
"""simple docstring"""





import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


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






class        _UpperCAmelCase(       lowercase_      ):



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






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

							if safety_checker is None:
												logger.warning(
												    F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
												    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
												    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
												    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
												    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
												    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''')

							self.register_modules(
							    speech_model=a__							,						speech_processor=a__							,						vae=a__							,						text_encoder=a__							,						tokenizer=a__							,						unet=a__							,						scheduler=a__							,						feature_extractor=a__							,						)



		def 		UpperCAmelCase				(   self							,						__a = "auto")			->			List[str]:






							'''simple docstring'''





							if slice_size == "auto":
												_UpperCamelCase									=    self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(a__)



		def 		UpperCAmelCase				(   self)			->			str:






							'''simple docstring'''
							self.enable_attention_slicing(a__)




		@torch.no_grad()
		def __call__(   self							,						__a							,						__a=1_60_00							,						__a = 5_12							,						__a = 5_12							,						__a = 50							,						__a = 7.5							,						__a = None							,						__a = 1							,						__a = 0.0							,						__a = None							,						__a = None							,						__a = "pil"							,						__a = True							,						__a = None							,						__a = 1							,						**__a							,						)			->			Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase									=    self.speech_processor.feature_extractor(
							    a__							,						return_tensors='''pt'''							,						sampling_rate=a__).input_features.to(self.device)
							_UpperCamelCase									=    self.speech_model.generate(a__							,						max_length=48_00_00)

							_UpperCamelCase									=    self.speech_processor.tokenizer.batch_decode(a__							,						skip_special_tokens=a__							,						normalize=a__)[
							    0
							]

							if isinstance(a__							,						a__):
												_UpperCamelCase									=    1
							elif isinstance(a__							,						a__):
												_UpperCamelCase									=    len(a__)
							else:
												raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(a__)}''')

							if height % 8 != 0 or width % 8 != 0:
												raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''')

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(a__							,						a__) or callback_steps <= 0)
							):
												raise ValueError(
												    F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
												    F''' {type(a__)}.''')

							# get prompt text embeddings
							_UpperCamelCase									=    self.tokenizer(
							    a__							,						padding='''max_length'''							,						max_length=self.tokenizer.model_max_length							,						return_tensors='''pt'''							,						)
							_UpperCamelCase									=    text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
												_UpperCamelCase									=    self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
												logger.warning(
												    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
												    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''')
												_UpperCamelCase									=    text_input_ids[:, : self.tokenizer.model_max_length]
							_UpperCamelCase									=    self.text_encoder(text_input_ids.to(self.device))[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							_UpperCamelCase									=    text_embeddings.shape
							_UpperCamelCase									=    text_embeddings.repeat(1							,						a__							,						1)
							_UpperCamelCase									=    text_embeddings.view(bs_embed * num_images_per_prompt							,						a__							,						-1)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							_UpperCamelCase									=    guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
												_UpperCamelCase									=    42
												if negative_prompt is None:
																	_UpperCamelCase									=    [""""""] * batch_size
												elif type(a__) is not type(a__):
																	raise TypeError(
																	    F'''`negative_prompt` should be the same type to `prompt`, but got {type(a__)} !='''
																	    F''' {type(a__)}.''')
												elif isinstance(a__							,						a__):
																	_UpperCamelCase									=    [negative_prompt]
												elif batch_size != len(a__):
																	raise ValueError(
																	    F'''`negative_prompt`: {negative_prompt} has batch size {len(a__)}, but `prompt`:'''
																	    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
																	    ''' the batch size of `prompt`.''')
												else:
																	_UpperCamelCase									=    negative_prompt

												_UpperCamelCase									=    text_input_ids.shape[-1]
												_UpperCamelCase									=    self.tokenizer(
												    a__							,						padding='''max_length'''							,						max_length=a__							,						truncation=a__							,						return_tensors='''pt'''							,						)
												_UpperCamelCase									=    self.text_encoder(uncond_input.input_ids.to(self.device))[0]

												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
												_UpperCamelCase									=    uncond_embeddings.shape[1]
												_UpperCamelCase									=    uncond_embeddings.repeat(1							,						a__							,						1)
												_UpperCamelCase									=    uncond_embeddings.view(batch_size * num_images_per_prompt							,						a__							,						-1)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												_UpperCamelCase									=    torch.cat([uncond_embeddings, text_embeddings])

