





'''simple docstring'''




import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 transformers import CvtForImageClassification, CvtModel
  from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor







class   A_	(  lowerCAmelCase_			):




 def      lowercase	(					self       :    Optional[int]    ):
      _UpperCAmelCase     							=   self.config_class(**self.inputs_dict    )
      self.parent.assertTrue(hasattr(snake_case_	,			"embed_dim"    )    )
      self.parent.assertTrue(hasattr(snake_case_	,			"num_heads"    )    )







class   A_	:




 def __init__(					self       :    Any	,			snake_case_       :    Tuple	,			snake_case_       :    List[Any]=1_3	,			snake_case_       :    Any=6_4	,			snake_case_       :    str=3	,			snake_case_       :    List[str]=[1_6, 4_8, 9_6]	,			snake_case_       :    Union[str, Any]=[1, 3, 6]	,			snake_case_       :    List[Any]=[1, 2, 1_0]	,			snake_case_       :    Union[str, Any]=[7, 3, 3]	,			snake_case_       :    Optional[Any]=[4, 2, 2]	,			snake_case_       :    str=[2, 1, 1]	,			snake_case_       :    List[Any]=[2, 2, 2]	,			snake_case_       :    str=[False, False, True]	,			snake_case_       :    Union[str, Any]=[0.0, 0.0, 0.0]	,			snake_case_       :    List[str]=0.0_2	,			snake_case_       :    List[Any]=1e-12	,			snake_case_       :    Any=True	,			snake_case_       :    Tuple=True	,			snake_case_       :    str=2	,			):
      _UpperCAmelCase     							=   parent
      _UpperCAmelCase     							=   batch_size
      _UpperCAmelCase     							=   image_size
      _UpperCAmelCase     							=   patch_sizes
      _UpperCAmelCase     							=   patch_stride
      _UpperCAmelCase     							=   patch_padding
      _UpperCAmelCase     							=   is_training
      _UpperCAmelCase     							=   use_labels
      _UpperCAmelCase     							=   num_labels
      _UpperCAmelCase     							=   num_channels
      _UpperCAmelCase     							=   embed_dim
      _UpperCAmelCase     							=   num_heads
      _UpperCAmelCase     							=   stride_kv
      _UpperCAmelCase     							=   depth
      _UpperCAmelCase     							=   cls_token
      _UpperCAmelCase     							=   attention_drop_rate
      _UpperCAmelCase     							=   initializer_range
      _UpperCAmelCase     							=   layer_norm_eps




 def      lowercase	(					self       :    List[str]    ):
      _UpperCAmelCase     							=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

      _UpperCAmelCase     							=   None
      if self.use_labels:
           _UpperCAmelCase     							=   ids_tensor([self.batch_size]	,			self.num_labels    )

      _UpperCAmelCase     							=   self.get_config()
      return config, pixel_values, labels




 def      lowercase	(					self       :    Any    ):
      return CvtConfig(
          image_size=self.image_size	,			num_labels=self.num_labels	,			num_channels=self.num_channels	,			embed_dim=self.embed_dim	,			num_heads=self.num_heads	,			patch_sizes=self.patch_sizes	,			patch_padding=self.patch_padding	,			patch_stride=self.patch_stride	,			stride_kv=self.stride_kv	,			depth=self.depth	,			cls_token=self.cls_token	,			attention_drop_rate=self.attention_drop_rate	,			initializer_range=self.initializer_range	,			)




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    int	,			snake_case_       :    Tuple    ):
      _UpperCAmelCase     							=   CvtModel(config=snake_case_    )
      model.to(snake_case_    )
      model.eval()
      _UpperCAmelCase     							=   model(snake_case_    )
      _UpperCAmelCase     							=   (self.image_size, self.image_size)
      _UpperCAmelCase							,  _UpperCAmelCase     							=   image_size[0], image_size[1]
      for i in range(len(self.depth    )    ):
           _UpperCAmelCase     							=   floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
           _UpperCAmelCase     							=   floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1    )
      self.parent.assertEqual(result.last_hidden_state.shape	,			(self.batch_size, self.embed_dim[-1], height, width)    )




 def      lowercase	(					self       :    List[Any]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    Any	,			snake_case_       :    int    ):
      _UpperCAmelCase     							=   self.num_labels
      _UpperCAmelCase     							=   CvtForImageClassification(snake_case_    )
      model.to(snake_case_    )
      model.eval()
      _UpperCAmelCase     							=   model(snake_case_	,			labels=snake_case_    )
      self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.num_labels)    )




 def      lowercase	(					self       :    Any    ):
      _UpperCAmelCase     							=   self.prepare_config_and_inputs()
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   config_and_inputs
      _UpperCAmelCase     							=   {"pixel_values": pixel_values}
      return config, inputs_dict







