


import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):
 _lowercase:       Union[str, Any]						=				XCLIPTextConfig()

 # derive patch size from model name
 _lowercase:       List[str]						=				model_name.find("patch"      )
 _lowercase:       Optional[int]						=				int(model_name[start_idx + len("patch"      ) : start_idx + len("patch"      ) + 2]      )
 _lowercase:       List[str]						=				XCLIPVisionConfig(patch_size=_lowercase ,   num_frames=_lowercase      )

 if "large" in model_name:
  _lowercase:       Optional[int]						=				7_6_8
  _lowercase:       Dict						=				3_0_7_2
  _lowercase:       Dict						=				1_2

  _lowercase:       str						=				1_0_2_4
  _lowercase:       Any						=				4_0_9_6
  _lowercase:       Union[str, Any]						=				1_6
  _lowercase:       Optional[Any]						=				2_4
  _lowercase:       List[Any]						=				7_6_8
  _lowercase:       Dict						=				3_0_7_2

 if model_name == "xclip-large-patch14-16-frames":
  _lowercase:       int						=				3_3_6

 _lowercase:       List[Any]						=				XCLIPConfig.from_text_vision_configs(_lowercase ,   _lowercase      )

 if "large" in model_name:
  _lowercase:       str						=				7_6_8

 return config


def 							__lowerCAmelCase      (			__magic_name__      ):


 if name == "token_embedding.weight":
  _lowercase:       Optional[int]						=				name.replace("token_embedding.weight" ,   "text_model.embeddings.token_embedding.weight"      )
 if name == "positional_embedding":
  _lowercase:       Any						=				name.replace("positional_embedding" ,   "text_model.embeddings.position_embedding.weight"      )
 if "ln_1" in name:
  _lowercase:       Any						=				name.replace("ln_1" ,   "layer_norm1"      )
 if "ln_2" in name:
  _lowercase:       List[str]						=				name.replace("ln_2" ,   "layer_norm2"      )
 if "c_fc" in name:
  _lowercase:       List[Any]						=				name.replace("c_fc" ,   "fc1"      )
 if "c_proj" in name:
  _lowercase:       Any						=				name.replace("c_proj" ,   "fc2"      )
 if name.startswith("transformer.resblocks"      ):
  _lowercase:       int						=				name.replace("transformer.resblocks" ,   "text_model.encoder.layers"      )
 if "attn.out_proj" in name and "message" not in name:
  _lowercase:       str						=				name.replace("attn.out_proj" ,   "self_attn.out_proj"      )
 if "ln_final" in name:
  _lowercase:       List[str]						=				name.replace("ln_final" ,   "text_model.final_layer_norm"      )
 # visual encoder
 if name == "visual.class_embedding":
  _lowercase:       List[str]						=				name.replace("visual.class_embedding" ,   "vision_model.embeddings.class_embedding"      )
 if name == "visual.positional_embedding":
  _lowercase:       Optional[Any]						=				name.replace("visual.positional_embedding" ,   "vision_model.embeddings.position_embedding.weight"      )
 if name.startswith("visual.transformer.resblocks"      ):
  _lowercase:       Union[str, Any]						=				name.replace("visual.transformer.resblocks" ,   "vision_model.encoder.layers"      )
 if "visual.conv1" in name:
  _lowercase:       Dict						=				name.replace("visual.conv1" ,   "vision_model.embeddings.patch_embedding"      )
 if "visual.ln_pre" in name:
  _lowercase:       List[Any]						=				name.replace("visual.ln_pre" ,   "vision_model.pre_layernorm"      )
 if "visual.ln_post" in name:
  _lowercase:       Optional[int]						=				name.replace("visual.ln_post" ,   "vision_model.post_layernorm"      )
 if "visual.proj" in name:
  _lowercase:       str						=				name.replace("visual.proj" ,   "visual_projection.weight"      )
 if "text_projection" in name:
  _lowercase:       Optional[int]						=				name.replace("text_projection" ,   "text_projection.weight"      )
 # things on top
 if "prompts_visual_proj" in name:
  _lowercase:       List[str]						=				name.replace("prompts_visual_proj" ,   "prompts_visual_projection"      )
 if "prompts_visual_ln" in name:
  _lowercase:       List[str]						=				name.replace("prompts_visual_ln" ,   "prompts_visual_layernorm"      )
 # mit
 if name == "mit.positional_embedding":
  _lowercase:       int						=				name.replace("positional" ,   "position"      )
 if name.startswith("mit.resblocks"      ):
  _lowercase:       Optional[int]						=				name.replace("mit.resblocks" ,   "mit.encoder.layers"      )
 # prompts generator
 if name.startswith("prompts_generator.norm"      ):
  _lowercase:       Optional[int]						=				name.replace("prompts_generator.norm" ,   "prompts_generator.layernorm"      )

