




import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


lowerCamelCase_  						=   {
    "vit_b32": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 32,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 16,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 768,
        "image_resolution": 840,
        "context_length": 16,
        "vocab_size": 49408,
        "vision_layers": 24,
        "vision_width": 1024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase="",				__UpperCamelCase="/"	):
    SCREAMING_SNAKE_CASE__								=[]

    for k, v in params.items():
        SCREAMING_SNAKE_CASE__								=parent_key + sep + k if parent_key else k

        if isinstance(__UpperCamelCase,				collections.MutableMapping	):
            items.extend(flatten_nested_dict(__UpperCamelCase,				__UpperCamelCase,				sep=__UpperCamelCase	).items()	)
        else:
            items.append((new_key, v)	)
    return dict(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    return jax.tree_util.tree_map(lambda __UpperCamelCase	: x.astype(jnp.floataa	) if x.dtype == jnp.bfloataa else x,				__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=pt_attn_layer.in_proj_weight.chunk(3,				dim=0	)
    SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__			,		SCREAMING_SNAKE_CASE__								=pt_attn_layer.in_proj_bias.chunk(3,				dim=0	)

    SCREAMING_SNAKE_CASE__								=pt_attn_layer.out_proj.weight
    SCREAMING_SNAKE_CASE__								=pt_attn_layer.out_proj.bias

    SCREAMING_SNAKE_CASE__								=q_proj
    SCREAMING_SNAKE_CASE__								=q_proj_bias

    SCREAMING_SNAKE_CASE__								=k_proj
    SCREAMING_SNAKE_CASE__								=k_proj_bias

    SCREAMING_SNAKE_CASE__								=v_proj
    SCREAMING_SNAKE_CASE__								=v_proj_bias

    SCREAMING_SNAKE_CASE__								=out_proj_weights
    SCREAMING_SNAKE_CASE__								=out_proj_bias



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    copy_linear(hf_mlp.fca,				pt_mlp.c_fc	)
    copy_linear(hf_mlp.fca,				pt_mlp.c_proj	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=pt_linear.weight
    SCREAMING_SNAKE_CASE__								=pt_linear.bias



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    # copy layer norms
    copy_linear(hf_layer.layer_norma,				pt_layer.ln_a	)
    copy_linear(hf_layer.layer_norma,				pt_layer.ln_a	)

    # copy MLP
    copy_mlp(hf_layer.mlp,				pt_layer.mlp	)

    # copy attn
    copy_attn_layer(hf_layer.self_attn,				pt_layer.attn	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    for hf_layer, pt_layer in zip(__UpperCamelCase,				__UpperCamelCase	):
        copy_layer(__UpperCamelCase,				__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    # copy  embeds
    SCREAMING_SNAKE_CASE__								=pt_model.token_embedding.weight
    SCREAMING_SNAKE_CASE__								=pt_model.positional_embedding

    # copy layer norm
    copy_linear(hf_encoder.final_layer_norm,				pt_model.ln_final	)

    # copy hidden layers
    copy_layers(hf_encoder.encoder.layers,				pt_model.transformer.resblocks	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    # copy projection
    SCREAMING_SNAKE_CASE__								=pt_model.text_projection.data.T

    # copy text encoder
    copy_encoder(hf_model.text_model,				__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    # copy projection
    SCREAMING_SNAKE_CASE__								=pt_model.visual.proj.data.T

    # copy layer norms
    copy_linear(hf_model.vision_model.pre_layernorm,				pt_model.visual.ln_pre	)
    copy_linear(hf_model.vision_model.post_layernorm,				pt_model.visual.ln_post	)

    # copy embeds
    SCREAMING_SNAKE_CASE__								=pt_model.visual.conva.weight.data
    SCREAMING_SNAKE_CASE__								=pt_model.visual.class_embedding
    SCREAMING_SNAKE_CASE__								=pt_model.visual.positional_embedding.data

