






import argparse
import hashlib  # hashlib is only used inside the Test class
import struct


class       __lowercase      :







     '''simple docstring'''




     def __init__(				self       :			Any ,	UpperCamelCase_       :			List[Any]				):


         """simple docstring"""



         __A         =	data
         __A         =	[0x6_7_4_5_2_3_0_1, 0xE_F_C_D_A_B_8_9, 0x9_8_B_A_D_C_F_E, 0x1_0_3_2_5_4_7_6, 0xC_3_D_2_E_1_F_0]




     @staticmethod
     def 		lowerCAmelCase_     (				UpperCamelCase_       :			Tuple ,	UpperCamelCase_       :			List[str]				):


         """simple docstring"""



         return ((n << b) | (n >> (32 - b))) & 0xF_F_F_F_F_F_F_F




     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         __A         =	B"""\x80""" + B"""\x00""" * (63 - (len(self.data				) + 8) % 64)
         __A         =	self.data + padding + struct.pack(""">Q""" ,	8 * len(self.data				)				)
         return padded_data




     def 		lowerCAmelCase_     (				self       :			str				):


         """simple docstring"""



         return [
             self.padded_data[i : i + 64] for i in range(0 ,	len(self.padded_data				) ,	64				)
         ]




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


         """simple docstring"""



         __A         =	list(struct.unpack(""">16L""" ,	UpperCamelCase_				)				) + [0] * 64
         for i in range(16 ,	80				):
             __A         =	self.rotate((w[i - 3] ^ w[i - 8] ^ w[i - 14] ^ w[i - 16]) ,	1				)
         return w





     def 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	self.padding()
         __A         =	self.split_blocks()
         for block in self.blocks:
             __A         =	self.expand_block(UpperCamelCase_				)
             __A    ,	__A    ,	__A    ,	__A    ,	__A         =	self.h
             for i in range(0 ,	80				):
                 if 0 <= i < 20:
                     __A         =	(b & c) | ((~b) & d)
                     __A         =	0x5_A_8_2_7_9_9_9
                 elif 20 <= i < 40:
                     __A         =	b ^ c ^ d
                     __A         =	0x6_E_D_9_E_B_A_1
                 elif 40 <= i < 60:
                     __A         =	(b & c) | (b & d) | (c & d)
                     __A         =	0x8_F_1_B_B_C_D_C
                 elif 60 <= i < 80:
                     __A         =	b ^ c ^ d
                     __A         =	0xC_A_6_2_C_1_D_6
                 __A    ,	__A    ,	__A    ,	__A    ,	__A         =	(
                     self.rotate(UpperCamelCase_ ,	5				) + f + e + k + expanded_block[i] & 0xF_F_F_F_F_F_F_F,
                     a,
                     self.rotate(UpperCamelCase_ ,	30				),
                     c,
                     d,
                 )
             __A         =	(
                 self.h[0] + a & 0xF_F_F_F_F_F_F_F,
                 self.h[1] + b & 0xF_F_F_F_F_F_F_F,
                 self.h[2] + c & 0xF_F_F_F_F_F_F_F,
                 self.h[3] + d & 0xF_F_F_F_F_F_F_F,
                 self.h[4] + e & 0xF_F_F_F_F_F_F_F,
             )
         return ("{:08x}" * 5).format(*self.h				)



def 			_SCREAMING_SNAKE_CASE						( )						->      int:





    """simple docstring"""

    __A         =	B"""Test String"""
    assert SHAaHash(__lowercase    ).final_hash() == hashlib.shaa(__lowercase    ).hexdigest()  # noqa: S324



def 			_SCREAMING_SNAKE_CASE						( )						->      Dict:





    """simple docstring"""

    __A         =	argparse.ArgumentParser(description="""Process some strings or files"""    )
    parser.add_argument(
        """--string"""			,				dest="""input_string"""			,				default="""Hello World!! Welcome to Cryptography"""			,				help="""Hash the string"""			,				)
    parser.add_argument("""--file"""			,				dest="""input_file"""			,				help="""Hash contents of a file"""    )
    __A         =	parser.parse_args()
    __A         =	args.input_string
    # In any case hash input should be a bytestring
    if args.input_file:
        with open(args.input_file			,				"""rb"""    ) as f:
            __A         =	f.read()
    else:
        __A         =	bytes(__lowercase			,				"""utf-8"""    )
    print(SHAaHash(__lowercase    ).final_hash()    )


if __name__ == "__main__":
  main()
  import doctest

  doctest.testmod()












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
  )




