import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax
     import jax.numpy as jnp

     from transformers.modeling_flax_pytorch_utils import (
         convert_pytorch_state_dict_to_flax,
         load_flax_weights_in_pytorch_model,
     )
     from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
     import torch







class 	__snake_case      :
 def __init__(     self						,      snake_case__						,      snake_case__=14						,      snake_case__=7						,      snake_case__=True						,      snake_case__=True						,      snake_case__=False						,      snake_case__=True						,      snake_case__=99						,      snake_case__=32						,      snake_case__=4						,      snake_case__=4						,      snake_case__=4						,      snake_case__=37						,      snake_case__="gelu"						,      snake_case__=0.1						,      snake_case__=0.1						,      snake_case__=512						,      snake_case__=0.02						,      )		->							int:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =parent
       UpperCAmelCase			:       Optional[int]			     =batch_size
       UpperCAmelCase			:       Dict			     =seq_length
       UpperCAmelCase			:       Union[str, Any]			     =is_training
       UpperCAmelCase			:       List[Any]			     =use_input_mask
       UpperCAmelCase			:       Tuple			     =use_token_type_ids
       UpperCAmelCase			:       Any			     =use_labels
       UpperCAmelCase			:       Optional[int]			     =vocab_size
       UpperCAmelCase			:       Optional[int]			     =hidden_size
       UpperCAmelCase			:       int			     =rotary_dim
       UpperCAmelCase			:       Optional[int]			     =num_hidden_layers
       UpperCAmelCase			:       Optional[Any]			     =num_attention_heads
       UpperCAmelCase			:       List[Any]			     =intermediate_size
       UpperCAmelCase			:       List[str]			     =hidden_act
       UpperCAmelCase			:       Any			     =hidden_dropout_prob
       UpperCAmelCase			:       Optional[Any]			     =attention_probs_dropout_prob
       UpperCAmelCase			:       Optional[int]			     =max_position_embeddings
       UpperCAmelCase			:       Optional[int]			     =initializer_range
       UpperCAmelCase			:       List[str]			     =None
       UpperCAmelCase			:       Tuple			     =vocab_size - 1
       UpperCAmelCase			:       Any			     =vocab_size - 1
       UpperCAmelCase			:       Dict			     =vocab_size - 1
 def 		UpperCAmelCase__				(     self  )		->							Any:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size  )

       UpperCAmelCase			:       Any			     =None
       if self.use_input_mask:
             UpperCAmelCase			:       Tuple			     =random_attention_mask([self.batch_size, self.seq_length]  )

       UpperCAmelCase			:       List[Any]			     =GPTJConfig(
           vocab_size=self.vocab_size						,      n_embd=self.hidden_size						,      n_layer=self.num_hidden_layers						,      n_head=self.num_attention_heads						,      n_positions=self.max_position_embeddings						,      use_cache=lowerCAmelCase__						,      bos_token_id=self.bos_token_id						,      eos_token_id=self.eos_token_id						,      pad_token_id=self.pad_token_id						,      rotary_dim=self.rotary_dim						,      )

       return (config, input_ids, input_mask)
 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =self.prepare_config_and_inputs()
       UpperCAmelCase			:       Any			     =config_and_inputs
       UpperCAmelCase			:       List[Any]			     ={"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict
 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__						,      snake_case__						,      snake_case__  )		->							Dict:
       '''simple docstring'''





       UpperCAmelCase			:       Union[str, Any]			     =20
       UpperCAmelCase			:       Union[str, Any]			     =model_class_name(lowerCAmelCase__  )

       UpperCAmelCase			:       str			     =model.init_cache(input_ids.shape[0]						,      lowerCAmelCase__  )
       UpperCAmelCase			:       Optional[Any]			     =jnp.ones((input_ids.shape[0], max_decoder_length)						,      dtype='''i4'''  )

