



"""simple docstring"""
import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
					import jax

					from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel




class 			snake_case_						(						unittest.TestCase				):






							def __init__(   self			,     a_			,     a_=1_3			,     a_=3_0			,     a_=2			,     a_=3			,     a_=True			,     a_=True			,     a_=3_2			,     a_=5			,     a_=4			,     a_=3_7			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=1_0			,     a_=0.02			,     ):
									a_     :      int 				=     parent
									a_     :      List[Any] 				=     batch_size
									a_     :      List[Any] 				=     image_size
									a_     :      Union[str, Any] 				=     patch_size
									a_     :      List[str] 				=     num_channels
									a_     :      Any 				=     is_training
									a_     :      Optional[int] 				=     use_labels
									a_     :      List[Any] 				=     hidden_size
									a_     :      Tuple 				=     num_hidden_layers
									a_     :      Optional[Any] 				=     num_attention_heads
									a_     :      Optional[int] 				=     intermediate_size
									a_     :      List[str] 				=     hidden_act
									a_     :      List[str] 				=     hidden_dropout_prob
									a_     :      str 				=     attention_probs_dropout_prob
									a_     :      Optional[Any] 				=     type_sequence_label_size
									a_     :      Any 				=     initializer_range

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






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

									a_     :      Any 				=     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=A_			,     initializer_range=self.initializer_range			,     )

									return config, pixel_values






							def snake_case_  (   self			,     a_			,     a_      ):
									a_     :      Any 				=     FlaxViTModel(config=A_      )
									a_     :      Any 				=     model(A_      )
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									a_     :      int 				=     (self.image_size, self.image_size)
									a_     :      str 				=     (self.patch_size, self.patch_size)
									a_     :      Optional[Any] 				=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, num_patches + 1, self.hidden_size)      )






							def snake_case_  (   self			,     a_			,     a_      ):
									a_     :      Tuple 				=     self.type_sequence_label_size
									a_     :      Optional[int] 				=     FlaxViTForImageClassification(config=A_      )
									a_     :      Union[str, Any] 				=     model(A_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )

									# test greyscale images
									a_     :      int 				=     1
									a_     :      Union[str, Any] 				=     FlaxViTForImageClassification(A_      )

									a_     :      List[Any] 				=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									a_     :      Union[str, Any] 				=     model(A_      )






							def snake_case_  (   self      ):
									a_     :      Any 				=     self.prepare_config_and_inputs()
									(
									    (
									    a_	
									)				,				(
									    a_	
									)				,					
									)     :      List[str] 				=     config_and_inputs
									a_     :      Dict 				=     {"pixel_values": pixel_values}
									return config, inputs_dict




@require_flax
class 			snake_case_						(						_UpperCAmelCase     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()






							def snake_case_  (   self      ):
									a_     :      Tuple 				=     FlaxViTModelTester(self      )
									a_     :      List[Any] 				=     ConfigTester(self			,     config_class=A_			,     has_text_modality=A_			,     hidden_size=3_7      )






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






							def snake_case_  (   self      ):
									a_     :      Tuple 				=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*A_      )






							def snake_case_  (   self      ):
									a_     :      Optional[int] 				=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*A_      )






							def snake_case_  (   self      ):
									a_				,				a_     :      str 				=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      Tuple 				=     model_class(A_      )
											a_     :      Optional[int] 				=     inspect.signature(model.__call__      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											a_     :      str 				=     [*signature.parameters.keys()]

											a_     :      List[str] 				=     ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,     A_      )






							def snake_case_  (   self      ):
									a_				,				a_     :      Tuple 				=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__      ):
													a_     :      Dict 				=     self._prepare_for_class(A_			,     A_      )
													a_     :      Optional[Any] 				=     model_class(A_      )

													@jax.jit
													def model_jitted(a_			,     **a_      ):
															return model(pixel_values=A_			,     **A_      )

													with self.subTest("JIT Enabled"      ):
															a_     :      Tuple 				=     model_jitted(**A_      ).to_tuple()

													with self.subTest("JIT Disabled"      ):
															with jax.disable_jit():
																	a_     :      Union[str, Any] 				=     model_jitted(**A_      ).to_tuple()

													self.assertEqual(len(A_      )			,     len(A_      )      )
													for jitted_output, output in zip(A_			,     A_      ):
															self.assertEqual(jitted_output.shape			,     output.shape      )






							@slow
							def snake_case_  (   self      ):
									for model_class_name in self.all_model_classes:
											a_     :      Any 				=     model_class_name.from_pretrained("google/vit-base-patch16-224"      )
											a_     :      int 				=     model(np.ones((1, 3, 2_2_4, 2_2_4)      )      )
											self.assertIsNotNone(A_      )




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

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