



import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  List[str]			,		A       :  List[Any]			,		A       :  Any			,		A       :  List[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       process
						_UpperCAmelCase			=       params



			def __len__(						self       :  Tuple)       ->   List[str]:



						"""simple docstring"""






						return len(self.dataset)



			def __getitem__(						self       :  Tuple			,		A       :  Optional[int])       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       self.dataset[i]
						_UpperCAmelCase			=       self.process(A			,		**self.params)
						return processed







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  Optional[Any]			,		A       :  Union[str, Any]			,		A       :  Optional[int]			,		A       :  List[Any]			,		A       :  Union[str, Any]=None)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       loader
						_UpperCAmelCase			=       infer
						_UpperCAmelCase			=       params
						if loader_batch_size == 1:
									# Let's spare some time by deactivating altogether
									_UpperCAmelCase			=       None
						_UpperCAmelCase			=       loader_batch_size

						# Internal bookkeeping
						_UpperCAmelCase			=       None
						_UpperCAmelCase			=       None



			def __len__(						self       :  List[str])       ->   Tuple:



						"""simple docstring"""






						return len(self.loader)



			def __iter__(						self       :  str)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       iter(self.loader)
						return self



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   Tuple:



						"""simple docstring"""






						if isinstance(self._loader_batch_data			,		torch.Tensor):
									# Batch data is simple tensor, just fetch the slice
									_UpperCAmelCase			=       self._loader_batch_data[self._loader_batch_index]
						else:
									# Batch data is assumed to be BaseModelOutput (or dict)
									_UpperCAmelCase			=       {}
									for k, element in self._loader_batch_data.items():
												if isinstance(A			,		A):
															# Convert ModelOutput to tuple first
															_UpperCAmelCase			=       element.to_tuple()
															if isinstance(element[0]			,		torch.Tensor):
																		_UpperCAmelCase			=       tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
															elif isinstance(element[0]			,		np.ndarray):
																		_UpperCAmelCase			=       tuple(np.expand_dims(el[self._loader_batch_index]			,		0) for el in element)
															continue
												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(A			,		A):
															# Those are stored as lists of tensors so need specific unbatching.
															if isinstance(element[0]			,		torch.Tensor):
																		_UpperCAmelCase			=       tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
															elif isinstance(element[0]			,		np.ndarray):
																		_UpperCAmelCase			=       tuple(np.expand_dims(el[self._loader_batch_index]			,		0) for el in element)
															continue
												if element is None:
															# This can happen for optional data that get passed around
															_UpperCAmelCase			=       None
												elif isinstance(element[self._loader_batch_index]			,		torch.Tensor):
															# Take correct batch data, but make it looked like batch_size=1
															# For compatibility with other methods within transformers

															_UpperCAmelCase			=       element[self._loader_batch_index].unsqueeze(0)
												elif isinstance(element[self._loader_batch_index]			,		np.ndarray):
															# Take correct batch data, but make it looked like batch_size=1
															# For compatibility with other methods within transformers
															_UpperCAmelCase			=       np.expand_dims(element[self._loader_batch_index]			,		0)
												else:
															# This is typically a list, so no need to `unsqueeze`.
															_UpperCAmelCase			=       element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
									_UpperCAmelCase			=       self._loader_batch_data.__class__(A)
						self._loader_batch_index += 1
						return result



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   int:



						"""simple docstring"""






						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
									# We are currently unrolling a batch so we just need to return
									# the current item within a batch
									return self.loader_batch_item()

						# We're out of items within a batch
						_UpperCAmelCase			=       next(self.iterator)
						_UpperCAmelCase			=       self.infer(A			,		**self.params)
						# We now have a batch of "inferred things".
						if self.loader_batch_size is not None:
									# Try to infer the size of the batch
									if isinstance(A			,		torch.Tensor):
												_UpperCAmelCase			=       processed
									else:
												_UpperCAmelCase			=       list(processed.keys())[0]
												_UpperCAmelCase			=       processed[key]
									if isinstance(A			,		A):
												_UpperCAmelCase			=       len(A)
									else:
												_UpperCAmelCase			=       first_tensor.shape[0]
									if 0 < observed_batch_size < self.loader_batch_size:
												# could be last batch so we can't unroll as many
												# elements.
												_UpperCAmelCase			=       observed_batch_size
									# Setting internal index to unwrap the batch
									_UpperCAmelCase			=       processed
									_UpperCAmelCase			=       0
									return self.loader_batch_item()
						else:
									# We're not unrolling batches
									return processed







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  Tuple			,		A       :  List[Any]			,		A       :  Any			,		A       :  int			,		A       :  Dict=None)       ->   Any:



						"""simple docstring"""






						super().__init__(A			,		A			,		A)



			def __iter__(						self       :  Dict)       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       iter(self.loader)
						_UpperCAmelCase			=       None
						return self



			def    _lowerCamelCase	(						self       :  int)       ->   Any:



