



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

from ..utils.generic import ModelOutput



class  __lowerCAmelCase     (	A_   ):




	def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



				'''simple docstring'''
				__lowerCamelCase													=    dataset
				__lowerCamelCase													=    process
				__lowerCamelCase													=    params




	def __len__(						self				):



				'''simple docstring'''
				return len(self.dataset				)




	def __getitem__(						self	,				__UpperCAmelCase				):



				'''simple docstring'''
				__lowerCamelCase													=    self.dataset[i]
				__lowerCamelCase													=    self.process(__UpperCAmelCase	,				**self.params				)
				return processed



class  __lowerCAmelCase     (	A_   ):




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



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

				# Internal bookkeeping
				__lowerCamelCase													=    None
				__lowerCamelCase													=    None




	def __len__(						self				):



				'''simple docstring'''
				return len(self.loader				)




	def __iter__(						self				):



				'''simple docstring'''
				__lowerCamelCase													=    iter(self.loader				)
				return self




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				if isinstance(self._loader_batch_data	,				torch.Tensor				):
							# Batch data is simple tensor, just fetch the slice
							__lowerCamelCase													=    self._loader_batch_data[self._loader_batch_index]
				else:
							# Batch data is assumed to be BaseModelOutput (or dict)
							__lowerCamelCase													=    {}
							for k, element in self._loader_batch_data.items():
										if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
													# Convert ModelOutput to tuple first
													__lowerCamelCase													=    element.to_tuple()
													if isinstance(element[0]	,				torch.Tensor				):
																__lowerCamelCase													=    tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
													elif isinstance(element[0]	,				np.ndarray				):
																__lowerCamelCase													=    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(__UpperCAmelCase	,				__UpperCAmelCase				):
													# Those are stored as lists of tensors so need specific unbatching.
													if isinstance(element[0]	,				torch.Tensor				):
																__lowerCamelCase													=    tuple(el[self._loader_batch_index].unsqueeze(0				) for el in element				)
													elif isinstance(element[0]	,				np.ndarray				):
																__lowerCamelCase													=    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
													__lowerCamelCase													=    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

													__lowerCamelCase													=    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
													__lowerCamelCase													=    np.expand_dims(element[self._loader_batch_index]	,				0				)
										else:
													# This is typically a list, so no need to `unsqueeze`.
													__lowerCamelCase													=    element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
							__lowerCamelCase													=    self._loader_batch_data.__class__(__UpperCAmelCase				)
				self._loader_batch_index += 1
				return result




	def        lowerCamelCase      (						self				):



				'''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
				__lowerCamelCase													=    next(self.iterator				)
				__lowerCamelCase													=    self.infer(__UpperCAmelCase	,				**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(__UpperCAmelCase	,				torch.Tensor				):
										__lowerCamelCase													=    processed
							else:
										__lowerCamelCase													=    list(processed.keys()				)[0]
										__lowerCamelCase													=    processed[key]
							if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
										__lowerCamelCase													=    len(__UpperCAmelCase				)
							else:
										__lowerCamelCase													=    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.
										__lowerCamelCase													=    observed_batch_size
							# Setting internal index to unwrap the batch
							__lowerCamelCase													=    processed
							__lowerCamelCase													=    0
							return self.loader_batch_item()
				else:
							# We're not unrolling batches
							return processed



class  __lowerCAmelCase     (	A_   ):




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



				'''simple docstring'''
				super().__init__(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




	def __iter__(						self				):



				'''simple docstring'''
				__lowerCamelCase													=    iter(self.loader				)
				__lowerCamelCase													=    None
				return self




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				if self.subiterator is None:
							__lowerCamelCase													=    self.infer(next(self.iterator				)	,				**self.params				)
				try:
							# Try to return next item
							__lowerCamelCase													=    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
							__lowerCamelCase													=    self.infer(next(self.iterator				)	,				**self.params				)
							__lowerCamelCase													=    next(self.subiterator				)
				return processed



class  __lowerCAmelCase     (	A_   ):




	def __iter__(						self				):



				'''simple docstring'''
				__lowerCamelCase													=    iter(self.loader				)
				return self




	def        lowerCamelCase      (						self				):



				'''simple docstring'''
				# Extremely similar to PipelineIterator in its unpacking mechanism
				# BUT, we have an extra required item which is the presence of `is_last`
				# That is because everything is flattened by `PipelineChunkIterator` we
				# need to keep track of how to regroup here in the original `process`
				# boundaries so that `process` and `postprocess` see the same data.

				# This iterator accumulates items (possibly while unbatching) until it
				# its a `is_last` and then just passes it on to the caller.
				__lowerCamelCase													=    False
				__lowerCamelCase													=    []
				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:
										__lowerCamelCase													=    self.loader_batch_item()
										__lowerCamelCase													=    item.pop('''is_last'''				)
										accumulator.append(__UpperCAmelCase				)
										if is_last:
													return accumulator

				while not is_last:
							__lowerCamelCase													=    self.infer(next(self.iterator				)	,				**self.params				)
							if self.loader_batch_size is not None:
										if isinstance(__UpperCAmelCase	,				torch.Tensor				):
													__lowerCamelCase													=    processed
										else:
													__lowerCamelCase													=    list(processed.keys()				)[0]
													__lowerCamelCase													=    processed[key]
										if isinstance(__UpperCAmelCase	,				__UpperCAmelCase				):
													__lowerCamelCase													=    len(__UpperCAmelCase				)
										else:
													__lowerCamelCase													=    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.
													__lowerCamelCase													=    observed_batch_size
										__lowerCamelCase													=    processed
										__lowerCamelCase													=    0
										while self._loader_batch_index < self.loader_batch_size:
													__lowerCamelCase													=    self.loader_batch_item()
													__lowerCamelCase													=    item.pop('''is_last'''				)
													accumulator.append(__UpperCAmelCase				)
													if is_last:
																return accumulator
							else:
										__lowerCamelCase													=    processed
										__lowerCamelCase													=    item.pop('''is_last'''				)
										accumulator.append(__UpperCAmelCase				)
				return accumulator



class  __lowerCAmelCase     (	A_   ):




	def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



				'''simple docstring'''
				__lowerCamelCase													=    dataset
				__lowerCamelCase													=    key




	def __len__(						self				):



				'''simple docstring'''
				return len(self.dataset				)




	def __getitem__(						self	,				__UpperCAmelCase				):



				'''simple docstring'''
				return self.dataset[i][self.key]



class  __lowerCAmelCase     (	A_   ):




	def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



				'''simple docstring'''
				__lowerCamelCase													=    dataset
				__lowerCamelCase													=    keya
				__lowerCamelCase													=    keya




	def __len__(						self				):



				'''simple docstring'''
				return len(self.dataset				)




	def __getitem__(						self	,				__UpperCAmelCase				):



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











import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_rjieba
@require_tokenizers
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  RoFormerTokenizer
 lowerCAmelCase__       					=  RoFormerTokenizerFast
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  True




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''永和服装饰品有限公司,今天天气非常好'''
    __lowerCamelCase													=    '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
    return input_text, output_text




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)

    self.assertListEqual(__UpperCAmelCase	,				output_text.split()				)

    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_rust_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)
    self.assertListEqual(__UpperCAmelCase	,				output_text.split()				)
    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass






