





import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


snake_case__					:							Dict													=     get_tests_dir('fixtures/test_sentencepiece_no_bos.model')





@require_sentencepiece
@require_tokenizers
class A_					(			_lowerCamelCase ,      unittest.TestCase   ):
   lowerCAmelCase__        = PegasusTokenizer
   lowerCAmelCase__        = PegasusTokenizerFast
   lowerCAmelCase__        = True
   lowerCAmelCase__        = True




   def _lowerCAmelCase      (self	:Optional[Any] )->					Any:
         super().setUp()

         # We have a SentencePiece fixture for testing
         __A          =				PegasusTokenizer(_UpperCamelCase )
         tokenizer.save_pretrained(self.tmpdirname )




   @cached_property
   def _lowerCAmelCase      (self	:Dict )->					str:
         return PegasusTokenizer.from_pretrained('''google/pegasus-large''' )




   def _lowerCAmelCase      (self	:List[Any]    ,   **_UpperCamelCase	:str )->					PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:List[str] )->					List[str]:
         return ("This is a test", "This is a test")




   def _lowerCAmelCase      (self	:Any )->					str:
         __A          =				'''</s>'''
         __A          =				1

         self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase )    ,   _UpperCamelCase )
         self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase )    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[Any] )->					Optional[Any]:
         __A          =				list(self.get_tokenizer().get_vocab().keys() )

         self.assertEqual(vocab_keys[0]    ,   '''<pad>''' )
         self.assertEqual(vocab_keys[1]    ,   '''</s>''' )
         self.assertEqual(vocab_keys[-1]    ,   '''v''' )
         self.assertEqual(len(_UpperCamelCase )    ,   1103 )




   def _lowerCAmelCase      (self	:List[Any] )->					Union[str, Any]:
         self.assertEqual(self.get_tokenizer().vocab_size    ,   1103 )




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A          =				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
         __A          =				self.tokenizer_class.from_pretrained(self.tmpdirname )
         __A          =				(
             '''Let\'s see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important'''
             ''' </s> <pad> <pad> <pad>'''
         )
         __A          =				rust_tokenizer([raw_input_str]    ,   return_tensors=_UpperCamelCase    ,   add_special_tokens=_UpperCamelCase ).input_ids[0]
         __A          =				py_tokenizer([raw_input_str]    ,   return_tensors=_UpperCamelCase    ,   add_special_tokens=_UpperCamelCase ).input_ids[0]
         self.assertListEqual(_UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:str )->					str:
         __A          =				self._large_tokenizer
         # <mask_1> masks whole sentence while <mask_2> masks single word
         __A          =				'''<mask_1> To ensure a <mask_2> flow of bank resolutions.'''
         __A          =				[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
         __A          =				tokenizer([raw_input_str]    ,   return_tensors=_UpperCamelCase ).input_ids[0]
         self.assertListEqual(_UpperCamelCase    ,   _UpperCamelCase )




   def _lowerCAmelCase      (self	:Optional[int] )->					int:
         __A          =				self._large_tokenizer
         # The tracebacks for the following asserts are **better** without messages or self.assertEqual
         assert tokenizer.vocab_size == 9_6103
         assert tokenizer.pad_token_id == 0
         assert tokenizer.eos_token_id == 1
         assert tokenizer.offset == 103
         assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
         assert tokenizer.unk_token == "<unk>"
         assert tokenizer.model_max_length == 1024
         __A          =				'''To ensure a smooth flow of bank resolutions.'''
         __A          =				[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
         __A          =				tokenizer([raw_input_str]    ,   return_tensors=_UpperCamelCase ).input_ids[0]
         self.assertListEqual(_UpperCamelCase    ,   _UpperCamelCase )
         assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]




   @require_torch
   def _lowerCAmelCase      (self	:Union[str, Any] )->					Tuple:
         __A          =				['''This is going to be way too long.''' * 150, '''short example''']
         __A          =				['''not super long but more than 5 tokens''', '''tiny''']
         __A          =				self._large_tokenizer(_UpperCamelCase    ,   padding=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   return_tensors='''pt''' )
         __A          =				self._large_tokenizer(
             text_target=_UpperCamelCase    ,   max_length=5    ,   padding=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   return_tensors='''pt''' )

         assert batch.input_ids.shape == (2, 1024)
         assert batch.attention_mask.shape == (2, 1024)
         assert targets["input_ids"].shape == (2, 5)
         assert len(_UpperCamelCase ) == 2  # input_ids, attention_mask.