       UpperCAmelCase			:       Optional[int]			     =jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1  )[None, :]						,      (input_ids.shape[0], input_ids.shape[-1] - 1)  )
       UpperCAmelCase			:       int			     =model(
           input_ids[:, :-1]						,      attention_mask=lowerCAmelCase__						,      past_key_values=lowerCAmelCase__						,      position_ids=lowerCAmelCase__						,      )

       UpperCAmelCase			:       int			     =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,      dtype='''i4'''  )
       UpperCAmelCase			:       Union[str, Any]			     =model(
           input_ids[:, -1:]						,      attention_mask=lowerCAmelCase__						,      past_key_values=outputs_cache.past_key_values						,      position_ids=lowerCAmelCase__						,      )

       UpperCAmelCase			:       Union[str, Any]			     =model(lowerCAmelCase__  )

       UpperCAmelCase			:       int			     =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
       self.parent.assertTrue(diff < 1e-3						,      msg=f'''Max diff is {diff}'''  )






 def 		UpperCAmelCase__				(     self						,      snake_case__						,      snake_case__						,      snake_case__						,      snake_case__  )		->							Tuple:
       '''simple docstring'''





       UpperCAmelCase			:       Optional[int]			     =20
       UpperCAmelCase			:       List[Any]			     =model_class_name(lowerCAmelCase__  )

       UpperCAmelCase			:       List[Any]			     =jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])  )]						,      axis=-1						,      )

       UpperCAmelCase			:       Optional[int]			     =model.init_cache(input_ids.shape[0]						,      lowerCAmelCase__  )
       UpperCAmelCase			:       Union[str, Any]			     =jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1  )[None, :]						,      (input_ids.shape[0], input_ids.shape[-1] - 1)  )

       UpperCAmelCase			:       Dict			     =model(
           input_ids[:, :-1]						,      attention_mask=lowerCAmelCase__						,      past_key_values=lowerCAmelCase__						,      position_ids=lowerCAmelCase__						,      )
       UpperCAmelCase			:       List[str]			     =jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,      dtype='''i4'''  )
       UpperCAmelCase			:       Optional[Any]			     =model(
           input_ids[:, -1:]						,      past_key_values=outputs_cache.past_key_values						,      attention_mask=lowerCAmelCase__						,      position_ids=lowerCAmelCase__						,      )

       UpperCAmelCase			:       int			     =model(lowerCAmelCase__						,      attention_mask=lowerCAmelCase__  )

       UpperCAmelCase			:       Union[str, Any]			     =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])  )  )
       self.parent.assertTrue(diff < 1e-3						,      msg=f'''Max diff is {diff}'''  )







@require_flax
class 	__snake_case      (   UpperCAmelCase_    ,      UpperCAmelCase_    ,      unittest.TestCase      ):
 __lowerCamelCase			:      int           =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
 __lowerCamelCase			:      Optional[Any]           =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()
 def 		UpperCAmelCase__				(     self  )		->							Union[str, Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Dict			     =FlaxGPTJModelTester(self  )
 def 		UpperCAmelCase__				(     self  )		->							Union[str, Any]:
       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             UpperCAmelCase			:       Union[str, Any]			     =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(lowerCAmelCase__						,      lowerCAmelCase__						,      lowerCAmelCase__						,      lowerCAmelCase__  )
 def 		UpperCAmelCase__				(     self  )		->							int:
       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             UpperCAmelCase			:       int			     =self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 lowerCAmelCase__						,      lowerCAmelCase__						,      lowerCAmelCase__						,      lowerCAmelCase__  )
 @tooslow
 def 		UpperCAmelCase__				(     self  )		->							List[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       Any			     =GPTaTokenizer.from_pretrained('''gpt2'''						,      pad_token='''<|endoftext|>'''						,      padding_side='''left'''  )
       UpperCAmelCase			:       Any			     =tokenizer(['''Hello this is a long string''', '''Hey''']						,      return_tensors='''np'''						,      padding=lowerCAmelCase__						,      truncation=lowerCAmelCase__  )

       UpperCAmelCase			:       Tuple			     =FlaxGPTJForCausalLM.from_pretrained('''EleutherAI/gpt-j-6B'''  )
       UpperCAmelCase			:       Optional[int]			     =False
       UpperCAmelCase			:       int			     =model.config.eos_token_id

