


"""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 					__lowerCAmelCase					(   unittest.TestCase       ):


		'''simple docstring'''



		def __init__(  self							, _a							, _a=13							, _a=30							, _a=2							, _a=3							, _a=True							, _a=True							, _a=32							, _a=5							, _a=4							, _a=37							, _a="gelu"							, _a=0.1							, _a=0.1							, _a=10							, _a=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 			__UpperCAmelCase       (  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=__UpperCamelCase							, initializer_range=self.initializer_range							, )

				return config, pixel_values

		def 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			FlaxViTModel(config=__UpperCamelCase      )
				__a           =			model(__UpperCamelCase      )
				# 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 			__UpperCAmelCase       (  self							, _a							, _a      ):
				__a           =			self.type_sequence_label_size
				__a           =			FlaxViTForImageClassification(config=__UpperCamelCase      )
				__a           =			model(__UpperCamelCase      )
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)      )

				# test greyscale images
				__a           =			1
				__a           =			FlaxViTForImageClassification(__UpperCamelCase      )

				__a           =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
				__a           =			model(__UpperCamelCase      )





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







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


		'''simple docstring'''



		__UpperCAmelCase		:			Optional[int]										=				(FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()

		def 			__UpperCAmelCase       (  self      ):
				__a           =			FlaxViTModelTester(self      )
				__a           =			ConfigTester(self							, config_class=__UpperCamelCase							, has_text_modality=__UpperCamelCase							, hidden_size=37      )

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

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase      )

		def 			__UpperCAmelCase       (  self      ):
				__a           =			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase      )

		def 			__UpperCAmelCase       (  self      ):
				__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.__call__      )
						# 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 			__UpperCAmelCase       (  self      ):
				__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(__UpperCamelCase							, __UpperCamelCase      )
								__a           =			model_class(__UpperCamelCase      )

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

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

								with self.subTest('''JIT Disabled'''      ):
										with jax.disable_jit():
												__a           =			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 			__UpperCAmelCase       (  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(__UpperCamelCase      )







"""simple docstring"""







from argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info



def 			lowercase__(       __SCREAMING_SNAKE_CASE							:     Optional[int]      ):
     return {key.lstrip('-'      ): value for key, value in zip(unknown_args[::2]     ,			unknown_args[1::2]      )}



def 			lowercase__(       ):
     lowercase_	:				Any													=	ArgumentParser(
         'HuggingFace Datasets CLI tool'     ,			usage='datasets-cli <command> [<args>]'     ,			allow_abbrev=__SCREAMING_SNAKE_CASE      )
     lowercase_	:				Tuple													=	parser.add_subparsers(help='datasets-cli command helpers'      )
     set_verbosity_info()

     # Register commands
     ConvertCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     TestCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     RunBeamCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )
     DummyDataCommand.register_subcommand(__SCREAMING_SNAKE_CASE      )

     # Parse args
     lowercase_							,  lowercase_	:				Dict													=	parser.parse_known_args()
     if not hasattr(__SCREAMING_SNAKE_CASE     ,			'func'      ):
          parser.print_help()
          exit(1      )
     lowercase_	:				int													=	parse_unknown_args(__SCREAMING_SNAKE_CASE      )

     # Run
     lowercase_	:				List[Any]													=	args.func(__SCREAMING_SNAKE_CASE     ,			**__SCREAMING_SNAKE_CASE      )
     service.run()


if __name__ == "__main__":
       main()

