


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 					UpperCAmelCase__   (      __lowerCamelCase		):



     '''simple docstring'''
     UpperCAmelCase_         =  DPRContextEncoderTokenizer
     UpperCAmelCase_         =  DPRContextEncoderTokenizerFast
     UpperCAmelCase_         =  True




@require_tokenizers
class 					UpperCAmelCase__   (      __lowerCamelCase		):



     '''simple docstring'''
     UpperCAmelCase_         =  DPRQuestionEncoderTokenizer
     UpperCAmelCase_         =  DPRQuestionEncoderTokenizerFast
     UpperCAmelCase_         =  True




@require_tokenizers
class 					UpperCAmelCase__   (      __lowerCamelCase		):



     '''simple docstring'''
     UpperCAmelCase_         =  DPRReaderTokenizer
     UpperCAmelCase_         =  DPRReaderTokenizerFast
     UpperCAmelCase_         =  True




     @slow
     def   lowerCAmelCase_   ( self       :       Optional[int]		):





            """simple docstring"""



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

            _lowercase					:				Optional[int]           =			tokenizer.encode('''question sequence'''	,      add_special_tokens=UpperCamelCase		)
            _lowercase					:				Union[str, Any]           =			tokenizer.encode('''title sequence'''	,      add_special_tokens=UpperCamelCase		)
            _lowercase					:				Any           =			tokenizer.encode('''text sequence ''' * 4	,      add_special_tokens=UpperCamelCase		)
            _lowercase					:				Dict           =			[[1_01] + text_a + [1_02] + text_a + [1_02] + text_a]
            _lowercase					:				List[Any]           =			BatchEncoding({'''input_ids''': input_ids}		)

            _lowercase					:				Any           =			[[0] * len(input_ids[0]		)]
            _lowercase					:				Optional[int]           =			[[0] * len(input_ids[0]		)]
            _lowercase					:				Dict           =			[0]
            _lowercase					:				Optional[Any]           =			DPRReaderOutput(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		)

            _lowercase       ,	_lowercase					:				Dict           =			8, 9
            _lowercase					:				Dict           =			10
            _lowercase					:				Dict           =			10
            _lowercase					:				Optional[Any]           =			tokenizer.decode_best_spans(UpperCamelCase	,      UpperCamelCase		)
            self.assertEqual(predicted_spans[0].start_index	,      UpperCamelCase		)
            self.assertEqual(predicted_spans[0].end_index	,      UpperCamelCase		)
            self.assertEqual(predicted_spans[0].doc_id	,      0		)




     @slow
     def   lowerCAmelCase_   ( self       :       Tuple		):





            """simple docstring"""



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

            _lowercase					:				List[str]           =			tokenizer.encode('''question sequence'''	,      add_special_tokens=UpperCamelCase		)
            _lowercase					:				Optional[int]           =			tokenizer.encode('''title sequence'''	,      add_special_tokens=UpperCamelCase		)
            _lowercase					:				Any           =			tokenizer.encode('''text sequence'''	,      add_special_tokens=UpperCamelCase		)
            _lowercase					:				Tuple           =			[1_01] + text_a + [1_02] + text_a + [1_02] + text_a
            _lowercase					:				List[str]           =			tokenizer(questions=['''question sequence''']	,      titles=['''title sequence''']	,      texts=['''text sequence''']		)
            self.assertIn('''input_ids'''	,      UpperCamelCase		)
            self.assertIn('''attention_mask'''	,      UpperCamelCase		)
            self.assertListEqual(encoded_input['''input_ids'''][0]	,      UpperCamelCase		)



# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						UpperCAmelCase__   (      A_		):



     '''simple docstring'''




     def __init__( self       :       List[str]	,      UpperCamelCase       :       Union[str, Any]	,      UpperCamelCase       :       int		):





            """simple docstring"""



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




     @torch.no_grad()
     def __call__( self       :       Any	,      UpperCamelCase       :       int = 1	,      UpperCamelCase       :       Optional[torch.Generator] = None	,      UpperCamelCase       :       int = 50	,      UpperCamelCase       :       Optional[str] = "pil"	,      UpperCamelCase       :       bool = True	,      **UpperCamelCase       :       int	,      ):





            """simple docstring"""



            _lowercase					:				Optional[int]           =			torch.randn(
                (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)	,      generator=UpperCamelCase	,      )
            _lowercase					:				Dict           =			image.to(self.device		)

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

            for t in self.progress_bar(self.scheduler.timesteps		):
                   # 1. predict noise model_output
                   _lowercase					:				Dict           =			self.unet(UpperCamelCase	,      UpperCamelCase		).sample

                   # 2. predict previous mean of image x_t-1 and add variance depending on eta
                   # eta corresponds to η in paper and should be between [0, 1]
                   # do x_t -> x_t-1
                   _lowercase					:				Tuple           =			self.scheduler.step(UpperCamelCase	,      UpperCamelCase	,      UpperCamelCase		).prev_sample

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

            if not return_dict:
                   return (image,), "This is a local test"

            return ImagePipelineOutput(images=UpperCamelCase		), "This is a local test"