       UpperCAmelCase			:       Union[str, Any]			     =jax.jit(model.generate  )

       UpperCAmelCase			:       int			     =jit_generate(
           inputs['''input_ids''']						,      attention_mask=inputs['''attention_mask''']						,      pad_token_id=tokenizer.pad_token_id  ).sequences

       UpperCAmelCase			:       str			     =tokenizer.batch_decode(lowerCAmelCase__						,      skip_special_tokens=lowerCAmelCase__  )

       UpperCAmelCase			:       str			     =[
           "Hello this is a long string of text.\n\nI'm trying to get the text of the",
           "Hey, I'm a little late to the party. I'm going to",
       ]

       self.assertListEqual(lowerCAmelCase__						,      lowerCAmelCase__  )
 @is_pt_flax_cross_test
 def 		UpperCAmelCase__				(     self  )		->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase			:       List[str]			     =self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__  ):
                   # prepare inputs
                   UpperCAmelCase			:       int			     =self._prepare_for_class(lowerCAmelCase__						,      lowerCAmelCase__  )
                   UpperCAmelCase			:       Any			     ={k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   UpperCAmelCase			:       Tuple			     =model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   UpperCAmelCase			:       Tuple			     =getattr(lowerCAmelCase__						,      lowerCAmelCase__  )

                   UpperCAmelCase			:       str			     =pt_inputs["input_ids"].shape
                   UpperCAmelCase			:       Optional[int]			     =np.random.randint(0						,      seq_length - 1						,      size=(batch_size,)  )
                   for batch_idx, start_index in enumerate(lowerCAmelCase__  ):
                         UpperCAmelCase			:       Dict			     =0
                         UpperCAmelCase			:       Union[str, Any]			     =1
                         UpperCAmelCase			:       Optional[int]			     =0
                         UpperCAmelCase			:       Optional[int]			     =1
                   UpperCAmelCase			:       Any			     =pt_model_class(lowerCAmelCase__  ).eval()
                   UpperCAmelCase			:       Any			     =model_class(lowerCAmelCase__						,      dtype=jnp.floataa  )

                   UpperCAmelCase			:       List[Any]			     =convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,      lowerCAmelCase__  )
                   UpperCAmelCase			:       int			     =fx_state

                   with torch.no_grad():
                         UpperCAmelCase			:       Union[str, Any]			     =pt_model(**lowerCAmelCase__  ).to_tuple()

                   UpperCAmelCase			:       List[Any]			     =fx_model(**lowerCAmelCase__  ).to_tuple()
                   self.assertEqual(len(lowerCAmelCase__  )						,      len(lowerCAmelCase__  )						,      '''Output lengths differ between Flax and PyTorch'''  )
                   for fx_output, pt_output in zip(lowerCAmelCase__						,      lowerCAmelCase__  ):
                         self.assert_almost_equals(fx_output[:, -1]						,      pt_output[:, -1].numpy()						,      4e-2  )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         pt_model.save_pretrained(lowerCAmelCase__  )
                         UpperCAmelCase			:       List[str]			     =model_class.from_pretrained(lowerCAmelCase__						,      from_pt=lowerCAmelCase__  )

                   UpperCAmelCase			:       Tuple			     =fx_model_loaded(**lowerCAmelCase__  ).to_tuple()
                   self.assertEqual(
                       len(lowerCAmelCase__  )						,      len(lowerCAmelCase__  )						,      '''Output lengths differ between Flax and PyTorch'''  )
                   for fx_output_loaded, pt_output in zip(lowerCAmelCase__						,      lowerCAmelCase__  ):
                         self.assert_almost_equals(fx_output_loaded[:, -1]						,      pt_output[:, -1].numpy()						,      4e-2  )
 @is_pt_flax_cross_test
 def 		UpperCAmelCase__				(     self  )		->							Optional[int]:
       '''simple docstring'''





