



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 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				int					  =       XCLIPTextConfig()

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

							if "large" in model_name:
														_lowerCAmelCase	:				Optional[Any]					  =       768
														_lowerCAmelCase	:				Tuple					  =       3_072
														_lowerCAmelCase	:				Union[str, Any]					  =       12

														_lowerCAmelCase	:				List[str]					  =       1_024
														_lowerCAmelCase	:				Any					  =       4_096
														_lowerCAmelCase	:				str					  =       16
														_lowerCAmelCase	:				Union[str, Any]					  =       24
														_lowerCAmelCase	:				List[Any]					  =       768
														_lowerCAmelCase	:				List[Any]					  =       3_072

							if model_name == "xclip-large-patch14-16-frames":
														_lowerCAmelCase	:				Union[str, Any]					  =       336

							_lowerCAmelCase	:				List[str]					  =       XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_    )

							if "large" in model_name:
														_lowerCAmelCase	:				Tuple					  =       768

							return config






def 							A							(   _lowerCamelCase    ):





							'''simple docstring'''




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

							return name






def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							for key in orig_state_dict.copy().keys():
														_lowerCAmelCase	:				str					  =       orig_state_dict.pop(SCREAMING_SNAKE_CASE_    )

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

							return orig_state_dict






def 							A							(   _lowerCamelCase    ):





							'''simple docstring'''




							if num_frames == 8:
														_lowerCAmelCase	:				List[str]					  =       "eating_spaghetti_8_frames.npy"
							elif num_frames == 16:
														_lowerCAmelCase	:				Optional[int]					  =       "eating_spaghetti.npy"
							elif num_frames == 32:
														_lowerCAmelCase	:				str					  =       "eating_spaghetti_32_frames.npy"
							_lowerCAmelCase	:				List[Any]					  =       hf_hub_download(
							    repo_id="hf-internal-testing/spaghetti-video"							,					filename=SCREAMING_SNAKE_CASE_							,					repo_type="dataset"							,					)
							_lowerCAmelCase	:				List[Any]					  =       np.load(SCREAMING_SNAKE_CASE_    )
							return list(SCREAMING_SNAKE_CASE_    )






def 							A							(   _lowerCamelCase							,					_lowerCamelCase=None							,					_lowerCamelCase=False    ):





							'''simple docstring'''




							_lowerCAmelCase	:				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",
							}

							_lowerCAmelCase	:				Union[str, Any]					  =       model_to_url[model_name]
							_lowerCAmelCase	:				Optional[Any]					  =       8
							if "16-frames" in model_name:
														_lowerCAmelCase	:				List[str]					  =       16
							elif "shot" in model_name:
														_lowerCAmelCase	:				Optional[int]					  =       32

							_lowerCAmelCase	:				Tuple					  =       get_xclip_config(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_    )
							_lowerCAmelCase	:				List[str]					  =       XCLIPModel(SCREAMING_SNAKE_CASE_    )
							model.eval()

							if "drive" in checkpoint_url:
														_lowerCAmelCase	:				Tuple					  =       "pytorch_model.bin"
														gdown.cached_download(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					quiet=SCREAMING_SNAKE_CASE_    )
														_lowerCAmelCase	:				Dict					  =       torch.load(SCREAMING_SNAKE_CASE_							,					map_location="cpu"    )["model"]
							else:
														_lowerCAmelCase	:				List[Any]					  =       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_    )["model"]

							_lowerCAmelCase	:				Optional[int]					  =       convert_state_dict(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_    )

							_lowerCAmelCase	:				Dict					  =       XCLIPModel(SCREAMING_SNAKE_CASE_    )
							_lowerCAmelCase  ,       _lowerCAmelCase	:				Dict					  =       model.load_state_dict(SCREAMING_SNAKE_CASE_							,					strict=SCREAMING_SNAKE_CASE_    )
							assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
							model.eval()

							_lowerCAmelCase	:				Dict					  =       336 if model_name == "xclip-large-patch14-16-frames" else 224
							_lowerCAmelCase	:				str					  =       VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_    )
							_lowerCAmelCase	:				Union[str, Any]					  =       CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"    )
							_lowerCAmelCase	:				Optional[Any]					  =       CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"    )
							_lowerCAmelCase	:				List[Any]					  =       XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_							,					tokenizer=SCREAMING_SNAKE_CASE_    )