@require_torch
class   A_	(  lowerCAmelCase_							,						lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : Any           =							(CvtModel, CvtForImageClassification) if is_torch_available() else ()
 _lowerCamelCase  : str           =							(
     {"""feature-extraction""": CvtModel, """image-classification""": CvtForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCamelCase  : int           =							False
 _lowerCamelCase  : Dict           =							False
 _lowerCamelCase  : Optional[Any]           =							False
 _lowerCamelCase  : Optional[int]           =							False
 _lowerCamelCase  : Union[str, Any]           =							False




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   CvtModelTester(self    )
      _UpperCAmelCase     							=   ConfigTester(self	,			config_class=snake_case_	,			has_text_modality=snake_case_	,			hidden_size=3_7    )




 def      lowercase	(					self       :    Any    ):
      self.create_and_test_config_common_properties()
      self.config_tester.create_and_test_config_to_json_string()
      self.config_tester.create_and_test_config_to_json_file()
      self.config_tester.create_and_test_config_from_and_save_pretrained()
      self.config_tester.create_and_test_config_with_num_labels()
      self.config_tester.check_config_can_be_init_without_params()
      self.config_tester.check_config_arguments_init()




 def      lowercase	(					self       :    Optional[Any]    ):
      return




 @unittest.skip(reason="Cvt does not output attentions"    )
 def      lowercase	(					self       :    Tuple    ):
      pass




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




 @unittest.skip(reason="Cvt does not support input and output embeddings"    )
 def      lowercase	(					self       :    int    ):
      pass




 def      lowercase	(					self       :    str    ):
      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

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

           _UpperCAmelCase     							=   ["pixel_values"]
           self.assertListEqual(arg_names[:1]	,			snake_case_    )




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




 def      lowercase	(					self       :    Optional[int]    ):
      def check_hidden_states_output(snake_case_       :    List[str]	,			snake_case_       :    Dict	,			snake_case_       :    int    ):
           _UpperCAmelCase     							=   model_class(snake_case_    )
           model.to(snake_case_    )
           model.eval()

           with torch.no_grad():
                _UpperCAmelCase     							=   model(**self._prepare_for_class(snake_case_	,			snake_case_    )    )

           _UpperCAmelCase     							=   outputs.hidden_states

           _UpperCAmelCase     							=   len(self.model_tester.depth    )
           self.assertEqual(len(snake_case_    )	,			snake_case_    )

           # verify the first hidden states (first block)
           self.assertListEqual(
               list(hidden_states[0].shape[-3:]    )	,			[
                   self.model_tester.embed_dim[0],
                   self.model_tester.image_size // 4,
                   self.model_tester.image_size // 4,
               ]	,			)

      _UpperCAmelCase							,  _UpperCAmelCase     							=   self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           _UpperCAmelCase     							=   True
           check_hidden_states_output(snake_case_	,			snake_case_	,			snake_case_    )

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           _UpperCAmelCase     							=   True

           check_hidden_states_output(snake_case_	,			snake_case_	,			snake_case_    )




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




 @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."    )
 def      lowercase	(					self       :    Dict    ):
      pass




 @slow
 def      lowercase	(					self       :    Optional[int]    ):
      for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           _UpperCAmelCase     							=   CvtModel.from_pretrained(snake_case_    )
           self.assertIsNotNone(snake_case_    )





def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



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







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




 @cached_property
 def      lowercase	(					self       :    Any    ):
      return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    )




 @slow
 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(snake_case_    )

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

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

      # verify the logits
      _UpperCAmelCase     							=   torch.Size((1, 1_0_0_0)    )
      self.assertEqual(outputs.logits.shape	,			snake_case_    )

      _UpperCAmelCase     							=   torch.tensor([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0]    ).to(snake_case_    )

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










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCAmelCase		:  List[str]	=   {
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   ["BlenderbotTokenizerFast"]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  int	=   [
         "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BlenderbotForCausalLM",
         "BlenderbotForConditionalGeneration",
         "BlenderbotModel",
         "BlenderbotPreTrainedModel",
     ]


try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Any	=   [
         "TFBlenderbotForConditionalGeneration",
         "TFBlenderbotModel",
         "TFBlenderbotPreTrainedModel",
     ]


try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase		:  Optional[int]	=   [
         "FlaxBlenderbotForConditionalGeneration",
         "FlaxBlenderbotModel",
         "FlaxBlenderbotPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_blenderbot import (
         BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         BlenderbotConfig,
         BlenderbotOnnxConfig,
     )
     from .tokenization_blenderbot import BlenderbotTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_blenderbot import (
              BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BlenderbotForCausalLM,
              BlenderbotForConditionalGeneration,
              BlenderbotModel,
              BlenderbotPreTrainedModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_blenderbot import (
              TFBlenderbotForConditionalGeneration,
              TFBlenderbotModel,
              TFBlenderbotPreTrainedModel,
          )

     try:
          if not is_flax_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flax_blenderbot import (
              FlaxBlenderbotForConditionalGeneration,
              FlaxBlenderbotModel,
              FlaxBlenderbotPreTrainedModel,
          )

else:
     import sys

     _UpperCAmelCase		:  Union[str, Any]	=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