       UpperCAmelCase			:       Tuple			     =self.model_tester.prepare_config_and_inputs_for_common()
       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__  ):
                   # prepare inputs
                   UpperCAmelCase			:       Any			     =self._prepare_for_class(lowerCAmelCase__						,      lowerCAmelCase__  )
                   UpperCAmelCase			:       Dict			     ={k: torch.tensor(v.tolist()  ) for k, v in prepared_inputs_dict.items()}

                   # load corresponding PyTorch class
                   UpperCAmelCase			:       int			     =model_class.__name__[4:]  # Skip the "Flax" at the beginning
                   UpperCAmelCase			:       Optional[int]			     =getattr(lowerCAmelCase__						,      lowerCAmelCase__  )

                   UpperCAmelCase			:       Union[str, Any]			     =pt_model_class(lowerCAmelCase__  ).eval()
                   UpperCAmelCase			:       Dict			     =model_class(lowerCAmelCase__						,      dtype=jnp.floataa  )

                   UpperCAmelCase			:       List[str]			     =load_flax_weights_in_pytorch_model(lowerCAmelCase__						,      fx_model.params  )
                   UpperCAmelCase			:       List[str]			     =pt_inputs["input_ids"].shape
                   UpperCAmelCase			:       List[str]			     =np.random.randint(0						,      seq_length - 1						,      size=(batch_size,)  )
                   for batch_idx, start_index in enumerate(lowerCAmelCase__  ):
                         UpperCAmelCase			:       Optional[Any]			     =0
                         UpperCAmelCase			:       List[str]			     =1
                         UpperCAmelCase			:       Any			     =0
                         UpperCAmelCase			:       List[str]			     =1

                   # make sure weights are tied in PyTorch
                   pt_model.tie_weights()

                   with torch.no_grad():
                         UpperCAmelCase			:       List[str]			     =pt_model(**lowerCAmelCase__  ).to_tuple()

                   UpperCAmelCase			:       Union[str, Any]			     =fx_model(**lowerCAmelCase__  ).to_tuple()
                   self.assertEqual(len(lowerCAmelCase__  )						,      len(lowerCAmelCase__  )						,      '''Output lengths differ between Flax and PyTorch'''  )
                   for fx_output, pt_output in zip(lowerCAmelCase__						,      lowerCAmelCase__  ):
                         self.assert_almost_equals(fx_output[:, -1]						,      pt_output[:, -1].numpy()						,      4e-2  )

                   with tempfile.TemporaryDirectory() as tmpdirname:
                         fx_model.save_pretrained(lowerCAmelCase__  )
                         UpperCAmelCase			:       int			     =pt_model_class.from_pretrained(lowerCAmelCase__						,      from_flax=lowerCAmelCase__  )

                   with torch.no_grad():
                         UpperCAmelCase			:       Optional[Any]			     =pt_model_loaded(**lowerCAmelCase__  ).to_tuple()

                   self.assertEqual(
                       len(lowerCAmelCase__  )						,      len(lowerCAmelCase__  )						,      '''Output lengths differ between Flax and PyTorch'''  )
                   for fx_output, pt_output in zip(lowerCAmelCase__						,      lowerCAmelCase__  ):
                         self.assert_almost_equals(fx_output[:, -1]						,      pt_output[:, -1].numpy()						,      4e-2  )






 @tooslow
 def 		UpperCAmelCase__				(     self  )		->							Dict:
       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             UpperCAmelCase			:       Dict			     =model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''  )
             UpperCAmelCase			:       List[Any]			     =model(np.ones((1, 1)  )  )
             self.assertIsNotNone(lowerCAmelCase__  )



import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import (
    require_accelerate,
    require_torch,
    require_torch_gpu,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import ViTForImageClassification, ViTForMaskedImageModeling, ViTModel
						from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor



class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Any   ,						lowerCAmelCase__  :     Union[str, Any]   ,						lowerCAmelCase__  :     Any=13   ,						lowerCAmelCase__  :     Tuple=30   ,						lowerCAmelCase__  :     List[str]=2   ,						lowerCAmelCase__  :     int=3   ,						lowerCAmelCase__  :     Optional[int]=True   ,						lowerCAmelCase__  :     List[str]=True   ,						lowerCAmelCase__  :     str=32   ,						lowerCAmelCase__  :     Any=5   ,						lowerCAmelCase__  :     str=4   ,						lowerCAmelCase__  :     int=37   ,						lowerCAmelCase__  :     Optional[Any]="gelu"   ,						lowerCAmelCase__  :     Optional[int]=0.1   ,						lowerCAmelCase__  :     Dict=0.1   ,						lowerCAmelCase__  :     Tuple=10   ,						lowerCAmelCase__  :     Optional[Any]=0.02   ,						lowerCAmelCase__  :     List[str]=None   ,						lowerCAmelCase__  :     Union[str, Any]=2   ,						):
									SCREAMING_SNAKE_CASE_: str  		=       parent
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       batch_size
									SCREAMING_SNAKE_CASE_: str  		=       image_size
									SCREAMING_SNAKE_CASE_: Tuple  		=       patch_size
									SCREAMING_SNAKE_CASE_: int  		=       num_channels
									SCREAMING_SNAKE_CASE_: List[str]  		=       is_training
									SCREAMING_SNAKE_CASE_: str  		=       use_labels
									SCREAMING_SNAKE_CASE_: int  		=       hidden_size
									SCREAMING_SNAKE_CASE_: List[Any]  		=       num_hidden_layers
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       num_attention_heads
									SCREAMING_SNAKE_CASE_: Any  		=       intermediate_size
									SCREAMING_SNAKE_CASE_: str  		=       hidden_act
									SCREAMING_SNAKE_CASE_: str  		=       hidden_dropout_prob
									SCREAMING_SNAKE_CASE_: List[str]  		=       attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_: int  		=       type_sequence_label_size
									SCREAMING_SNAKE_CASE_: Dict  		=       initializer_range
									SCREAMING_SNAKE_CASE_: Dict  		=       scope
									SCREAMING_SNAKE_CASE_: Dict  		=       encoder_stride

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



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_config()

									return config, pixel_values, labels



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									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=lowerCAmelCase__   ,						initializer_range=self.initializer_range   ,						encoder_stride=self.encoder_stride   ,						)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Tuple):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ViTModel(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size))



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]   ,						lowerCAmelCase__  :     Tuple   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       ViTForMaskedImageModeling(config=lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: str  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(
									    result.reconstruction.shape   ,						(self.batch_size, self.num_channels, self.image_size, self.image_size))

									# test greyscale images
									SCREAMING_SNAKE_CASE_: Dict  		=       1
									SCREAMING_SNAKE_CASE_: List[str]  		=       ViTForMaskedImageModeling(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									SCREAMING_SNAKE_CASE_: List[str]  		=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									SCREAMING_SNAKE_CASE_: str  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(result.reconstruction.shape   ,						(self.batch_size, 1, self.image_size, self.image_size))



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.type_sequence_label_size
									SCREAMING_SNAKE_CASE_: List[str]  		=       ViTForImageClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()
									SCREAMING_SNAKE_CASE_: Any  		=       model(lowerCAmelCase__   ,						labels=lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size))

									# test greyscale images
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       1
									SCREAMING_SNAKE_CASE_: List[str]  		=       ViTForImageClassification(lowerCAmelCase__)
									model.to(lowerCAmelCase__)
									model.eval()

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									SCREAMING_SNAKE_CASE_: Dict  		=       model(lowerCAmelCase__)
									self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.type_sequence_label_size))




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







@require_torch
class   __lowercase				( UpperCAmelCase_		,       UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     (
			    (
			        ViTModel,
			        ViTForImageClassification,
			        ViTForMaskedImageModeling,
			    )
			    if is_torch_available()
			    else ()
			)
			_UpperCAmelCase   :							Tuple										=     (
			    {'''feature-extraction''': ViTModel, '''image-classification''': ViTForImageClassification}
			    if is_torch_available()
			    else {}
			)
			_UpperCAmelCase   :							List[str]										=     True

