






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			lowerCAmelCase_							(					__UpperCamelCase       ):
							UpperCamelCase_		:Optional[int]		   =     ['image_processor', 'tokenizer']
							UpperCamelCase_		:List[Any]		   =     'LayoutLMv2ImageProcessor'
							UpperCamelCase_		:Dict		   =     ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')




							def __init__( self  : Tuple			,				SCREAMING_SNAKE_CASE_  : List[str]=None			,				SCREAMING_SNAKE_CASE_  : Optional[Any]=None			,				**SCREAMING_SNAKE_CASE_  : Optional[Any]						):
											if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''			,				SCREAMING_SNAKE_CASE_			,				)
															lowerCAmelCase__         =    kwargs.pop('''feature_extractor'''						)

											lowerCAmelCase__         =    image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''						)
											if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''						)

											super().__init__(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						)




							def __call__( self  : Optional[Any]			,				SCREAMING_SNAKE_CASE_  : Tuple			,				SCREAMING_SNAKE_CASE_  : int = None			,				SCREAMING_SNAKE_CASE_  : Optional[int] = None			,				SCREAMING_SNAKE_CASE_  : Optional[Any] = None			,				SCREAMING_SNAKE_CASE_  : List[Any] = None			,				SCREAMING_SNAKE_CASE_  : Optional[int] = True			,				SCREAMING_SNAKE_CASE_  : str = False			,				SCREAMING_SNAKE_CASE_  : Tuple = None			,				SCREAMING_SNAKE_CASE_  : Dict = None			,				SCREAMING_SNAKE_CASE_  : Tuple = 0			,				SCREAMING_SNAKE_CASE_  : Union[str, Any] = None			,				SCREAMING_SNAKE_CASE_  : List[Any] = None			,				SCREAMING_SNAKE_CASE_  : int = None			,				SCREAMING_SNAKE_CASE_  : int = False			,				SCREAMING_SNAKE_CASE_  : Dict = False			,				SCREAMING_SNAKE_CASE_  : int = False			,				SCREAMING_SNAKE_CASE_  : int = False			,				SCREAMING_SNAKE_CASE_  : Tuple = True			,				SCREAMING_SNAKE_CASE_  : Tuple = None			,				**SCREAMING_SNAKE_CASE_  : Optional[int]			,				):
											# verify input
											if self.image_processor.apply_ocr and (boxes is not None):
															raise ValueError(
															    '''You cannot provide bounding boxes '''
															    '''if you initialized the image processor with apply_ocr set to True.'''						)

											if self.image_processor.apply_ocr and (word_labels is not None):
															raise ValueError(
															    '''You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'''						)

											if return_overflowing_tokens is True and return_offsets_mapping is False:
															raise ValueError('''You cannot return overflowing tokens without returning the offsets mapping.'''						)

											# first, apply the image processor
											lowerCAmelCase__         =    self.image_processor(images=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_						)

											# second, apply the tokenizer
											if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_						):
																			lowerCAmelCase__         =    [text]  # add batch dimension (as the image processor always adds a batch dimension)
															lowerCAmelCase__         =    features['''words''']

											lowerCAmelCase__         =    self.tokenizer(
											    text=text if text is not None else features['''words''']			,				text_pair=text_pair if text_pair is not None else None			,				boxes=boxes if boxes is not None else features['''boxes''']			,				word_labels=SCREAMING_SNAKE_CASE_			,				add_special_tokens=SCREAMING_SNAKE_CASE_			,				padding=SCREAMING_SNAKE_CASE_			,				truncation=SCREAMING_SNAKE_CASE_			,				max_length=SCREAMING_SNAKE_CASE_			,				stride=SCREAMING_SNAKE_CASE_			,				pad_to_multiple_of=SCREAMING_SNAKE_CASE_			,				return_token_type_ids=SCREAMING_SNAKE_CASE_			,				return_attention_mask=SCREAMING_SNAKE_CASE_			,				return_overflowing_tokens=SCREAMING_SNAKE_CASE_			,				return_special_tokens_mask=SCREAMING_SNAKE_CASE_			,				return_offsets_mapping=SCREAMING_SNAKE_CASE_			,				return_length=SCREAMING_SNAKE_CASE_			,				verbose=SCREAMING_SNAKE_CASE_			,				return_tensors=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

