


'''simple docstring'''






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel


def  _snake_case       (					A	)						->   Optional[int]:
							# vision encoder
							if "img_encoder.pos_embed" in name:
														lowerCAmelCase__	       =     name.replace('''img_encoder.pos_embed'''			,   '''vision_model.embeddings.position_embeddings'''	)
							if "img_encoder.patch_embed.proj" in name:
														lowerCAmelCase__	       =     name.replace('''img_encoder.patch_embed.proj'''			,   '''vision_model.embeddings.patch_embeddings.projection'''	)
							if "img_encoder.patch_embed.norm" in name:
														lowerCAmelCase__	       =     name.replace('''img_encoder.patch_embed.norm'''			,   '''vision_model.embeddings.layernorm'''	)
							if "img_encoder.layers" in name:
														lowerCAmelCase__	       =     name.replace('''img_encoder.layers'''			,   '''vision_model.encoder.stages'''	)
							if "blocks" in name and "res" not in name:
														lowerCAmelCase__	       =     name.replace('''blocks'''			,   '''layers'''	)
							if "attn" in name and "pre_assign" not in name:
														lowerCAmelCase__	       =     name.replace('''attn'''			,   '''self_attn'''	)
							if "proj" in name and "self_attn" in name and "text" not in name:
														lowerCAmelCase__	       =     name.replace('''proj'''			,   '''out_proj'''	)
							if "pre_assign_attn.attn.proj" in name:
														lowerCAmelCase__	       =     name.replace('''pre_assign_attn.attn.proj'''			,   '''pre_assign_attn.attn.out_proj'''	)
							if "norm1" in name:
														lowerCAmelCase__	       =     name.replace('''norm1'''			,   '''layer_norm1'''	)
							if "norm2" in name and "pre_assign" not in name:
														lowerCAmelCase__	       =     name.replace('''norm2'''			,   '''layer_norm2'''	)
							if "img_encoder.norm" in name:
														lowerCAmelCase__	       =     name.replace('''img_encoder.norm'''			,   '''vision_model.layernorm'''	)
							# text encoder
							if "text_encoder.token_embedding" in name:
														lowerCAmelCase__	       =     name.replace('''text_encoder.token_embedding'''			,   '''text_model.embeddings.token_embedding'''	)
							if "text_encoder.positional_embedding" in name:
														lowerCAmelCase__	       =     name.replace('''text_encoder.positional_embedding'''			,   '''text_model.embeddings.position_embedding.weight'''	)
							if "text_encoder.transformer.resblocks." in name:
														lowerCAmelCase__	       =     name.replace('''text_encoder.transformer.resblocks.'''			,   '''text_model.encoder.layers.'''	)
							if "ln_1" in name:
														lowerCAmelCase__	       =     name.replace('''ln_1'''			,   '''layer_norm1'''	)
							if "ln_2" in name:
														lowerCAmelCase__	       =     name.replace('''ln_2'''			,   '''layer_norm2'''	)
							if "c_fc" in name:
														lowerCAmelCase__	       =     name.replace('''c_fc'''			,   '''fc1'''	)
							if "c_proj" in name:
														lowerCAmelCase__	       =     name.replace('''c_proj'''			,   '''fc2'''	)
							if "text_encoder" in name:
														lowerCAmelCase__	       =     name.replace('''text_encoder'''			,   '''text_model'''	)
							if "ln_final" in name:
														lowerCAmelCase__	       =     name.replace('''ln_final'''			,   '''final_layer_norm'''	)
							# projection layers
							if "img_projector.linear_hidden." in name:
														lowerCAmelCase__	       =     name.replace('''img_projector.linear_hidden.'''			,   '''visual_projection.'''	)
							if "img_projector.linear_out." in name:
														lowerCAmelCase__	       =     name.replace('''img_projector.linear_out.'''			,   '''visual_projection.3.'''	)
							if "text_projector.linear_hidden" in name:
														lowerCAmelCase__	       =     name.replace('''text_projector.linear_hidden'''			,   '''text_projection'''	)
							if "text_projector.linear_out" in name:
														lowerCAmelCase__	       =     name.replace('''text_projector.linear_out'''			,   '''text_projection.3'''	)

							return name


def  _snake_case       (					A			,   A	)						->   str:
							for key in orig_state_dict.copy().keys():
														lowerCAmelCase__	       =     orig_state_dict.pop(__SCREAMING_SNAKE_CASE	)