    # copy encoder
    copy_layers(hf_model.vision_model.encoder.layers,				pt_model.visual.transformer.resblocks	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]	)

    SCREAMING_SNAKE_CASE__								=torch.from_numpy(flax_class_token_params["""scale"""]	)
    SCREAMING_SNAKE_CASE__								=torch.from_numpy(flax_class_token_params["""bias"""]	)
    SCREAMING_SNAKE_CASE__								=nn.Parameter(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__								=nn.Parameter(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=hf_model.state_dict()
    SCREAMING_SNAKE_CASE__								={}

    # Rename class prediction head flax params to pytorch HF
    SCREAMING_SNAKE_CASE__								=flatten_nested_dict(flax_params["""class_head"""]	)

    for flax_key, v in flax_class_params.items():
        SCREAMING_SNAKE_CASE__								=flax_key.replace("""/""",				"""."""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace(""".kernel""",				""".weight"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""Dense_0""",				"""dense0"""	)
        SCREAMING_SNAKE_CASE__								="""class_head.""" + torch_key

        if "weight" in torch_key and v.ndim == 2:
            SCREAMING_SNAKE_CASE__								=v.T

        SCREAMING_SNAKE_CASE__								=nn.Parameter(torch.from_numpy(__UpperCamelCase	)	)

    # Rename box prediction box flax params to pytorch HF
    SCREAMING_SNAKE_CASE__								=flatten_nested_dict(flax_params["""obj_box_head"""]	)

    for flax_key, v in flax_box_params.items():
        SCREAMING_SNAKE_CASE__								=flax_key.replace("""/""",				"""."""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace(""".kernel""",				""".weight"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""_""",				""""""	).lower()
        SCREAMING_SNAKE_CASE__								="""box_head.""" + torch_key

        if "weight" in torch_key and v.ndim == 2:
            SCREAMING_SNAKE_CASE__								=v.T

        SCREAMING_SNAKE_CASE__								=nn.Parameter(torch.from_numpy(__UpperCamelCase	)	)

    # Copy flax params to PyTorch params
    for name, param in new_params.items():
        if name in pt_params.keys():
            pt_params[name].copy_(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    for k, v in flax_attn_params.items():
        if k.startswith("""transformer"""	):
            SCREAMING_SNAKE_CASE__								=k.replace("""transformer.resblocks""",				"""text_model.encoder.layers"""	)
        else:
            SCREAMING_SNAKE_CASE__								=k.replace("""visual.transformer.resblocks""",				"""vision_model.encoder.layers"""	)

        SCREAMING_SNAKE_CASE__								=torch_key.replace("""attn""",				"""self_attn"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""key""",				"""k_proj"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""value""",				"""v_proj"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""query""",				"""q_proj"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""out""",				"""out_proj"""	)

        if "bias" in torch_key and v.ndim == 2:
            SCREAMING_SNAKE_CASE__								=v.shape[0] * v.shape[1]
            SCREAMING_SNAKE_CASE__								=v.reshape(__UpperCamelCase	)

        if "weight" in torch_key and "out" in torch_key:
            SCREAMING_SNAKE_CASE__								=(v.shape[0] * v.shape[1], v.shape[2])
            SCREAMING_SNAKE_CASE__								=v.reshape(__UpperCamelCase	).T

        if "weight" in torch_key and "out" not in torch_key:
            SCREAMING_SNAKE_CASE__								=(v.shape[0], v.shape[1] * v.shape[2])
            SCREAMING_SNAKE_CASE__								=v.reshape(__UpperCamelCase	).T

