
import inspect
import unittest
import warnings

from transformers import DeiTConfig
from transformers.models.auto import get_values
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 (
					    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
					    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
					    MODEL_MAPPING,
					    DeiTForImageClassification,
					    DeiTForImageClassificationWithTeacher,
					    DeiTForMaskedImageModeling,
					    DeiTModel,
					)
					from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import DeiTImageProcessor





class 							UpperCAmelCase      :




	'''simple docstring'''





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

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






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

					__A   			= self.get_config()

					return config, pixel_values, labels






	def 				UpperCamelCase_  (       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=SCREAMING_SNAKE_CASE_							,initializer_range=self.initializer_range							,encoder_stride=self.encoder_stride							,)






	def 				UpperCamelCase_  (       self					: Optional[Any]							,A					: Tuple							,A					: List[Any]							,A					: List[str] ):
					__A   			= DeiTModel(config=SCREAMING_SNAKE_CASE_ )
					model.to(SCREAMING_SNAKE_CASE_ )
					model.eval()
					__A   			= model(SCREAMING_SNAKE_CASE_ )
					self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size) )






	def 				UpperCamelCase_  (       self					: Optional[int]							,A					: Optional[int]							,A					: int							,A					: Dict ):
					__A   			= DeiTForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_ )
					model.to(SCREAMING_SNAKE_CASE_ )
					model.eval()
					__A   			= model(SCREAMING_SNAKE_CASE_ )
					self.parent.assertEqual(
					    result.reconstruction.shape							,(self.batch_size, self.num_channels, self.image_size, self.image_size) )

					# test greyscale images
					__A   			= 1
					__A   			= DeiTForMaskedImageModeling(SCREAMING_SNAKE_CASE_ )
					model.to(SCREAMING_SNAKE_CASE_ )
					model.eval()

					__A   			= floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
					__A   			= model(SCREAMING_SNAKE_CASE_ )
					self.parent.assertEqual(result.reconstruction.shape							,(self.batch_size, 1, self.image_size, self.image_size) )






	def 				UpperCamelCase_  (       self					: str							,A					: Dict							,A					: Union[str, Any]							,A					: Tuple ):
					__A   			= self.type_sequence_label_size
					__A   			= DeiTForImageClassification(SCREAMING_SNAKE_CASE_ )
					model.to(SCREAMING_SNAKE_CASE_ )
					model.eval()
					__A   			= model(SCREAMING_SNAKE_CASE_							,labels=SCREAMING_SNAKE_CASE_ )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.type_sequence_label_size) )

					# test greyscale images
					__A   			= 1
					__A   			= DeiTForImageClassification(SCREAMING_SNAKE_CASE_ )
					model.to(SCREAMING_SNAKE_CASE_ )
					model.eval()

					__A   			= floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
					__A   			= model(SCREAMING_SNAKE_CASE_							,labels=SCREAMING_SNAKE_CASE_ )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.type_sequence_label_size) )







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





@require_torch
class 							UpperCAmelCase      (  a__			,			a__			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (
	    (
	        DeiTModel,
	        DeiTForImageClassification,
	        DeiTForImageClassificationWithTeacher,
	        DeiTForMaskedImageModeling,
	    )
	    if is_torch_available()
	    else ()
	)
	snake_case_					    =     (
	    {
	        "feature-extraction": DeiTModel,
	        "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= DeiTModelTester(self )
					__A   			= ConfigTester(self							,config_class=SCREAMING_SNAKE_CASE_							,has_text_modality=SCREAMING_SNAKE_CASE_							,hidden_size=37 )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					self.config_tester.run_common_tests()






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






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A   			= model_class(SCREAMING_SNAKE_CASE_ )
									self.assertIsInstance(model.get_input_embeddings()							,(nn.Module) )
									__A   			= model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_							,nn.Linear ) )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs_for_common()

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

									__A   			= ['pixel_values']
									self.assertListEqual(arg_names[:1]							,SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_ )






	def 				UpperCamelCase_  (       self					: Tuple							,A					: Any							,A					: List[str]							,A					: List[Any]=False ):
					__A   			= super()._prepare_for_class(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,return_labels=SCREAMING_SNAKE_CASE_ )

					if return_labels:
									if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
													del inputs_dict["labels"]

					return inputs_dict






	def 				UpperCamelCase_  (       self					: str ):
					if not self.model_tester.is_training:
									return

					__A   			= self.model_tester.prepare_config_and_inputs_for_common()
					__A   			= True

					for model_class in self.all_model_classes:
									# DeiTForImageClassificationWithTeacher supports inference-only
									if (
									    model_class in get_values(SCREAMING_SNAKE_CASE_ )
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
													continue
									__A   			= model_class(SCREAMING_SNAKE_CASE_ )
									model.to(SCREAMING_SNAKE_CASE_ )
									model.train()
									__A   			= self._prepare_for_class(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,return_labels=SCREAMING_SNAKE_CASE_ )
									__A   			= model(**SCREAMING_SNAKE_CASE_ ).loss
									loss.backward()






	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.model_tester.prepare_config_and_inputs_for_common()
					if not self.model_tester.is_training:
									return

					__A   			= False
					__A   			= True

					for model_class in self.all_model_classes:
									if model_class in get_values(SCREAMING_SNAKE_CASE_ ) or not model_class.supports_gradient_checkpointing:
													continue
									# DeiTForImageClassificationWithTeacher supports inference-only
									if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
													continue
									__A   			= model_class(SCREAMING_SNAKE_CASE_ )
									model.gradient_checkpointing_enable()
									model.to(SCREAMING_SNAKE_CASE_ )
									model.train()
									__A   			= self._prepare_for_class(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,return_labels=SCREAMING_SNAKE_CASE_ )
									__A   			= model(**SCREAMING_SNAKE_CASE_ ).loss
									loss.backward()






	def 				UpperCamelCase_  (       self					: str ):
					__A   			= self.model_tester.prepare_config_and_inputs_for_common()

					__A   			= [
					    {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
					    {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
					    {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
					]

					for model_class in self.all_model_classes:
									if (
									    model_class
									    not in [
									        *get_values(SCREAMING_SNAKE_CASE_ ),
									        *get_values(SCREAMING_SNAKE_CASE_ ),
									    ]
									    or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
									):
													continue

									for problem_type in problem_types:
													with self.subTest(msg=f'''Testing {model_class} with {problem_type['title']}''' ):
																	__A   			= problem_type['title']
																	__A   			= problem_type['num_labels']

																	__A   			= model_class(SCREAMING_SNAKE_CASE_ )
																	model.to(SCREAMING_SNAKE_CASE_ )
																	model.train()

																	__A   			= self._prepare_for_class(SCREAMING_SNAKE_CASE_							,SCREAMING_SNAKE_CASE_							,return_labels=SCREAMING_SNAKE_CASE_ )

																	if problem_type["num_labels"] > 1:
																					__A   			= inputs['labels'].unsqueeze(1 ).repeat(1							,problem_type["num_labels"] )

																	__A   			= inputs['labels'].to(problem_type["dtype"] )

																	# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																	# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																	# they have the same size." which is a symptom something in wrong for the regression problem.
																	# See https://github.com/huggingface/transformers/issues/11780
																	with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE_ ) as warning_list:
																					__A   			= model(**SCREAMING_SNAKE_CASE_ ).loss
																	for w in warning_list:
																					if "Using a target size that is different to the input size" in str(w.message ):
																									raise ValueError(
																									    f'''Something is going wrong in the regression problem: intercepted {w.message}''' )

																	loss.backward()







	@slow
	def 				UpperCamelCase_  (       self					: List[Any] ):
					for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= DeiTModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )





def 						UpperCAmelCase			(	)		->	List[Any]:







				"""simple docstring"""



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





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




	'''simple docstring'''





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






	@slow
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
					    SCREAMING_SNAKE_CASE_ )

					__A   			= self.default_image_processor
					__A   			= prepare_img()
					__A   			= image_processor(images=SCREAMING_SNAKE_CASE_							,return_tensors="pt" ).to(SCREAMING_SNAKE_CASE_ )

					# forward pass
					with torch.no_grad():
									__A   			= model(**SCREAMING_SNAKE_CASE_ )

					# verify the logits
					__A   			= torch.Size((1, 10_00) )
					self.assertEqual(outputs.logits.shape							,SCREAMING_SNAKE_CASE_ )

					__A   			= torch.tensor([-1.02_66, 0.19_12, -1.28_61] ).to(SCREAMING_SNAKE_CASE_ )

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







	@slow
	@require_accelerate
	@require_torch_gpu
	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= DeiTModel.from_pretrained(
					    "facebook/deit-base-distilled-patch16-224"							,torch_dtype=torch.floataa							,device_map="auto" )
					__A   			= self.default_image_processor

					__A   			= prepare_img()
					__A   			= image_processor(images=SCREAMING_SNAKE_CASE_							,return_tensors="pt" )
					__A   			= inputs.pixel_values.to(SCREAMING_SNAKE_CASE_ )

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





import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__UpperCAmelCase          =       get_tests_dir() + '''/test_data/fsmt/fsmt_val_data.json'''
with io.open(filename, '''r''', encoding='''utf-8''') as f:
    __UpperCAmelCase          =       json.load(f)



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	FSMTForConditionalGeneration.from_pretrained(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
          if torch_device == "cuda":
             model.half()
          return model






       @parameterized.expand(
           [
               ['en-ru', 26.0],
               ['ru-en', 22.0],
               ['en-de', 22.0],
               ['de-en', 29.0],
           ]       )
       @slow
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

          UpperCamelCase    :  List[Any]							=	tokenizer(SCREAMING_SNAKE_CASE_,			return_tensors='pt',			truncation=SCREAMING_SNAKE_CASE_,			padding='longest'       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Any							=	model.generate(
              input_ids=batch.input_ids,			num_beams=8,			)
          UpperCamelCase    :  Tuple							=	tokenizer.batch_decode(
              SCREAMING_SNAKE_CASE_,			skip_special_tokens=SCREAMING_SNAKE_CASE_,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[Any]							=	calculate_bleu(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )
          print(SCREAMING_SNAKE_CASE_       )
          self.assertGreaterEqual(scores['bleu'],			SCREAMING_SNAKE_CASE_       )