 return name


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__      ):


 for key in orig_state_dict.copy().keys():
  _lowercase:       Any						=				orig_state_dict.pop(_lowercase      )

  if "attn.in_proj" in key:
   _lowercase:       Optional[int]						=				key.split("."      )
   if key.startswith("visual"      ):
    _lowercase:       List[Any]						=				key_split[3]
    _lowercase:       Optional[int]						=				config.vision_config.hidden_size
    if "message_attn" in key:
     if "weight" in key:
      _lowercase:       List[str]						=				val[
          :dim, :
      ]
      _lowercase:       Optional[int]						=				val[
          dim : dim * 2, :
      ]
      _lowercase:       Optional[int]						=				val[
          -dim:, :
      ]
     else:
      _lowercase:       List[Any]						=				val[
          :dim
      ]
      _lowercase:       Any						=				val[
          dim : dim * 2
      ]
      _lowercase:       List[str]						=				val[
          -dim:
      ]
    else:
     if "weight" in key:
      _lowercase:       Optional[int]						=				val[
          :dim, :
      ]
      _lowercase:       List[Any]						=				val[
          dim : dim * 2, :
      ]
      _lowercase:       List[str]						=				val[
          -dim:, :
      ]
     else:
      _lowercase:       List[Any]						=				val[:dim]
      _lowercase:       List[Any]						=				val[
          dim : dim * 2
      ]
      _lowercase:       Optional[Any]						=				val[-dim:]
   elif key.startswith("mit"      ):
    _lowercase:       Optional[Any]						=				key_split[2]
    _lowercase:       Tuple						=				config.vision_config.mit_hidden_size
    if "weight" in key:
     _lowercase:       Optional[Any]						=				val[:dim, :]
     _lowercase:       Optional[int]						=				val[dim : dim * 2, :]
     _lowercase:       Optional[int]						=				val[-dim:, :]
    else:
     _lowercase:       List[str]						=				val[:dim]
     _lowercase:       List[Any]						=				val[dim : dim * 2]
     _lowercase:       int						=				val[-dim:]
   else:
    _lowercase:       List[str]						=				key_split[2]
    _lowercase:       List[str]						=				config.text_config.hidden_size
    if "weight" in key:
     _lowercase:       Tuple						=				val[:dim, :]
     _lowercase:       Any						=				val[
         dim : dim * 2, :
     ]
     _lowercase:       str						=				val[-dim:, :]
    else:
     _lowercase:       Optional[int]						=				val[:dim]
     _lowercase:       Tuple						=				val[
         dim : dim * 2
     ]
     _lowercase:       List[Any]						=				val[-dim:]
  else:
   _lowercase:       int						=				rename_key(_lowercase      )
   if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
    _lowercase:       Dict						=				val.T
   _lowercase:       Tuple						=				val

 return orig_state_dict


def 							__lowerCAmelCase      (			__magic_name__      ):


 if num_frames == 8:
  _lowercase:       Tuple						=				'''eating_spaghetti_8_frames.npy'''
 elif num_frames == 1_6:
  _lowercase:       Union[str, Any]						=				'''eating_spaghetti.npy'''
 elif num_frames == 3_2:
  _lowercase:       Optional[Any]						=				'''eating_spaghetti_32_frames.npy'''
 _lowercase:       List[Any]						=				hf_hub_download(
     repo_id="hf-internal-testing/spaghetti-video" ,   filename=_lowercase ,   repo_type="dataset" ,   )
 _lowercase:       Union[str, Any]						=				np.load(_lowercase      )
 return list(_lowercase      )


def 							__lowerCAmelCase      (			__magic_name__ ,   __magic_name__=None ,   __magic_name__=False      ):
 _lowercase:       Optional[Any]						=				{
     # fully supervised kinetics-400 checkpoints
     '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
     '''xclip-base-patch32-16-frames''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
     ),
     '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
     '''xclip-base-patch16-16-frames''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
     ),
     '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
     '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
     # fully supervised kinetics-600 checkpoints
     '''xclip-base-patch16-kinetics-600''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
     ),
     '''xclip-base-patch16-kinetics-600-16-frames''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
     ),
     '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
     # few shot
     '''xclip-base-patch16-hmdb-2-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
     ),
     '''xclip-base-patch16-hmdb-4-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
     ),
     '''xclip-base-patch16-hmdb-8-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
     ),
     '''xclip-base-patch16-hmdb-16-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
     ),
     '''xclip-base-patch16-ucf-2-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
     ),
     '''xclip-base-patch16-ucf-4-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
     ),
     '''xclip-base-patch16-ucf-8-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
     ),
     '''xclip-base-patch16-ucf-16-shot''': (
         '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
     ),
     # zero shot
     '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
 }

