



'''simple docstring'''





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        lowerCamelCase  (				_snake_case  :						Union[str, Any]       ):





    '''simple docstring'''


    lowercase__		       =						checkpoints.load_tax_checkpoint(_snake_case       )
    lowercase__		       =						flatten_dict(_snake_case       )
    return flax_params




def        lowerCamelCase  (				_snake_case  :						Optional[Any]       ):





    '''simple docstring'''


    lowercase__		       =						{}

    lowercase__		       =						{
        "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",
    }

    lowercase__		       =						{
        "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
            lowercase__		       =						".".join(key[1:]       )

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

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

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

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

            lowercase__		       =						flax_dict[key]

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

    return converted_torch_dict




def        lowerCamelCase  (				_snake_case  :						int     ,_snake_case  :						int     ,_snake_case  :						int=False     ,_snake_case  :						Tuple=False       ):





    '''simple docstring'''


    lowercase__		       =						get_flax_param(_snake_case       )

    if not use_large:
        lowercase__		       =						PixaStructVisionConfig()
        lowercase__		       =						PixaStructTextConfig()
    else:
        lowercase__		       =						PixaStructVisionConfig(
            hidden_size=1_536     ,d_ff=3_968     ,num_attention_heads=24     ,num_hidden_layers=18       )
        lowercase__		       =						PixaStructTextConfig(hidden_size=1_536     ,d_ff=3_968     ,num_heads=24     ,num_layers=18       )
    lowercase__		       =						PixaStructConfig(
        vision_config=encoder_config.to_dict()     ,text_config=decoder_config.to_dict()     ,is_vqa=_snake_case       )

    lowercase__		       =						PixaStructForConditionalGeneration(_snake_case       )

    lowercase__		       =						rename_and_convert_flax_params(_snake_case       )
    model.load_state_dict(_snake_case       )

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

    if use_large:
        lowercase__		       =						4_096

    lowercase__		       =						True

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

    model.save_pretrained(_snake_case       )
    processor.save_pretrained(_snake_case       )

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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__      					=     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.")
 SCREAMING_SNAKE_CASE__      					=     parser.parse_args()

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









'''simple docstring'''





def        lowerCamelCase  (				_snake_case  :						list[int]     ,_snake_case  :						list[int]       ):





    '''simple docstring'''


    lowercase__		       =						len(_snake_case       )
    print("The following activities are selected:"       )

    # The first activity is always selected
    lowercase__		       =						0
    print(_snake_case     ,end=","       )

    # Consider rest of the activities
    for j in range(_snake_case       ):
        # If this activity has start time greater than
        # or equal to the finish time of previously
        # selected activity, then select it
        if start[j] >= finish[i]:
            print(_snake_case     ,end=","       )
            lowercase__		       =						j


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 SCREAMING_SNAKE_CASE__      					=     [1, 3, 0, 5, 8, 5]
 SCREAMING_SNAKE_CASE__      					=     [2, 4, 6, 7, 9, 9]
 print_max_activities(start, finish)




