






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 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     int	=   DPRContextEncoderTokenizer
 _SCREAMING_SNAKE_CASE							:     List[Any]	=   DPRContextEncoderTokenizerFast
 _SCREAMING_SNAKE_CASE							:     List[str]	=   True



@require_tokenizers
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   DPRQuestionEncoderTokenizer
 _SCREAMING_SNAKE_CASE							:     Any	=   DPRQuestionEncoderTokenizerFast
 _SCREAMING_SNAKE_CASE							:     int	=   True



@require_tokenizers
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     str	=   DPRReaderTokenizer
 _SCREAMING_SNAKE_CASE							:     List[str]	=   DPRReaderTokenizerFast
 _SCREAMING_SNAKE_CASE							:     Optional[int]	=   True


 @slow
 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			self.tokenizer_class.from_pretrained('''bert-base-uncased''')

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

     _UpperCAmelCase			: Optional[Any]     					=			[[0] * len(input_ids[0])]
     _UpperCAmelCase			: Union[str, Any]     					=			[[0] * len(input_ids[0])]
     _UpperCAmelCase			: Any     					=			[0]
     _UpperCAmelCase			: Optional[int]     					=			DPRReaderOutput(_A							,					_A							,					_A)

     _UpperCAmelCase			: Optional[Any]     					=			8, 9
     _UpperCAmelCase			: List[str]     					=			10
     _UpperCAmelCase			: str     					=			10
     _UpperCAmelCase			: Optional[Any]     					=			tokenizer.decode_best_spans(_A							,					_A)
     self.assertEqual(predicted_spans[0].start_index							,					_A)
     self.assertEqual(predicted_spans[0].end_index							,					_A)
     self.assertEqual(predicted_spans[0].doc_id							,					0)


 @slow
 def 	snake_case__			(		self)	->						Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			self.tokenizer_class.from_pretrained('''bert-base-uncased''')

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












from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def __init__(		self							,					_A							,					_A)	->						Dict:

     """simple docstring"""



     super().__init__()
     self.register_modules(unet=_A							,					scheduler=_A)


 @torch.no_grad()
 def __call__(		self							,					_A = 1							,					_A = None							,					_A = 1000							,					_A = "pil"							,					_A = True							,					)	->						Union[ImagePipelineOutput, Tuple]:

     """simple docstring"""



     if isinstance(self.unet.config.sample_size							,					_A):
         _UpperCAmelCase			: List[str]     					=			(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
     else:
         _UpperCAmelCase			: Union[str, Any]     					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

     if self.device.type == "mps":
         # randn does not work reproducibly on mps
         _UpperCAmelCase			: int     					=			randn_tensor(_A							,					generator=_A)
         _UpperCAmelCase			: Tuple     					=			image.to(self.device)
     else:
         _UpperCAmelCase			: Any     					=			randn_tensor(_A							,					generator=_A							,					device=self.device)

     # set step values
     self.scheduler.set_timesteps(_A)

     for t in self.progress_bar(self.scheduler.timesteps):
         # 1. predict noise model_output
         _UpperCAmelCase			: Optional[Any]     					=			self.unet(_A							,					_A).sample

         # 2. compute previous image: x_t -> x_t-1
         _UpperCAmelCase			: Dict     					=			self.scheduler.step(_A							,					_A							,					_A							,					generator=_A).prev_sample

     _UpperCAmelCase			: Optional[int]     					=			(image / 2 + 0.5).clamp(0							,					1)
     _UpperCAmelCase			: int     					=			image.cpu().permute(0							,					2							,					3							,					1).numpy()
     if output_type == "pil":
         _UpperCAmelCase			: str     					=			self.numpy_to_pil(_A)

     if not return_dict:
         return (image,)

     return ImagePipelineOutput(images=_A)




