





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      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=13				,     snake_case_=30				,     snake_case_=2				,     snake_case_=3				,     snake_case_=True				,     snake_case_=True				,     snake_case_=32				,     snake_case_=5				,     snake_case_=4				,     snake_case_=37				,     snake_case_="gelu"				,     snake_case_=0.1				,     snake_case_=0.1				,     snake_case_=10				,     snake_case_=0.02				,     ):
         _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

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




     def        lowerCAmelCase__		(    self							):
         _A     =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

         _A     =							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=snake_case_				,     initializer_range=self.initializer_range				,     )

         return config, pixel_values




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							FlaxViTModel(config=snake_case_							)
         _A     =							model(snake_case_							)
         # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
         _A     =							(self.image_size, self.image_size)
         _A     =							(self.patch_size, self.patch_size)
         _A     =							(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        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_							):
         _A     =							self.type_sequence_label_size
         _A     =							FlaxViTForImageClassification(config=snake_case_							)
         _A     =							model(snake_case_							)
         self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.type_sequence_label_size)							)

         # test greyscale images
         _A     =							1
         _A     =							FlaxViTForImageClassification(snake_case_							)

         _A     =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
         _A     =							model(snake_case_							)




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







@require_flax
class      lowerCamelCase(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()




     def        lowerCAmelCase__		(    self							):
         _A     =							FlaxViTModelTester(self							)
         _A     =							ConfigTester(self				,     config_class=snake_case_				,     has_text_modality=snake_case_				,     hidden_size=37							)




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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

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

             _A     =							['pixel_values']
             self.assertListEqual(arg_names[:1]				,     snake_case_							)




     def        lowerCAmelCase__		(    self							):
         _A,			_A     =							self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__							):
                 _A     =							self._prepare_for_class(snake_case_				,     snake_case_							)
                 _A     =							model_class(snake_case_							)

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

                 with self.subTest('JIT Enabled'							):
                     _A     =							model_jitted(**snake_case_							).to_tuple()

                 with self.subTest('JIT Disabled'							):
                     with jax.disable_jit():
                         _A     =							model_jitted(**snake_case_							).to_tuple()

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




     @slow
     def        lowerCAmelCase__		(    self							):
         for model_class_name in self.all_model_classes:
             _A     =							model_class_name.from_pretrained('google/vit-base-patch16-224'							)
             _A     =							model(np.ones((1, 3, 224, 224)							)							)
             self.assertIsNotNone(snake_case_							)









from typing import TYPE_CHECKING

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


UpperCAmelCase   :    Tuple     =		{
    "configuration_blenderbot_small": [
        "BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotSmallConfig",
        "BlenderbotSmallOnnxConfig",
    ],
    "tokenization_blenderbot_small": ["BlenderbotSmallTokenizer"],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		["BlenderbotSmallTokenizerFast"]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[str]     =		[
		    "BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "BlenderbotSmallForCausalLM",
		    "BlenderbotSmallForConditionalGeneration",
		    "BlenderbotSmallModel",
		    "BlenderbotSmallPreTrainedModel",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    Any     =		[
		    "TFBlenderbotSmallForConditionalGeneration",
		    "TFBlenderbotSmallModel",
		    "TFBlenderbotSmallPreTrainedModel",
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase   :    List[Any]     =		[
		    "FlaxBlenderbotSmallForConditionalGeneration",
		    "FlaxBlenderbotSmallModel",
		    "FlaxBlenderbotSmallPreTrainedModel",
		]

if TYPE_CHECKING:
		from .configuration_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    BlenderbotSmallConfig,
		    BlenderbotSmallOnnxConfig,
		)
		from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_blenderbot_small import (
				    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BlenderbotSmallForCausalLM,
				    BlenderbotSmallForConditionalGeneration,
				    BlenderbotSmallModel,
				    BlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_blenderbot_small import (
				    TFBlenderbotSmallForConditionalGeneration,
				    TFBlenderbotSmallModel,
				    TFBlenderbotSmallPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_blenderbot_small import (
				    FlaxBlenderbotSmallForConditionalGeneration,
				    FlaxBlenderbotSmallModel,
				    FlaxBlenderbotSmallPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase   :    List[Any]     =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

