



"""simple docstring"""

import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__					:						List[Any]												=			logging.get_logger(__name__)

lowercase__					:						str												=			{
    '''google/pix2struct-textcaps-base''': (
        '''https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'''
    ),
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    str	      =	"""pix2struct_text_model"""
 _lowerCAmelCase     :    List[Any]	      =	["""past_key_values"""]
 _lowerCAmelCase     :    Tuple	      =	{
     """hidden_size""": """hidden_size""",
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(  self	: List[Any]						,  lowercase_	: Dict=50244						,  lowercase_	: List[str]=768						,  lowercase_	: Optional[int]=64						,  lowercase_	: Optional[int]=2048						,  lowercase_	: Optional[Any]=12						,  lowercase_	: Optional[int]=12						,  lowercase_	: Optional[Any]=32						,  lowercase_	: List[Any]=128						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Optional[Any]=1E-6						,  lowercase_	: List[str]=1.0						,  lowercase_	: List[str]="gelu_new"						,  lowercase_	: Tuple=0						,  lowercase_	: List[str]=False						,  lowercase_	: Tuple=0						,  lowercase_	: Optional[int]=1						,  lowercase_	: Optional[int]=False						,  lowercase_	: Union[str, Any]=True						,  **lowercase_	: List[str]						,  ):
     snake_case_				:  Optional[int]       =			vocab_size
     snake_case_				:  Tuple       =			hidden_size
     snake_case_				:  List[Any]       =			d_kv
     snake_case_				:  List[Any]       =			d_ff
     snake_case_				:  List[Any]       =			num_layers
     snake_case_				:  Union[str, Any]       =			num_heads
     snake_case_				:  Optional[int]       =			relative_attention_num_buckets
     snake_case_				:  Optional[Any]       =			relative_attention_max_distance
     snake_case_				:  int       =			dropout_rate
     snake_case_				:  Optional[Any]       =			layer_norm_epsilon
     snake_case_				:  Union[str, Any]       =			initializer_factor
     snake_case_				:  List[str]       =			use_cache

     snake_case_				:  Dict       =			eos_token_id
     snake_case_				:  Dict       =			decoder_start_token_id

     # for backwards compatibility
     snake_case_				:  str       =			dense_act_fn

     super().__init__(
         pad_token_id=lowercase_						,  eos_token_id=lowercase_						,  decoder_start_token_id=lowercase_						,  tie_word_embeddings=lowercase_						,  is_decoder=lowercase_						,  **lowercase_						,  )






 @classmethod
 def 		_snake_case      (  cls	: Tuple						,  lowercase_	: Union[str, os.PathLike]						,  **lowercase_	: List[str]       ):
     cls._set_token_in_kwargs(lowercase_       )

     snake_case_				:  Union[str, Any]       =			cls.get_config_dict(lowercase_						,  **lowercase_       )

     # get the text config dict if we are loading from Pix2StructConfig
     if config_dict.get('''model_type'''       ) == "pix2struct":
         snake_case_				:  List[Any]       =			config_dict['''text_config''']

     if "model_type" in config_dict and hasattr(cls						,  '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

     return cls.from_dict(lowercase_						,  **lowercase_       )







class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""pix2struct_vision_model"""

 def __init__(  self	: List[str]						,  lowercase_	: Tuple=768						,  lowercase_	: str=768						,  lowercase_	: Optional[Any]=2048						,  lowercase_	: int=64						,  lowercase_	: Optional[int]=12						,  lowercase_	: str=12						,  lowercase_	: str="gelu_new"						,  lowercase_	: Optional[int]=1E-6						,  lowercase_	: Tuple=0.0						,  lowercase_	: List[Any]=0.0						,  lowercase_	: Tuple=1E-10						,  lowercase_	: Optional[int]=1.0						,  lowercase_	: Tuple=4096						,  lowercase_	: Optional[Any]=32						,  lowercase_	: List[str]=128						,  **lowercase_	: Optional[Any]						,  ):
     super().__init__(**lowercase_       )

     snake_case_				:  Any       =			hidden_size
     snake_case_				:  List[str]       =			patch_embed_hidden_size
     snake_case_				:  List[Any]       =			d_ff
     snake_case_				:  int       =			dropout_rate
     snake_case_				:  int       =			num_hidden_layers
     snake_case_				:  List[Any]       =			num_attention_heads
     snake_case_				:  int       =			initializer_range
     snake_case_				:  Union[str, Any]       =			initializer_factor
     snake_case_				:  Tuple       =			attention_dropout
     snake_case_				:  Optional[Any]       =			layer_norm_eps
     snake_case_				:  str       =			dense_act_fn
     snake_case_				:  Union[str, Any]       =			seq_len
     snake_case_				:  str       =			relative_attention_num_buckets
     snake_case_				:  Optional[Any]       =			relative_attention_max_distance
     snake_case_				:  int       =			d_kv