							_lowerCAmelCase	:				int					  =       prepare_video(SCREAMING_SNAKE_CASE_    )
							_lowerCAmelCase	:				Union[str, Any]					  =       processor(
							    text=["playing sports", "eating spaghetti", "go shopping"]							,					videos=SCREAMING_SNAKE_CASE_							,					return_tensors="pt"							,					padding=SCREAMING_SNAKE_CASE_    )

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

							with torch.no_grad():
														_lowerCAmelCase	:				List[Any]					  =       model(**SCREAMING_SNAKE_CASE_    )

							# Verify outputs
							_lowerCAmelCase	:				List[Any]					  =       outputs.logits_per_video
							_lowerCAmelCase	:				str					  =       logits_per_video.softmax(dim=1    )
							print("Probs:"							,					SCREAMING_SNAKE_CASE_    )
							# kinetics-400
							if model_name == "xclip-base-patch32":
														_lowerCAmelCase	:				Optional[int]					  =       torch.tensor([[0.00_19, 0.99_51, 0.00_30]]    )
							elif model_name == "xclip-base-patch32-16-frames":
														_lowerCAmelCase	:				Optional[int]					  =       torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]    )
							elif model_name == "xclip-base-patch16":
														_lowerCAmelCase	:				int					  =       torch.tensor([[0.00_83, 0.96_81, 0.02_36]]    )
							elif model_name == "xclip-base-patch16-16-frames":
														_lowerCAmelCase	:				Dict					  =       torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]    )
							elif model_name == "xclip-large-patch14":
														_lowerCAmelCase	:				Any					  =       torch.tensor([[0.00_62, 0.98_64, 0.00_75]]    )
							elif model_name == "xclip-large-patch14-16-frames":
														_lowerCAmelCase	:				Union[str, Any]					  =       torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]    )
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														_lowerCAmelCase	:				Dict					  =       torch.tensor([[0.05_55, 0.89_14, 0.05_31]]    )
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														_lowerCAmelCase	:				List[str]					  =       torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]    )
							elif model_name == "xclip-large-patch14-kinetics-600":
														_lowerCAmelCase	:				List[str]					  =       torch.tensor([[0.00_36, 0.99_20, 0.00_45]]    )
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														_lowerCAmelCase	:				Dict					  =       torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]    )
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														_lowerCAmelCase	:				Any					  =       torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]    )
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														_lowerCAmelCase	:				Any					  =       torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]    )
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														_lowerCAmelCase	:				Union[str, Any]					  =       torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]    )
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														_lowerCAmelCase	:				Dict					  =       torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														_lowerCAmelCase	:				Tuple					  =       torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														_lowerCAmelCase	:				str					  =       torch.tensor([[0.00_27, 0.99_04, 0.00_70]]    )
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														_lowerCAmelCase	:				List[Any]					  =       torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]    )
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														_lowerCAmelCase	:				Any					  =       torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]    )
							else:
														raise ValueError(F"Model name {model_name} not supported"    )
							assert torch.allclose(SCREAMING_SNAKE_CASE_							,					SCREAMING_SNAKE_CASE_							,					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(SCREAMING_SNAKE_CASE_    )

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


if __name__ == "__main__":
						_snake_case						=    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."
						)

						_snake_case						=    parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

'''simple docstring'''


from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
       import PIL

# soft dependency
if is_pytesseract_available():
       import pytesseract

__snake_case						  =		logging.get_logger(__name__)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Tuple:
  return [
      int(10_00 * (box[0] / width)						),
      int(10_00 * (box[1] / height)						),
      int(10_00 * (box[2] / width)						),
      int(10_00 * (box[3] / height)						),
  ]

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ = None						)			->List[Any]:
  lowercase_     =       tesseract_config if tesseract_config is not None else """"""

  # apply OCR
  lowercase_     =       to_pil_image(SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     =       pil_image.size
  lowercase_     =       pytesseract.image_to_data(SCREAMING_SNAKE_CASE_ ,	lang=SCREAMING_SNAKE_CASE_ ,	output_type="""dict""" ,	config=SCREAMING_SNAKE_CASE_						)
  lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     ,	lowercase_     =       data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

  # filter empty words and corresponding coordinates
  lowercase_     =       [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if not word.strip()]
  lowercase_     =       [word for idx, word in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]
  lowercase_     =       [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_						) if idx not in irrelevant_indices]

