






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,
)


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

__UpperCAmelCase								=						'\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'




def      lowercase__      (     __snake_case    :							List[str]     ,       __snake_case    :							int     ,       __snake_case    :							Tuple=8  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Dict            =						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					UpperCAmelCase_    :      List[Any]            =						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor




def      lowercase__      (     __snake_case    :							Any     ,       __snake_case    :							int=512     ,       __snake_case    :							Dict=512  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Tuple            =						pil_image.resize((w, h)     ,       resample=Image.BICUBIC     ,       reducing_gap=1  )
					UpperCAmelCase_    :      Dict            =						np.array(pil_image.convert('RGB'  )  )
					UpperCAmelCase_    :      Any            =						arr.astype(np.floataa  ) / 127.5 - 1
					UpperCAmelCase_    :      Dict            =						np.transpose(__snake_case     ,       [2, 0, 1]  )
					UpperCAmelCase_    :      List[str]            =						torch.from_numpy(__snake_case  ).unsqueeze(0  )
					return image






class   lowerCamelCase      (_snake_case			):







		'''simple docstring'''



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

							self.register_modules(
							    unet=_UpperCamelCase				,		scheduler=_UpperCamelCase				,		movq=_UpperCamelCase				,		)
							UpperCAmelCase_    :      Tuple            =						2 ** (len(self.movq.config.block_out_channels   ) - 1)



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )	->							Dict:
							# get the original timestep using init_timestep
							UpperCAmelCase_    :      Any            =						min(int(num_inference_steps * strength   )				,		_UpperCamelCase   )

							UpperCAmelCase_    :      List[Any]            =						max(num_inference_steps - init_timestep				,		0   )
							UpperCAmelCase_    :      str            =						self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase=None   )	->							Tuple:
							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   )}"   )

							UpperCAmelCase_    :      List[str]            =						image.to(device=_UpperCamelCase				,		dtype=_UpperCamelCase   )

							UpperCAmelCase_    :      List[str]            =						batch_size * num_images_per_prompt

							if image.shape[1] == 4:
												UpperCAmelCase_    :      List[str]            =						image

							else:
												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."   )

												elif isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
																	UpperCAmelCase_    :      Any            =						[
																	    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(_UpperCamelCase   )
																	]
																	UpperCAmelCase_    :      Tuple            =						torch.cat(_UpperCamelCase				,		dim=0   )
												else:
																	UpperCAmelCase_    :      Union[str, Any]            =						self.movq.encode(_UpperCamelCase   ).latent_dist.sample(_UpperCamelCase   )

												UpperCAmelCase_    :      int            =						self.movq.config.scaling_factor * init_latents

							UpperCAmelCase_    :      Optional[int]            =						torch.cat([init_latents]				,		dim=0   )

							UpperCAmelCase_    :      Tuple            =						init_latents.shape
							UpperCAmelCase_    :      List[Any]            =						randn_tensor(_UpperCamelCase				,		generator=_UpperCamelCase				,		device=_UpperCamelCase				,		dtype=_UpperCamelCase   )

							# get latents
							UpperCAmelCase_    :      str            =						self.scheduler.add_noise(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )

							UpperCAmelCase_    :      Union[str, Any]            =						init_latents

							return latents



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Any:
							if is_accelerate_available():
												from accelerate import cpu_offload
							else:
												raise ImportError('Please install accelerate via `pip install accelerate`'   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.device(f"cuda:{gpu_id}"   )

							UpperCAmelCase_    :      Optional[Any]            =						[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
												if cpu_offloaded_model is not None:
																	cpu_offload(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase=0   )	->							Union[str, Any]:
							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.'   )

							UpperCAmelCase_    :      str            =						torch.device(f"cuda:{gpu_id}"   )

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

							UpperCAmelCase_    :      Dict            =						None
							for cpu_offloaded_model in [self.unet, self.movq]:
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Dict            =						cpu_offload_with_hook(_UpperCamelCase				,		_UpperCamelCase				,		prev_module_hook=_UpperCamelCase   )

							# We'll offload the last model manually.
							UpperCAmelCase_    :      Any            =						hook



		@property
		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
		def 					__UpperCAmelCase			(							self   )	->							Dict:
							if not hasattr(self.unet				,		'_hf_hook'   ):
												return self.device
							for module in self.unet.modules():
												if (
												    hasattr(_UpperCamelCase				,		'_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(_UpperCamelCase   )
		def __call__(							self				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 5_1_2				,		_UpperCamelCase = 1_0_0				,		_UpperCamelCase = 4.0				,		_UpperCamelCase = 0.3				,		_UpperCamelCase = 1				,		_UpperCamelCase = None				,		_UpperCamelCase = "pil"				,		_UpperCamelCase = True				,		)	->							str:
							UpperCAmelCase_    :      Any            =						self._execution_device