 _lowercase:       Optional[int]						=				model_to_url[model_name]
 _lowercase:       Dict						=				8
 if "16-frames" in model_name:
  _lowercase:       List[Any]						=				1_6
 elif "shot" in model_name:
  _lowercase:       Tuple						=				3_2

 _lowercase:       Any						=				get_xclip_config(_lowercase ,   _lowercase      )
 _lowercase:       str						=				XCLIPModel(_lowercase      )
 model.eval()

 if "drive" in checkpoint_url:
  _lowercase:       int						=				'''pytorch_model.bin'''
  gdown.cached_download(_lowercase ,   _lowercase ,   quiet=_lowercase      )
  _lowercase:       Union[str, Any]						=				torch.load(_lowercase ,   map_location="cpu"      )['''model''']
 else:
  _lowercase:       Optional[int]						=				torch.hub.load_state_dict_from_url(_lowercase      )['''model''']

 _lowercase:       Dict						=				convert_state_dict(_lowercase ,   _lowercase      )

 _lowercase:       int						=				XCLIPModel(_lowercase      )
 _lowercase:       List[Any]						=				model.load_state_dict(_lowercase ,   strict=_lowercase      )
 assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
 model.eval()

 _lowercase:       Optional[Any]						=				3_3_6 if model_name == '''xclip-large-patch14-16-frames''' else 2_2_4
 _lowercase:       List[str]						=				VideoMAEImageProcessor(size=_lowercase      )
 _lowercase:       Tuple						=				CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"      )
 _lowercase:       Tuple						=				CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"      )
 _lowercase:       List[Any]						=				XCLIPProcessor(image_processor=_lowercase ,   tokenizer=_lowercase      )

 _lowercase:       Dict						=				prepare_video(_lowercase      )
 _lowercase:       List[str]						=				processor(
     text=["playing sports", "eating spaghetti", "go shopping"] ,   videos=_lowercase ,   return_tensors="pt" ,   padding=_lowercase      )

 print("Shape of pixel values:" ,   inputs.pixel_values.shape      )

 with torch.no_grad():
  _lowercase:       str						=				model(**_lowercase      )

 # Verify outputs
 _lowercase:       Dict						=				outputs.logits_per_video
 _lowercase:       Union[str, Any]						=				logits_per_video.softmax(dim=1      )
 print("Probs:" ,   _lowercase      )
 # kinetics-400
 if model_name == "xclip-base-patch32":
  _lowercase:       Tuple						=				torch.tensor([[0.0_019, 0.9_951, 0.0_030]]      )
 elif model_name == "xclip-base-patch32-16-frames":
  _lowercase:       Any						=				torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]      )
 elif model_name == "xclip-base-patch16":
  _lowercase:       Tuple						=				torch.tensor([[0.0_083, 0.9_681, 0.0_236]]      )
 elif model_name == "xclip-base-patch16-16-frames":
  _lowercase:       Union[str, Any]						=				torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]      )
 elif model_name == "xclip-large-patch14":
  _lowercase:       str						=				torch.tensor([[0.0_062, 0.9_864, 0.0_075]]      )
 elif model_name == "xclip-large-patch14-16-frames":
  _lowercase:       Optional[int]						=				torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]      )
 # kinetics-600
 elif model_name == "xclip-base-patch16-kinetics-600":
  _lowercase:       Optional[int]						=				torch.tensor([[0.0_555, 0.8_914, 0.0_531]]      )
 elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
  _lowercase:       List[str]						=				torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]      )
 elif model_name == "xclip-large-patch14-kinetics-600":
  _lowercase:       Optional[Any]						=				torch.tensor([[0.0_036, 0.9_920, 0.0_045]]      )
 # few shot
 elif model_name == "xclip-base-patch16-hmdb-2-shot":
  _lowercase:       List[Any]						=				torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]      )
 elif model_name == "xclip-base-patch16-hmdb-4-shot":
  _lowercase:       str						=				torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]      )
 elif model_name == "xclip-base-patch16-hmdb-8-shot":
  _lowercase:       List[str]						=				torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]      )
 elif model_name == "xclip-base-patch16-hmdb-16-shot":
  _lowercase:       Union[str, Any]						=				torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]      )
 elif model_name == "xclip-base-patch16-ucf-2-shot":
  _lowercase:       Dict						=				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]      )
 elif model_name == "xclip-base-patch16-ucf-4-shot":
  _lowercase:       Union[str, Any]						=				torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]      )
 elif model_name == "xclip-base-patch16-ucf-8-shot":
  _lowercase:       Optional[Any]						=				torch.tensor([[0.0_027, 0.9_904, 0.0_070]]      )
 elif model_name == "xclip-base-patch16-ucf-16-shot":
  _lowercase:       Any						=				torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]      )
 # zero shot
 elif model_name == "xclip-base-patch16-zero-shot":
  _lowercase:       List[str]						=				torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]      )
 else:
  raise ValueError(f"Model name {model_name} not supported"      )
 assert torch.allclose(_lowercase ,   _lowercase ,   atol=1e-3      )
 print("Looks ok!"      )