        # Copy flax CLIP attn params to HF PyTorch params
        SCREAMING_SNAKE_CASE__								=torch.from_numpy(__UpperCamelCase	)
        hf_backbone.state_dict()[torch_key].copy_(__UpperCamelCase	)



def 	UpperCAmelCase_    (	__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								={}
    SCREAMING_SNAKE_CASE__								=[]

    for k, v in params.items():
        if "attn." in k:
            SCREAMING_SNAKE_CASE__								=k[: k.rindex("""attn."""	) + 5]
            if base in processed_attn_layers:
                continue

            processed_attn_layers.append(__UpperCamelCase	)
            SCREAMING_SNAKE_CASE__								=params[base + """out.weight"""].shape[-1]
            SCREAMING_SNAKE_CASE__								=params[base + """out.weight"""].reshape(__UpperCamelCase,				__UpperCamelCase	).T
            SCREAMING_SNAKE_CASE__								=params[base + """out.bias"""]
        else:
            SCREAMING_SNAKE_CASE__								=v
    return new_params



def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase	):
    SCREAMING_SNAKE_CASE__								=CLIP(**__UpperCamelCase	)
    torch_model.eval()
    SCREAMING_SNAKE_CASE__								=torch_model.state_dict()

    SCREAMING_SNAKE_CASE__								=flatten_nested_dict(flax_params["""backbone"""]["""clip"""]	)
    SCREAMING_SNAKE_CASE__								={}

    for flax_key, v in flax_clip_params.items():
        SCREAMING_SNAKE_CASE__								=flax_key.replace("""/""",				"""."""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""text.token_embedding.embedding""",				"""token_embedding.kernel"""	)

        if (
            torch_key.startswith("""text.transformer"""	)
            or torch_key.startswith("""text.text_projection"""	)
            or torch_key.startswith("""text.ln_final"""	)
            or torch_key.startswith("""text.positional_embedding"""	)
        ):
            SCREAMING_SNAKE_CASE__								=torch_key[5:]

        SCREAMING_SNAKE_CASE__								=torch_key.replace("""text_projection.kernel""",				"""text_projection"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace("""visual.proj.kernel""",				"""visual.proj"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace(""".scale""",				""".weight"""	)
        SCREAMING_SNAKE_CASE__								=torch_key.replace(""".kernel""",				""".weight"""	)

        if "conv" in torch_key or "downsample.0.weight" in torch_key:
            SCREAMING_SNAKE_CASE__								=v.transpose(3,				2,				0,				1	)

        elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
            # Fully connected layers are transposed, embeddings are not
            SCREAMING_SNAKE_CASE__								=v.T

        SCREAMING_SNAKE_CASE__								=v

    SCREAMING_SNAKE_CASE__								=_convert_attn_layers(__UpperCamelCase	)
    new_torch_params.update(__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__								={}

    # Copy flax CLIP backbone params to PyTorch params
    for name, param in new_torch_params.items():
        if name in torch_clip_params.keys():
            SCREAMING_SNAKE_CASE__								=torch.from_numpy(new_torch_params[name]	)
            torch_clip_params[name].copy_(__UpperCamelCase	)
        else:
            SCREAMING_SNAKE_CASE__								=param

    return torch_clip_params, torch_model, attn_params



@torch.no_grad()
def 	UpperCAmelCase_    (	__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase,				__UpperCamelCase=None	):
    SCREAMING_SNAKE_CASE__								=Repository(__UpperCamelCase,				clone_from=f"""google/{pytorch_dump_folder_path}"""	)
    repo.git_pull()

    if config_path is not None:
        SCREAMING_SNAKE_CASE__								=OwlViTConfig.from_pretrained(__UpperCamelCase	)
    else:
        SCREAMING_SNAKE_CASE__								=OwlViTConfig()

    SCREAMING_SNAKE_CASE__								=OwlViTModel(__UpperCamelCase	).eval()
    SCREAMING_SNAKE_CASE__								=OwlViTForObjectDetection(__UpperCamelCase	).eval()

    copy_text_model_and_projection(__UpperCamelCase,				__UpperCamelCase	)
    copy_vision_model_and_projection(__UpperCamelCase,				__UpperCamelCase	)
    SCREAMING_SNAKE_CASE__								=pt_backbone.logit_scale
    copy_flax_attn_params(__UpperCamelCase,				__UpperCamelCase	)

