'''simple docstring'''





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_big_bird import BigBirdTokenizer
else:
			lowerCAmelCase_	:		int			=     None

lowerCAmelCase_	:		List[Any]			=     logging.get_logger(__name__)
lowerCAmelCase_	:		Optional[int]			=     {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase_	:		Tuple			=     {
    """vocab_file""": {
        """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model""",
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"""
        ),
    },
    """tokenizer_file""": {
        """google/bigbird-roberta-base""": (
            """https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-roberta-large""": (
            """https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"""
        ),
        """google/bigbird-base-trivia-itc""": (
            """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase_	:		List[Any]			=     {
    """google/bigbird-roberta-base""": 4096,
    """google/bigbird-roberta-large""": 4096,
    """google/bigbird-base-trivia-itc""": 4096,
}


lowerCAmelCase_	:		Tuple			=     """▁"""




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							VOCAB_FILES_NAMES
				UpperCAmelCase__							=							PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase__							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase__							=							BigBirdTokenizer
				UpperCAmelCase__							=							['''input_ids''', '''attention_mask''']
				UpperCAmelCase__							=							[]





				def __init__(						self					:						List[Any]       ,		lowercase__					:						Union[str, Any]=None       ,		lowercase__					:						Dict=None       ,		lowercase__					:						Any="<unk>"       ,		lowercase__					:						Tuple="<s>"       ,		lowercase__					:						Union[str, Any]="</s>"       ,		lowercase__					:						Tuple="<pad>"       ,		lowercase__					:						int="[SEP]"       ,		lowercase__					:						Union[str, Any]="[MASK]"       ,		lowercase__					:						Optional[Any]="[CLS]"       ,		**lowercase__					:						Any       ,		)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else bos_token
										_UpperCamelCase			:			int   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else eos_token
										_UpperCamelCase			:			Dict   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else unk_token
										_UpperCamelCase			:			Dict   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else pad_token
										_UpperCamelCase			:			List[str]   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else cls_token
										_UpperCamelCase			:			Optional[int]   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else sep_token

										# Mask token behave like a normal word, i.e. include the space before it
										_UpperCamelCase			:			Union[str, Any]   			=						AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__					) if isinstance(lowercase__       ,		lowercase__					) else mask_token

										super().__init__(
										    lowercase__       ,		tokenizer_file=lowercase__       ,		bos_token=lowercase__       ,		eos_token=lowercase__       ,		unk_token=lowercase__       ,		sep_token=lowercase__       ,		pad_token=lowercase__       ,		cls_token=lowercase__       ,		mask_token=lowercase__       ,		**lowercase__       ,		)

										_UpperCamelCase			:			Optional[Any]   			=						vocab_file
										_UpperCamelCase			:			Optional[int]   			=						False if not self.vocab_file else True





				def  snake_case__				(						self					:						Optional[int]       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None					)							->List[int]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						[self.sep_token_id]
										_UpperCamelCase			:			int   			=						[self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep





				def  snake_case__				(						self					:						List[Any]       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None       ,		lowercase__					:						bool = False					)							->List[int]:


										'''simple docstring'''




										if already_has_special_tokens:
																if token_ids_a is not None:
																						raise ValueError(
																						    "You should not supply a second sequence if the provided sequence of "
																						    "ids is already formatted with special tokens for the model."					)
																return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

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





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						List[int]       ,		lowercase__					:						Optional[List[int]] = None					)							->List[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						[self.sep_token_id]
										_UpperCamelCase			:			Tuple   			=						[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  snake_case__				(						self					:						List[str]       ,		lowercase__					:						str       ,		lowercase__					:						Optional[str] = None					)							->Tuple[str]:


										'''simple docstring'''




										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																    "tokenizer."					)

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

										if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase__					):
																copyfile(self.vocab_file       ,		lowercase__					)

										return (out_vocab_file,)



'''simple docstring'''





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase_	:		int			=     logging.get_logger(__name__)

lowerCAmelCase_	:		Any			=     {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}




class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''
				UpperCAmelCase__							=							'''gptj'''
				UpperCAmelCase__							=							{
				    '''max_position_embeddings''': '''n_positions''',
				    '''hidden_size''': '''n_embd''',
				    '''num_attention_heads''': '''n_head''',
				    '''num_hidden_layers''': '''n_layer''',
				}