 @classmethod
 def 		_snake_case      (  cls	: Any						,  lowercase_	: Union[str, os.PathLike]						,  **lowercase_	: List[str]       ):
     cls._set_token_in_kwargs(lowercase_       )

     snake_case_				:  int       =			cls.get_config_dict(lowercase_						,  **lowercase_       )

     # get the vision config dict if we are loading from Pix2StructConfig
     if config_dict.get('''model_type'''       ) == "pix2struct":
         snake_case_				:  List[str]       =			config_dict['''vision_config''']

     if "model_type" in config_dict and hasattr(cls						,  '''model_type'''       ) and config_dict["model_type"] != cls.model_type:
         logger.warning(
             f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
             f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."       )

     return cls.from_dict(lowercase_						,  **lowercase_       )







class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Any	      =	"""pix2struct"""
 _lowerCAmelCase     :    int	      =	True

 def __init__(  self	: List[Any]						,  lowercase_	: Dict=None						,  lowercase_	: Optional[int]=None						,  lowercase_	: Dict=1.0						,  lowercase_	: List[Any]=0.02						,  lowercase_	: List[str]=False						,  lowercase_	: Tuple=False						,  lowercase_	: Any=True						,  **lowercase_	: Optional[Any]						,  ):
     super().__init__(tie_word_embeddings=lowercase_						,  is_encoder_decoder=lowercase_						,  **lowercase_       )

     if text_config is None:
         snake_case_				:  Any       =			{}
         logger.info('''text_config is None. Initializing the Pix2StructTextConfig with default values.'''       )

     if vision_config is None:
         snake_case_				:  Dict       =			{}
         logger.info('''vision_config is None. Initializing the Pix2StructVisionConfig with default values.'''       )

     snake_case_				:  str       =			PixaStructTextConfig(**lowercase_       )
     snake_case_				:  int       =			PixaStructVisionConfig(**lowercase_       )

     snake_case_				:  List[str]       =			self.text_config.decoder_start_token_id
     snake_case_				:  int       =			self.text_config.pad_token_id
     snake_case_				:  Optional[int]       =			self.text_config.eos_token_id

     snake_case_				:  Any       =			initializer_factor
     snake_case_				:  Union[str, Any]       =			initializer_range

     snake_case_				:  List[Any]       =			self.initializer_range
     snake_case_				:  List[str]       =			self.initializer_range

     snake_case_				:  Tuple       =			is_vqa

 @classmethod
 def 		_snake_case      (  cls	: Optional[int]						,  lowercase_	: PixaStructTextConfig						,  lowercase_	: PixaStructVisionConfig						,  **lowercase_	: Union[str, Any]       ):
     return cls(text_config=text_config.to_dict()						,  vision_config=vision_config.to_dict()						,  **lowercase_       )






 def 		_snake_case      (  self	: int       ):
     snake_case_				:  List[str]       =			copy.deepcopy(self.__dict__       )
     snake_case_				:  int       =			self.text_config.to_dict()
     snake_case_				:  List[Any]       =			self.vision_config.to_dict()
     snake_case_				:  Any       =			self.__class__.model_type
     return output










