


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

from ..utils.generic import ModelOutput

class 						A__							(		lowerCAmelCase__  ):




						def __init__(       self					:      Dict				,    _a					:      Optional[int]				,    _a					:      Dict				,    _a					:      Optional[Any]  )							->     Union[str, Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =dataset
										_SCREAMING_SNAKE_CASE			  =process
										_SCREAMING_SNAKE_CASE			  =params




						def __len__(       self					:      int  )							->     str:


										"""simple docstring"""





										return len(self.dataset  )




						def __getitem__(       self					:      Union[str, Any]				,    _a					:      Dict  )							->     Dict:


										"""simple docstring"""





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

class 						A__							(		lowerCAmelCase__  ):




						def __init__(       self					:      Any				,    _a					:      Dict				,    _a					:      Optional[Any]				,    _a					:      List[Any]				,    _a					:      Union[str, Any]=None  )							->     List[str]:


										"""simple docstring"""





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

										# Internal bookkeeping
										_SCREAMING_SNAKE_CASE			  =None
										_SCREAMING_SNAKE_CASE			  =None




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


										"""simple docstring"""





										return len(self.loader  )




						def __iter__(       self					:      str  )							->     Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =iter(self.loader  )
										return self




						def 							__UpperCamelCase				(       self					:      List[str]  )							->     int:


										"""simple docstring"""





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

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




						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Union[str, Any]:


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

class 						A__							(		lowerCAmelCase__  ):




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


										"""simple docstring"""





										super().__init__(_SCREAMING_SNAKE_CASE				,    _SCREAMING_SNAKE_CASE				,    _SCREAMING_SNAKE_CASE  )




						def __iter__(       self					:      str  )							->     int:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Optional[Any]:


										"""simple docstring"""





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

class 						A__							(		lowerCAmelCase__  ):




						def __iter__(       self					:      Optional[int]  )							->     Any:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =iter(self.loader  )
										return self




						def 							__UpperCamelCase				(       self					:      Union[str, Any]  )							->     List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =False
										_SCREAMING_SNAKE_CASE			  =[]
										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:
																		_SCREAMING_SNAKE_CASE			  =self.loader_batch_item()
																		_SCREAMING_SNAKE_CASE			  =item.pop('''is_last'''  )
																		accumulator.append(_SCREAMING_SNAKE_CASE  )
																		if is_last:
																						return accumulator

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

class 						A__							(		lowerCAmelCase__  ):




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =dataset
										_SCREAMING_SNAKE_CASE			  =key




						def __len__(       self					:      Any  )							->     Optional[int]:


										"""simple docstring"""





										return len(self.dataset  )




						def __getitem__(       self					:      Tuple				,    _a					:      Dict  )							->     Any:


										"""simple docstring"""





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

class 						A__							(		lowerCAmelCase__  ):




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =dataset
										_SCREAMING_SNAKE_CASE			  =keya
										_SCREAMING_SNAKE_CASE			  =keya




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


										"""simple docstring"""





										return len(self.dataset  )




						def __getitem__(       self					:      Dict				,    _a					:      str  )							->     Union[str, Any]:


										"""simple docstring"""





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



import os
import unittest

from transformers import LxmertTokenizer, LxmertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						A__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LxmertTokenizer
						UpperCAmelCase             =						LxmertTokenizerFast
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     Tuple:


										"""simple docstring"""





										super().setUp()

										_SCREAMING_SNAKE_CASE			  =[
										    '''[UNK]''',
										    '''[CLS]''',
										    '''[SEP]''',
										    '''want''',
										    '''##want''',
										    '''##ed''',
										    '''wa''',
										    '''un''',
										    '''runn''',
										    '''##ing''',
										    ''',''',
										    '''low''',
										    '''lowest''',
										]
										_SCREAMING_SNAKE_CASE			  =os.path.join(self.tmpdirname				,    VOCAB_FILES_NAMES['''vocab_file''']  )
										with open(self.vocab_file				,    '''w'''				,    encoding='''utf-8'''  ) as vocab_writer:
														vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )




						def 							__UpperCamelCase				(       self					:      List[str]				,    _a					:      Tuple  )							->     Optional[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ='''UNwant\u00E9d,running'''
										_SCREAMING_SNAKE_CASE			  ='''unwanted, running'''
										return input_text, output_text




						def 							__UpperCamelCase				(       self					:      Tuple  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.tokenizer_class(self.vocab_file  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize('''UNwant\u00E9d,running'''  )
										self.assertListEqual(_a				,    ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']  )
										self.assertListEqual(tokenizer.convert_tokens_to_ids(_a  )				,    [7, 4, 5, 10, 8, 9]  )




						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										if not self.test_rust_tokenizer:
														return

										_SCREAMING_SNAKE_CASE			  =self.get_tokenizer()
										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()

										_SCREAMING_SNAKE_CASE			  ='''I was born in 92000, and this is falsé.'''

										_SCREAMING_SNAKE_CASE			  =tokenizer.tokenize(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.tokenize(_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a				,    add_special_tokens=_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a				,    add_special_tokens=_a  )
										self.assertListEqual(_a				,    _a  )

										_SCREAMING_SNAKE_CASE			  =self.get_rust_tokenizer()
										_SCREAMING_SNAKE_CASE			  =tokenizer.encode(_a  )
										_SCREAMING_SNAKE_CASE			  =rust_tokenizer.encode(_a  )
										self.assertListEqual(_a				,    _a  )