				def __init__(						self					:						Optional[Any]       ,		lowercase__					:						Union[str, Any]=50_400       ,		lowercase__					:						Union[str, Any]=2_048       ,		lowercase__					:						Tuple=4_096       ,		lowercase__					:						List[str]=28       ,		lowercase__					:						Optional[int]=16       ,		lowercase__					:						str=64       ,		lowercase__					:						Any=None       ,		lowercase__					:						Any="gelu_new"       ,		lowercase__					:						Union[str, Any]=0.0       ,		lowercase__					:						Optional[Any]=0.0       ,		lowercase__					:						Any=0.0       ,		lowercase__					:						Tuple=1e-5       ,		lowercase__					:						Any=0.0_2       ,		lowercase__					:						int=True       ,		lowercase__					:						int=50_256       ,		lowercase__					:						Any=50_256       ,		lowercase__					:						Tuple=False       ,		**lowercase__					:						str       ,		)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						vocab_size
										_UpperCamelCase			:			List[str]   			=						n_positions
										_UpperCamelCase			:			Union[str, Any]   			=						n_embd
										_UpperCamelCase			:			Union[str, Any]   			=						n_layer
										_UpperCamelCase			:			Optional[Any]   			=						n_head
										_UpperCamelCase			:			Dict   			=						n_inner
										_UpperCamelCase			:			Optional[Any]   			=						rotary_dim
										_UpperCamelCase			:			Tuple   			=						activation_function
										_UpperCamelCase			:			List[Any]   			=						resid_pdrop
										_UpperCamelCase			:			Any   			=						embd_pdrop
										_UpperCamelCase			:			Optional[Any]   			=						attn_pdrop
										_UpperCamelCase			:			Optional[Any]   			=						layer_norm_epsilon
										_UpperCamelCase			:			Union[str, Any]   			=						initializer_range
										_UpperCamelCase			:			Optional[int]   			=						use_cache

										_UpperCamelCase			:			str   			=						bos_token_id
										_UpperCamelCase			:			Any   			=						eos_token_id

										super().__init__(
										    bos_token_id=lowercase__       ,		eos_token_id=lowercase__       ,		tie_word_embeddings=lowercase__       ,		**lowercase__					)





class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE	):







				'''simple docstring'''





				def __init__(						self					:						Optional[int]       ,		lowercase__					:						PretrainedConfig       ,		lowercase__					:						str = "default"       ,		lowercase__					:						List[PatchingSpec] = None       ,		lowercase__					:						bool = False       ,		)							->Union[str, Any]:


										'''simple docstring'''




										super().__init__(lowercase__       ,		task=lowercase__       ,		patching_specs=lowercase__       ,		use_past=lowercase__					)
										if not getattr(self._config       ,		"pad_token_id"       ,		lowercase__					):
																# TODO: how to do that better?
																_UpperCamelCase			:			Optional[int]   			=						0





				@property
				def  snake_case__				(						self					:						List[str]					)							->Mapping[str, Mapping[int, str]]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
										if self.use_past:
																self.fill_with_past_key_values_(lowercase__       ,		direction="inputs"					)
																_UpperCamelCase			:			str   			=						{0: "batch", 1: "past_sequence + sequence"}
										else:
																_UpperCamelCase			:			Optional[Any]   			=						{0: "batch", 1: "sequence"}

										return common_inputs





				@property
				def  snake_case__				(						self					:						int					)							->int:


										'''simple docstring'''




										return self._config.n_layer





				@property
				def  snake_case__				(						self					:						Dict					)							->int:


										'''simple docstring'''




										return self._config.n_head





				def  snake_case__				(						self					:						int       ,		lowercase__					:						PreTrainedTokenizer       ,		lowercase__					:						int = -1       ,		lowercase__					:						int = -1       ,		lowercase__					:						bool = False       ,		lowercase__					:						Optional[TensorType] = None       ,		)							->Mapping[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						super(lowercase__       ,		self					).generate_dummy_inputs(
										    lowercase__       ,		batch_size=lowercase__       ,		seq_length=lowercase__       ,		is_pair=lowercase__       ,		framework=lowercase__					)

										# We need to order the input in the way they appears in the forward()
										_UpperCamelCase			:			Tuple   			=						OrderedDict({"input_ids": common_inputs["input_ids"]}					)

										# Need to add the past_keys
										if self.use_past:
																if not is_torch_available():
																						raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
																else:
																						import torch

																						_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						common_inputs["input_ids"].shape
																						# Not using the same length for past_key_values
																						_UpperCamelCase			:			Optional[int]   			=						seqlen + 2
																						_UpperCamelCase			:			str   			=						(
																						    batch,
																						    self.num_attention_heads,
																						    past_key_values_length,
																						    self._config.hidden_size // self.num_attention_heads,
																						)
																						_UpperCamelCase			:			Dict   			=						[
																						    (torch.zeros(lowercase__					), torch.zeros(lowercase__					)) for _ in range(self.num_layers					)
																						]

										_UpperCamelCase			:			str   			=						common_inputs["attention_mask"]
										if self.use_past:
																_UpperCamelCase			:			int   			=						ordered_inputs["attention_mask"].dtype
																_UpperCamelCase			:			Optional[int]   			=						torch.cat(
																    [ordered_inputs["attention_mask"], torch.ones(lowercase__       ,		lowercase__       ,		dtype=lowercase__					)]       ,		dim=1					)

										return ordered_inputs







				@property
				def  snake_case__				(						self					:						Tuple					)							->int:


										'''simple docstring'''




										return 13