"""simple docstring"""

import importlib.metadata
import warnings
from copy import deepcopy

from packaging import version

from ..utils import logging
from .import_utils import is_accelerate_available, is_bitsandbytes_available


if is_bitsandbytes_available():
     import bitsandbytes as bnb
     import torch
     import torch.nn as nn

     from ..pytorch_utils import ConvaD

if is_accelerate_available():
     from accelerate import init_empty_weights
     from accelerate.utils import find_tied_parameters

lowercase__					:						Optional[int]												=			logging.get_logger(__name__)




def __lowercase    (							_a	,    _a	,    _a	,    _a=None	,    _a=None					):
    # Recurse if needed
    if "." in tensor_name:
        snake_case_				:  Union[str, Any]       =			tensor_name.split('''.'''					)
        for split in splits[:-1]:
            snake_case_				:  Any       =			getattr(_a	,    _a					)
            if new_module is None:
                raise ValueError(f"{module} has no attribute {split}."					)
            snake_case_				:  int       =			new_module
        snake_case_				:  str       =			splits[-1]

    if tensor_name not in module._parameters and tensor_name not in module._buffers:
        raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}."					)
    snake_case_				:  Tuple       =			tensor_name in module._buffers
    snake_case_				:  Optional[int]       =			getattr(_a	,    _a					)

    if old_value.device == torch.device('''meta'''					) and device not in ["meta", torch.device('''meta'''					)] and value is None:
        raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}."					)

    snake_case_				:  Optional[Any]       =			False
    snake_case_				:  List[Any]       =			False
    if is_buffer or not is_bitsandbytes_available():
        snake_case_				:  Optional[Any]       =			False
        snake_case_				:  Tuple       =			False
    else:
        snake_case_				:  Tuple       =			hasattr(bnb.nn	,    '''Params4bit'''					) and isinstance(module._parameters[tensor_name]	,    bnb.nn.Paramsabit					)
        snake_case_				:  int       =			isinstance(module._parameters[tensor_name]	,    bnb.nn.IntaParams					)

    if is_abit or is_abit:
        snake_case_				:  List[str]       =			module._parameters[tensor_name]
        if param.device.type != "cuda":
            if value is None:
                snake_case_				:  Any       =			old_value.to(_a					)
            elif isinstance(_a	,    torch.Tensor					):
                snake_case_				:  str       =			value.to('''cpu'''					)
                if value.dtype == torch.inta:
                    snake_case_				:  List[Any]       =			version.parse(importlib.metadata.version('''bitsandbytes'''					)					) > version.parse(
                        '''0.37.2'''					)
                    if not is_abit_serializable:
                        raise ValueError(
                            '''Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. '''
                            '''Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`.'''					)
            else:
                snake_case_				:  Tuple       =			torch.tensor(_a	,    device='''cpu'''					)

            # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
            # Since weights are saved in the correct "orientation", we skip transposing when loading.
            if issubclass(module.source_cls	,    _a					) and fpaa_statistics is None:
                snake_case_				:  Any       =			new_value.T

            snake_case_				:  Tuple       =			old_value.__dict__
            if is_abit:
                snake_case_				:  Tuple       =			bnb.nn.IntaParams(_a	,    requires_grad=_a	,    **_a					).to(_a					)
            elif is_abit:
                snake_case_				:  Any       =			bnb.nn.Paramsabit(_a	,    requires_grad=_a	,    **_a					).to(_a					)

            snake_case_				:  Union[str, Any]       =			new_value
            if fpaa_statistics is not None:
                setattr(module.weight	,    '''SCB'''	,    fpaa_statistics.to(_a					)					)

    else:
        if value is None:
            snake_case_				:  Dict       =			old_value.to(_a					)
        elif isinstance(_a	,    torch.Tensor					):
            snake_case_				:  Dict       =			value.to(_a					)
        else:
            snake_case_				:  str       =			torch.tensor(_a	,    device=_a					)

        if is_buffer:
            snake_case_				:  Optional[int]       =			new_value
        else:
            snake_case_				:  Optional[Any]       =			nn.Parameter(_a	,    requires_grad=old_value.requires_grad					)
            snake_case_				:  List[Any]       =			new_value




def __lowercase    (							_a	,    _a=None	,    _a=None	,    _a=None	,    _a=False					):
    for name, module in model.named_children():
        if current_key_name is None:
            snake_case_				:  List[str]       =			[]
        current_key_name.append(_a					)

        if (isinstance(_a	,    nn.Linear					) or isinstance(_a	,    _a					)) and name not in modules_to_not_convert:
            # Check if the current key is not in the `modules_to_not_convert`
            if not any(key in '''.'''.join(_a					) for key in modules_to_not_convert					):
                with init_empty_weights():
                    if isinstance(_a	,    _a					):
                        snake_case_,			snake_case_				:  List[Any]       =			module.weight.shape
                    else:
                        snake_case_				:  Dict       =			module.in_features
                        snake_case_				:  Tuple       =			module.out_features

