
"""simple docstring"""

from transformers import (
    DPRContextEncoderTokenizer,
    DPRContextEncoderTokenizerFast,
    DPRQuestionEncoderTokenizer,
    DPRQuestionEncoderTokenizerFast,
    DPRReaderOutput,
    DPRReaderTokenizer,
    DPRReaderTokenizerFast,
)
from transformers.testing_utils import require_tokenizers, slow
from transformers.tokenization_utils_base import BatchEncoding

from ..bert.test_tokenization_bert import BertTokenizationTest






@require_tokenizers
class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      DPRContextEncoderTokenizer
	lowercase_								=      DPRContextEncoderTokenizerFast
	lowercase_								=      True






@require_tokenizers
class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      DPRQuestionEncoderTokenizer
	lowercase_								=      DPRQuestionEncoderTokenizerFast
	lowercase_								=      True






@require_tokenizers
class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      DPRReaderTokenizer
	lowercase_								=      DPRReaderTokenizerFast
	lowercase_								=      True




	@slow
	def      _UpperCamelCase       (					self   )						->				List[str]:
			lowerCamelCase	:     Optional[int]            =       self.tokenizer_class.from_pretrained('bert-base-uncased'   )

			lowerCamelCase	:     Any            =       tokenizer.encode('question sequence'						,					add_special_tokens=_snake_case   )
			lowerCamelCase	:     Dict            =       tokenizer.encode('title sequence'						,					add_special_tokens=_snake_case   )
			lowerCamelCase	:     Optional[int]            =       tokenizer.encode('text sequence ' * 4						,					add_special_tokens=_snake_case   )
			lowerCamelCase	:     Optional[Any]            =       [[101] + text_a + [102] + text_a + [102] + text_a]
			lowerCamelCase	:     Tuple            =       BatchEncoding({'input_ids': input_ids}   )

			lowerCamelCase	:     str            =       [[0] * len(input_ids[0]   )]
			lowerCamelCase	:     int            =       [[0] * len(input_ids[0]   )]
			lowerCamelCase	:     Union[str, Any]            =       [0]
			lowerCamelCase	:     Any            =       DPRReaderOutput(_snake_case						,					_snake_case						,					_snake_case   )

			lowerCamelCase	:     Optional[int]            =       8, 9
			lowerCamelCase	:     List[Any]            =       10
			lowerCamelCase	:     Any            =       10
			lowerCamelCase	:     Any            =       tokenizer.decode_best_spans(_snake_case						,					_snake_case   )
			self.assertEqual(predicted_spans[0].start_index						,					_snake_case   )
			self.assertEqual(predicted_spans[0].end_index						,					_snake_case   )
			self.assertEqual(predicted_spans[0].doc_id						,					0   )




	@slow
	def      _UpperCamelCase       (					self   )						->				str:
			lowerCamelCase	:     Optional[Any]            =       self.tokenizer_class.from_pretrained('bert-base-uncased'   )

			lowerCamelCase	:     str            =       tokenizer.encode('question sequence'						,					add_special_tokens=_snake_case   )
			lowerCamelCase	:     Union[str, Any]            =       tokenizer.encode('title sequence'						,					add_special_tokens=_snake_case   )
			lowerCamelCase	:     str            =       tokenizer.encode('text sequence'						,					add_special_tokens=_snake_case   )
			lowerCamelCase	:     Optional[int]            =       [101] + text_a + [102] + text_a + [102] + text_a
			lowerCamelCase	:     List[str]            =       tokenizer(questions=['question sequence']						,					titles=['title sequence']						,					texts=['text sequence']   )
			self.assertIn('input_ids'						,					_snake_case   )
			self.assertIn('attention_mask'						,					_snake_case   )
			self.assertListEqual(encoded_input['input_ids'][0]						,					_snake_case   )














from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 # The first operation in a checkpoint can't be in-place, but it's
 # nice to have in-place addition during inference. Thus...
 if not inplace:
  lowerCamelCase_							:    Tuple							=     ma + ma
 else:
  ma += ma