   @slow
   def _lowerCAmelCase      (self	:Optional[int] )->					Tuple:
         # fmt: off
         __A          =				{'''input_ids''': [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
         # fmt: on

         self.tokenizer_integration_test_util(
             expected_encoding=_UpperCamelCase    ,   model_name='''google/bigbird-pegasus-large-arxiv'''    ,   revision='''ba85d0851d708441f91440d509690f1ab6353415'''    ,   )





@require_sentencepiece
@require_tokenizers
class A_					(			_lowerCamelCase ,      unittest.TestCase   ):
   lowerCAmelCase__        = PegasusTokenizer
   lowerCAmelCase__        = PegasusTokenizerFast
   lowerCAmelCase__        = True
   lowerCAmelCase__        = True




   def _lowerCAmelCase      (self	:Any )->					Union[str, Any]:
         super().setUp()

         # We have a SentencePiece fixture for testing
         __A          =				PegasusTokenizer(_UpperCamelCase    ,   offset=0    ,   mask_token_sent=_UpperCamelCase    ,   mask_token='''[MASK]''' )
         tokenizer.save_pretrained(self.tmpdirname )




   @cached_property
   def _lowerCAmelCase      (self	:int )->					Optional[int]:
         return PegasusTokenizer.from_pretrained('''google/bigbird-pegasus-large-arxiv''' )




   def _lowerCAmelCase      (self	:str    ,   **_UpperCamelCase	:int )->					PegasusTokenizer:
         return PegasusTokenizer.from_pretrained(self.tmpdirname    ,   **_UpperCamelCase )




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:str )->					Optional[int]:
         return ("This is a test", "This is a test")




   def _lowerCAmelCase      (self	:Union[str, Any] )->					Optional[int]:
         __A          =				self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
         __A          =				self.tokenizer_class.from_pretrained(self.tmpdirname )
         __A          =				(
             '''Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'''
             ''' <pad> <pad> <pad>'''
         )
         __A          =				rust_tokenizer([raw_input_str]    ,   return_tensors=_UpperCamelCase    ,   add_special_tokens=_UpperCamelCase ).input_ids[0]
         __A          =				py_tokenizer([raw_input_str]    ,   return_tensors=_UpperCamelCase    ,   add_special_tokens=_UpperCamelCase ).input_ids[0]
         self.assertListEqual(_UpperCamelCase    ,   _UpperCamelCase )




   @require_torch
   def _lowerCAmelCase      (self	:Dict )->					str:
         __A          =				['''This is going to be way too long.''' * 1000, '''short example''']
         __A          =				['''not super long but more than 5 tokens''', '''tiny''']
         __A          =				self._large_tokenizer(_UpperCamelCase    ,   padding=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   return_tensors='''pt''' )
         __A          =				self._large_tokenizer(
             text_target=_UpperCamelCase    ,   max_length=5    ,   padding=_UpperCamelCase    ,   truncation=_UpperCamelCase    ,   return_tensors='''pt''' )

         assert batch.input_ids.shape == (2, 4096)
         assert batch.attention_mask.shape == (2, 4096)
         assert targets["input_ids"].shape == (2, 5)
         assert len(_UpperCamelCase ) == 2  # input_ids, attention_mask.






   def _lowerCAmelCase      (self	:Any )->					Any:
         __A          =				(
             '''This is an example string that is used to test the original TF implementation against the HF'''
             ''' implementation'''
         )

         __A          =				self._large_tokenizer(_UpperCamelCase ).input_ids

         self.assertListEqual(
             _UpperCamelCase    ,   [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]    ,   )












from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


snake_case__					:							int													=     transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)



def _a				(	lowerCamelCase:   List[Any] )	->						List[Any]:







      '''simple docstring'''






      if isinstance(lowerCamelCase					,				torch.Tensor ):
            return image
      elif isinstance(lowerCamelCase					,				PIL.Image.Image ):
            __A          =				[image]