							UpperCAmelCase_    :      Union[str, Any]            =						guidance_scale > 1.0

							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      str            =						torch.cat(_UpperCamelCase				,		dim=0   )
							UpperCAmelCase_    :      Optional[Any]            =						image_embeds.shape[0]
							if isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Union[str, Any]            =						torch.cat(_UpperCamelCase				,		dim=0   )

							if do_classifier_free_guidance:
												UpperCAmelCase_    :      int            =						image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )
												UpperCAmelCase_    :      int            =						negative_image_embeds.repeat_interleave(_UpperCamelCase				,		dim=0   )

							UpperCAmelCase_    :      Optional[Any]            =						torch.cat([negative_image_embeds, image_embeds]				,		dim=0   ).to(dtype=self.unet.dtype				,		device=_UpperCamelCase   )

							if not isinstance(_UpperCamelCase				,		_UpperCamelCase   ):
												UpperCAmelCase_    :      Tuple            =						[image]
							if not all(isinstance(_UpperCamelCase				,		(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
												raise ValueError(
												    f"Input is in incorrect format: {[type(_UpperCamelCase   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"   )

							UpperCAmelCase_    :      str            =						torch.cat([prepare_image(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   ) for i in image]				,		dim=0   )
							UpperCAmelCase_    :      Any            =						image.to(dtype=image_embeds.dtype				,		device=_UpperCamelCase   )

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

												UpperCAmelCase_    :      str            =						{'image_embeds': image_embeds}
												UpperCAmelCase_    :      Union[str, Any]            =						self.unet(
												    sample=_UpperCamelCase				,		timestep=_UpperCamelCase				,		encoder_hidden_states=_UpperCamelCase				,		added_cond_kwargs=_UpperCamelCase				,		return_dict=_UpperCamelCase				,		)[0]

												if do_classifier_free_guidance:
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						noise_pred.split(latents.shape[1]				,		dim=1   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						noise_pred.chunk(2   )
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						variance_pred.chunk(2   )
																	UpperCAmelCase_    :      Dict            =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																	UpperCAmelCase_    :      Tuple            =						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"]
												):
																	UpperCAmelCase_       ,  UpperCAmelCase_    :      int            =						noise_pred.split(latents.shape[1]				,		dim=1   )

												# compute the previous noisy sample x_t -> x_t-1
												UpperCAmelCase_    :      List[str]            =						self.scheduler.step(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		generator=_UpperCamelCase				,		)[0]

							# post-processing
							UpperCAmelCase_    :      Optional[Any]            =						self.movq.decode(_UpperCamelCase				,		force_not_quantize=_UpperCamelCase   )['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"]:
												UpperCAmelCase_    :      List[str]            =						image * 0.5 + 0.5
												UpperCAmelCase_    :      List[Any]            =						image.clamp(0				,		1   )
												UpperCAmelCase_    :      Dict            =						image.cpu().permute(0				,		2				,		3				,		1   ).float().numpy()

							if output_type == "pil":
												UpperCAmelCase_    :      List[Any]            =						self.numpy_to_pil(_UpperCamelCase   )

							if not return_dict:
												return (image,)

							return ImagePipelineOutput(images=_UpperCamelCase   )










import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class     UpperCAmelCase__       (					A_   ,  unittest.TestCase		):



    """simple docstring"""






    UpperCAmelCase__						:		Dict      =  GPTaTokenizer
    UpperCAmelCase__						:		Any      =  GPTaTokenizerFast
    UpperCAmelCase__						:		Tuple      =  True
    UpperCAmelCase__						:		int      =  {"add_prefix_space": True}
    UpperCAmelCase__						:		Any      =  False






    def _a					(			self    )  ->       Optional[int]:
         super().setUp()

         # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
         __UpperCamelCase    							=[
             'l',
             'o',
             'w',
             'e',
             'r',
             's',
             't',
             'i',
             'd',
             'n',
             '\u0120',
             '\u0120l',
             '\u0120n',
             '\u0120lo',
             '\u0120low',
             'er',
             '\u0120lowest',
             '\u0120newer',
             '\u0120wider',
             '<unk>',
             '<|endoftext|>',
         ]
         __UpperCamelCase    							=dict(zip(A_					,   range(len(A_    )    )    )    )
         __UpperCamelCase    							=['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
         __UpperCamelCase    							={'unk_token': '<unk>'}

