



import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available


def 						_UpperCAmelCase							( snake_case      ,		snake_case      ,		snake_case      ,		snake_case=None     ):





					"""simple docstring"""

					_lowerCAmelCase         =       None
					# Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
					if str(weight.dtype     ) == "torch.bfloat16":
										# Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
										_lowerCAmelCase         =       weight.view(torch.intaa     )
										_lowerCAmelCase         =       """bfloat16"""
					_lowerCAmelCase         =       weight.cpu().numpy()
					_lowerCAmelCase         =       os.path.join(snake_case      ,		F'{weight_name}.dat'     )
					if index is not None:
										if dtype is None:
															_lowerCAmelCase         =       str(array.dtype     )
										_lowerCAmelCase         =       {"""dtype""": dtype, """shape""": list(array.shape     )}
					if array.ndim == 0:
										_lowerCAmelCase         =       array[None]
					_lowerCAmelCase         =       np.memmap(snake_case      ,		dtype=array.dtype      ,		mode="""w+"""      ,		shape=array.shape     )
					_lowerCAmelCase         =       array[:]
					file_array.flush()
					return index


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       tuple(weight_info["""shape"""]     )
					if shape == ():
										# NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
										_lowerCAmelCase         =       (1,)

					_lowerCAmelCase         =       weight_info["""dtype"""]
					if dtype == "bfloat16":
										# NumPy does not support bfloat16 so this was saved as a int16
										_lowerCAmelCase         =       """int16"""

					_lowerCAmelCase         =       np.memmap(snake_case      ,		dtype=snake_case      ,		shape=snake_case      ,		mode="""r"""     )

					if len(weight_info["""shape"""]     ) == 0:
										_lowerCAmelCase         =       weight[0]
					_lowerCAmelCase         =       torch.tensor(snake_case     )
					if weight_info["dtype"] == "bfloat16":
										_lowerCAmelCase         =       weight.view(torch.bfloataa     )

					return weight


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					if index is None or len(snake_case     ) == 0:
										# Nothing to save
										return

					_lowerCAmelCase         =       os.path.join(snake_case      ,		"""index.json"""     )
					if os.path.isfile(snake_case     ):
										with open(snake_case      ,		"""r"""      ,		encoding="""utf-8"""     ) as f:
															_lowerCAmelCase         =       json.load(snake_case     )
					else:
										_lowerCAmelCase         =       {}
					current_index.update(snake_case     )

					with open(snake_case      ,		"""w"""      ,		encoding="""utf-8"""     ) as f:
										json.dump(snake_case      ,		snake_case      ,		indent=2     )


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					os.makedirs(snake_case      ,		exist_ok=snake_case     )
					_lowerCAmelCase         =       {}
					for name, parameter in state_dict.items():
										_lowerCAmelCase         =       offload_weight(snake_case      ,		snake_case      ,		snake_case      ,		index=snake_case     )

					# Update index
					save_offload_index(snake_case      ,		snake_case     )





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       dataset
									_lowerCAmelCase         =       prefix




				def __getitem__(				self		,							_snake_case						):



									"""simple docstring"""





									return self.dataset[F'{self.prefix}{key}']




				def __iter__(				self						):



									"""simple docstring"""





									return iter([key for key in self.dataset if key.startswith(self.prefix						)]						)




				def __len__(				self						):



									"""simple docstring"""





									return len(self.dataset						)





class    __lowerCAmelCase					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case = None		,							_snake_case = None		,							_snake_case = None		,							_snake_case=None		,							):



									"""simple docstring"""





									if state_dict is None and save_folder is None:
														raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights."""						)

									_lowerCAmelCase         =       {} if state_dict is None else state_dict
									_lowerCAmelCase         =       save_folder
									if index is None and save_folder is not None:
														with open(os.path.join(_snake_case		,							"""index.json"""						)						) as f:
																			_lowerCAmelCase         =       json.load(_snake_case						)
									_lowerCAmelCase         =       {} if index is None else index
									_lowerCAmelCase         =       list(self.state_dict.keys()						)
									self.all_keys.extend([key for key in self.index if key not in self.all_keys]						)
									_lowerCAmelCase         =       device




				def __getitem__(				self		,							_snake_case						):



									"""simple docstring"""





									if key in self.state_dict:
														return self.state_dict[key]
									_lowerCAmelCase         =       self.index[key]
									if weight_info.get("""safetensors_file"""						) is not None:
														if not is_safetensors_available():
																			raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`."""						)

														from safetensors import safe_open

														_lowerCAmelCase         =       """cpu""" if self.device is None else self.device
														with safe_open(weight_info["""safetensors_file"""]		,							framework="""pt"""		,							device=_snake_case						) as f:
																			_lowerCAmelCase         =       f.get_tensor(weight_info.get("""weight_name"""		,							_snake_case						)						)

														if "dtype" in weight_info:
																			return tensor.to(getattr(_snake_case		,							weight_info["""dtype"""]						)						)
														else:
																			return tensor

									_lowerCAmelCase         =       os.path.join(self.save_folder		,							F'{key}.dat'						)
									return load_offloaded_weight(_snake_case		,							_snake_case						)




				def __iter__(				self						):



									"""simple docstring"""





									return iter(self.all_keys						)




				def __len__(				self						):



									"""simple docstring"""





									return len(self.all_keys						)


def 						_UpperCAmelCase							( snake_case      ,		snake_case     ):





					"""simple docstring"""

					_lowerCAmelCase         =       {}
					for module_name in submodule_names:
										# We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
										# submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
										result.update(
										    {
										        key: param
										        for key, param in state_dict.items()
										        if key == module_name or key.startswith(module_name + """."""     )
										    }     )
					return result










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	 ={
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """CamembertForCausalLM""",
						    """CamembertForMaskedLM""",
						    """CamembertForMultipleChoice""",
						    """CamembertForQuestionAnswering""",
						    """CamembertForSequenceClassification""",
						    """CamembertForTokenClassification""",
						    """CamembertModel""",
						    """CamembertPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFCamembertForCausalLM""",
						    """TFCamembertForMaskedLM""",
						    """TFCamembertForMultipleChoice""",
						    """TFCamembertForQuestionAnswering""",
						    """TFCamembertForSequenceClassification""",
						    """TFCamembertForTokenClassification""",
						    """TFCamembertModel""",
						    """TFCamembertPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase_	 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



