






import argparse
import json

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.models import create_model

from transformers import (
    BeitImageProcessor,
    DataaVecVisionConfig,
    DataaVecVisionForImageClassification,
    DataaVecVisionModel,
)







def   snake_case		(				UpperCAmelCase						:							Tuple,    UpperCAmelCase						:							List[Any]=False,    UpperCAmelCase						:							Dict=False,    UpperCAmelCase						:							List[str]="data2vec." ):
				A									=   'backbone.' if is_semantic else ''

				A									=   []
				for i in range(config.num_hidden_layers ):
								# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
								rename_keys.append(
								    (f'{prefix}blocks.{i}.norm1.weight', f'{hf_prefix}encoder.layer.{i}.layernorm_before.weight') )
								rename_keys.append((f'{prefix}blocks.{i}.norm1.bias', f'{hf_prefix}encoder.layer.{i}.layernorm_before.bias') )
								rename_keys.append(
								    (f'{prefix}blocks.{i}.attn.proj.weight', f'{hf_prefix}encoder.layer.{i}.attention.output.dense.weight') )
								rename_keys.append(
								    (f'{prefix}blocks.{i}.attn.proj.bias', f'{hf_prefix}encoder.layer.{i}.attention.output.dense.bias') )
								rename_keys.append(
								    (f'{prefix}blocks.{i}.norm2.weight', f'{hf_prefix}encoder.layer.{i}.layernorm_after.weight') )
								rename_keys.append((f'{prefix}blocks.{i}.norm2.bias', f'{hf_prefix}encoder.layer.{i}.layernorm_after.bias') )
								rename_keys.append(
								    (f'{prefix}blocks.{i}.mlp.fc1.weight', f'{hf_prefix}encoder.layer.{i}.intermediate.dense.weight') )
								rename_keys.append(
								    (f'{prefix}blocks.{i}.mlp.fc1.bias', f'{hf_prefix}encoder.layer.{i}.intermediate.dense.bias') )
								rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.weight', f'{hf_prefix}encoder.layer.{i}.output.dense.weight') )
								rename_keys.append((f'{prefix}blocks.{i}.mlp.fc2.bias', f'{hf_prefix}encoder.layer.{i}.output.dense.bias') )

				# projection layer + position embeddings
				rename_keys.extend(
				    [
				        (f'{prefix}cls_token', f'{hf_prefix}embeddings.cls_token'),
				        (f'{prefix}patch_embed.proj.weight', f'{hf_prefix}embeddings.patch_embeddings.projection.weight'),
				        (f'{prefix}patch_embed.proj.bias', f'{hf_prefix}embeddings.patch_embeddings.projection.bias'),
				    ] )

				if has_lm_head:
								# mask token + shared relative position bias + layernorm
								rename_keys.extend(
								    [
								        ('mask_token', f'{hf_prefix}embeddings.mask_token'),
								        (
								            'rel_pos_bias.relative_position_bias_table',
								            f'{hf_prefix}encoder.relative_position_bias.relative_position_bias_table',
								        ),
								        (
								            'rel_pos_bias.relative_position_index',
								            f'{hf_prefix}encoder.relative_position_bias.relative_position_index',
								        ),
								        ('norm.weight', 'layernorm.weight'),
								        ('norm.bias', 'layernorm.bias'),
								    ] )
				elif is_semantic:
								# semantic segmentation classification heads
								rename_keys.extend(
								    [
								        ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
								        ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
								        ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
								        ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
								    ] )
				else:
								# layernorm + classification head
								rename_keys.extend(
								    [
								        ('fc_norm.weight', f'{hf_prefix}pooler.layernorm.weight'),
								        ('fc_norm.bias', f'{hf_prefix}pooler.layernorm.bias'),
								        ('head.weight', 'classifier.weight'),
								        ('head.bias', 'classifier.bias'),
								    ] )

				return rename_keys







def   snake_case		(				UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							Any,    UpperCAmelCase						:							Dict=False,    UpperCAmelCase						:							Any=False,    UpperCAmelCase						:							Any="data2vec_vision." ):
				for i in range(config.num_hidden_layers ):
								A									=   'backbone.' if is_semantic else ''
								# queries, keys and values
								A									=   state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight' )
								A									=   state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias' )
								A									=   state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias' )

