





"""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 				__lowercase			(			unittest.TestCase							):







					'''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=0.0_2	,			):
									__a :						List[Any]			       =  parent
									__a :						List[Any]			       =  batch_size
									__a :						Union[str, Any]			       =  image_size
									__a :						List[str]			       =  patch_size
									__a :						List[str]			       =  num_channels
									__a :						int			       =  is_training
									__a :						Optional[Any]			       =  use_labels
									__a :						Tuple			       =  hidden_size
									__a :						int			       =  num_hidden_layers
									__a :						Optional[int]			       =  num_attention_heads
									__a :						Optional[int]			       =  intermediate_size
									__a :						List[str]			       =  hidden_act
									__a :						int			       =  hidden_dropout_prob
									__a :						List[str]			       =  attention_probs_dropout_prob
									__a :						Union[str, Any]			       =  type_sequence_label_size
									__a :						Union[str, Any]			       =  initializer_range

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







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

									__a :						List[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=_UpperCAmelCase	,			initializer_range=self.initializer_range	,			)

									return config, pixel_values







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Optional[Any]			       =  FlaxViTModel(config=_UpperCAmelCase     )
									__a :						Any			       =  model(_UpperCAmelCase     )
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									__a :						List[str]			       =  (self.image_size, self.image_size)
									__a :						Any			       =  (self.patch_size, self.patch_size)
									__a :						str			       =  (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	,			_UpperCAmelCase	,			_UpperCAmelCase     ):
									__a :						Any			       =  self.type_sequence_label_size
									__a :						Dict			       =  FlaxViTForImageClassification(config=_UpperCAmelCase     )
									__a :						Any			       =  model(_UpperCAmelCase     )
									self.parent.assertEqual(result.logits.shape	,			(self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									__a :						Union[str, Any]			       =  1
									__a :						Any			       =  FlaxViTForImageClassification(_UpperCAmelCase     )

									__a :						List[str]			       =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									__a :						Optional[Any]			       =  model(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.prepare_config_and_inputs()
									(
									    (
									    __a
									)       ,						(
									    __a
									)       ,						
									) :						Tuple			       =  config_and_inputs
									__a :						Any			       =  {'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_flax
class 				__lowercase			(			_UpperCamelCase   ,      unittest.TestCase							):







					'''simple docstring'''




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







					def  _lowerCamelCase		(						self     ):
									__a :						Tuple			       =  FlaxViTModelTester(self     )
									__a :						str			       =  ConfigTester(self	,			config_class=_UpperCAmelCase	,			has_text_modality=_UpperCAmelCase	,			hidden_size=37     )







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







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a       ,						__a :						List[Any]			       =  self.model_tester.prepare_config_and_inputs_for_common()

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

													__a :						Optional[Any]			       =  ['''pixel_values''']
													self.assertListEqual(arg_names[:1]	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a       ,						__a :						Union[str, Any]			       =  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__     ):
																	__a :						Union[str, Any]			       =  self._prepare_for_class(_UpperCAmelCase	,			_UpperCAmelCase     )
																	__a :						Optional[int]			       =  model_class(_UpperCAmelCase     )

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

																	with self.subTest('''JIT Enabled'''     ):
																					__a :						Dict			       =  model_jitted(**_UpperCAmelCase     ).to_tuple()

																	with self.subTest('''JIT Disabled'''     ):
																					with jax.disable_jit():
																									__a :						Optional[int]			       =  model_jitted(**_UpperCAmelCase     ).to_tuple()

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







					@slow
					def  _lowerCamelCase		(						self     ):
									for model_class_name in self.all_model_classes:
													__a :						str			       =  model_class_name.from_pretrained('''google/vit-base-patch16-224'''     )
													__a :						int			       =  model(np.ones((1, 3, 224, 224)     )     )
													self.assertIsNotNone(_UpperCAmelCase     )






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A 			=  {
    '''configuration_jukebox''': [
        '''JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''JukeboxConfig''',
        '''JukeboxPriorConfig''',
        '''JukeboxVQVAEConfig''',
    ],
    '''tokenization_jukebox''': ['''JukeboxTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A 			=  [
		    '''JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''JukeboxModel''',
		    '''JukeboxPreTrainedModel''',
		    '''JukeboxVQVAE''',
		    '''JukeboxPrior''',
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		A 			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)