    SCREAMING_SNAKE_CASE__								=hf_backbone
    copy_class_merge_token(__UpperCamelCase,				__UpperCamelCase	)
    copy_class_box_heads(__UpperCamelCase,				__UpperCamelCase	)

    # Save HF model
    hf_model.save_pretrained(repo.local_dir	)

    # Initialize image processor
    SCREAMING_SNAKE_CASE__								=OwlViTImageProcessor(
        size=config.vision_config.image_size,				crop_size=config.vision_config.image_size	)
    # Initialize tokenizer
    SCREAMING_SNAKE_CASE__								=CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""",				pad_token="""!""",				model_max_length=16	)

    # Initialize processor
    SCREAMING_SNAKE_CASE__								=OwlViTProcessor(image_processor=__UpperCamelCase,				tokenizer=__UpperCamelCase	)
    image_processor.save_pretrained(repo.local_dir	)
    processor.save_pretrained(repo.local_dir	)

    repo.git_add()
    repo.git_commit("""Upload model and processor"""	)
    repo.git_push()


if __name__ == "__main__":
    lowerCamelCase_  						=   argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--owlvit_version",
        default=None,
        type=str,
        required=True,
        help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
    )
    parser.add_argument(
        "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
    )
    parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
    parser.add_argument(
        "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
    )
    lowerCamelCase_  						=   parser.parse_args()

    # Initialize PyToch clip model
    lowerCamelCase_  						=   args.owlvit_version
    if model_name == "clip_b16":
        lowerCamelCase_  						=   CONFIGS["vit_b16"]
    elif model_name == "clip_b32":
        lowerCamelCase_  						=   CONFIGS["vit_b32"]
    elif model_name == "clip_l14":
        lowerCamelCase_  						=   CONFIGS["vit_l14"]

    # Load from checkpoint and convert params to float-32
    lowerCamelCase_  						=   checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
    lowerCamelCase_  						=   jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
    del variables

    # Convert CLIP backbone
    lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_  						=   convert_clip_backbone(flax_params, torch_config)

    convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)












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 ConvNextImageProcessor
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def __init__(	self			: List[str]				,_UpperCamelCase			: int				,_UpperCamelCase			: int=7				,_UpperCamelCase			: int=3				,_UpperCamelCase			: List[str]=1_8				,_UpperCamelCase			: Dict=3_0				,_UpperCamelCase			: Tuple=4_0_0				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: int=None				,_UpperCamelCase			: str=0.875				,_UpperCamelCase			: Tuple=True				,_UpperCamelCase			: Dict=[0.5, 0.5, 0.5]				,_UpperCamelCase			: str=[0.5, 0.5, 0.5]				,)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=size if size is not None else {"""shortest_edge""": 2_0}
         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=min_resolution
         SCREAMING_SNAKE_CASE__								=max_resolution
         SCREAMING_SNAKE_CASE__								=do_resize
         SCREAMING_SNAKE_CASE__								=size
         SCREAMING_SNAKE_CASE__								=crop_pct
         SCREAMING_SNAKE_CASE__								=do_normalize
         SCREAMING_SNAKE_CASE__								=image_mean
         SCREAMING_SNAKE_CASE__								=image_std




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "crop_pct": self.crop_pct,
         }






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


     """simple docstring"""


     _A				:    Dict									=  ConvNextImageProcessor if is_vision_available() else None




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=ConvNextImageProcessingTester(self						)




     @property
     def   __A				(	self			: str						)		->   Optional[int]:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_resize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""size"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""crop_pct"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_normalize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_mean"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_std"""						)						)




     def   __A				(	self			: Tuple						)		->   Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 2_0}						)

         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict				,size=4_2						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 4_2}						)




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


         '''simple docstring'''





         pass




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,Image.Image						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: str						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,numpify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,np.ndarray						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,torchify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,torch.Tensor						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)