			_UpperCAmelCase   :							List[Any]										=     False
			_UpperCAmelCase   :							Optional[Any]										=     False
			_UpperCAmelCase   :							Tuple										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       ViTModelTester(self)
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ConfigTester(self   ,						config_class=lowerCAmelCase__   ,						has_text_modality=lowerCAmelCase__   ,						hidden_size=37)



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									self.config_tester.run_common_tests()



			@unittest.skip(reason="ViT does not use inputs_embeds")
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: Dict  		=       model_class(lowerCAmelCase__)
															self.assertIsInstance(model.get_input_embeddings()   ,						(nn.Module))
															SCREAMING_SNAKE_CASE_: List[Any]  		=       model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(lowerCAmelCase__   ,						nn.Linear))



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															SCREAMING_SNAKE_CASE_: List[Any]  		=       model_class(lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_: int  		=       inspect.signature(model.forward)
															# signature.parameters is an OrderedDict => so arg_names order is deterministic
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [*signature.parameters.keys()]

															SCREAMING_SNAKE_CASE_: Optional[int]  		=       ["pixel_values"]
															self.assertListEqual(arg_names[:1]   ,						lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: int  		=       self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       ViTModel.from_pretrained(lowerCAmelCase__)
															self.assertIsNotNone(lowerCAmelCase__)







def 			A_      (    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
						return image



@require_torch
@require_vision
class   __lowercase				( unittest.TestCase	):




			"""simple docstring"""







			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return ViTImageProcessor.from_pretrained("google/vit-base-patch16-224") if is_vision_available() else None



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: int  		=       ViTForImageClassification.from_pretrained("google/vit-base-patch16-224").to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.default_image_processor
									SCREAMING_SNAKE_CASE_: str  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt").to(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(**lowerCAmelCase__)

									# verify the logits
									SCREAMING_SNAKE_CASE_: Any  		=       torch.Size((1, 1000))
									self.assertEqual(outputs.logits.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       torch.tensor([-0.2744, 0.8215, -0.0836]).to(lowerCAmelCase__)

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



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									# ViT models have an `interpolate_pos_encoding` argument in their forward method,
									# allowing to interpolate the pre-trained position embeddings in order to use
									# the model on higher resolutions. The DINO model by Facebook AI leverages this
									# to visualize self-attention on higher resolution images.
									SCREAMING_SNAKE_CASE_: str  		=       ViTModel.from_pretrained("facebook/dino-vits8").to(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       ViTImageProcessor.from_pretrained("facebook/dino-vits8"   ,						size=480)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_: int  		=       inputs.pixel_values.to(lowerCAmelCase__)

									# forward pass
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(lowerCAmelCase__   ,						interpolate_pos_encoding=lowerCAmelCase__)

									# verify the logits
									SCREAMING_SNAKE_CASE_: Tuple  		=       torch.Size((1, 3601, 384))
									self.assertEqual(outputs.last_hidden_state.shape   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       torch.tensor(
									    [[4.2340, 4.3906, -6.6692], [4.5463, 1.8928, -6.7257], [4.4429, 0.8496, -5.8585]]).to(lowerCAmelCase__)

									self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]   ,						lowerCAmelCase__   ,						atol=1E-4))




			@slow
			@require_accelerate
			@require_torch_gpu
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       ViTModel.from_pretrained("facebook/dino-vits8"   ,						torch_dtype=torch.floataa   ,						device_map="auto")
									SCREAMING_SNAKE_CASE_: int  		=       self.default_image_processor

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       prepare_img()
									SCREAMING_SNAKE_CASE_: Dict  		=       image_processor(images=lowerCAmelCase__   ,						return_tensors="pt")
									SCREAMING_SNAKE_CASE_: str  		=       inputs.pixel_values.to(lowerCAmelCase__)

									# forward pass to make sure inference works in fp16
									with torch.no_grad():
															SCREAMING_SNAKE_CASE_: str  		=       model(lowerCAmelCase__)






