



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 (       lowerCamelCase__						):

    """simple docstring"""


    lowercase__					:   Any													=					checkpoints.load_tax_checkpoint(lowerCamelCase__						)
    lowercase__					:   Dict													=					flatten_dict(lowerCamelCase__						)
    return flax_params


def  __lowerCamelCase (       lowerCamelCase__						):

    """simple docstring"""


    lowercase__					:   int													=					{}

    lowercase__					:   Union[str, Any]													=					{
        "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__					:   List[str]													=					{
        "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__					:   Tuple													=					".".join(key[1:]						)

            # rename the key
            for old, new in CONVERSION_MAPPING.items():
                lowercase__					:   List[Any]													=					new_key.replace(lowerCamelCase__					,			lowerCamelCase__						)

            if "decoder" in new_key:
                for old, new in DECODER_CONVERSION_MAPPING.items():
                    lowercase__					:   Union[str, Any]													=					new_key.replace(lowerCamelCase__					,			lowerCamelCase__						)

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

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

            lowercase__					:   List[str]													=					flax_dict[key]

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

    return converted_torch_dict


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__=False					,			lowerCamelCase__=False						):

    """simple docstring"""


    lowercase__					:   Optional[Any]													=					get_flax_param(lowerCamelCase__						)

    if not use_large:
        lowercase__					:   List[str]													=					PixaStructVisionConfig()
        lowercase__					:   int													=					PixaStructTextConfig()
    else:
        lowercase__					:   List[Any]													=					PixaStructVisionConfig(
            hidden_size=1_536					,			d_ff=3_968					,			num_attention_heads=24					,			num_hidden_layers=18						)
        lowercase__					:   Union[str, Any]													=					PixaStructTextConfig(hidden_size=1_536					,			d_ff=3_968					,			num_heads=24					,			num_layers=18						)
    lowercase__					:   Tuple													=					PixaStructConfig(
        vision_config=encoder_config.to_dict()					,			text_config=decoder_config.to_dict()					,			is_vqa=lowerCamelCase__						)

    lowercase__					:   str													=					PixaStructForConditionalGeneration(lowerCamelCase__						)

    lowercase__					:   Optional[int]													=					rename_and_convert_flax_params(lowerCamelCase__						)
    model.load_state_dict(lowerCamelCase__						)

    lowercase__					:   Optional[int]													=					AutoTokenizer.from_pretrained("ybelkada/test-pix2struct-tokenizer"						)
    lowercase__					:   List[Any]													=					PixaStructImageProcessor()
    lowercase__					:   str													=					PixaStructProcessor(image_processor=lowerCamelCase__					,			tokenizer=lowerCamelCase__						)

    if use_large:
        lowercase__					:   Dict													=					4_096

    lowercase__					:   Dict													=					True

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

    model.save_pretrained(lowerCamelCase__						)
    processor.save_pretrained(lowerCamelCase__						)

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


if __name__ == "__main__":
    lowerCAmelCase__					  =  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.''')
    lowerCAmelCase__					  =  parser.parse_args()

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









def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Union[str, Any]													=					[]
				lowercase__					:   Tuple													=					[]
				lowercase__					:   Any													=					{
				    "^": 3,
				    "*": 2,
				    "/": 2,
				    "%": 2,
				    "+": 1,
				    "-": 1,
				}  # Priority of each operator
				lowercase__					:   Any													=					len(lowerCamelCase__						) if (len(lowerCamelCase__						) > 7) else 7

				# Print table header for output
				print(
				    "Symbol".center(8						)					,			"Stack".center(lowerCamelCase__						)					,			"Postfix".center(lowerCamelCase__						)					,			sep=" | "					,			)
				print("-" * (print_width * 3 + 7)						)

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(lowerCamelCase__						)  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(lowerCamelCase__						)  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()						)  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(lowerCamelCase__						) == 0:
																stack.append(lowerCamelCase__						)  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(lowerCamelCase__						) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()						)  # pop stack & add to Postfix
																stack.append(lowerCamelCase__						)  # push x to stack

								print(
								    x.center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				while len(lowerCamelCase__						) > 0:  # while stack is not empty
								post_fix.append(stack.pop()						)  # pop stack & add to Postfix
								print(
								    " ".center(8						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			("".join(lowerCamelCase__						)).ljust(lowerCamelCase__						)					,			sep=" | "					,			)  # Output in tabular format

				return "".join(lowerCamelCase__						)  # return Postfix as str


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Optional[int]													=					list(infix[::-1]						)  # reverse the infix equation

				for i in range(len(lowerCamelCase__						)						):
								if infix[i] == "(":
												lowercase__					:   Tuple													=					")"  # change "(" to ")"
								elif infix[i] == ")":
												lowercase__					:   Optional[Any]													=					"("  # change ")" to "("

				return (infix_2_postfix("".join(lowerCamelCase__						)						))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
				lowerCAmelCase__					  =  input('''\nEnter an Infix Equation = ''')  # Input an Infix equation
				lowerCAmelCase__					  =  ''''''.join(Infix.split())  # Remove spaces from the input
				print('''\n\t''', Infix, '''(Infix) -> ''', infix_2_prefix(Infix), '''(Prefix)''')




