






import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


__a      :       List[str]				  =   {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class       __lowercase      (	nn.Module  ):







					'''simple docstring'''




					def __init__(				self       :			Optional[int] ,	UpperCamelCase_       :			Tuple				):


									"""simple docstring"""



									super().__init__()
									__A         =	torchvision.models.resnetaaa(pretrained=UpperCamelCase_				)
									__A         =	list(model.children()				)[:-2]
									__A         =	nn.Sequential(*UpperCamelCase_				)
									__A         =	nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]				)





					def 		lowerCAmelCase_     (				self       :			Optional[Any] ,	UpperCamelCase_       :			str				):


									"""simple docstring"""



									__A         =	self.pool(self.model(UpperCamelCase_				)				)
									__A         =	torch.flatten(UpperCamelCase_ ,	start_dim=2				)
									__A         =	out.transpose(1 ,	2				).contiguous()
									return out  # BxNx2048


class       __lowercase      (	UpperCamelCase_  ):







					'''simple docstring'''




					def __init__(				self       :			List[str] ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			List[str] ,	UpperCamelCase_       :			str ,	UpperCamelCase_       :			Dict ,	UpperCamelCase_       :			int				):


									"""simple docstring"""



									__A         =	[json.loads(UpperCamelCase_				) for l in open(UpperCamelCase_				)]
									__A         =	os.path.dirname(UpperCamelCase_				)
									__A         =	tokenizer
									__A         =	labels
									__A         =	len(UpperCamelCase_				)
									__A         =	max_seq_length

									__A         =	transforms




					def __len__(				self       :			int				):


									"""simple docstring"""



									return len(self.data				)




					def __getitem__(				self       :			List[str] ,	UpperCamelCase_       :			Tuple				):


									"""simple docstring"""



									__A         =	torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,	add_special_tokens=UpperCamelCase_				)				)
									__A         =	sentence[0], sentence[1:-1], sentence[-1]
									__A         =	sentence[: self.max_seq_length]

									__A         =	torch.zeros(self.n_classes				)
									__A         =	1

									__A         =	Image.open(os.path.join(self.data_dir ,	self.data[index]["""img"""]				)				).convert("""RGB"""				)
									__A         =	self.transforms(UpperCamelCase_				)

									return {
									    "image_start_token": start_token,
									    "image_end_token": end_token,
									    "sentence": sentence,
									    "image": image,
									    "label": label,
									}





					def 		lowerCAmelCase_     (				self       :			Any				):


									"""simple docstring"""



									__A         =	Counter()
									for row in self.data:
													label_freqs.update(row["""label"""]				)
									return label_freqs



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Union[str, Any]:





				"""simple docstring"""

				__A         =	[len(row["""sentence"""]    ) for row in batch]
				__A         =	len(snake_case_    ), max(snake_case_    )

				__A         =	torch.zeros(snake_case_			,				snake_case_			,				dtype=torch.long    )
				__A         =	torch.zeros(snake_case_			,				snake_case_			,				dtype=torch.long    )

				for i_batch, (input_row, length) in enumerate(zip(snake_case_			,				snake_case_    )    ):
								__A         =	input_row['''sentence''']
								__A         =	1

				__A         =	torch.stack([row["""image"""] for row in batch]    )
				__A         =	torch.stack([row["""label"""] for row in batch]    )
				__A         =	torch.stack([row["""image_start_token"""] for row in batch]    )
				__A         =	torch.stack([row["""image_end_token"""] for row in batch]    )

				return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor



def 			_SCREAMING_SNAKE_CASE						( )						->      Union[str, Any]:





				"""simple docstring"""

				return [
				    "Crime",
				    "Drama",
				    "Thriller",
				    "Action",
				    "Comedy",
				    "Romance",
				    "Documentary",
				    "Short",
				    "Mystery",
				    "History",
				    "Family",
				    "Adventure",
				    "Fantasy",
				    "Sci-Fi",
				    "Western",
				    "Horror",
				    "Sport",
				    "War",
				    "Music",
				    "Musical",
				    "Animation",
				    "Biography",
				    "Film-Noir",
				]






def 			_SCREAMING_SNAKE_CASE						( )						->      Union[str, Any]:





				"""simple docstring"""

				return transforms.Compose(
				    [
				        transforms.Resize(2_5_6    ),
				        transforms.CenterCrop(2_2_4    ),
				        transforms.ToTensor(),
				        transforms.Normalize(
				            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]			,				std=[0.12_221_994, 0.12_145_835, 0.14_380_469]			,				),
				    ]    )












