



"""simple docstring"""






import inspect
import unittest

from transformers import BitConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
	from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		Any       ,			_SCREAMING_SNAKE_CASE:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		Dict=3       ,			_SCREAMING_SNAKE_CASE:		List[str]=32       ,			_SCREAMING_SNAKE_CASE:		List[str]=3       ,			_SCREAMING_SNAKE_CASE:		Dict=10       ,			_SCREAMING_SNAKE_CASE:		List[Any]=[8, 16, 32, 64]       ,			_SCREAMING_SNAKE_CASE:		str=[1, 1, 2, 1]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]=True       ,			_SCREAMING_SNAKE_CASE:		Optional[Any]=True       ,			_SCREAMING_SNAKE_CASE:		int="relu"       ,			_SCREAMING_SNAKE_CASE:		str=3       ,			_SCREAMING_SNAKE_CASE:		List[str]=None       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any]=["stage2", "stage3", "stage4"]       ,			_SCREAMING_SNAKE_CASE:		List[str]=[2, 3, 4]       ,			_SCREAMING_SNAKE_CASE:		str=1       ,			)				->       Tuple:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       parent
				__lowerCAmelCase      :  List[Any]												=       batch_size
				__lowerCAmelCase      :  Optional[Any]												=       image_size
				__lowerCAmelCase      :  List[Any]												=       num_channels
				__lowerCAmelCase      :  int												=       embeddings_size
				__lowerCAmelCase      :  Tuple												=       hidden_sizes
				__lowerCAmelCase      :  List[Any]												=       depths
				__lowerCAmelCase      :  int												=       is_training
				__lowerCAmelCase      :  int												=       use_labels
				__lowerCAmelCase      :  List[Any]												=       hidden_act
				__lowerCAmelCase      :  List[Any]												=       num_labels
				__lowerCAmelCase      :  Optional[Any]												=       scope
				__lowerCAmelCase      :  int												=       len(_UpperCAmelCase)
				__lowerCAmelCase      :  Optional[int]												=       out_features
				__lowerCAmelCase      :  Dict												=       out_indices
				__lowerCAmelCase      :  Dict												=       num_groups

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

				__lowerCAmelCase      :  str												=       None
				if self.use_labels:
							__lowerCAmelCase      :  Optional[Any]												=       ids_tensor([self.batch_size]       ,			self.num_labels)

				__lowerCAmelCase      :  Tuple												=       self.get_config()

				return config, pixel_values, labels

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Optional[int]:

				"""simple docstring"""

				return BitConfig(
				    num_channels=self.num_channels       ,			embeddings_size=self.embeddings_size       ,			hidden_sizes=self.hidden_sizes       ,			depths=self.depths       ,			hidden_act=self.hidden_act       ,			num_labels=self.num_labels       ,			out_features=self.out_features       ,			out_indices=self.out_indices       ,			num_groups=self.num_groups       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Optional[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       BitModel(config=_UpperCAmelCase)
				model.to(_UpperCAmelCase)
				model.eval()
				__lowerCAmelCase      :  int												=       model(_UpperCAmelCase)
				self.parent.assertEqual(
				    result.last_hidden_state.shape       ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any       ,			_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		Dict       ,			_SCREAMING_SNAKE_CASE:		Union[str, Any])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       self.num_labels
				__lowerCAmelCase      :  List[Any]												=       BitForImageClassification(_UpperCAmelCase)
				model.to(_UpperCAmelCase)
				model.eval()
				__lowerCAmelCase      :  Tuple												=       model(_UpperCAmelCase       ,			labels=_UpperCAmelCase)
				self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.num_labels))

