
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_ (	a_  ,				a_  ,				a_  ,				a_=None		)     ->Any:
		A				=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.
				A				=weight.view(torch.intaa		)
				A				="bfloat16"
		A				=weight.cpu().numpy()
		A				=os.path.join(a_  ,				f'''{weight_name}.dat'''		)
		if index is not None:
				if dtype is None:
						A				=str(array.dtype		)
				A				={"dtype": dtype, "shape": list(array.shape		)}
		if array.ndim == 0:
				A				=array[None]
		A				=np.memmap(a_  ,				dtype=array.dtype  ,				mode="w+"  ,				shape=array.shape		)
		A				=array[:]
		file_array.flush()
		return index

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

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

		A				=np.memmap(a_  ,				dtype=a_  ,				shape=a_  ,				mode="r"		)

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

		return weight

def    UpperCamelCase_ (	a_  ,				a_		)     ->int:
		if index is None or len(a_		) == 0:
				# Nothing to save
				return

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

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

def    UpperCamelCase_ (	a_  ,				a_		)     ->str:
		os.makedirs(a_  ,				exist_ok=a_		)
		A				={}
		for name, parameter in state_dict.items():
				A				=offload_weight(a_  ,				a_  ,				a_  ,				index=a_		)

		# Update index
		save_offload_index(a_  ,				a_		)

class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	def __init__(      self				:			List[str]							,			snake_case__				:			Mapping							,			snake_case__				:			str ):
			"""simple docstring"""





			A				=dataset
			A				=prefix







	def __getitem__(      self				:			Optional[int]							,			snake_case__				:			str ):
			"""simple docstring"""





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







	def __iter__(      self				:			Tuple ):
			"""simple docstring"""





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







	def __len__(      self				:			List[Any] ):
			"""simple docstring"""





			return len(self.dataset )


class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	def __init__(      self				:			Dict							,			snake_case__				:			Dict[str, torch.Tensor] = None							,			snake_case__				:			Optional[Union[str, os.PathLike]] = None							,			snake_case__				:			Mapping = None							,			snake_case__				:			Union[str, Any]=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." )

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







	def __getitem__(      self				:			Union[str, Any]							,			snake_case__				:			str ):
			"""simple docstring"""





			if key in self.state_dict:
					return self.state_dict[key]
			A				=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

					A				="cpu" if self.device is None else self.device
					with safe_open(weight_info["safetensors_file"]							,			framework="pt"							,			device=snake_case__ ) as f:
							A				=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

			A				=os.path.join(self.save_folder							,			f'''{key}.dat''' )
			return load_offloaded_weight(snake_case__							,			snake_case__ )







	def __iter__(      self				:			Optional[Any] ):
			"""simple docstring"""





			return iter(self.all_keys )







	def __len__(      self				:			Optional[Any] ):
			"""simple docstring"""





			return len(self.all_keys )


def    UpperCamelCase_ (	a_  ,				a_		)     ->Optional[int]:
		A				={}
		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






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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

__a							  =						logging.get_logger(__name__)

__a							  =						{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

__a							  =						{
    """vocab_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """microsoft/deberta-base""": """https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt""",
        """microsoft/deberta-large""": """https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge""": """https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt""",
        """microsoft/deberta-base-mnli""": """https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-large-mnli""": """https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt""",
        """microsoft/deberta-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt"""
        ),
    },
}

__a							  =						{
    """microsoft/deberta-base""": 5_1_2,
    """microsoft/deberta-large""": 5_1_2,
    """microsoft/deberta-xlarge""": 5_1_2,
    """microsoft/deberta-base-mnli""": 5_1_2,
    """microsoft/deberta-large-mnli""": 5_1_2,
    """microsoft/deberta-xlarge-mnli""": 5_1_2,
}