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			int    )						->      Union[str, Any]:





    """simple docstring"""

    __A         =	checkpoints.load_tax_checkpoint(__lowercase    )
    __A         =	flatten_dict(__lowercase    )
    return flax_params



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			Optional[int]    )						->      List[str]:





    """simple docstring"""

    __A         =	{}

    __A         =	{
        """token_embedder""": """embeddings""",
        """encoder_norm""": """layernorm""",
        """kernel""": """weight""",
        """.out""": """.output""",
        """scale""": """weight""",
        """embedders_0.pos_embedding""": """row_embedder.weight""",
        """embedders_1.pos_embedding""": """column_embedder.weight""",
    }

    __A         =	{
        """query""": """attention.query""",
        """key""": """attention.key""",
        """value""": """attention.value""",
        """output.dense""": """output""",
        """encoder_decoder_attention.o""": """encoder_decoder_attention.attention.o""",
        """pre_self_attention_layer_norm""": """self_attention.layer_norm""",
        """pre_cross_attention_layer_norm""": """encoder_decoder_attention.layer_norm""",
        """mlp.""": """mlp.DenseReluDense.""",
        """pre_mlp_layer_norm""": """mlp.layer_norm""",
        """self_attention.o""": """self_attention.attention.o""",
        """decoder.embeddings.embedding""": """decoder.embed_tokens.weight""",
        """decoder.relpos_bias.rel_embedding""": """decoder.layer.0.self_attention.attention.relative_attention_bias.weight""",
        """decoder.decoder_norm.weight""": """decoder.final_layer_norm.weight""",
        """decoder.logits_dense.weight""": """decoder.lm_head.weight""",
    }

    for key in flax_dict.keys():
        if "target" in key:
            # remove the first prefix from the key
            __A         =	""".""".join(key[1:]    )

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    __A         =	new_key.replace(__lowercase			,				__lowercase    )

            if "layers" in new_key and "decoder" not in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )
                __A         =	new_key.replace("""encoder"""			,				"""encoder.encoder"""    )

            elif "layers" in new_key and "decoder" in new_key:
                # use regex to replace the layer number
                __A         =	re.sub(R"""layers_(\d+)"""			,				R"""layer.\1"""			,				__lowercase    )

            __A         =	flax_dict[key]

    __A         =	{}
    # convert converted_dict into torch format
    for key in converted_dict.keys():
        if ("embed_tokens" not in key) and ("embedder" not in key):
            __A         =	torch.from_numpy(converted_dict[key].T    )
        else:
            __A         =	torch.from_numpy(converted_dict[key]    )

    return converted_torch_dict



def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			str			,				__lowercase   :			List[Any]			,				__lowercase   :			Dict=False			,				__lowercase   :			List[str]=False    )						->      Any:





    """simple docstring"""

    __A         =	get_flax_param(__lowercase    )

    if not use_large:
        __A         =	PixaStructVisionConfig()
        __A         =	PixaStructTextConfig()
    else:
        __A         =	PixaStructVisionConfig(
            hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_attention_heads=2_4			,				num_hidden_layers=1_8    )
        __A         =	PixaStructTextConfig(hidden_size=1_5_3_6			,				d_ff=3_9_6_8			,				num_heads=2_4			,				num_layers=1_8    )
    __A         =	PixaStructConfig(
        vision_config=encoder_config.to_dict()			,				text_config=decoder_config.to_dict()			,				is_vqa=__lowercase    )

    __A         =	PixaStructForConditionalGeneration(__lowercase    )

    __A         =	rename_and_convert_flax_params(__lowercase    )
    model.load_state_dict(__lowercase    )

    __A         =	AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer"""    )
    __A         =	PixaStructImageProcessor()
    __A         =	PixaStructProcessor(image_processor=__lowercase			,				tokenizer=__lowercase    )

    if use_large:
        __A         =	4_0_9_6

    __A         =	True

    # mkdir if needed
    os.makedirs(__lowercase			,				exist_ok=__lowercase    )

    model.save_pretrained(__lowercase    )
    processor.save_pretrained(__lowercase    )

    print("""Model saved in {}""".format(__lowercase    )    )


if __name__ == "__main__":
  __a      :       Union[str, Any]				  =   argparse.ArgumentParser()
  parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
  parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
  parser.add_argument("--use_large", action="store_true", help="Use large model.")
  parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
  __a      :       Dict				  =   parser.parse_args()

  convert_pixastruct_original_pytorch_checkpoint_to_hf(
      args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
  )