														if "qkv" in key:
																					# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
																					# we need to split them up into separate matrices/vectors
																					lowerCAmelCase__	       =     key.split('''.'''	)
																					lowerCAmelCase__	       =     int(key_split[2]	), int(key_split[4]	)
																					lowerCAmelCase__	       =     config.vision_config.hidden_size
																					if "weight" in key:
																												lowerCAmelCase__	       =     val[:dim, :]
																												lowerCAmelCase__	       =     val[dim : dim * 2, :]
																												lowerCAmelCase__	       =     val[-dim:, :]
																					else:
																												lowerCAmelCase__	       =     val[:dim]
																												lowerCAmelCase__	       =     val[dim : dim * 2]
																												lowerCAmelCase__	       =     val[-dim:]
														elif "in_proj" in key:
																					# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
																					# we need to split them up into separate matrices/vectors
																					lowerCAmelCase__	       =     key.split('''.'''	)
																					lowerCAmelCase__	       =     int(key_split[3]	)
																					lowerCAmelCase__	       =     config.text_config.hidden_size
																					if "weight" in key:
																												lowerCAmelCase__	       =     val[:dim, :]
																												lowerCAmelCase__	       =     val[
																												    dim : dim * 2, :
																												]
																												lowerCAmelCase__	       =     val[-dim:, :]
																					else:
																												lowerCAmelCase__	       =     val[:dim]
																												lowerCAmelCase__	       =     val[dim : dim * 2]
																												lowerCAmelCase__	       =     val[-dim:]
														else:
																					lowerCAmelCase__	       =     rename_key(__SCREAMING_SNAKE_CASE	)
																					# squeeze if necessary
																					if (
																					    "text_projection.0" in new_name
																					    or "text_projection.3" in new_name
																					    or "visual_projection.0" in new_name
																					    or "visual_projection.3" in new_name
																					):
																												lowerCAmelCase__	       =     val.squeeze_()
																					else:
																												lowerCAmelCase__	       =     val

							return orig_state_dict


def  _snake_case       (					)						->   Optional[int]:
							lowerCAmelCase__	       =     "http://images.cocodataset.org/val2017/000000039769.jpg"
							lowerCAmelCase__	       =     Image.open(requests.get(__SCREAMING_SNAKE_CASE			,   stream=__SCREAMING_SNAKE_CASE	).raw	)
							return im


@torch.no_grad()
def  _snake_case       (					A			,   A			,   A="groupvit-gcc-yfcc"			,   A=False	)						->   List[str]:
							lowerCAmelCase__	       =     GroupViTConfig()
							lowerCAmelCase__	       =     GroupViTModel(__SCREAMING_SNAKE_CASE	).eval()

							lowerCAmelCase__	       =     torch.load(__SCREAMING_SNAKE_CASE			,   map_location='''cpu'''	)["model"]
							lowerCAmelCase__	       =     convert_state_dict(__SCREAMING_SNAKE_CASE			,   __SCREAMING_SNAKE_CASE	)
							lowerCAmelCase__	       =     model.load_state_dict(__SCREAMING_SNAKE_CASE			,   strict=__SCREAMING_SNAKE_CASE	)
							assert missing_keys == ["text_model.embeddings.position_ids"]
							assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__SCREAMING_SNAKE_CASE	) == 0)

							# verify result
							lowerCAmelCase__	       =     CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''	)
							lowerCAmelCase__	       =     prepare_img()
							lowerCAmelCase__	       =     processor(text=['''a photo of a cat''', '''a photo of a dog''']			,   images=__SCREAMING_SNAKE_CASE			,   padding=__SCREAMING_SNAKE_CASE			,   return_tensors='''pt'''	)

							with torch.no_grad():
														lowerCAmelCase__	       =     model(**__SCREAMING_SNAKE_CASE	)

							if model_name == "groupvit-gcc-yfcc":
														lowerCAmelCase__	       =     torch.tensor([[13.3_523, 6.3_629]]	)
							elif model_name == "groupvit-gcc-redcaps":
														lowerCAmelCase__	       =     torch.tensor([[16.1_873, 8.6_230]]	)
							else:
														raise ValueError(F"""Model name {model_name} not supported."""	)
							assert torch.allclose(outputs.logits_per_image			,   __SCREAMING_SNAKE_CASE			,   atol=1E-3	)

							processor.save_pretrained(__SCREAMING_SNAKE_CASE	)
							model.save_pretrained(__SCREAMING_SNAKE_CASE	)
							print('''Successfully saved processor and model to'''			,   __SCREAMING_SNAKE_CASE	)

							if push_to_hub:
														print('''Pushing to the hub...'''	)
														processor.push_to_hub(__SCREAMING_SNAKE_CASE			,   organization='''nielsr'''	)
														model.push_to_hub(__SCREAMING_SNAKE_CASE			,   organization='''nielsr'''	)


if __name__ == "__main__":
			__UpperCAmelCase       =							argparse.ArgumentParser()
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
			)
			parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
			parser.add_argument(
			    '''--model_name''',
			    default='''groupvit-gccy-fcc''',
			    type=str,
			    help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			    help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
			)
			__UpperCAmelCase       =							parser.parse_args()

			convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)



'''simple docstring'''






from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    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