 if pytorch_dump_folder_path is not None:
  print(f"Saving model {model_name} to {pytorch_dump_folder_path}"      )
  model.save_pretrained(_lowercase      )

 if push_to_hub:
  print("Pushing model, processor and slow tokenizer files to the hub..."      )
  model.push_to_hub(_lowercase ,   organization="nielsr"      )
  processor.push_to_hub(_lowercase ,   organization="nielsr"      )
  slow_tokenizer.push_to_hub(_lowercase ,   organization="nielsr"      )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE     :			Union[str, Any]        =							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--model_name',
           default='xclip-base-patch32',
           type=str,
           help='Name of the model.',
       )
       parser.add_argument(
           '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
       )
       parser.add_argument(
           '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
       )

       _SCREAMING_SNAKE_CASE     :			List[str]        =							parser.parse_args()
       convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







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, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor

class     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=0.9  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,)					->						Optional[int]:
         UpperCAmelCase_     :				int  =     size if size is not None else {'''shortest_edge''': 30}
         UpperCAmelCase_     :				List[str]  =     crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         UpperCAmelCase_     :				Dict  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     min_resolution
         UpperCAmelCase_     :				Tuple  =     max_resolution
         UpperCAmelCase_     :				Optional[int]  =     do_resize_and_center_crop
         UpperCAmelCase_     :				Tuple  =     size
         UpperCAmelCase_     :				List[str]  =     crop_pct
         UpperCAmelCase_     :				List[str]  =     crop_size
         UpperCAmelCase_     :				Any  =     do_normalize
         UpperCAmelCase_     :				str  =     image_mean
         UpperCAmelCase_     :				List[Any]  =     image_std





    def 				a__ ( self    )					->						str:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }


@require_torch
@require_vision
class     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = PoolFormerImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				str  =     PoolFormerImageProcessingTester(self    )





    @property
    def 				a__ ( self    )					->						Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_resize_and_center_crop'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''size'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''crop_pct'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''do_normalize'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_mean'''    )    )
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE  ,'''image_std'''    )    )





    def 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Any  =     self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 30}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 30, '''width''': 30}    )

         UpperCAmelCase_     :				Optional[Any]  =     self.image_processing_class.from_dict(self.image_processor_dict  ,size=42  ,crop_size=84    )
         self.assertEqual(image_processor.size  ,{'''shortest_edge''': 42}    )
         self.assertEqual(image_processor.crop_size  ,{'''height''': 84, '''width''': 84}    )





    def 				a__ ( self    )					->						Optional[int]:
         pass





    def 				a__ ( self    )					->						Dict:
         # Initialize image_processing
         UpperCAmelCase_     :				str  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_     :				int  =     prepare_image_inputs(self.image_processor_tester  ,equal_resolution=_SCREAMING_SNAKE_CASE    )
         for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE  ,Image.Image    )

         # Test not batched input
         UpperCAmelCase_     :				Optional[int]  =     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
         UpperCAmelCase_     :				Union[str, Any]  =     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    )					->						List[Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				Any  =     self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_     :				List[str]  =     prepare_image_inputs(self.image_processor_tester  ,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
         UpperCAmelCase_     :				Tuple  =     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
         UpperCAmelCase_     :				List[Any]  =     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    )					->						Union[str, Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_     :				Optional[Any]  =     prepare_image_inputs(self.image_processor_tester  ,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
         UpperCAmelCase_     :				Tuple  =     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
         UpperCAmelCase_     :				List[Any]  =     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'''],
             )  ,)