								A									=   in_proj_weight[
								    : config.hidden_size, :
								]
								A									=   q_bias
								A									=   in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								A									=   in_proj_weight[
								    -config.hidden_size :, :
								]
								A									=   v_bias

								# gamma_1 and gamma_2
								# we call them lambda because otherwise they are renamed when using .from_pretrained
								A									=   state_dict.pop(f'{prefix}blocks.{i}.gamma_1' )
								A									=   state_dict.pop(f'{prefix}blocks.{i}.gamma_2' )

								A									=   gamma_a
								A									=   gamma_a

								# relative_position bias table + index
								if not has_lm_head:
												# each layer has its own relative position bias
												A									=   state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_bias_table' )
												A									=   state_dict.pop(f'{prefix}blocks.{i}.attn.relative_position_index' )

												A									=   table
												A									=   index







def   snake_case		(				):
				A									=   argparse.ArgumentParser(
				    'Convert Data2VecVision to HF for image classification and pretraining',    add_help=UpperCAmelCase )
				parser.add_argument('--hf_checkpoint_name',    type=UpperCAmelCase )
				parser.add_argument('--input_size',    default=2_24,    type=UpperCAmelCase,    help='images input size' )
				parser.add_argument('--beit_checkpoint',    default='',    help='beit checkpoint' )

				return parser.parse_args()







def   snake_case		(				UpperCAmelCase						:							Optional[Any],    UpperCAmelCase						:							int,    UpperCAmelCase						:							Union[str, Any] ):
				def load_state_dict(UpperCAmelCase						:							Any,    UpperCAmelCase						:							List[str],    UpperCAmelCase						:							Optional[int]="",    UpperCAmelCase						:							str="relative_position_index" ):
								A									=   []
								A									=   []
								A									=   []
								# copy state_dict so _load_from_state_dict can modify it
								A									=   getattr(UpperCAmelCase,    '_metadata',    UpperCAmelCase )
								A									=   state_dict.copy()
								if metadata is not None:
												A									=   metadata

								def load(UpperCAmelCase						:							List[str],    UpperCAmelCase						:							Tuple="" ):
												A									=   {} if metadata is None else metadata.get(prefix[:-1],    {} )
												module._load_from_state_dict(
												    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
												for name, child in module._modules.items():
																if child is not None:
																				load(UpperCAmelCase,    prefix + name + '.' )

								load(UpperCAmelCase,    prefix=UpperCAmelCase )

								A									=   []
								A									=   []
								for key in missing_keys:
												A									=   True
												for ignore_key in ignore_missing.split('|' ):
																if ignore_key in key:
																				A									=   False
																				break
												if keep_flag:
																warn_missing_keys.append(UpperCAmelCase )
												else:
																ignore_missing_keys.append(UpperCAmelCase )

								A									=   warn_missing_keys

								if len(UpperCAmelCase ) > 0:
												print(
												    'Weights of {} not initialized from pretrained model: {}'.format(
												        model.__class__.__name__,    UpperCAmelCase ) )
								if len(UpperCAmelCase ) > 0:
												print('Weights from pretrained model not used in {}: {}'.format(model.__class__.__name__,    UpperCAmelCase ) )
								if len(UpperCAmelCase ) > 0:
												print(
												    'Ignored weights of {} not initialized from pretrained model: {}'.format(
												        model.__class__.__name__,    UpperCAmelCase ) )
								if len(UpperCAmelCase ) > 0:
												print('\n'.join(UpperCAmelCase ) )

				A									=   {
				    'pretrained': False,
				    'use_shared_rel_pos_bias': True,
				    'use_abs_pos_emb': False,
				    'init_values': 0.1,
				}

				if is_finetuned:
								model_kwargs.update(
								    {
								        'num_classes': 10_00,
								        'use_mean_pooling': True,
								        'init_scale': 0.001,
								        'use_rel_pos_bias': True,
								    } )

				A									=   create_model(
				    'beit_large_patch16_224' if is_large else 'beit_base_patch16_224',    **UpperCAmelCase,    )
				A									=   model.patch_embed.patch_size
				A									=   (args.input_size // patch_size[0], args.input_size // patch_size[1])
				A									=   torch.load(args.beit_checkpoint,    map_location='cpu' )

				print(f'Load ckpt from {args.beit_checkpoint}' )
				A									=   None
				for model_key in ("model", "module"):
								if model_key in checkpoint:
												A									=   checkpoint[model_key]
												print(f'Load state_dict by model_key = {model_key}' )
												break