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any]       ,			_SCREAMING_SNAKE_CASE:		List[str]       ,			_SCREAMING_SNAKE_CASE:		Optional[int]       ,			_SCREAMING_SNAKE_CASE:		Any)				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       BitBackbone(config=_UpperCAmelCase)
				model.to(_UpperCAmelCase)
				model.eval()
				__lowerCAmelCase      :  Union[str, Any]												=       model(_UpperCAmelCase)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps)       ,			len(config.out_features))
				self.parent.assertListEqual(list(result.feature_maps[0].shape)       ,			[self.batch_size, self.hidden_sizes[1], 4, 4])

				# verify channels
				self.parent.assertEqual(len(model.channels)       ,			len(config.out_features))
				self.parent.assertListEqual(model.channels       ,			config.hidden_sizes[1:])

				# verify backbone works with out_features=None
				__lowerCAmelCase      :  Optional[int]												=       None
				__lowerCAmelCase      :  Optional[Any]												=       BitBackbone(config=_UpperCAmelCase)
				model.to(_UpperCAmelCase)
				model.eval()
				__lowerCAmelCase      :  Optional[Any]												=       model(_UpperCAmelCase)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps)       ,			1)
				self.parent.assertListEqual(list(result.feature_maps[0].shape)       ,			[self.batch_size, self.hidden_sizes[-1], 1, 1])

				# verify channels
				self.parent.assertEqual(len(model.channels)       ,			1)
				self.parent.assertListEqual(model.channels       ,			[config.hidden_sizes[-1]])







	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.prepare_config_and_inputs()
				__lowerCAmelCase      :  List[str]												=       config_and_inputs
				__lowerCAmelCase      :  Optional[int]												=       {'pixel_values': pixel_values}
				return config, inputs_dict






@require_torch
class 			A__   (  a__  ,		a__  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE          =	(
	    {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False
	SCREAMING_SNAKE_CASE          =	False

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  List[Any]												=       BitModelTester(self)
				__lowerCAmelCase      :  Dict												=       ConfigTester(self       ,			config_class=_UpperCAmelCase       ,			has_text_modality=_UpperCAmelCase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       int:

				"""simple docstring"""

				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()

	def 							_SCREAMING_SNAKE_CASE   (     self:		str)				->       Any:

				"""simple docstring"""

				return

	@unittest.skip(reason="Bit does not output attentions")
	def 							_SCREAMING_SNAKE_CASE   (     self:		Tuple)				->       Dict:

				"""simple docstring"""

				pass

	@unittest.skip(reason="Bit does not use inputs_embeds")
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Optional[int]:

				"""simple docstring"""

				pass

	@unittest.skip(reason="Bit does not support input and output embeddings")
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       self.model_tester.prepare_config_and_inputs_for_common()

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

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

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       Any:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_UpperCAmelCase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  List[str]												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*_UpperCAmelCase)

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase      :  Union[str, Any]												=       model_class(config=_UpperCAmelCase)
							for name, module in model.named_modules():
										if isinstance(_UpperCAmelCase       ,			(nn.BatchNormad, nn.GroupNorm)):
													self.assertTrue(
													    torch.all(module.weight == 1)       ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,			)
													self.assertTrue(
													    torch.all(module.bias == 0)       ,			msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       ,			)

	def 							_SCREAMING_SNAKE_CASE   (     self:		Any)				->       Tuple:

				"""simple docstring"""

				def check_hidden_states_output(_SCREAMING_SNAKE_CASE:		str       ,			_SCREAMING_SNAKE_CASE:		Any       ,			_SCREAMING_SNAKE_CASE:		Optional[int]):
							__lowerCAmelCase      :  str												=       model_class(_UpperCAmelCase)
							model.to(_UpperCAmelCase)
							model.eval()

							with torch.no_grad():
										__lowerCAmelCase      :  str												=       model(**self._prepare_for_class(_UpperCAmelCase       ,			_UpperCAmelCase))

							__lowerCAmelCase      :  int												=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

							__lowerCAmelCase      :  Any												=       self.model_tester.num_stages
							self.assertEqual(len(_UpperCAmelCase)       ,			expected_num_stages + 1)