  # turn coordinates into (left, top, left+width, top+height) format
  lowercase_     =       []
  for x, y, w, h in zip(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       [x, y, x + w, y + h]
    actual_boxes.append(SCREAMING_SNAKE_CASE_						)

  # finally, normalize the bounding boxes
  lowercase_     =       []
  for box in actual_boxes:
    normalized_boxes.append(normalize_box(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)						)

  assert len(SCREAMING_SNAKE_CASE_						) == len(SCREAMING_SNAKE_CASE_						), "Not as many words as there are bounding boxes"

  return words, normalized_boxes





class       _a    (		__a      ):


  """simple docstring"""

  A_    							=						['''pixel_values''']




  def __init__(		self      :       Tuple   ,				lowercase_      :       bool = True   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       bool = True   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = ""   ,				**lowercase_      :       Optional[int]   ,				):







    '''simple docstring'''


    super().__init__(**lowercase_      )
    lowercase_     =       size if size is not None else {"""height""": 224, """width""": 224}
    lowercase_     =       get_size_dict(lowercase_      )

    lowercase_     =       do_resize
    lowercase_     =       size
    lowercase_     =       resample
    lowercase_     =       apply_ocr
    lowercase_     =       ocr_lang
    lowercase_     =       tesseract_config




  def 		lowerCamelCase__  (		self      :       Optional[int]   ,				lowercase_      :       np.ndarray   ,				lowercase_      :       Dict[str, int]   ,				lowercase_      :       PILImageResampling = PILImageResampling.BILINEAR   ,				lowercase_      :       Optional[Union[str, ChannelDimension]] = None   ,				**lowercase_      :       int   ,				):







    '''simple docstring'''


    lowercase_     =       get_size_dict(lowercase_      )
    if "height" not in size or "width" not in size:
      raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""      )
    lowercase_     =       (size["""height"""], size["""width"""])
    return resize(lowercase_   ,				size=lowercase_   ,				resample=lowercase_   ,				data_format=lowercase_   ,				**lowercase_      )





  def 		lowerCamelCase__  (		self      :       List[Any]   ,				lowercase_      :       ImageInput   ,				lowercase_      :       bool = None   ,				lowercase_      :       Dict[str, int] = None   ,				lowercase_      :       PILImageResampling = None   ,				lowercase_      :       bool = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[str] = None   ,				lowercase_      :       Optional[Union[str, TensorType]] = None   ,				lowercase_      :       ChannelDimension = ChannelDimension.FIRST   ,				**lowercase_      :       Dict   ,				):







    '''simple docstring'''


    lowercase_     =       do_resize if do_resize is not None else self.do_resize
    lowercase_     =       size if size is not None else self.size
    lowercase_     =       get_size_dict(lowercase_      )
    lowercase_     =       resample if resample is not None else self.resample
    lowercase_     =       apply_ocr if apply_ocr is not None else self.apply_ocr
    lowercase_     =       ocr_lang if ocr_lang is not None else self.ocr_lang
    lowercase_     =       tesseract_config if tesseract_config is not None else self.tesseract_config

    lowercase_     =       make_list_of_images(lowercase_      )

    if not valid_images(lowercase_      ):
      raise ValueError(
          """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
          """torch.Tensor, tf.Tensor or jax.ndarray."""      )

    if do_resize and size is None:
      raise ValueError("""Size must be specified if do_resize is True."""      )

    # All transformations expect numpy arrays.
    lowercase_     =       [to_numpy_array(lowercase_      ) for image in images]

    if apply_ocr:
      requires_backends(self   ,				"""pytesseract"""      )
      lowercase_     =       []
      lowercase_     =       []
      for image in images:
        lowercase_     ,	lowercase_     =       apply_tesseract(lowercase_   ,				lowercase_   ,				lowercase_      )
        words_batch.append(lowercase_      )
        boxes_batch.append(lowercase_      )

    if do_resize:
      lowercase_     =       [self.resize(image=lowercase_   ,				size=lowercase_   ,				resample=lowercase_      ) for image in images]

    # flip color channels from RGB to BGR (as Detectron2 requires this)
    lowercase_     =       [flip_channel_order(lowercase_      ) for image in images]
    lowercase_     =       [to_channel_dimension_format(lowercase_   ,				lowercase_      ) for image in images]

    lowercase_     =       BatchFeature(data={"""pixel_values""": images}   ,				tensor_type=lowercase_      )

    if apply_ocr:
      lowercase_     =       words_batch
      lowercase_     =       boxes_batch
    return data