 return ma




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    Union[str, Any]							=     -1 * len(lowerCamelCase__				)
 lowerCamelCase_							:    Optional[Any]							=     list(range(len(tensor.shape[:zero_index]				)				)				)
 return tensor.permute(first_inds + [zero_index + i for i in inds]				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 return t.reshape(t.shape[:-no_dims] + (-1,)				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 1e-4				)  ->      torch.Tensor:
 lowerCamelCase_							:    Optional[int]							=     mask.expand(*value.shape				)
 return torch.sum(mask * value ,			dim=lowerCamelCase__				) / (eps + torch.sum(lowerCamelCase__ ,			dim=lowerCamelCase__				))




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ = 2.3125 ,			lowerCamelCase__ = 21.6875 ,			lowerCamelCase__ = 64				)  ->      torch.Tensor:
 lowerCamelCase_							:    int							=     torch.linspace(lowerCamelCase__ ,			lowerCamelCase__ ,			no_bins - 1 ,			device=pts.device				)
 lowerCamelCase_							:    Tuple							=     torch.sqrt(torch.sum((pts.unsqueeze(-2				) - pts.unsqueeze(-3				)) ** 2 ,			dim=-1				)				)
 return torch.bucketize(lowerCamelCase__ ,			lowerCamelCase__				)




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 lowerCamelCase_							:    Optional[Any]							=     dicts[0]
 lowerCamelCase_							:    Optional[Any]							=     {}
 for k, v in first.items():
  lowerCamelCase_							:    Tuple							=     [d[k] for d in dicts]
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[int]							=     dict_multimap(lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    List[Any]							=     fn(lowerCamelCase__				)

 return new_dict




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__				)  ->      torch.Tensor:
 lowerCamelCase_							:    str							=     v_bins.view(((1,) * len(x.shape				)) + (len(lowerCamelCase__				),)				)
 lowerCamelCase_							:    List[Any]							=     x[..., None] - reshaped_bins
 lowerCamelCase_							:    List[Any]							=     torch.argmin(torch.abs(lowerCamelCase__				) ,			dim=-1				)
 return nn.functional.one_hot(lowerCamelCase__ ,			num_classes=len(lowerCamelCase__				)				).float()




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ = 0 ,			lowerCamelCase__ = 0				)  ->      torch.Tensor:
 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     []
 for i, s in enumerate(data.shape[:no_batch_dims]				):
  lowerCamelCase_							:    int							=     torch.arange(lowerCamelCase__				)
  lowerCamelCase_							:    Any							=     r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape				) - i - 1)))				)
  ranges.append(lowerCamelCase__				)

 lowerCamelCase_							:    List[Union[slice, torch.Tensor]]							=     [slice(lowerCamelCase__				) for _ in range(len(data.shape				) - no_batch_dims				)]
 lowerCamelCase_							:    Optional[int]							=     inds
 ranges.extend(lowerCamelCase__				)
 # Matt note: Editing this to get around the behaviour of using a list as an array index changing
 # in recent Numpy versions
 return data[tuple(lowerCamelCase__				)]


UpperCamelCase											=      TypeVar('''T''')




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Dict[Any, Union[dict, list, tuple, Any]]:
 lowerCamelCase_							:    Dict[Any, Union[dict, list, tuple, Any]]							=     {}
 for k, v in dic.items():
  if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
   lowerCamelCase_							:    Optional[Any]							=     dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
  else:
   lowerCamelCase_							:    Dict							=     tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)

 return new_dict




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      dict:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      list:
 ...




@overload
def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      tuple:
 ...




def       _a   (							lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)  ->      Any:
 if isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return dict_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return [tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree]
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return tuple(tree_map(lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__				) for x in tree				)
 elif isinstance(lowerCamelCase__ ,			lowerCamelCase__				):
  return fn(lowerCamelCase__				)
 else:
  print(type(lowerCamelCase__				)				)
  raise ValueError('Not supported'				)





UpperCamelCase											=      partial(tree_map, leaf_type=torch.Tensor)