         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['vocab_file']    )
         __UpperCamelCase    							=os.path.join(self.tmpdirname					,   VOCAB_FILES_NAMES['merges_file']    )
         with open(self.vocab_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write(json.dumps(A_    ) + '\n'    )
         with open(self.merges_file					,   'w'					,   encoding='utf-8'    ) as fp:
              fp.write('\n'.join(A_    )    )






    def _a					(			self					,   **A_    )  ->       str:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizer.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   **A_    )  ->       Optional[Any]:
         kwargs.update(self.special_tokens_map    )
         return GPTaTokenizerFast.from_pretrained(self.tmpdirname					,   **A_    )






    def _a					(			self					,   A_    )  ->       Tuple:
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							='lower newer'
         return input_text, output_text






    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=GPTaTokenizer(self.vocab_file					,   self.merges_file					,   **self.special_tokens_map    )
         __UpperCamelCase    							='lower newer'
         __UpperCamelCase    							=['\u0120low', 'er', '\u0120', 'n', 'e', 'w', 'er']
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         self.assertListEqual(A_					,   A_    )

         __UpperCamelCase    							=tokens + [tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self    )  ->       int:
         if not self.test_rust_tokenizer:
              return

         __UpperCamelCase    							=self.get_tokenizer()
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )

         __UpperCamelCase    							='lower newer'

         # Testing tokenization
         __UpperCamelCase    							=tokenizer.tokenize(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.tokenize(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids without special tokens
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_					,   add_special_tokens=A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing conversion to ids with special tokens
         __UpperCamelCase    							=self.get_rust_tokenizer(add_prefix_space=A_    )
         __UpperCamelCase    							=tokenizer.encode(A_					,   add_prefix_space=A_    )
         __UpperCamelCase    							=rust_tokenizer.encode(A_    )
         self.assertListEqual(A_					,   A_    )

         # Testing the unknown token
         __UpperCamelCase    							=tokens + [rust_tokenizer.unk_token]
         __UpperCamelCase    							=[14, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(A_    )					,   A_    )






    def _a					(			self					,   *A_					,   **A_    )  ->       Optional[int]:
         # It's very difficult to mix/test pretokenization with byte-level
         # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
         pass






    def _a					(			self					,   A_=15    )  ->       List[str]:
         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
                   __UpperCamelCase    							=self.rust_tokenizer_class.from_pretrained(A_					,   **A_    )

                   # Simple input
                   __UpperCamelCase    							='This is a simple input'
                   __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']
                   __UpperCamelCase    							=('This is a simple input', 'This is a pair')
                   __UpperCamelCase    							=[
                       ('This is a simple input 1', 'This is a simple input 2'),
                       ('This is a simple pair 1', 'This is a simple pair 2'),
                   ]

                   # Simple input tests
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Simple input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(A_					,   tokenizer_r.encode_plus					,   A_					,   max_length=A_					,   padding='max_length'    )

                   # Pair input
                   self.assertRaises(
                       A_					,   tokenizer_r.batch_encode_plus					,   A_					,   max_length=A_					,   padding='max_length'					,   )






    def _a					(			self    )  ->       int:
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   pad_token='<pad>'    )

         # Simple input
         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input looooooooong', 'This is a simple input']
         __UpperCamelCase    							=('This is a simple input', 'This is a pair')
         __UpperCamelCase    							=[
             ('This is a simple input loooooong', 'This is a simple input'),
             ('This is a simple pair loooooong', 'This is a simple pair'),
         ]

         __UpperCamelCase    							=tokenizer.pad_token_id

         __UpperCamelCase    							=tokenizer(A_					,   padding='max_length'					,   max_length=30					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(*A_					,   padding='max_length'					,   max_length=60					,   return_tensors='np'    )
         __UpperCamelCase    							=tokenizer(A_					,   padding=A_					,   truncate=A_					,   return_tensors='np'    )

         # s
         # test single string max_length padding
         self.assertEqual(out_s['input_ids'].shape[-1]					,   30    )
         self.assertTrue(pad_token_id in out_s['input_ids']    )
         self.assertTrue(0 in out_s['attention_mask']    )

