


import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
   import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class lowerCAmelCase_		(				unittest.TestCase      ,		lowerCamelCase__ ):

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   load_tool('''text-to-speech'''      )
       self.tool.setup()



 def 		UpperCamelCase__  (			self      ):
       # SpeechT5 isn't deterministic
       torch.manual_seed(0      )
       snake_case_ =   self.tool('''hey'''      )
       snake_case_ =   result.to_raw()
       self.assertTrue(
           torch.allclose(
               resulting_tensor[:3]	,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]      )	,   )      )





 def 		UpperCamelCase__  (			self      ):
       # SpeechT5 isn't deterministic
       torch.manual_seed(0      )
       snake_case_ =   self.tool('''hey'''      )
       snake_case_ =   result.to_raw()
       self.assertTrue(
           torch.allclose(
               resulting_tensor[:3]	,   torch.tensor([-0.0_005_966_668_832_115_829, -0.0_003_657_640_190_795_064, -0.00_013_439_502_799_883_485]      )	,   )      )



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 lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase	,   _UpperCAmelCase=13	,   _UpperCAmelCase=30	,   _UpperCAmelCase=2	,   _UpperCAmelCase=3	,   _UpperCAmelCase=True	,   _UpperCAmelCase=True	,   _UpperCAmelCase=32	,   _UpperCAmelCase=5	,   _UpperCAmelCase=4	,   _UpperCAmelCase=37	,   _UpperCAmelCase="gelu"	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=0.1	,   _UpperCAmelCase=10	,   _UpperCAmelCase=0.02	,   _UpperCAmelCase=3	,   _UpperCAmelCase=None	,   _UpperCAmelCase=2	,   ):
       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
       snake_case_ =   encoder_stride

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



 def 		UpperCamelCase__  (			self      ):
       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 		UpperCamelCase__  (			self      ):
       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=_UpperCAmelCase	,   initializer_range=self.initializer_range	,   encoder_stride=self.encoder_stride	,   )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   DeiTModel(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size)      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   DeiTForMaskedImageModeling(config=_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase      )
       self.parent.assertEqual(
           result.reconstruction.shape	,   (self.batch_size, self.num_channels, self.image_size, self.image_size)      )

       # test greyscale images
       snake_case_ =   1
       snake_case_ =   DeiTForMaskedImageModeling(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()

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



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       snake_case_ =   self.type_sequence_label_size
       snake_case_ =   DeiTForImageClassification(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()
       snake_case_ =   model(_UpperCAmelCase	,   labels=_UpperCAmelCase      )
       self.parent.assertEqual(result.logits.shape	,   (self.batch_size, self.type_sequence_label_size)      )

       # test greyscale images
       snake_case_ =   1
       snake_case_ =   DeiTForImageClassification(_UpperCAmelCase      )
       model.to(_UpperCAmelCase      )
       model.eval()

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





 def 		UpperCamelCase__  (			self      ):
       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_torch
class lowerCAmelCase_		(				lowerCamelCase__      ,		lowerCamelCase__      ,		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      ):
       snake_case_ =   DeiTModelTester(self      )
       snake_case_ =   ConfigTester(self	,   config_class=_UpperCAmelCase	,   has_text_modality=_UpperCAmelCase	,   hidden_size=37      )



 def 		UpperCamelCase__  (			self      ):
       self.config_tester.run_common_tests()



 @unittest.skip(reason='''DeiT does not use inputs_embeds'''      )
 def 		UpperCamelCase__  (			self      ):
       pass



 def 		UpperCamelCase__  (			self      ):
       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()	,   (nn.Module)      )
             snake_case_ =   model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(_UpperCAmelCase	,   nn.Linear      )      )



 def 		UpperCamelCase__  (			self      ):
       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.forward      )
             # 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 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase      )



 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase	,   _UpperCAmelCase=False      ):
       snake_case_ =   super()._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )

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

       return inputs_dict



 def 		UpperCamelCase__  (			self      ):
       if not self.model_tester.is_training:
             return

       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()
       snake_case_ =   True

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()
       if not self.model_tester.is_training:
             return

       snake_case_ =   False
       snake_case_ =   True

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



 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   self.model_tester.prepare_config_and_inputs_for_common()

       snake_case_ =   [
           {'''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(_UpperCAmelCase      ),
                     *get_values(_UpperCAmelCase      ),
                 ]
                 or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
             ):
                   continue

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

                         snake_case_ =   model_class(_UpperCAmelCase      )
                         model.to(_UpperCAmelCase      )
                         model.train()

                         snake_case_ =   self._prepare_for_class(_UpperCAmelCase	,   _UpperCAmelCase	,   return_labels=_UpperCAmelCase      )

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

                         snake_case_ =   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=_UpperCAmelCase      ) as warning_list:
                               snake_case_ =   model(**_UpperCAmelCase      ).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      ):
       for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             snake_case_ =   DeiTModel.from_pretrained(_UpperCAmelCase      )
             self.assertIsNotNone(_UpperCAmelCase      )






def  __lowerCAmelCase					()->						Optional[int]:




      """simple docstring"""

      snake_case_ =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
      return image







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

 '''simple docstring'''

 @cached_property
 def 		UpperCamelCase__  (			self      ):
       return (
           DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      )
           if is_vision_available()
           else None
       )



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   DeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224'''      ).to(
           _UpperCAmelCase      )

       snake_case_ =   self.default_image_processor
       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''pt'''      ).to(_UpperCAmelCase      )

       # forward pass
       with torch.no_grad():
             snake_case_ =   model(**_UpperCAmelCase      )

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

       snake_case_ =   torch.tensor([-1.0_266, 0.1_912, -1.2_861]      ).to(_UpperCAmelCase      )

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





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

       snake_case_ =   prepare_img()
       snake_case_ =   image_processor(images=_UpperCAmelCase	,   return_tensors='''pt'''      )
       snake_case_ =   inputs.pixel_values.to(_UpperCAmelCase      )

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