							# Bit's feature maps are of shape (batch_size, num_channels, height, width)
							self.assertListEqual(
							    list(hidden_states[0].shape[-2:])       ,			[self.model_tester.image_size // 4, self.model_tester.image_size // 4]       ,			)

				__lowerCAmelCase      :  List[Any]												=       self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase      :  Dict												=       ['preactivation', 'bottleneck']
				for model_class in self.all_model_classes:
							for layer_type in layers_type:
										__lowerCAmelCase      :  Dict												=       layer_type
										__lowerCAmelCase      :  List[Any]												=       True
										check_hidden_states_output(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										__lowerCAmelCase      :  Union[str, Any]												=       True

										check_hidden_states_output(_UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase)

	@unittest.skip(reason="Bit does not use feedforward chunking")
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       str:

				"""simple docstring"""

				pass

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[str])				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase)







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       Dict:

				"""simple docstring"""

				for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase      :  Any												=       BitModel.from_pretrained(_UpperCAmelCase)
							self.assertIsNotNone(_UpperCAmelCase)






def 			_lowercase				(  )						->   str:
			__lowerCAmelCase      :  Tuple												=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
			return image



@require_torch
@require_vision
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@cached_property
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       int:

				"""simple docstring"""

				return (
				    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
				)







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase      :  Dict												=       BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(_UpperCAmelCase)

				__lowerCAmelCase      :  Dict												=       self.default_image_processor
				__lowerCAmelCase      :  Union[str, Any]												=       prepare_img()
				__lowerCAmelCase      :  int												=       image_processor(images=_UpperCAmelCase       ,			return_tensors="pt").to(_UpperCAmelCase)

				# forward pass
				with torch.no_grad():
							__lowerCAmelCase      :  List[Any]												=       model(**_UpperCAmelCase)

				# verify the logits
				__lowerCAmelCase      :  str												=       torch.Size((1, 1000))
				self.assertEqual(outputs.logits.shape       ,			_UpperCAmelCase)

				__lowerCAmelCase      :  List[Any]												=       torch.tensor([[-0.6526, -0.5263, -1.4398]]).to(_UpperCAmelCase)

				self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,			_UpperCAmelCase       ,			atol=1e-4))






@require_torch
class 			A__   (  a__  ,		unittest.TestCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE          =	(BitBackbone,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE          =	BitConfig

	SCREAMING_SNAKE_CASE          =	False

	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       List[str]:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       BitModelTester(self)






'''simple docstring'''




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       tempfile.mkdtemp()

          # fmt: off
          __A						: Optional[int]      =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
          # fmt: on
          __A						: Tuple      =       dict(zip(_UpperCAmelCase						,	range(len(_UpperCAmelCase))))
          __A						: Any      =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          __A						: List[str]      =       {'unk_token': '<unk>'}

          __A						: int      =       os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['vocab_file'])
          __A						: Optional[int]      =       os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES['merges_file'])
          with open(self.vocab_file						,	'w'						,	encoding='utf-8') as fp:
               fp.write(json.dumps(_UpperCAmelCase) + '\n')
          with open(self.merges_file						,	'w'						,	encoding='utf-8') as fp:
               fp.write('\n'.join(_UpperCAmelCase))

          __A						: str      =       {
              'do_resize': True,
              'size': 20,
              'do_center_crop': True,
              'crop_size': 18,
              'do_normalize': True,
              'image_mean': [0.48145466, 0.4578275, 0.40821073],
              'image_std': [0.26862954, 0.26130258, 0.27577711],
          }
          __A						: Union[str, Any]      =       os.path.join(self.tmpdirname						,	_UpperCAmelCase)
          with open(self.image_processor_file						,	'w'						,	encoding='utf-8') as fp:
               json.dump(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizer.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	**_UpperCAmelCase):

