




"""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 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  self.config_class(**self.inputs_dict						)
       self.parent.assertTrue(hasattr(_UpperCAmelCase       ,				'embed_dim'						)						)
       self.parent.assertTrue(hasattr(_UpperCAmelCase       ,				'num_heads'						)						)





class 							lowercase_			:



 '''simple docstring'''

 def __init__(					self			:			Any       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Tuple=13       ,				_UpperCAmelCase			:			Tuple=64       ,				_UpperCAmelCase			:			str=3       ,				_UpperCAmelCase			:			List[Any]=[16, 48, 96]       ,				_UpperCAmelCase			:			Any=[1, 3, 6]       ,				_UpperCAmelCase			:			Optional[int]=[1, 2, 10]       ,				_UpperCAmelCase			:			Optional[Any]=[7, 3, 3]       ,				_UpperCAmelCase			:			List[Any]=[4, 2, 2]       ,				_UpperCAmelCase			:			Optional[Any]=[2, 1, 1]       ,				_UpperCAmelCase			:			Optional[Any]=[2, 2, 2]       ,				_UpperCAmelCase			:			Dict=[False, False, True]       ,				_UpperCAmelCase			:			Optional[Any]=[0.0, 0.0, 0.0]       ,				_UpperCAmelCase			:			Tuple=0.02       ,				_UpperCAmelCase			:			Tuple=1E-1_2       ,				_UpperCAmelCase			:			List[str]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Tuple=2       ,				):
       _A     =  parent
       _A     =  batch_size
       _A     =  image_size
       _A     =  patch_sizes
       _A     =  patch_stride
       _A     =  patch_padding
       _A     =  is_training
       _A     =  use_labels
       _A     =  num_labels
       _A     =  num_channels
       _A     =  embed_dim
       _A     =  num_heads
       _A     =  stride_kv
       _A     =  depth
       _A     =  cls_token
       _A     =  attention_drop_rate
       _A     =  initializer_range
       _A     =  layer_norm_eps

 def      lowerCAmelCase_				(					self			:			Dict						):
       _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.num_labels						)

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

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       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      lowerCAmelCase_				(					self			:			List[str]       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			str       ,				_UpperCAmelCase			:			Union[str, Any]						):
       _A     =  CvtModel(config=_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase						)
       _A     =  (self.image_size, self.image_size)
       _A  ,       _A     =  image_size[0], image_size[1]
       for i in range(len(self.depth						)						):
             _A     =  floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
             _A     =  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      lowerCAmelCase_				(					self			:			str       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Any       ,				_UpperCAmelCase			:			List[Any]						):
       _A     =  self.num_labels
       _A     =  CvtForImageClassification(_UpperCAmelCase						)
       model.to(_UpperCAmelCase						)
       model.eval()
       _A     =  model(_UpperCAmelCase       ,				labels=_UpperCAmelCase						)
       self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels)						)



 def      lowerCAmelCase_				(					self			:			Tuple						):
       _A     =  self.prepare_config_and_inputs()
       _A  ,       _A  ,       _A     =  config_and_inputs
       _A     =  {'pixel_values': pixel_values}
       return config, inputs_dict





@require_torch
class 							lowercase_			(	__lowerCAmelCase     ,      __lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Any     =      (CvtModel, CvtForImageClassification) if is_torch_available() else ()
 UpperCAmelCase       :	List[Any]     =      (
     {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
     if is_torch_available()
     else {}
 )

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

 def      lowerCAmelCase_				(					self			:			Any						):
       _A     =  CvtModelTester(self						)
       _A     =  ConfigTester(self       ,				config_class=_UpperCAmelCase       ,				has_text_modality=_UpperCAmelCase       ,				hidden_size=37						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       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      lowerCAmelCase_				(					self			:			List[str]						):
       return

 @unittest.skip(reason='Cvt does not output attentions'						)
 def      lowerCAmelCase_				(					self			:			Dict						):
       pass

 @unittest.skip(reason='Cvt does not use inputs_embeds'						)
 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       pass

 @unittest.skip(reason='Cvt does not support input and output embeddings'						)
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       pass

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A  ,       _A     =  self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             _A     =  model_class(_UpperCAmelCase						)
             _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]       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			str						):
       def check_hidden_states_output(_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			Dict       ,				_UpperCAmelCase			:			Optional[Any]						):
             _A     =  model_class(_UpperCAmelCase						)
             model.to(_UpperCAmelCase						)
             model.eval()

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

             _A     =  outputs.hidden_states

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

             # 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,
                 ]       ,				)

       _A  ,       _A     =  self.model_tester.prepare_config_and_inputs_for_common()

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

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

             check_hidden_states_output(_UpperCAmelCase       ,				_UpperCAmelCase       ,				_UpperCAmelCase						)

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)

 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       pass



 @slow
 def      lowerCAmelCase_				(					self			:			Optional[Any]						):
       for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             _A     =  CvtModel.from_pretrained(_UpperCAmelCase						)
             self.assertIsNotNone(_UpperCAmelCase						)






def 		_snake_case	(   )       ->   List[Any]:






      '''simple docstring'''


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







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



 '''simple docstring'''

 @cached_property
 def      lowerCAmelCase_				(					self			:			int						):
       return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)



 @slow
 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_UpperCAmelCase						)

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

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

       # verify the logits
       _A     =  torch.Size((1, 1_000)						)
       self.assertEqual(outputs.logits.shape       ,				_UpperCAmelCase						)

       _A     =  torch.tensor([0.9285, 0.9015, -0.3150]						).to(_UpperCAmelCase						)

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







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase  =			{
    """configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """PegasusXForConditionalGeneration""",
	    """PegasusXModel""",
	    """PegasusXPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_pegasus_x import (
		    PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PegasusXForConditionalGeneration,
		    PegasusXModel,
		    PegasusXPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