__a							  =						{
    """microsoft/deberta-base""": {"""do_lower_case""": False},
    """microsoft/deberta-large""": {"""do_lower_case""": False},
}

def    UpperCamelCase_ (	)     ->Optional[int]:
		A				=(
		    list(range(ord("!"		)  ,				ord("~"		) + 1		)		) + list(range(ord("¡"		)  ,				ord("¬"		) + 1		)		) + list(range(ord("®"		)  ,				ord("ÿ"		) + 1		)		)
		)
		A				=bs[:]
		A				=0
		for b in range(2**8		):
				if b not in bs:
						bs.append(a_		)
						cs.append(2**8 + n		)
						n += 1
		A				=[chr(a_		) for n in cs]
		return dict(zip(a_  ,				a_		)		)

def    UpperCamelCase_ (	a_		)     ->Dict:
		A				=set()
		A				=word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)		)
				A				=char
		return pairs






class        UpperCamelCase__(    lowerCAmelCase__	):

	"""simple docstring"""

	_A  =		VOCAB_FILES_NAMES
	_A  =		PRETRAINED_VOCAB_FILES_MAP
	_A  =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A  =		["input_ids", "attention_mask", "token_type_ids"]







	def __init__(      self				:			Union[str, Any]							,			snake_case__				:			Tuple							,			snake_case__				:			Union[str, Any]							,			snake_case__				:			Union[str, Any]="replace"							,			snake_case__				:			Any="[CLS]"							,			snake_case__				:			List[Any]="[SEP]"							,			snake_case__				:			str="[SEP]"							,			snake_case__				:			Tuple="[CLS]"							,			snake_case__				:			Optional[Any]="[UNK]"							,			snake_case__				:			Union[str, Any]="[PAD]"							,			snake_case__				:			List[Any]="[MASK]"							,			snake_case__				:			Any=False							,			snake_case__				:			List[str]=False							,			**snake_case__				:			str							,			):
			"""simple docstring"""





			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else bos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else eos_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else sep_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else cls_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else unk_token
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else pad_token

			# Mask token behave like a normal word, i.e. include the space before it
			A				=AddedToken(snake_case__							,			lstrip=snake_case__							,			rstrip=snake_case__ ) if isinstance(snake_case__							,			snake_case__ ) else mask_token

			super().__init__(
			    errors=snake_case__							,			bos_token=snake_case__							,			eos_token=snake_case__							,			unk_token=snake_case__							,			sep_token=snake_case__							,			cls_token=snake_case__							,			pad_token=snake_case__							,			mask_token=snake_case__							,			add_prefix_space=snake_case__							,			add_bos_token=snake_case__							,			**snake_case__							,			)
			A				=add_bos_token

			with open(snake_case__							,			encoding="utf-8" ) as vocab_handle:
					A				=json.load(snake_case__ )
			A				={v: k for k, v in self.encoder.items()}
			A				=errors  # how to handle errors in decoding
			A				=bytes_to_unicode()
			A				={v: k for k, v in self.byte_encoder.items()}
			with open(snake_case__							,			encoding="utf-8" ) as merges_handle:
					A				=merges_handle.read().split("\n" )[1:-1]
			A				=[tuple(merge.split() ) for merge in bpe_merges]
			A				=dict(zip(snake_case__							,			range(len(snake_case__ ) ) ) )
			A				={}
			A				=add_prefix_space

			# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
			A				=re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )







	@property
	# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
	def        _a					(      self				:			Optional[int] ):
			"""simple docstring"""





			return len(self.encoder )







	def        _a					(      self				:			int ):
			"""simple docstring"""





			return dict(self.encoder							,			**self.added_tokens_encoder )







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			if token in self.cache:
					return self.cache[token]
			A				=tuple(snake_case__ )
			A				=get_pairs(snake_case__ )

			if not pairs:
					return token

			while True:
					A				=min(snake_case__							,			key=lambda snake_case__ : self.bpe_ranks.get(snake_case__							,			float("inf" ) ) )
					if bigram not in self.bpe_ranks:
							break
					A			,    A				=bigram
					A				=[]
					A				=0
					while i < len(snake_case__ ):
							try:
									A				=word.index(snake_case__							,			snake_case__ )
							except ValueError:
									new_word.extend(word[i:] )
									break
							else:
									new_word.extend(word[i:j] )
									A				=j

							if word[i] == first and i < len(snake_case__ ) - 1 and word[i + 1] == second:
									new_word.append(first + second )
									i += 2
							else:
									new_word.append(word[i] )
									i += 1
					A				=tuple(snake_case__ )
					A				=new_word
					if len(snake_case__ ) == 1:
							break
					else:
							A				=get_pairs(snake_case__ )
			A				=" ".join(snake_case__ )
			A				=word
			return word







	def        _a					(      self				:			Any							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None ):
			"""simple docstring"""





