shape    ,      (self.batch_size, self.type_sequence_label_size)			)







     def      lowercase	(							self			:			Dict			):
       _snake_case					     =       self.prepare_config_and_inputs()
       _snake_case     ,					_snake_case     ,					_snake_case					     =       config_and_inputs
       _snake_case					     =       {'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class       lowerCAmelCase__				(						lowercase_ ,	lowercase_ ,	unittest.TestCase	):
     __a						=					(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
     __a						=					(
         {"""feature-extraction""": TFViTModel, """image-classification""": TFViTForImageClassification}
         if is_tf_available()
         else {}
     )

     __a						=					False
     __a						=					False
     __a						=					False







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case					     =       TFViTModelTester(self			)
       _snake_case					     =       ConfigTester(self    ,      config_class=__UpperCamelCase    ,      has_text_modality=__UpperCamelCase    ,      hidden_size=37			)







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







     @unittest.skip(reason='''ViT does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Any			):
       pass







     @unittest.skip(reason='''ViT does not use inputs_embeds'''			)
     def      lowercase	(							self			:			Optional[int]			):
       pass







     def      lowercase	(							self			:			List[str]			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

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







     def      lowercase	(							self			:			Union[str, Any]			):
       _snake_case     ,					_snake_case					     =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case					     =       model_class(__UpperCamelCase			)
         _snake_case					     =       inspect.signature(model.call			)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _snake_case					     =       [*signature.parameters.keys()]

         _snake_case					     =       ['''pixel_values''']
         self.assertListEqual(arg_names[:1]    ,      __UpperCamelCase			)







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCamelCase			)







     def      lowercase	(							self			:			Tuple			):
       _snake_case					     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase			)







     @slow
     def      lowercase	(							self			:			Any			):
       _snake_case					     =       TFViTModel.from_pretrained('''google/vit-base-patch16-224'''			)
       self.assertIsNotNone(__UpperCamelCase			)






def 					_UpperCAmelCase      ( )     ->					Union[str, Any]:
  _snake_case					     =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
  return image

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







     @cached_property
     def      lowercase	(							self			:			Union[str, Any]			):
       return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''			) if is_vision_available() else None







     @slow
     def      lowercase	(							self			:			str			):
       _snake_case					     =       TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''			)

       _snake_case					     =       self.default_image_processor
       _snake_case					     =       prepare_img()
       _snake_case					     =       image_processor(images=__UpperCamelCase    ,      return_tensors='''tf'''			)

       # forward pass
       _snake_case					     =       model(**__UpperCamelCase			)

       # verify the logits
       _snake_case					     =       tf.TensorShape((1, 1000)			)
       self.assertEqual(outputs.logits.shape    ,      __UpperCamelCase			)

       _snake_case					     =       tf.constant([-0.2_7_4_4, 0.8_2_1_5, -0.0_8_3_6]			)

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










"""simple docstring"""
import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_rjieba
@require_tokenizers
class 					_UpperCAmelCase   (	lowercase_     ,							unittest.TestCase ):
					UpperCamelCase      =							RoFormerTokenizer
					UpperCamelCase      =							RoFormerTokenizerFast
					UpperCamelCase      =							True
					UpperCamelCase      =							True


					def 	lowerCamelCase       (					self			:List[str]		):
												super().setUp()


					def 	lowerCamelCase       (					self			:int     ,       **__UpperCamelCase			:List[Any]		):
												return self.tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Optional[int]		):
												return self.rust_tokenizer_class.from_pretrained("junnyu/roformer_chinese_base"     ,       **__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												A		=  "永和服装饰品有限公司,今天天气非常好"
												A		=  "永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"
												return input_text, output_text


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.get_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)

												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)

												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												A		=  self.get_rust_tokenizer()
												A,	A		=  self.get_chinese_input_output_texts()
												A		=  tokenizer.tokenize(__UpperCamelCase		)
												self.assertListEqual(__UpperCamelCase     ,       output_text.split()		)
												A		=  tokens + [tokenizer.unk_token]
												A		=  [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCamelCase		)     ,       __UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Tuple		):
												pass


					def 	lowerCamelCase       (					self			:List[str]		):
												pass


