"""simple docstring"""




from argparse import ArgumentParser

from . import BaseTransformersCLICommand





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]	)       ->      int:
  return DownloadCommand(args.model			,args.cache_dir			,args.force			,args.trust_remote_code	)






class 						__A							( SCREAMING_SNAKE_CASE_		):






     @staticmethod
     def     __A  (		a__  ):
       _lowerCAmelCase						:       Optional[int]  			=     parser.add_parser("""download"""  )
       download_parser.add_argument(
           """--cache-dir"""				,     type=a__				,     default=a__				,     help="""Path to location to store the models"""  )
       download_parser.add_argument(
           """--force"""				,     action="""store_true"""				,     help="""Force the model to be download even if already in cache-dir"""  )
       download_parser.add_argument(
           """--trust-remote-code"""				,     action="""store_true"""				,     help="""Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you've reviewed the code as it will execute on your local machine"""				,     )
       download_parser.add_argument("""model"""				,     type=a__				,     help="""Name of the model to download"""  )
       download_parser.set_defaults(func=a__  )






     def __init__(		self				,     a__				,     a__				,     a__				,     a__  ):
       _lowerCAmelCase						:       str  			=     model
       _lowerCAmelCase						:       List[Any]  			=     cache
       _lowerCAmelCase						:       Tuple  			=     force
       _lowerCAmelCase						:       List[Any]  			=     trust_remote_code






     def     __A  (		self  ):
       from ..models.auto import AutoModel, AutoTokenizer

       AutoModel.from_pretrained(
           self._model				,     cache_dir=self._cache				,     force_download=self._force				,     trust_remote_code=self._trust_remote_code  )
       AutoTokenizer.from_pretrained(
           self._model				,     cache_dir=self._cache				,     force_download=self._force				,     trust_remote_code=self._trust_remote_code  )











from __future__ import annotations

import inspect
import unittest

from transformers import ViTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFViTForImageClassification, TFViTModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor

class 	A_       :
	def __init__(    self							,					_A							,					_A=1_3							,					_A=3_0							,					_A=2							,					_A=3							,					_A=True							,					_A=True							,					_A=3_2							,					_A=2							,					_A=4							,					_A=3_7							,					_A="gelu"							,					_A=0.1							,					_A=0.1							,					_A=1_0							,					_A=0.02							,					_A=3							,					_A=None							,					):






				'''simple docstring'''







				UpperCAmelCase   		=				parent
				UpperCAmelCase   		=				batch_size
				UpperCAmelCase   		=				image_size
				UpperCAmelCase   		=				patch_size
				UpperCAmelCase   		=				num_channels
				UpperCAmelCase   		=				is_training
				UpperCAmelCase   		=				use_labels
				UpperCAmelCase   		=				hidden_size
				UpperCAmelCase   		=				num_hidden_layers
				UpperCAmelCase   		=				num_attention_heads
				UpperCAmelCase   		=				intermediate_size
				UpperCAmelCase   		=				hidden_act
				UpperCAmelCase   		=				hidden_dropout_prob
				UpperCAmelCase   		=				attention_probs_dropout_prob
				UpperCAmelCase   		=				type_sequence_label_size
				UpperCAmelCase   		=				initializer_range
				UpperCAmelCase   		=				scope

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






				'''simple docstring'''







				UpperCAmelCase   		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

				UpperCAmelCase   		=				None
				if self.use_labels:
							UpperCAmelCase   		=				ids_tensor([self.batch_size]							,					self.type_sequence_label_size		)

				UpperCAmelCase   		=				self.get_config()

				return config, pixel_values, labels
	def      _lowercase				(    self		):






				'''simple docstring'''







				return 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							,					)
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModel(config=_A		)
				UpperCAmelCase   		=				model(_A							,					training=_A		)
				self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, self.seq_length, self.hidden_size)		)

