


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


__lowerCAmelCase		       =							logging.get_logger(__name__)  # pylint: disable=invalid-name

__lowerCAmelCase		       =							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save("red_frog.png")
        ```
'''




def    snake_case_     (  snake_case			,							snake_case			,							snake_case=8     )  ->				Tuple:
       lowercase__:   Tuple  			=   height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       lowercase__:   Union[str, Any]  			=   width // scale_factor**2
       if width % scale_factor**2 != 0:
              new_width += 1
       return new_height * scale_factor, new_width * scale_factor




def    snake_case_     (  snake_case			,							snake_case=5_12			,							snake_case=5_12     )  ->				Dict:
       lowercase__:   Optional[Any]  			=   pil_image.resize((w, h)			,							resample=Image.BICUBIC			,							reducing_gap=1     )
       lowercase__:   Optional[int]  			=   np.array(pil_image.convert('RGB'     )     )
       lowercase__:   Optional[Any]  			=   arr.astype(np.floataa     ) / 1_2_7.5 - 1
       lowercase__:   str  			=   np.transpose(snake_case			,							[2, 0, 1]     )
       lowercase__:   Dict  			=   torch.from_numpy(snake_case     ).unsqueeze(0     )
       return image





class   __a			(    __UpperCamelCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				Any:




        '''simple docstring'''
        super().__init__()

        self.register_modules(
            unet=lowerCAmelCase__				, scheduler=lowerCAmelCase__				, movq=lowerCAmelCase__				, )
        lowercase__:   Optional[int]  			=   2 ** (len(self.movq.config.block_out_channels ) - 1)
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				int:




        '''simple docstring'''
        # get the original timestep using init_timestep
        lowercase__:   Dict  			=   min(int(num_inference_steps * strength )				, lowerCAmelCase__ )

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

        return timesteps, num_inference_steps - t_start
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__=None )					->				Dict:




        '''simple docstring'''
        if not isinstance(lowerCAmelCase__				, (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(lowerCAmelCase__ )}' )

        lowercase__:   Tuple  			=   image.to(device=lowerCAmelCase__				, dtype=lowerCAmelCase__ )

        lowercase__:   str  			=   batch_size * num_images_per_prompt

        if image.shape[1] == 4:
               lowercase__:   int  			=   image

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

               elif isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
                      lowercase__:   str  			=   [
                          self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCAmelCase__ )
                      ]
                      lowercase__:   Dict  			=   torch.cat(lowerCAmelCase__				, dim=0 )
               else:
                      lowercase__:   Optional[int]  			=   self.movq.encode(lowerCAmelCase__ ).latent_dist.sample(lowerCAmelCase__ )

               lowercase__:   Optional[int]  			=   self.movq.config.scaling_factor * init_latents

        lowercase__:   str  			=   torch.cat([init_latents]				, dim=0 )

        lowercase__:   List[Any]  			=   init_latents.shape
        lowercase__:   Optional[int]  			=   randn_tensor(lowerCAmelCase__				, generator=lowerCAmelCase__				, device=lowerCAmelCase__				, dtype=lowerCAmelCase__ )

        # get latents
        lowercase__:   str  			=   self.scheduler.add_noise(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )

        lowercase__:   Union[str, Any]  			=   init_latents

        return latents
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=0 )					->				Optional[int]:




        '''simple docstring'''
        if is_accelerate_available():
               from accelerate import cpu_offload
        else:
               raise ImportError('Please install accelerate via `pip install accelerate`' )

        lowercase__:   Optional[int]  			=   torch.device(F'cuda:{gpu_id}' )

        lowercase__:   Any  			=   [
            self.unet,
            self.movq,
        ]
        for cpu_offloaded_model in models:
               if cpu_offloaded_model is not None:
                      cpu_offload(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=0 )					->				str:




        '''simple docstring'''
        if is_accelerate_available() and is_accelerate_version('>='				, '0.17.0.dev0' ):
               from accelerate import cpu_offload_with_hook
        else:
               raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )

        lowercase__:   Optional[int]  			=   torch.device(F'cuda:{gpu_id}' )

        if self.device.type != "cpu":
               self.to('cpu'				, silence_dtype_warnings=lowerCAmelCase__ )
               torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        lowercase__:   int  			=   None
        for cpu_offloaded_model in [self.unet, self.movq]:
               lowercase__			,						lowercase__:   List[str]  			=   cpu_offload_with_hook(lowerCAmelCase__				, lowerCAmelCase__				, prev_module_hook=lowerCAmelCase__ )

        # We'll offload the last model manually.
        lowercase__:   Optional[Any]  			=   hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        if not hasattr(self.unet				, '_hf_hook' ):
               return self.device
        for module in self.unet.modules():
               if (
                   hasattr(lowerCAmelCase__				, '_hf_hook' )
                   and hasattr(module._hf_hook				, 'execution_device' )
                   and module._hf_hook.execution_device is not None
               ):
                      return torch.device(module._hf_hook.execution_device )
        return self.device
 @torch.no_grad()
 @replace_example_docstring(lowerCAmelCase__ )
 def __call__(   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = 512				, lowerCAmelCase__ = 512				, lowerCAmelCase__ = 100				, lowerCAmelCase__ = 4.0				, lowerCAmelCase__ = 0.3				, lowerCAmelCase__ = 1				, lowerCAmelCase__ = None				, lowerCAmelCase__ = "pil"				, lowerCAmelCase__ = True				, )					->				Optional[int]:




        '''simple docstring'''
        lowercase__:   int  			=   self._execution_device

        lowercase__:   Tuple  			=   guidance_scale > 1.0

        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   Optional[int]  			=   torch.cat(lowerCAmelCase__				, dim=0 )
        lowercase__:   str  			=   image_embeds.shape[0]
        if isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   List[str]  			=   torch.cat(lowerCAmelCase__				, dim=0 )

        if do_classifier_free_guidance:
               lowercase__:   int  			=   image_embeds.repeat_interleave(lowerCAmelCase__				, dim=0 )
               lowercase__:   int  			=   negative_image_embeds.repeat_interleave(lowerCAmelCase__				, dim=0 )

        lowercase__:   str  			=   torch.cat([negative_image_embeds, image_embeds]				, dim=0 ).to(dtype=self.unet.dtype				, device=lowerCAmelCase__ )

        if not isinstance(lowerCAmelCase__				, lowerCAmelCase__ ):
               lowercase__:   str  			=   [image]
        if not all(isinstance(lowerCAmelCase__				, (PIL.Image.Image, torch.Tensor) ) for i in image ):
               raise ValueError(
                   F'Input is in incorrect format: {[type(lowerCAmelCase__ ) for i in image]}. Currently, we only support  PIL image and pytorch tensor' )

        lowercase__:   Optional[Any]  			=   torch.cat([prepare_image(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ ) for i in image]				, dim=0 )
        lowercase__:   List[str]  			=   image.to(dtype=image_embeds.dtype				, device=lowerCAmelCase__ )

        lowercase__:   int  			=   self.movq.encode(lowerCAmelCase__ )['latents']
        lowercase__:   List[str]  			=   latents.repeat_interleave(lowerCAmelCase__				, dim=0 )
        self.scheduler.set_timesteps(lowerCAmelCase__				, device=lowerCAmelCase__ )
        lowercase__			,						lowercase__:   Dict  			=   self.get_timesteps(lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )
        lowercase__:   List[Any]  			=   timesteps[:1].repeat(batch_size * num_images_per_prompt )
        lowercase__			,						lowercase__:   Optional[Any]  			=   downscale_height_and_width(lowerCAmelCase__				, lowerCAmelCase__				, self.movq_scale_factor )
        lowercase__:   List[Any]  			=   self.prepare_latents(
            lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, image_embeds.dtype				, lowerCAmelCase__				, lowerCAmelCase__ )
        for i, t in enumerate(self.progress_bar(lowerCAmelCase__ ) ):
               # expand the latents if we are doing classifier free guidance
               lowercase__:   List[Any]  			=   torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

               lowercase__:   Optional[Any]  			=   {'image_embeds': image_embeds}
               lowercase__:   List[str]  			=   self.unet(
                   sample=lowerCAmelCase__				, timestep=lowerCAmelCase__				, encoder_hidden_states=lowerCAmelCase__				, added_cond_kwargs=lowerCAmelCase__				, return_dict=lowerCAmelCase__				, )[0]

               if do_classifier_free_guidance:
                      lowercase__			,						lowercase__:   str  			=   noise_pred.split(latents.shape[1]				, dim=1 )
                      lowercase__			,						lowercase__:   Union[str, Any]  			=   noise_pred.chunk(2 )
                      lowercase__			,						lowercase__:   Union[str, Any]  			=   variance_pred.chunk(2 )
                      lowercase__:   Optional[Any]  			=   noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                      lowercase__:   int  			=   torch.cat([noise_pred, variance_pred_text]				, dim=1 )

               if not (
                   hasattr(self.scheduler.config				, 'variance_type' )
                   and self.scheduler.config.variance_type in ["learned", "learned_range"]
               ):
                      lowercase__			,						lowercase__:   Union[str, Any]  			=   noise_pred.split(latents.shape[1]				, dim=1 )

               # compute the previous noisy sample x_t -> x_t-1
               lowercase__:   int  			=   self.scheduler.step(
                   lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, generator=lowerCAmelCase__				, )[0]