			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			A				=[self.cls_token_id]
			A				=[self.sep_token_id]
			return cls + token_ids_a + sep + token_ids_a + sep







	def        _a					(      self				:			List[Any]							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None							,			snake_case__				:			bool = False ):
			"""simple docstring"""





			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=snake_case__							,			token_ids_a=snake_case__							,			already_has_special_tokens=snake_case__ )

			if token_ids_a is None:
					return [1] + ([0] * len(snake_case__ )) + [1]
			return [1] + ([0] * len(snake_case__ )) + [1] + ([0] * len(snake_case__ )) + [1]







	def        _a					(      self				:			str							,			snake_case__				:			List[int]							,			snake_case__				:			Optional[List[int]] = None ):
			"""simple docstring"""





			A				=[self.sep_token_id]
			A				=[self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep ) * [0]
			return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]







	def        _a					(      self				:			List[str]							,			snake_case__				:			List[Any] ):
			"""simple docstring"""





			A				=[]
			for token in re.findall(self.pat							,			snake_case__ ):
					A				="".join(
					    self.byte_encoder[b] for b in token.encode("utf-8" ) )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
					bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case__ ).split(" " ) )
			return bpe_tokens







	def        _a					(      self				:			List[Any]							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			return self.encoder.get(snake_case__							,			self.encoder.get(self.unk_token ) )







	def        _a					(      self				:			int							,			snake_case__				:			List[str] ):
			"""simple docstring"""





			return self.decoder.get(snake_case__ )







	def        _a					(      self				:			Union[str, Any]							,			snake_case__				:			Optional[int] ):
			"""simple docstring"""





			A				="".join(snake_case__ )
			A				=bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8"							,			errors=self.errors )
			return text







	def        _a					(      self				:			int							,			snake_case__				:			str							,			snake_case__				:			Optional[str] = None ):
			"""simple docstring"""





			if not os.path.isdir(snake_case__ ):
					logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
					return
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
			A				=os.path.join(
			    snake_case__							,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )

			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as f:
					f.write(json.dumps(self.encoder							,			indent=2							,			sort_keys=snake_case__							,			ensure_ascii=snake_case__ ) + "\n" )

			A				=0
			with open(snake_case__							,			"w"							,			encoding="utf-8" ) as writer:
					writer.write("#version: 0.2\n" )
					for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,			key=lambda snake_case__ : kv[1] ):
							if index != token_index:
									logger.warning(
									    f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
									    " Please check that the tokenizer is not corrupted!" )
									A				=token_index
							writer.write(" ".join(snake_case__ ) + "\n" )
							index += 1

			return vocab_file, merge_file







	def        _a					(      self				:			List[Any]							,			snake_case__				:			Optional[Any]							,			snake_case__				:			Any=False							,			**snake_case__				:			Optional[int] ):
			"""simple docstring"""





			A				=kwargs.pop("add_prefix_space"							,			self.add_prefix_space )
			if (is_split_into_words or add_prefix_space) and (len(snake_case__ ) > 0 and not text[0].isspace()):
					A				=" " + text
			return (text, kwargs)







	def        _a					(      self				:			List[str]							,			snake_case__				:			"Conversation" ):
			"""simple docstring"""





			A				=[]
			for is_user, text in conversation.iter_texts():
					input_ids.extend(self.encode(snake_case__							,			add_special_tokens=snake_case__ ) + [self.eos_token_id] )
			if len(snake_case__ ) > self.model_max_length:
					A				=input_ids[-self.model_max_length :]
			return input_ids




