



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union

from packaging import version

from ..utils import is_torch_available, logging


if is_torch_available():
   import torch


UpperCAmelCase_		 =					logging.get_logger(__name__)


@dataclass
class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					List[Any]			,						__lowerCAmelCase					:					Dict=False			,						__lowerCAmelCase					:					str=False			,						__lowerCAmelCase					:					Optional[int]=6.0			,						__lowerCAmelCase					:					Dict=None			,						__lowerCAmelCase					:					Dict=False			,						__lowerCAmelCase					:					Any=False			,						__lowerCAmelCase					:					str=None			,						__lowerCAmelCase					:					Tuple="fp4"			,						__lowerCAmelCase					:					Dict=False			,						**__lowerCAmelCase					:					List[str]			,						)   ->	List[Any]:
    _A      =       load_in_abit
    _A      =       load_in_abit
    _A      =       llm_inta_threshold
    _A      =       llm_inta_skip_modules
    _A      =       llm_inta_enable_fpaa_cpu_offload
    _A      =       llm_inta_has_fpaa_weight
    _A      =       bnb_abit_quant_type
    _A      =       bnb_abit_use_double_quant

    if bnb_abit_compute_dtype is None:
       _A      =       torch.floataa
    elif isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
       _A      =       getattr(__lowerCAmelCase			,						__lowerCAmelCase      )
    elif isinstance(__lowerCAmelCase			,						torch.dtype      ):
       _A      =       bnb_abit_compute_dtype
    else:
       raise ValueError('''bnb_4bit_compute_dtype must be a string or a torch.dtype'''      )

    self.post_init()

 def     snake_case_      (    self					:					Optional[Any]      )   ->	str:
    if not isinstance(self.llm_inta_threshold			,						__lowerCAmelCase      ):
       raise ValueError('''llm_int8_threshold must be a float'''      )

    if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules			,						__lowerCAmelCase      ):
       raise ValueError('''llm_int8_skip_modules must be a list of strings'''      )
    if not isinstance(self.llm_inta_enable_fpaa_cpu_offload			,						__lowerCAmelCase      ):
       raise ValueError('''llm_int8_enable_fp32_cpu_offload must be a boolean'''      )

    if not isinstance(self.llm_inta_has_fpaa_weight			,						__lowerCAmelCase      ):
       raise ValueError('''llm_int8_has_fp16_weight must be a boolean'''      )

    if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype			,						torch.dtype      ):
       raise ValueError('''bnb_4bit_compute_dtype must be torch.dtype'''      )

    if not isinstance(self.bnb_abit_quant_type			,						__lowerCAmelCase      ):
       raise ValueError('''bnb_4bit_quant_type must be a string'''      )

    if not isinstance(self.bnb_abit_use_double_quant			,						__lowerCAmelCase      ):
       raise ValueError('''bnb_4bit_use_double_quant must be a boolean'''      )

    if self.load_in_abit and not version.parse(importlib.metadata.version('''bitsandbytes'''      )      ) >= version.parse(
        '''0.39.0'''      ):
       raise ValueError(
           '''4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version'''      )

 def     snake_case_      (    self					:					Dict      )   ->	str:
    return self.load_in_abit or self.load_in_abit

 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Tuple:
    if self.load_in_abit:
       return "llm_int8"
    elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
       return "fp4"
    elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
       return "nf4"
    else:
       return None

 @classmethod
 def     snake_case_      (    cls					:					List[Any]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[Any]			,						**__lowerCAmelCase					:					Optional[Any]      )   ->	Optional[Any]:
    _A      =       cls(**__lowerCAmelCase      )

    _A      =       []
    for key, value in kwargs.items():
       if hasattr(__lowerCAmelCase			,						__lowerCAmelCase      ):
          setattr(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )
          to_remove.append(__lowerCAmelCase      )
    for key in to_remove:
       kwargs.pop(__lowerCAmelCase			,						__lowerCAmelCase      )

    if return_unused_kwargs:
       return config, kwargs
    else:
       return config

 def     snake_case_      (    self					:					Dict			,						__lowerCAmelCase					:					Union[str, os.PathLike]      )   ->	Optional[int]:
    with open(__lowerCAmelCase			,						'''w'''			,						encoding='''utf-8'''      ) as writer:
       _A      =       self.to_dict()
       _A      =       json.dumps(__lowerCAmelCase			,						indent=2			,						sort_keys=__lowerCAmelCase      ) + '''\n'''

       writer.write(__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[int]      )   ->	Dict[str, Any]:
    _A      =       copy.deepcopy(self.__dict__      )
    _A      =       str(output['''bnb_4bit_compute_dtype''']      ).split('''.'''      )[1]

    return output

 def __repr__(    self					:					Optional[int]      )   ->	List[str]:
    return f'''{self.__class__.__name__} {self.to_json_string()}'''

 def     snake_case_      (    self					:					str			,						__lowerCAmelCase					:					bool = True      )   ->	str:
    if use_diff is True:
       _A      =       self.to_diff_dict()
    else:
       _A      =       self.to_dict()
    return json.dumps(__lowerCAmelCase			,						indent=2			,						sort_keys=__lowerCAmelCase      ) + "\n"

 def     snake_case_      (    self					:					Tuple      )   ->	Dict[str, Any]:
    _A      =       self.to_dict()

    # get the default config dict
    _A      =       BitsAndBytesConfig().to_dict()

    _A      =       {}

    # only serialize values that differ from the default config
    for key, value in config_dict.items():
       if value != default_config_dict[key]:
          _A      =       value

    return serializable_config_dict








'''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
	)