				A									=   list(checkpoint_model.keys() )
				for key in all_keys:
								if "relative_position_index" in key:
												checkpoint_model.pop(UpperCAmelCase )

								if "relative_position_bias_table" in key:
												A									=   checkpoint_model[key]
												A			,		A									=   rel_pos_bias.size()
												A			,		A									=   model.state_dict()[key].size()
												A									=   model.patch_embed.patch_shape
												if dst_patch_shape[0] != dst_patch_shape[1]:
																raise NotImplementedError()

				load_state_dict(UpperCAmelCase,    UpperCAmelCase,    prefix='' )

				return model







def   snake_case		(				):
				A									=   get_args()

				A									=   'ft1k' in args.hf_checkpoint_name
				A									=   'large' in args.hf_checkpoint_name

				if is_finetuned:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_finetune.py
								# into this folder.
								import modeling_finetune  # noqa: F401
				else:
								# To convert Beit's data2vec_vision to HF you need to copy
								# https://github.com/facebookresearch/data2vec_vision/blob/main/beit/modeling_cyclical.py
								# into this folder
								# IMPORTANT: Note that for now we've only converted the down-stream
								# model and not the full pretrained model. This means for the integration
								# test you need to add a `return x` after the following line:
								# https://github.com/facebookresearch/data2vec_vision/blob/af9a36349aaed59ae66e69b5dabeef2d62fdc5da/beit/modeling_cyclical.py#L197
								# to make the integration test pass.
								import modeling_cyclical  # noqa: F401

				# 1. Create model config
				A									=   DataaVecVisionConfig()
				if is_finetuned:
								A									=   True
								A									=   False
								A									=   True
								A									=   10_00

								A									=   'huggingface/label-files'
								A									=   'imagenet-1k-id2label.json'
								A									=   json.load(open(hf_hub_download(UpperCAmelCase,    UpperCAmelCase,    repo_type='dataset' ),    'r' ) )
								A									=   {int(UpperCAmelCase ): v for k, v in idalabel.items()}
								A									=   idalabel
								A									=   {v: k for k, v in idalabel.items()}
				else:
								A									=   False
								A									=   True
								A									=   False

				if is_large:
								A									=   10_24
								A									=   40_96
								A									=   24
								A									=   16

				# 2. Load Beit model
				A									=   load_beit_model(UpperCAmelCase,    UpperCAmelCase,    UpperCAmelCase )
				orig_model.eval()

				# 3. Forward Beit model
				A									=   BeitImageProcessor(size=config.image_size,    do_center_crop=UpperCAmelCase )
				A									=   Image.open('../../../../tests/fixtures/tests_samples/COCO/000000039769.png' )
				A									=   image_processor(images=UpperCAmelCase,    return_tensors='pt' )
				A									=   encoding['pixel_values']

				A									=   (pixel_values,) if is_finetuned else (pixel_values, None)
				with torch.no_grad():
								A									=   orig_model(*UpperCAmelCase )

				# 4. Load HF Data2VecVision model
				if is_finetuned:
								A									=   DataaVecVisionForImageClassification(UpperCAmelCase )
								hf_model.eval()
								A									=   False
								A									=   'data2vec_vision.'
				else:
								A									=   DataaVecVisionModel(UpperCAmelCase )
								hf_model.eval()
								A									=   True
								A									=   ''

				A									=   create_rename_keys(UpperCAmelCase,    hf_prefix=UpperCAmelCase,    has_lm_head=UpperCAmelCase )
				A									=   orig_model.state_dict()
				for src, dest in rename_keys:
								A									=   state_dict.pop(UpperCAmelCase )
								A									=   val

				read_in_q_k_v(UpperCAmelCase,    UpperCAmelCase,    hf_prefix=UpperCAmelCase,    has_lm_head=UpperCAmelCase )
				A			,		A									=   hf_model.load_state_dict(UpperCAmelCase,    strict=UpperCAmelCase )
				print('HF missing',    UpperCAmelCase )
				print('HF unexpected_keys',    UpperCAmelCase )

				# 5. Forward HF Data2VecVision model
				with torch.no_grad():
								A									=   hf_model(UpperCAmelCase )

				A									=   hf_model_output.logits if is_finetuned else hf_model_output.last_hidden_state