        # post-processing
        lowercase__:   List[str]  			=   self.movq.decode(lowerCAmelCase__				, force_not_quantize=lowerCAmelCase__ )['sample']

        if output_type not in ["pt", "np", "pil"]:
               raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}' )

        if output_type in ["np", "pil"]:
               lowercase__:   List[Any]  			=   image * 0.5 + 0.5
               lowercase__:   int  			=   image.clamp(0				, 1 )
               lowercase__:   Optional[int]  			=   image.cpu().permute(0				, 2				, 3				, 1 ).float().numpy()

        if output_type == "pil":
               lowercase__:   Dict  			=   self.numpy_to_pil(lowerCAmelCase__ )

        if not return_dict:
               return (image,)

        return ImagePipelineOutput(images=lowerCAmelCase__ )






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


__lowerCAmelCase		       =							get_tests_dir('''fixtures/test_sentencepiece_no_bos.model''')





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Optional[int]        = True
 __lowercase :							Tuple        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase__:   List[str]  			=   PegasusTokenizer(lowerCAmelCase__ )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/pegasus-large' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   '</s>'
        lowercase__:   Union[str, Any]  			=   1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCAmelCase__ )				, lowerCAmelCase__ )
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCAmelCase__ )				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Tuple  			=   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(lowerCAmelCase__ )				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Any:




        '''simple docstring'''
        self.assertEqual(self.get_tokenizer().vocab_size				, 1_103 )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Dict  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[int]  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Optional[Any]  			=   (
            '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>'
        )
        lowercase__:   Dict  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Tuple  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        lowercase__:   int  			=   self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        lowercase__:   Any  			=   '<mask_1> To ensure a <mask_2> flow of bank resolutions.'
        lowercase__:   Union[str, Any]  			=   [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   int  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 96_103
        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 == 1_024
        lowercase__:   int  			=   'To ensure a smooth flow of bank resolutions.'
        lowercase__:   Any  			=   [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
        lowercase__:   str  			=   tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   Any  			=   ['This is going to be way too long.' * 150, 'short example']
        lowercase__:   Tuple  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Dict  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Any  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 1_024)
        assert batch.attention_mask.shape == (2, 1_024)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 @slow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[Any]:




        '''simple docstring'''
        # fmt: off
        lowercase__:   List[str]  			=   {'input_ids': [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 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, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 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=lowerCAmelCase__				, model_name='google/bigbird-pegasus-large-arxiv'				, revision='ba85d0851d708441f91440d509690f1ab6353415'				, )





@require_sentencepiece
@require_tokenizers
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							int        = PegasusTokenizer
 __lowercase :							Any        = PegasusTokenizerFast
 __lowercase :							Any        = True
 __lowercase :							Dict        = True
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Union[str, Any]:




        '''simple docstring'''
        super().setUp()

        # We have a SentencePiece fixture for testing
        lowercase__:   Union[str, Any]  			=   PegasusTokenizer(lowerCAmelCase__				, offset=0				, mask_token_sent=lowerCAmelCase__				, mask_token='[MASK]' )
        tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained('google/bigbird-pegasus-large-arxiv' )
 def 		SCREAMING_SNAKE_CASE__   (   self				, **lowerCAmelCase__ )					->				PegasusTokenizer:




        '''simple docstring'''
        return PegasusTokenizer.from_pretrained(self.tmpdirname				, **lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				Optional[int]:




        '''simple docstring'''
        return ("This is a test", "This is a test")
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   str  			=   self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   str  			=   self.tokenizer_class.from_pretrained(self.tmpdirname )
        lowercase__:   Tuple  			=   (
            'Let\'s see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>'
            ' <pad> <pad> <pad>'
        )
        lowercase__:   List[Any]  			=   rust_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        lowercase__:   Any  			=   py_tokenizer([raw_input_str]				, return_tensors=lowerCAmelCase__				, add_special_tokens=lowerCAmelCase__ ).input_ids[0]
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 @require_torch
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        lowercase__:   List[Any]  			=   ['This is going to be way too long.' * 1_000, 'short example']
        lowercase__:   str  			=   ['not super long but more than 5 tokens', 'tiny']
        lowercase__:   Tuple  			=   self._large_tokenizer(lowerCAmelCase__				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )
        lowercase__:   Dict  			=   self._large_tokenizer(
            text_target=lowerCAmelCase__				, max_length=5				, padding=lowerCAmelCase__				, truncation=lowerCAmelCase__				, return_tensors='pt' )

        assert batch.input_ids.shape == (2, 4_096)
        assert batch.attention_mask.shape == (2, 4_096)
        assert targets["input_ids"].shape == (2, 5)
        assert len(lowerCAmelCase__ ) == 2  # input_ids, attention_mask.
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   str  			=   (
            'This is an example string that is used to test the original TF implementation against the HF'
            ' implementation'
        )

        lowercase__:   Optional[int]  			=   self._large_tokenizer(lowerCAmelCase__ ).input_ids

        self.assertListEqual(
            lowerCAmelCase__				, [182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]				, )