          '''simple docstring'''






          return CLIPImageProcessor.from_pretrained(self.tmpdirname						,	**_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Tuple      =       [np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta)]

          __A						: Optional[int]      =       [Image.fromarray(np.moveaxis(_UpperCAmelCase						,	0						,	-1)) for x in image_inputs]

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       self.get_tokenizer()
          __A						: int      =       self.get_rust_tokenizer()
          __A						: Any      =       self.get_image_processor()

          __A						: Union[str, Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          processor_slow.save_pretrained(self.tmpdirname)
          __A						: Dict      =       CLIPProcessor.from_pretrained(self.tmpdirname						,	use_fast=_UpperCAmelCase)

          __A						: Union[str, Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          processor_fast.save_pretrained(self.tmpdirname)
          __A						: Union[str, Any]      =       CLIPProcessor.from_pretrained(self.tmpdirname)

          self.assertEqual(processor_slow.tokenizer.get_vocab()						,	tokenizer_slow.get_vocab())
          self.assertEqual(processor_fast.tokenizer.get_vocab()						,	tokenizer_fast.get_vocab())
          self.assertEqual(tokenizer_slow.get_vocab()						,	tokenizer_fast.get_vocab())
          self.assertIsInstance(processor_slow.tokenizer						,	_UpperCAmelCase)
          self.assertIsInstance(processor_fast.tokenizer						,	_UpperCAmelCase)

          self.assertEqual(processor_slow.image_processor.to_json_string()						,	image_processor.to_json_string())
          self.assertEqual(processor_fast.image_processor.to_json_string()						,	image_processor.to_json_string())
          self.assertIsInstance(processor_slow.image_processor						,	_UpperCAmelCase)
          self.assertIsInstance(processor_fast.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       CLIPProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						: Dict      =       self.get_tokenizer(bos_token='(BOS)'						,	eos_token='(EOS)')
          __A						: Any      =       self.get_image_processor(do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          __A						: Tuple      =       CLIPProcessor.from_pretrained(
              self.tmpdirname						,	bos_token='(BOS)'						,	eos_token='(EOS)'						,	do_normalize=_UpperCAmelCase						,	padding_value=1.0)

          self.assertEqual(processor.tokenizer.get_vocab()						,	tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.tokenizer						,	_UpperCAmelCase)

          self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.get_image_processor()
          __A						: List[Any]      =       self.get_tokenizer()

          __A						: List[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: List[str]      =       self.prepare_image_inputs()

          __A						: Dict      =       image_processor(_UpperCAmelCase						,	return_tensors='np')
          __A						: str      =       processor(images=_UpperCAmelCase						,	return_tensors='np')

          for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()						,	input_processor[key].sum()						,	delta=1e-2)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       self.get_image_processor()
          __A						: List[str]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       'lower newer'

          __A						: List[Any]      =       processor(text=_UpperCAmelCase)

          __A						: Optional[int]      =       tokenizer(_UpperCAmelCase)

          for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]						,	encoded_processor[key])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Dict      =       self.get_image_processor()
          __A						: Optional[int]      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: int      =       'lower newer'
          __A						: Any      =       self.prepare_image_inputs()

          __A						: Tuple      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,	['input_ids', 'attention_mask', 'pixel_values'])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCAmelCase):
               processor()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       self.get_image_processor()
          __A						: str      =       self.get_tokenizer()

          __A						: Optional[Any]      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Dict      =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						: str      =       processor.batch_decode(_UpperCAmelCase)
          __A						: Optional[Any]      =       tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       self.get_image_processor()
          __A						: int      =       self.get_tokenizer()

          __A						: Any      =       CLIPProcessor(tokenizer=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)

          __A						: Any      =       'lower newer'
          __A						: Any      =       self.prepare_image_inputs()

          __A						: Any      =       processor(text=_UpperCAmelCase						,	images=_UpperCAmelCase)

          self.assertListEqual(list(inputs.keys())						,	processor.model_input_names)