				# 6. Compare
				A									=   torch.max(torch.abs(hf_output - orig_model_output ) ).item()

				print(f'max_absolute_diff = {max_absolute_diff}' )
				A									=   torch.allclose(UpperCAmelCase,    UpperCAmelCase,    atol=1E-3 )
				print('Do both models output the same tensors?',    '🔥' if success else '💩' )
				if not success:
								raise Exception('Something went wRoNg' )

				# 7. Save
				print(f'Saving to {args.hf_checkpoint_name}' )
				hf_model.save_pretrained(args.hf_checkpoint_name )
				image_processor.save_pretrained(args.hf_checkpoint_name )


if __name__ == "__main__":
					main()
					# Run the following to convert checkpoints
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_base.pt \
					#          --hf_checkpoint_name "./data2vec-vision-base-ft1k"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./pretrained_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large"
					#  python ./convert_data2vec_vision_original_pytorch_checkpoint_to_pytorch.py \
					#          --beit_checkpoint ./finetuned_large.pt \
					#          --hf_checkpoint_name "./data2vec-vision-large-ft1k"













import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def __init__(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Any    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=7    ,_SCREAMING_SNAKE_CASE		:							List[Any]=3    ,_SCREAMING_SNAKE_CASE		:							int=1_8    ,_SCREAMING_SNAKE_CASE		:							Tuple=3_0    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=4_0_0    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							int=None    ,_SCREAMING_SNAKE_CASE		:							Tuple=True    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=[0.48145466, 0.4578275, 0.40821073]    ,_SCREAMING_SNAKE_CASE		:							int=[0.26862954, 0.26130258, 0.27577711]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=True    ,)     -> List[str]:


						'''simple docstring'''
						A									=   size if size is not None else {'shortest_edge': 2_0}
						A									=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
						A									=   parent
						A									=   batch_size
						A									=   num_channels
						A									=   image_size
						A									=   min_resolution
						A									=   max_resolution
						A									=   do_resize
						A									=   size
						A									=   do_center_crop
						A									=   crop_size
						A									=   do_normalize
						A									=   image_mean
						A									=   image_std
						A									=   do_convert_rgb
		def    A(	self		:							List[str]		)     -> Optional[int]:


						'''simple docstring'''
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_center_crop": self.do_center_crop,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_convert_rgb": self.do_convert_rgb,
						}







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


						'''simple docstring'''
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
										A									=   []
										for i in range(self.batch_size		):
														image_inputs.append(
														    np.random.randint(
														        2_5_5    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta		)		)
						else:
										A									=   []
										for i in range(self.batch_size		):
														A			,		A									=   np.random.choice(np.arange(self.min_resolution    ,self.max_resolution		)    ,2		)
														image_inputs.append(np.random.randint(2_5_5    ,size=(self.num_channels, width, height)    ,dtype=np.uinta		)		)

						if not numpify and not torchify:
										# PIL expects the channel dimension as last dimension
										A									=   [Image.fromarray(np.moveaxis(_SCREAMING_SNAKE_CASE    ,0    ,-1		)		) for x in image_inputs]

						if torchify:
										A									=   [torch.from_numpy(_SCREAMING_SNAKE_CASE		) for x in image_inputs]

						return image_inputs


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Optional[Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self		)
		@property
		def    A(	self		:							Optional[int]		)     -> Any:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Optional[int]		)     -> Tuple:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 2_0}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 1_8, 'width': 1_8}		)

						A									=   self.image_processing_class.from_dict(self.image_processor_dict    ,size=4_2    ,crop_size=8_4		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 4_2}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 8_4, 'width': 8_4}		)
		def    A(	self		:							Any		)     -> Union[str, Any]:


						'''simple docstring'''
						pass
		def    A(	self		:							Tuple		)     -> Optional[int]:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)
		def    A(	self		:							int		)     -> Tuple:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,np.ndarray		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)







		def    A(	self		:							str		)     -> str:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,torch.Tensor		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Tuple		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self    ,num_channels=4		)
						A									=   3
		@property
		def    A(	self		:							str		)     -> Dict:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Any		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Any		)     -> str:


						'''simple docstring'''
						pass







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


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   image_processing(image_inputs[0]    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        1,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)

						# Test batched
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,return_tensors='pt'		).pixel_values
						self.assertEqual(
						    encoded_images.shape    ,(
						        self.image_processor_tester.batch_size,
						        self.expected_encoded_image_num_channels,
						        self.image_processor_tester.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)