											# add pixel values
											lowerCAmelCase__         =    features.pop('''pixel_values'''						)
											if return_overflowing_tokens is True:
															lowerCAmelCase__         =    self.get_overflowing_images(SCREAMING_SNAKE_CASE_			,				encoded_inputs['''overflow_to_sample_mapping''']						)
											lowerCAmelCase__         =    images

											return encoded_inputs




							def     __snake_case       ( self  : List[Any]			,				SCREAMING_SNAKE_CASE_  : List[Any]			,				SCREAMING_SNAKE_CASE_  : List[Any]						):
											# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
											lowerCAmelCase__         =    []
											for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]						)

											if len(SCREAMING_SNAKE_CASE_						) != len(SCREAMING_SNAKE_CASE_						):
															raise ValueError(
															    '''Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'''
															    f' {len(SCREAMING_SNAKE_CASE_						)} and {len(SCREAMING_SNAKE_CASE_						)}'						)

											return images_with_overflow




							def     __snake_case       ( self  : Optional[Any]			,				*SCREAMING_SNAKE_CASE_  : Optional[int]			,				**SCREAMING_SNAKE_CASE_  : Optional[int]						):
											return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_						)




							def     __snake_case       ( self  : List[Any]			,				*SCREAMING_SNAKE_CASE_  : str			,				**SCREAMING_SNAKE_CASE_  : Any						):
											return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_						)




							@property
							def     __snake_case       ( self  : int						):
											return ["input_ids", "bbox", "attention_mask", "image"]




							@property
							def     __snake_case       ( self  : List[str]						):
											warnings.warn(
											    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''			,				SCREAMING_SNAKE_CASE_			,				)
											return self.image_processor_class




							@property
							def     __snake_case       ( self  : int						):
											warnings.warn(
											    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''			,				SCREAMING_SNAKE_CASE_			,				)
											return self.image_processor







import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





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

    return name





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase				):
    for key in orig_state_dict.copy().keys():
        snake_case__    =					orig_state_dict.pop(__lowerCAmelCase				)

        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
            snake_case__    =					key.split("."				)
            snake_case__    ,       snake_case__    =					int(key_split[2]				), int(key_split[4]				)
            snake_case__    =					config.vision_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[dim : dim * 2, :]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					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
            snake_case__    =					key.split("."				)
            snake_case__    =					int(key_split[3]				)
            snake_case__    =					config.text_config.hidden_size
            if "weight" in key:
                snake_case__    =					val[:dim, :]
                snake_case__    =					val[
                    dim : dim * 2, :
                ]
                snake_case__    =					val[-dim:, :]
            else:
                snake_case__    =					val[:dim]
                snake_case__    =					val[dim : dim * 2]
                snake_case__    =					val[-dim:]
        else:
            snake_case__    =					rename_key(__lowerCAmelCase				)
            # 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
            ):
                snake_case__    =					val.squeeze_()
            else:
                snake_case__    =					val

    return orig_state_dict





def 						SCREAMING_SNAKE_CASE__     (   ):
    snake_case__    =					"http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case__    =					Image.open(requests.get(__lowerCAmelCase     ,		stream=__lowerCAmelCase				).raw				)
    return im





@torch.no_grad()
def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase="groupvit-gcc-yfcc"     ,		__lowerCAmelCase=False				):
    snake_case__    =					GroupViTConfig()
    snake_case__    =					GroupViTModel(__lowerCAmelCase				).eval()

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

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

    with torch.no_grad():
        snake_case__    =					model(**__lowerCAmelCase				)

    if model_name == "groupvit-gcc-yfcc":
        snake_case__    =					torch.tensor([[13.3_523, 6.3_629]]				)
    elif model_name == "groupvit-gcc-redcaps":
        snake_case__    =					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     ,		__lowerCAmelCase     ,		atol=1E-3				)

    processor.save_pretrained(__lowerCAmelCase				)
    model.save_pretrained(__lowerCAmelCase				)
    print("Successfully saved processor and model to"     ,		__lowerCAmelCase				)

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


if __name__ == "__main__":
     __magic_name__  							=	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`.''',
     )
     __magic_name__  							=	parser.parse_args()

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