      __A          =				[trans(img.convert('''RGB''' ) ) for img in image]
      __A          =				torch.stack(lowerCamelCase )
      return image







class A_					(			_lowerCamelCase   ):




   def __init__(self	:List[str]    ,   _UpperCamelCase	:List[Any]    ,   _UpperCamelCase	:List[str] )->					List[Any]:
         super().__init__()

         # make sure scheduler can always be converted to DDIM
         __A          =				DDIMScheduler.from_config(scheduler.config )

         self.register_modules(unet=_UpperCamelCase    ,   scheduler=_UpperCamelCase )




   def _lowerCAmelCase      (self	:int    ,   _UpperCamelCase	:Optional[Any] )->					Union[str, Any]:
         if strength < 0 or strength > 1:
               raise ValueError(f"""The value of strength should in [0.0, 1.0] but is {strength}""" )




   def _lowerCAmelCase      (self	:Optional[Any]    ,   _UpperCamelCase	:Dict    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:List[str] )->					Union[str, Any]:
         # get the original timestep using init_timestep
         __A          =				min(int(num_inference_steps * strength )    ,   _UpperCamelCase )

         __A          =				max(num_inference_steps - init_timestep    ,   0 )
         __A          =				self.scheduler.timesteps[t_start:]

         return timesteps, num_inference_steps - t_start




   def _lowerCAmelCase      (self	:str    ,   _UpperCamelCase	:Tuple    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:Dict=None )->					List[str]:
         if not isinstance(_UpperCamelCase    ,   (torch.Tensor, PIL.Image.Image, list) ):
               raise ValueError(
                   f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_UpperCamelCase )}""" )

         __A          =				image.to(device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         if isinstance(_UpperCamelCase    ,   _UpperCamelCase ) and len(_UpperCamelCase ) != batch_size:
               raise ValueError(
                   f"""You have passed a list of generators of length {len(_UpperCamelCase )}, but requested an effective batch"""
                   f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

         __A          =				init_latents.shape
         __A          =				randn_tensor(_UpperCamelCase    ,   generator=_UpperCamelCase    ,   device=_UpperCamelCase    ,   dtype=_UpperCamelCase )

         # get latents
         print('''add noise to latents at timestep'''    ,   _UpperCamelCase )
         __A          =				self.scheduler.add_noise(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase )
         __A          =				init_latents

         return latents






   @torch.no_grad()
   def __call__(self	:List[str]    ,   _UpperCamelCase	:Union[torch.FloatTensor, PIL.Image.Image] = None    ,   _UpperCamelCase	:float = 0.8    ,   _UpperCamelCase	:int = 1    ,   _UpperCamelCase	:Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,   _UpperCamelCase	:float = 0.0    ,   _UpperCamelCase	:int = 50    ,   _UpperCamelCase	:Optional[bool] = None    ,   _UpperCamelCase	:Optional[str] = "pil"    ,   _UpperCamelCase	:bool = True    ,   )->					Union[ImagePipelineOutput, Tuple]:
         self.check_inputs(_UpperCamelCase )

         # 2. Preprocess image
         __A          =				preprocess(_UpperCamelCase )

         # 3. set timesteps
         self.scheduler.set_timesteps(_UpperCamelCase    ,   device=self.device )
         __A   ,						__A          =				self.get_timesteps(_UpperCamelCase    ,   _UpperCamelCase    ,   self.device )
         __A          =				timesteps[:1].repeat(_UpperCamelCase )

         # 4. Prepare latent variables
         __A          =				self.prepare_latents(_UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   self.unet.dtype    ,   self.device    ,   _UpperCamelCase )
         __A          =				latents

         # 5. Denoising loop
         for t in self.progress_bar(_UpperCamelCase ):
               # 1. predict noise model_output
               __A          =				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
               __A          =				self.scheduler.step(
                   _UpperCamelCase    ,   _UpperCamelCase    ,   _UpperCamelCase    ,   eta=_UpperCamelCase    ,   use_clipped_model_output=_UpperCamelCase    ,   generator=_UpperCamelCase    ,   ).prev_sample

         __A          =				(image / 2 + 0.5).clamp(0    ,   1 )
         __A          =				image.cpu().permute(0    ,   2    ,   3    ,   1 ).numpy()
         if output_type == "pil":
               __A          =				self.numpy_to_pil(_UpperCamelCase )

         if not return_dict:
               return (image, latent_timestep.item())

         return ImagePipelineOutput(images=_UpperCamelCase )





