
'''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      A (       A_							:					int       ):
						snake_case       :     str =    checkpoints.load_tax_checkpoint(A_       )
						snake_case       :     Optional[Any] =    flatten_dict(A_       )
						return flax_params

def      A (       A_							:					Optional[int]       ):
						snake_case       :     Tuple =    {}

						snake_case       :     List[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''',
						}

						snake_case       :     Optional[int] =    {
						    '''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
																		snake_case       :     Optional[Any] =    '''.'''.join(key[1:]       )

																		# rename the key
																		for old, new in CONVERSION_MAPPING.items():
																								snake_case       :     int =    new_key.replace(A_    ,							A_       )

																		if "decoder" in new_key:
																								for old, new in DECODER_CONVERSION_MAPPING.items():
																														snake_case       :     Tuple =    new_key.replace(A_    ,							A_       )

																		if "layers" in new_key and "decoder" not in new_key:
																								# use regex to replace the layer number
																								snake_case       :     Optional[int] =    re.sub(R'''layers_(\d+)'''    ,							R'''layer.\1'''    ,							A_       )
																								snake_case       :     str =    new_key.replace('''encoder'''    ,							'''encoder.encoder'''       )

																		elif "layers" in new_key and "decoder" in new_key:
																								# use regex to replace the layer number
																								snake_case       :     Optional[int] =    re.sub(R'''layers_(\d+)'''    ,							R'''layer.\1'''    ,							A_       )

																		snake_case       :     Optional[int] =    flax_dict[key]

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

						return converted_torch_dict

def      A (       A_							:					Dict    ,							A_							:					List[Any]    ,							A_							:					int=False    ,							A_							:					List[Any]=False       ):
						snake_case       :     List[Any] =    get_flax_param(A_       )

						if not use_large:
												snake_case       :     List[Any] =    PixaStructVisionConfig()
												snake_case       :     List[str] =    PixaStructTextConfig()
						else:
												snake_case       :     Optional[int] =    PixaStructVisionConfig(
												    hidden_size=1536    ,							d_ff=3968    ,							num_attention_heads=24    ,							num_hidden_layers=18       )
												snake_case       :     List[str] =    PixaStructTextConfig(hidden_size=1536    ,							d_ff=3968    ,							num_heads=24    ,							num_layers=18       )
						snake_case       :     str =    PixaStructConfig(
						    vision_config=encoder_config.to_dict()    ,							text_config=decoder_config.to_dict()    ,							is_vqa=A_       )

						snake_case       :     str =    PixaStructForConditionalGeneration(A_       )

						snake_case       :     List[Any] =    rename_and_convert_flax_params(A_       )
						model.load_state_dict(A_       )

						snake_case       :     Union[str, Any] =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''       )
						snake_case       :     int =    PixaStructImageProcessor()
						snake_case       :     List[Any] =    PixaStructProcessor(image_processor=A_    ,							tokenizer=A_       )

						if use_large:
												snake_case       :     str =    4096

						snake_case       :     Optional[int] =    True

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

						model.save_pretrained(A_       )
						processor.save_pretrained(A_       )

						print('''Model saved in {}'''.format(A_       )       )


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

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



'''simple docstring'''





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


UpperCAmelCase    					=   logging.get_logger(__name__)

UpperCAmelCase    					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase    					=   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase    					=   {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

class 						a   (				__magic_name__    ):
				_snake_case		    = VOCAB_FILES_NAMES
				_snake_case		    = PRETRAINED_VOCAB_FILES_MAP
				_snake_case		    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		    = ['''input_ids''', '''attention_mask''']
				_snake_case		    = GPTaTokenizer




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=None,    SCREAMING_SNAKE_CASE_	:				Any=None,    SCREAMING_SNAKE_CASE_	:				List[str]=None,    SCREAMING_SNAKE_CASE_	:				Union[str, Any]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Optional[int]="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Dict="<|endoftext|>",    SCREAMING_SNAKE_CASE_	:				Union[str, Any]=False,    **SCREAMING_SNAKE_CASE_	:				Tuple,    ):
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    tokenizer_file=SCREAMING_SNAKE_CASE_,    unk_token=SCREAMING_SNAKE_CASE_,    bos_token=SCREAMING_SNAKE_CASE_,    eos_token=SCREAMING_SNAKE_CASE_,    add_prefix_space=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )

										snake_case       :     Optional[Any] =    kwargs.pop('''add_bos_token''',    SCREAMING_SNAKE_CASE_      )

										snake_case       :     Dict =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
										if pre_tok_state.get('''add_prefix_space''',    SCREAMING_SNAKE_CASE_      ) != add_prefix_space:
																snake_case       :     List[Any] =    getattr(SCREAMING_SNAKE_CASE_,    pre_tok_state.pop('''type'''      )      )
																snake_case       :     Dict =    add_prefix_space
																snake_case       :     List[Any] =    pre_tok_class(**SCREAMING_SNAKE_CASE_      )

										snake_case       :     Optional[Any] =    add_prefix_space




				def 	__snake_case							(							self	:				Optional[Any],    *SCREAMING_SNAKE_CASE_	:				Dict,    **SCREAMING_SNAKE_CASE_	:				List[str]      ):
										snake_case       :     str =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )
										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Union[str, Any],    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case       :     int =    kwargs.get('''is_split_into_words''',    SCREAMING_SNAKE_CASE_      )

										assert self.add_prefix_space or not is_split_into_words, (
										    F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
										    "to use it with pretokenized inputs."
										)

										return super()._encode_plus(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[str] = None      ):
										snake_case       :     List[Any] =    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_,    name=SCREAMING_SNAKE_CASE_      )
										return tuple(SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				"Conversation"      ):
										snake_case       :     int =    []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_      ) + [self.eos_token_id]      )

										if len(SCREAMING_SNAKE_CASE_      ) > self.model_max_length:
																snake_case       :     str =    input_ids[-self.model_max_length :]
										return input_ids