         # s2
         # test automatic padding
         self.assertEqual(out_sa['input_ids'].shape[-1]					,   33    )
         # long slice doesn't have padding
         self.assertFalse(pad_token_id in out_sa['input_ids'][0]    )
         self.assertFalse(0 in out_sa['attention_mask'][0]    )
         # short slice does have padding
         self.assertTrue(pad_token_id in out_sa['input_ids'][1]    )
         self.assertTrue(0 in out_sa['attention_mask'][1]    )

         # p
         # test single pair max_length padding
         self.assertEqual(out_p['input_ids'].shape[-1]					,   60    )
         self.assertTrue(pad_token_id in out_p['input_ids']    )
         self.assertTrue(0 in out_p['attention_mask']    )

         # p2
         # test automatic padding pair
         self.assertEqual(out_pa['input_ids'].shape[-1]					,   52    )
         # long slice pair doesn't have padding
         self.assertFalse(pad_token_id in out_pa['input_ids'][0]    )
         self.assertFalse(0 in out_pa['attention_mask'][0]    )
         # short slice pair does have padding
         self.assertTrue(pad_token_id in out_pa['input_ids'][1]    )
         self.assertTrue(0 in out_pa['attention_mask'][1]    )






    def _a					(			self    )  ->       Union[str, Any]:
         __UpperCamelCase    							='$$$'
         __UpperCamelCase    							=GPTaTokenizer.from_pretrained(self.tmpdirname					,   bos_token=A_					,   add_bos_token=A_    )

         __UpperCamelCase    							='This is a simple input'
         __UpperCamelCase    							=['This is a simple input 1', 'This is a simple input 2']

         __UpperCamelCase    							=tokenizer.bos_token_id

         __UpperCamelCase    							=tokenizer(A_    )
         __UpperCamelCase    							=tokenizer(A_    )

         self.assertEqual(out_s.input_ids[0]					,   A_    )
         self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids    )    )

         __UpperCamelCase    							=tokenizer.decode(out_s.input_ids    )
         __UpperCamelCase    							=tokenizer.batch_decode(out_sa.input_ids    )

         self.assertEqual(decode_s.split()[0]					,   A_    )
         self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa    )    )






    def _a					(			self    )  ->       Optional[int]:
         pass






    def _a					(			self    )  ->       Any:
         # TODO: change to self.get_tokenizers() when the fast version is implemented
         __UpperCamelCase    							=[self.get_tokenizer(do_lower_case=A_					,   add_bos_token=A_    )]
         for tokenizer in tokenizers:
              with self.subTest(f'{tokenizer.__class__.__name__}'    ):
                   __UpperCamelCase    							='Encode this.'
                   __UpperCamelCase    							='This one too please.'
                   __UpperCamelCase    							=tokenizer.encode(A_					,   add_special_tokens=A_    )
                   encoded_sequence += tokenizer.encode(A_					,   add_special_tokens=A_    )
                   __UpperCamelCase    							=tokenizer.encode_plus(
                       A_					,   A_					,   add_special_tokens=A_					,   return_special_tokens_mask=A_					,   )
                   __UpperCamelCase    							=encoded_sequence_dict['input_ids']
                   __UpperCamelCase    							=encoded_sequence_dict['special_tokens_mask']
                   self.assertEqual(len(A_    )					,   len(A_    )    )

                   __UpperCamelCase    							=[
                       (x if not special_tokens_mask[i] else None) for i, x in enumerate(A_    )
                   ]
                   __UpperCamelCase    							=[x for x in filtered_sequence if x is not None]
                   self.assertEqual(A_					,   A_    )






@require_tokenizers
class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def _a					(			self    )  ->       Optional[Any]:
         # More context:
         # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
         # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
         # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('test_opt'    )

         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./test_opt'    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    def _a					(			self    )  ->       Dict:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   use_slow=A_    )
         __UpperCamelCase    							='A photo of a cat'

         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # Same as above
         self.assertEqual(A_					,   [2, 250, 1345, 9, 10, 4758]    )






    @unittest.skip('This test is failing because of a bug in the fast tokenizer'    )
    def _a					(			self    )  ->       List[Any]:
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('facebook/opt-350m'					,   from_slow=A_    )

         __UpperCamelCase    							='bos'
         __UpperCamelCase    							=tokenizer.get_vocab()['bos']

         __UpperCamelCase    							='A photo of a cat'
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         # We changed the bos token
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )
         tokenizer.save_pretrained('./tok'    )
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('./tok'    )
         self.assertTrue(tokenizer.is_fast    )
         __UpperCamelCase    							=tokenizer.encode(
             A_					,   )
         self.assertEqual(A_					,   [31957, 250, 1345, 9, 10, 4758]    )