				# Test with an image with different size than the one specified in config.
				UpperCAmelCase   		=				self.image_size // 2
				UpperCAmelCase   		=				pixel_values[:, :, :image_size, :image_size]
				UpperCAmelCase   		=				model(_A							,					interpolate_pos_encoding=_A							,					training=_A		)
				UpperCAmelCase   		=				(image_size // self.patch_size) ** 2 + 1
				self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, seq_length, self.hidden_size)		)
	def      _lowercase				(    self							,					_A							,					_A							,					_A		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.type_sequence_label_size
				UpperCAmelCase   		=				TFViTForImageClassification(_A		)
				UpperCAmelCase   		=				model(_A							,					labels=_A							,					training=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)

				# Test with an image with different size than the one specified in config.
				UpperCAmelCase   		=				self.image_size // 2
				UpperCAmelCase   		=				pixel_values[:, :, :image_size, :image_size]
				UpperCAmelCase   		=				model(_A							,					interpolate_pos_encoding=_A							,					training=_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)

				# test greyscale images
				UpperCAmelCase   		=				1
				UpperCAmelCase   		=				TFViTForImageClassification(_A		)
				UpperCAmelCase   		=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
				UpperCAmelCase   		=				model(_A		)
				self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.type_sequence_label_size)		)







	def      _lowercase				(    self		):






				'''simple docstring'''







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

@require_tf
class 	A_       (a_     ,							a_     ,							unittest.TestCase			):
	UpperCAmelCase__					=			(TFViTModel, TFViTForImageClassification) if is_tf_available() else ()
	UpperCAmelCase__					=			(
	    {'''feature-extraction''': TFViTModel, '''image-classification''': TFViTForImageClassification}
	    if is_tf_available()
	    else {}
	)

	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	UpperCAmelCase__					=			False
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModelTester(self		)
				UpperCAmelCase   		=				ConfigTester(self							,					config_class=_A							,					has_text_modality=_A							,					hidden_size=3_7		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				self.config_tester.run_common_tests()
	@unittest.skip(reason='''ViT does not use inputs_embeds'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	@unittest.skip(reason='''ViT does not use inputs_embeds'''		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				pass
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							UpperCAmelCase   		=				model_class(_A		)
							self.assertIsInstance(model.get_input_embeddings()							,					(tf.keras.layers.Layer)		)
							UpperCAmelCase   		=				model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(_A							,					tf.keras.layers.Layer		)		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase		,			UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs_for_common()

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

							UpperCAmelCase   		=				['''pixel_values''']
							self.assertListEqual(arg_names[:1]							,					_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_A		)
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_A		)







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTModel.from_pretrained('''google/vit-base-patch16-224'''		)
				self.assertIsNotNone(_A		)





def 						__SCREAMING_SNAKE_CASE  (				)  ->				Optional[Any]:







			'''simple docstring'''

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






@require_tf
@require_vision
class 	A_       (unittest.TestCase			):
	@cached_property
	def      _lowercase				(    self		):






				'''simple docstring'''







				return ViTImageProcessor.from_pretrained('''google/vit-base-patch16-224'''		) if is_vision_available() else None







	@slow
	def      _lowercase				(    self		):






				'''simple docstring'''







				UpperCAmelCase   		=				TFViTForImageClassification.from_pretrained('''google/vit-base-patch16-224'''		)

				UpperCAmelCase   		=				self.default_image_processor
				UpperCAmelCase   		=				prepare_img()
				UpperCAmelCase   		=				image_processor(images=_A							,					return_tensors='''tf'''		)

				# forward pass
				UpperCAmelCase   		=				model(**_A		)

				# verify the logits
				UpperCAmelCase   		=				tf.TensorShape((1, 1_0_0_0)		)
				self.assertEqual(outputs.logits.shape							,					_A		)

				UpperCAmelCase   		=				tf.constant([-0.27_44, 0.82_15, -0.08_36]		)

				tf.debugging.assert_near(outputs.logits[0, :3]							,					_A							,					atol=1E-4		)