						"""simple docstring"""






						if self.subiterator is None:
									_UpperCAmelCase			=       self.infer(next(self.iterator)			,		**self.params)
						try:
									# Try to return next item
									_UpperCAmelCase			=       next(self.subiterator)
						except StopIteration:
									# When a preprocess iterator ends, we can start lookig at the next item
									# ChunkIterator will keep feeding until ALL elements of iterator
									# all have created their subiterator and have been iterating against.
									#
									# Another way to look at it, is we're basically flattening lists of lists
									# into a single list, but with generators
									_UpperCAmelCase			=       self.infer(next(self.iterator)			,		**self.params)
									_UpperCAmelCase			=       next(self.subiterator)
						return processed







class       __lowerCAmelCase   (      A						):



			def __iter__(						self       :  List[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       iter(self.loader)
						return self



			def    _lowerCamelCase	(						self       :  str)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       False
						_UpperCAmelCase			=       []
						if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
									while self._loader_batch_index < self.loader_batch_size:
												_UpperCAmelCase			=       self.loader_batch_item()
												_UpperCAmelCase			=       item.pop('is_last')
												accumulator.append(A)
												if is_last:
															return accumulator

						while not is_last:
									_UpperCAmelCase			=       self.infer(next(self.iterator)			,		**self.params)
									if self.loader_batch_size is not None:
												if isinstance(A			,		torch.Tensor):
															_UpperCAmelCase			=       processed
												else:
															_UpperCAmelCase			=       list(processed.keys())[0]
															_UpperCAmelCase			=       processed[key]
												if isinstance(A			,		A):
															_UpperCAmelCase			=       len(A)
												else:
															_UpperCAmelCase			=       first_tensor.shape[0]
												if 0 < observed_batch_size < self.loader_batch_size:
															# could be last batch so we can't unroll as many
															# elements.
															_UpperCAmelCase			=       observed_batch_size
												_UpperCAmelCase			=       processed
												_UpperCAmelCase			=       0
												while self._loader_batch_index < self.loader_batch_size:
															_UpperCAmelCase			=       self.loader_batch_item()
															_UpperCAmelCase			=       item.pop('is_last')
															accumulator.append(A)
															if is_last:
																		return accumulator
									else:
												_UpperCAmelCase			=       processed
												_UpperCAmelCase			=       item.pop('is_last')
												accumulator.append(A)
						return accumulator







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  List[str]			,		A       :  Dataset			,		A       :  str)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       key



			def __len__(						self       :  List[str])       ->   Any:



						"""simple docstring"""






						return len(self.dataset)



			def __getitem__(						self       :  Tuple			,		A       :  Tuple)       ->   int:



						"""simple docstring"""






						return self.dataset[i][self.key]







class       __lowerCAmelCase   (      A						):



			def __init__(						self       :  int			,		A       :  Dataset			,		A       :  str			,		A       :  str)       ->   Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       dataset
						_UpperCAmelCase			=       keya
						_UpperCAmelCase			=       keya



			def __len__(						self       :  str)       ->   Dict:



						"""simple docstring"""






						return len(self.dataset)



			def __getitem__(						self       :  List[Any]			,		A       :  int)       ->   Dict:



						"""simple docstring"""






						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__       =	{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

UpperCAmelCase__       =	{
    "junnyu/roformer_chinese_small": 1536,
    "junnyu/roformer_chinese_base": 1536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


UpperCAmelCase__       =	{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					VOCAB_FILES_NAMES
			UpperCamelCase        =					PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase        =					PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase        =					RoFormerTokenizer



			def __init__(						self       :  Dict			,		A       :  Any=None			,		A       :  Optional[Any]=None			,		A       :  Union[str, Any]=True			,		A       :  List[str]="[UNK]"			,		A       :  List[str]="[SEP]"			,		A       :  Union[str, Any]="[PAD]"			,		A       :  Any="[CLS]"			,		A       :  str="[MASK]"			,		A       :  Optional[int]=True			,		A       :  str=None			,		**A       :  Dict			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						super().__init__(
						    A			,		tokenizer_file=A			,		do_lower_case=A			,		unk_token=A			,		sep_token=A			,		pad_token=A			,		cls_token=A			,		mask_token=A			,		tokenize_chinese_chars=A			,		strip_accents=A			,		**A			,		)

						_UpperCAmelCase			=       json.loads(self.backend_tokenizer.normalizer.__getstate__())
						if (
						    pre_tok_state.get('lowercase'			,		A) != do_lower_case
						    or pre_tok_state.get('strip_accents'			,		A) != strip_accents
						):
									_UpperCAmelCase			=       getattr(A			,		pre_tok_state.pop('type'))
									_UpperCAmelCase			=       do_lower_case
									_UpperCAmelCase			=       strip_accents
									_UpperCAmelCase			=       pre_tok_class(**A)

						_UpperCAmelCase			=       do_lower_case



			def __getstate__(						self       :  Optional[Any])       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.__dict__.copy()
						_UpperCAmelCase			=       BertPreTokenizer()
						return state



			def __setstate__(						self       :  List[Any]			,		A       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       d
						_UpperCAmelCase			=       self.__dict__['_tokenizer'].get_vocab()
						_UpperCAmelCase			=       PreTokenizer.custom(JiebaPreTokenizer(A))



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



						"""simple docstring"""






						_UpperCAmelCase			=       [self.cls_token_id] + token_ids_a + [self.sep_token_id]

						if token_ids_a:
									output += token_ids_a + [self.sep_token_id]

						return output



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  List[int]			,		A       :  Optional[List[int]] = None)       ->   List[int]:



						"""simple docstring"""






						_UpperCAmelCase			=       [self.sep_token_id]
						_UpperCAmelCase			=       [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    _lowerCamelCase	(						self       :  Union[str, Any]			,		A       :  str			,		A       :  Optional[str] = None)       ->   Tuple[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self._tokenizer.model.save(A			,		name=A)
						return tuple(A)



			def    _lowerCamelCase	(						self       :  List[Any]			,		A       :  Tuple			,		A       :  str=None			,		A       :  Union[str, Any]=None			,		A       :  Union[str, Any]=False			,		**A       :  Tuple			,		)       ->   str:



						"""simple docstring"""






						_UpperCAmelCase			=       BertPreTokenizer()
						return super().save_pretrained(A			,		A			,		A			,		A			,		**A)