                    if quantization_config.quantization_method() == "llm_int8":
                        snake_case_				:  str       =			bnb.nn.LinearabitLt(
                            _a	,    _a	,    module.bias is not None	,    has_fpaa_weights=quantization_config.llm_inta_has_fpaa_weight	,    threshold=quantization_config.llm_inta_threshold	,    )
                        snake_case_				:  str       =			True
                    else:
                        if (
                            quantization_config.llm_inta_skip_modules is not None
                            and name in quantization_config.llm_inta_skip_modules
                        ):
                            pass
                        else:
                            snake_case_				:  Union[str, Any]       =			bnb.nn.Linearabit(
                                _a	,    _a	,    module.bias is not None	,    quantization_config.bnb_abit_compute_dtype	,    compress_statistics=quantization_config.bnb_abit_use_double_quant	,    quant_type=quantization_config.bnb_abit_quant_type	,    )
                            snake_case_				:  List[Any]       =			True
                    # Store the module class in case we need to transpose the weight later
                    snake_case_				:  str       =			type(_a					)
                    # Force requires grad to False to avoid unexpected errors
                    model._modules[name].requires_grad_(_a					)
        if len(list(module.children()					)					) > 0:
            snake_case_,			snake_case_				:  Optional[int]       =			_replace_with_bnb_linear(
                _a	,    _a	,    _a	,    _a	,    has_been_replaced=_a	,    )
        # Remove the last key for recursion
        current_key_name.pop(-1					)
    return model, has_been_replaced




def __lowercase    (							_a	,    _a=None	,    _a=None	,    _a=None					):
    snake_case_				:  Any       =			['''lm_head'''] if modules_to_not_convert is None else modules_to_not_convert
    snake_case_,			snake_case_				:  List[Any]       =			_replace_with_bnb_linear(
        _a	,    _a	,    _a	,    _a					)

    if not has_been_replaced:
        logger.warning(
            '''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
            ''' Please double check your model architecture, or submit an issue on github if you think this is'''
            ''' a bug.'''					)

    return model




def __lowercase    (							*_a	,    **_a					):
    warnings.warn(
        '''`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead'''	,    _a	,    )
    return replace_with_bnb_linear(*_a	,    **_a					)




def __lowercase    (							*_a	,    **_a					):
    warnings.warn(
        '''`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead'''	,    _a	,    )
    return set_module_quantized_tensor_to_device(*_a	,    **_a					)






def __lowercase    (							_a					):
    snake_case_				:  List[str]       =			deepcopy(_a					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
    tied_model.tie_weights()

    snake_case_				:  Optional[Any]       =			find_tied_parameters(_a					)
    # For compatibility with Accelerate < 0.18
    if isinstance(_a	,    _a					):
        snake_case_				:  Any       =			sum(list(tied_params.values()					)	,    []					) + list(tied_params.keys()					)
    else:
        snake_case_				:  str       =			sum(_a	,    []					)
    snake_case_				:  str       =			len(_a					) > 0

    # Check if it is a base model
    snake_case_				:  Dict       =			not hasattr(_a	,    model.base_model_prefix					)

    # Ignore this for base models (BertModel, GPT2Model, etc.)
    if (not has_tied_params) and is_base_model:
        return []

    # otherwise they have an attached head
    snake_case_				:  Dict       =			list(model.named_children()					)
    snake_case_				:  List[Any]       =			[list_modules[-1][0]]

    # add last module together with tied weights
    snake_case_				:  Optional[int]       =			set(_a					) - set(_a					)
    snake_case_				:  List[Any]       =			list(set(_a					)					) + list(_a					)

    # remove ".weight" from the keys
    snake_case_				:  Any       =			['''.weight''', '''.bias''']
    snake_case_				:  List[str]       =			[]
    for name in list_untouched:
        for name_to_remove in names_to_remove:
            if name_to_remove in name:
                snake_case_				:  List[str]       =			name.replace(_a	,    ''''''					)
        filtered_module_names.append(_a					)

    return filtered_module_names