__UpperCAmelCase       =							logging.get_logger(__name__)


def  _snake_case       (					A			,   A			,   A	)						->   Optional[Any]:
							return [
							    int(1000 * (box[0] / width)	),
							    int(1000 * (box[1] / height)	),
							    int(1000 * (box[2] / width)	),
							    int(1000 * (box[3] / height)	),
							]


def  _snake_case       (					A			,   A			,   A	)						->   Union[str, Any]:
							lowerCAmelCase__	       =     to_pil_image(A	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     pil_image.size
							lowerCAmelCase__	       =     pytesseract.image_to_data(A			,   lang=A			,   output_type='''dict'''			,   config=A	)
							lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__       , lowerCAmelCase__	       =     data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

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

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

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

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

							return words, normalized_boxes






class 		a__    (							a__  ):





							'''simple docstring'''






							lowercase__      :       Any     						=				["pixel_values"]







							def __init__(		self ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   lowerCamelCase_ = True ,   lowerCamelCase_ = 1 / 2_55 ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = True ,   lowerCamelCase_ = None ,   lowerCamelCase_ = "" ,   **lowerCamelCase_ ,   )	->							None:
														super().__init__(**lowerCamelCase_       )
														lowerCAmelCase__	       =     size if size is not None else {'''height''': 2_24, '''width''': 2_24}
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )

														lowerCAmelCase__	       =     do_resize
														lowerCAmelCase__	       =     size
														lowerCAmelCase__	       =     resample
														lowerCAmelCase__	       =     do_rescale
														lowerCAmelCase__	       =     rescale_value
														lowerCAmelCase__	       =     do_normalize
														lowerCAmelCase__	       =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
														lowerCAmelCase__	       =     image_std if image_std is not None else IMAGENET_STANDARD_STD
														lowerCAmelCase__	       =     apply_ocr
														lowerCAmelCase__	       =     ocr_lang
														lowerCAmelCase__	       =     tesseract_config







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = PILImageResampling.BILINEAR ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														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()}"""       )
														lowerCAmelCase__	       =     (size['''height'''], size['''width'''])
														return resize(lowerCamelCase_ ,   size=lowerCamelCase_ ,   resample=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														return rescale(lowerCamelCase_ ,   scale=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   **lowerCamelCase_ ,   )	->							np.ndarray:
														return normalize(lowerCamelCase_ ,   mean=lowerCamelCase_ ,   std=lowerCamelCase_ ,   data_format=lowerCamelCase_ ,   **lowerCamelCase_       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_=None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = None ,   lowerCamelCase_ = ChannelDimension.FIRST ,   **lowerCamelCase_ ,   )	->							PIL.Image.Image:
														lowerCAmelCase__	       =     do_resize if do_resize is not None else self.do_resize
														lowerCAmelCase__	       =     size if size is not None else self.size
														lowerCAmelCase__	       =     get_size_dict(lowerCamelCase_       )
														lowerCAmelCase__	       =     resample if resample is not None else self.resample
														lowerCAmelCase__	       =     do_rescale if do_rescale is not None else self.do_rescale
														lowerCAmelCase__	       =     rescale_factor if rescale_factor is not None else self.rescale_factor
														lowerCAmelCase__	       =     do_normalize if do_normalize is not None else self.do_normalize
														lowerCAmelCase__	       =     image_mean if image_mean is not None else self.image_mean
														lowerCAmelCase__	       =     image_std if image_std is not None else self.image_std
														lowerCAmelCase__	       =     apply_ocr if apply_ocr is not None else self.apply_ocr
														lowerCAmelCase__	       =     ocr_lang if ocr_lang is not None else self.ocr_lang
														lowerCAmelCase__	       =     tesseract_config if tesseract_config is not None else self.tesseract_config

														lowerCAmelCase__	       =     make_list_of_images(lowerCamelCase_       )

														if not valid_images(lowerCamelCase_       ):
																					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.'''       )

														if do_rescale and rescale_factor is None:
																					raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

														if do_normalize and (image_mean is None or image_std is None):
																					raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''       )

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

														# Tesseract OCR to get words + normalized bounding boxes
														if apply_ocr:
																					requires_backends(self ,   '''pytesseract'''       )
																					lowerCAmelCase__	       =     []
																					lowerCAmelCase__	       =     []
																					for image in images:
																												lowerCAmelCase__       , lowerCAmelCase__	       =     apply_tesseract(lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )
																												words_batch.append(lowerCamelCase_       )
																												boxes_batch.append(lowerCamelCase_       )

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

														if do_rescale:
																					lowerCAmelCase__	       =     [self.rescale(image=lowerCamelCase_ ,   scale=lowerCamelCase_       ) for image in images]

														if do_normalize:
																					lowerCAmelCase__	       =     [self.normalize(image=lowerCamelCase_ ,   mean=lowerCamelCase_ ,   std=lowerCamelCase_       ) for image in images]

														lowerCAmelCase__	       =     [to_channel_dimension_format(lowerCamelCase_ ,   lowerCamelCase_       ) for image in images]

														lowerCAmelCase__	       =     BatchFeature(data={'''pixel_values''': images} ,   tensor_type=lowerCamelCase_       )

														if apply_ocr:
																					lowerCAmelCase__	       =     words_batch
																					lowerCAmelCase__	       =     boxes_batch
														return data