





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

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
     if is_torch_available():
          import torch
     if is_tf_available():
          import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


__A							   =							logging.get_logger(__name__)

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

__A							   =							{
    "vocab_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json",
    },
    "merges_file": {
        "Salesforce/codegen-350M-mono": "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "Salesforce/codegen-350M-mono": (
            "https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"
        ),
    },
}

__A							   =							{
    "Salesforce/codegen-350M-mono": 2048,
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     VOCAB_FILES_NAMES
 lowercase_						=     PRETRAINED_VOCAB_FILES_MAP
 lowercase_						=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_						=     ["""input_ids""", """attention_mask"""]
 lowercase_						=     CodeGenTokenizer

 def __init__(self							:  Optional[Any]			,					UpperCAmelCase_							:  Optional[int]=None			,					UpperCAmelCase_							:  Union[str, Any]=None			,					UpperCAmelCase_							:  List[str]=None			,					UpperCAmelCase_							:  Any="<|endoftext|>"			,					UpperCAmelCase_							:  Union[str, Any]="<|endoftext|>"			,					UpperCAmelCase_							:  Any="<|endoftext|>"			,					UpperCAmelCase_							:  Union[str, Any]=False			,					**UpperCAmelCase_							:  Optional[int]			,					)			->List[str]:



   '''simple docstring'''



   super().__init__(
       a_			,					a_			,					tokenizer_file=a_			,					unk_token=a_			,					bos_token=a_			,					eos_token=a_			,					add_prefix_space=a_			,					**a_			,					)

   if kwargs.pop("add_bos_token"			,					a_):
     lowerCamelCase__: List[str]         =kwargs.pop("name_or_path"			,					"")
     raise ValueError(
         "Currenty GPT2\'s fast tokenizer does NOT support adding a BOS token."
         "Instead you should use GPT2\'s slow tokenizer class `CodeGenTokenizer` as follows: \n"
         F"""`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n"""
         F"""`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n"""
         "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
         " so that the fast tokenizer works correctly.")

   lowerCamelCase__: int         =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
   if pre_tok_state.get("add_prefix_space"			,					a_) != add_prefix_space:
     lowerCamelCase__: List[str]         =getattr(a_			,					pre_tok_state.pop("type"))
     lowerCamelCase__: Any         =add_prefix_space
     lowerCamelCase__: Any         =pre_tok_class(**a_)

   lowerCamelCase__: List[Any]         =add_prefix_space

 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any]			,					*UpperCAmelCase_							:  Optional[Any]			,					**UpperCAmelCase_							:  Optional[int])			->BatchEncoding:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =kwargs.get("is_split_into_words"			,					a_)
   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(*a_			,					**a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  int			,					*UpperCAmelCase_							:  Any			,					**UpperCAmelCase_							:  Any)			->BatchEncoding:



   '''simple docstring'''



   lowerCamelCase__: List[Any]         =kwargs.get("is_split_into_words"			,					a_)

   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(*a_			,					**a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  List[Any]			,					UpperCAmelCase_							:  str			,					UpperCAmelCase_							:  Optional[str] = None)			->Tuple[str]:



   '''simple docstring'''



   lowerCamelCase__: int         =self._tokenizer.model.save(a_			,					name=a_)
   return tuple(a_)

 def 				SCREAMING_SNAKE_CASE_   (self							:  Tuple			,					UpperCAmelCase_							:  Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]			,					UpperCAmelCase_							:  bool = False			,					UpperCAmelCase_							:  bool = None			,					UpperCAmelCase_							:  Optional[List[str]] = None			,					**UpperCAmelCase_							:  List[str]			,					)			->str:



   '''simple docstring'''



   lowerCamelCase__: Any         =super().decode(
       token_ids=a_			,					skip_special_tokens=a_			,					clean_up_tokenization_spaces=a_			,					**a_			,					)

   if truncate_before_pattern is not None and len(a_) > 0:
     lowerCamelCase__: int         =self.truncate(a_			,					a_)

   return decoded_text







 def 				SCREAMING_SNAKE_CASE_   (self							:  Dict			,					UpperCAmelCase_							:  List[str]			,					UpperCAmelCase_							:  str)			->Optional[int]:



   '''simple docstring'''



   def find_re(UpperCAmelCase_							:  Dict			,					UpperCAmelCase_							:  Optional[Any]			,					UpperCAmelCase_							:  Tuple):
     lowerCamelCase__: Optional[Any]         =pattern.search(a_			,					a_)
     return m.start() if m else -1

   lowerCamelCase__: Union[str, Any]         =[re.compile(a_			,					re.MULTILINE) for pattern in truncate_before_pattern]

   lowerCamelCase__: List[Any]         =list(re.finditer("^print"			,					a_			,					re.MULTILINE))

   if len(a_) > 1:
     lowerCamelCase__: List[str]         =completion[: prints[1].start()]

   lowerCamelCase__: Tuple         =list(re.finditer("^def"			,					a_			,					re.MULTILINE))

   if len(a_) > 1:
     lowerCamelCase__: int         =completion[: defs[1].start()]

   lowerCamelCase__: Any         =0

   lowerCamelCase__: List[str]         =[
       pos for pos in [find_re(a_			,					a_			,					a_) for terminal in terminals] if pos != -1
   ]

   if len(a_) > 0:
     return completion[: min(a_)]
   else:
     return completion






'''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_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			snake_case:    List[str]      							=checkpoints.load_tax_checkpoint(__UpperCAmelCase					)
			snake_case:    str      							=flatten_dict(__UpperCAmelCase					)
			return flax_params







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    int      							={}

			snake_case:    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:    Union[str, Any]      							={
			    '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:    Dict      							='.'.join(key[1:]					)

									# rename the key
									for old, new in CONVERSION_MAPPING.items():
												snake_case:    Union[str, Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

									if "decoder" in new_key:
												for old, new in DECODER_CONVERSION_MAPPING.items():
															snake_case:    Optional[Any]      							=new_key.replace(__UpperCAmelCase				,							__UpperCAmelCase					)

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

									snake_case:    Optional[int]      							=flax_dict[key]

			snake_case:    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):
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key].T					)
						else:
									snake_case:    Union[str, Any]      							=torch.from_numpy(converted_dict[key]					)

			return converted_torch_dict







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=False				,							__UpperCAmelCase=False					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Any      							=get_flax_param(__UpperCAmelCase					)

			if not use_large:
						snake_case:    Optional[int]      							=PixaStructVisionConfig()
						snake_case:    int      							=PixaStructTextConfig()
			else:
						snake_case:    Tuple      							=PixaStructVisionConfig(
						    hidden_size=15_36				,							d_ff=39_68				,							num_attention_heads=24				,							num_hidden_layers=18					)
						snake_case:    List[Any]      							=PixaStructTextConfig(hidden_size=15_36				,							d_ff=39_68				,							num_heads=24				,							num_layers=18					)
			snake_case:    Any      							=PixaStructConfig(
			    vision_config=encoder_config.to_dict()				,							text_config=decoder_config.to_dict()				,							is_vqa=__UpperCAmelCase					)

			snake_case:    List[Any]      							=PixaStructForConditionalGeneration(__UpperCAmelCase					)

			snake_case:    str      							=rename_and_convert_flax_params(__UpperCAmelCase					)
			model.load_state_dict(__UpperCAmelCase					)

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

			if use_large:
						snake_case:    Optional[Any]      							=40_96

			snake_case:    str      							=True

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

			model.save_pretrained(__UpperCAmelCase					)
			processor.save_pretrained(__UpperCAmelCase					)

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


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

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