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							_UpperCamelCase									=    (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							_UpperCamelCase									=    text_embeddings.dtype
							if latents is None:
												if self.device.type == "mps":
																	# randn does not exist on mps
																	_UpperCamelCase									=    torch.randn(a__							,						generator=a__							,						device='''cpu'''							,						dtype=a__).to(
																	    self.device)
												else:
																	_UpperCamelCase									=    torch.randn(a__							,						generator=a__							,						device=self.device							,						dtype=a__)
							else:
												if latents.shape != latents_shape:
																	raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''')
												_UpperCamelCase									=    latents.to(self.device)

							# set timesteps
							self.scheduler.set_timesteps(a__)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							_UpperCamelCase									=    self.scheduler.timesteps.to(self.device)

							# scale the initial noise by the standard deviation required by the scheduler
							_UpperCamelCase									=    latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							_UpperCamelCase									=    """eta""" in set(inspect.signature(self.scheduler.step).parameters.keys())
							_UpperCamelCase									=    {}
							if accepts_eta:
												_UpperCamelCase									=    eta

							for i, t in enumerate(self.progress_bar(a__)):
												# expand the latents if we are doing classifier free guidance
												_UpperCamelCase									=    torch.cat([latents] * 2) if do_classifier_free_guidance else latents
												_UpperCamelCase									=    self.scheduler.scale_model_input(a__							,						a__)

												# predict the noise residual
												_UpperCamelCase									=    self.unet(a__							,						a__							,						encoder_hidden_states=a__).sample

												# perform guidance
												if do_classifier_free_guidance:
																	_UpperCamelCase									=    noise_pred.chunk(2)
																	_UpperCamelCase									=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

												# compute the previous noisy sample x_t -> x_t-1
												_UpperCamelCase									=    self.scheduler.step(a__							,						a__							,						a__							,						**a__).prev_sample

												# call the callback, if provided
												if callback is not None and i % callback_steps == 0:
																	callback(a__							,						a__							,						a__)

							_UpperCamelCase									=    1 / 0.1_8215 * latents
							_UpperCamelCase									=    self.vae.decode(a__).sample

							_UpperCamelCase									=    (image / 2 + 0.5).clamp(0							,						1)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							_UpperCamelCase									=    image.cpu().permute(0							,						2							,						3							,						1).float().numpy()

							if output_type == "pil":
												_UpperCamelCase									=    self.numpy_to_pil(a__)

							if not return_dict:
												return image

							return StableDiffusionPipelineOutput(images=a__							,						nsfw_content_detected=a__)














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       snake_case__ (				lowercase_						,     unittest.TestCase):

       '''simple docstring'''
       lowerCamelCase				:					List[str]								=			GPTaTokenizer
       lowerCamelCase				:					Optional[int]								=			GPTaTokenizerFast
       lowerCamelCase				:					List[Any]								=			True
       lowerCamelCase				:					List[str]								=			{"add_prefix_space": True}
       lowerCamelCase				:					Optional[int]								=			False



       def        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            super().setUp()

            # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
            __snake_case      :Optional[Any] 							=    [
                """l""",
                """o""",
                """w""",
                """e""",
                """r""",
                """s""",
                """t""",
                """i""",
                """d""",
                """n""",
                """\u0120""",
                """\u0120l""",
                """\u0120n""",
                """\u0120lo""",
                """\u0120low""",
                """er""",
                """\u0120lowest""",
                """\u0120newer""",
                """\u0120wider""",
                """<unk>""",
                """<|endoftext|>""",
            ]
            __snake_case      :Dict 							=    dict(zip(a__      ,    range(len(a__     )     )     )     )
            __snake_case      :List[Any] 							=    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
            __snake_case      :List[str] 							=    {"""unk_token""": """<unk>"""}

            __snake_case      :Dict 							=    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""]     )
            __snake_case      :Optional[Any] 							=    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        __lowercase    (				self      ,    **a__     )			->   Any:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizer.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    **a__     )			->   Tuple:






            '''simple docstring'''





            kwargs.update(self.special_tokens_map     )
            return GPTaTokenizerFast.from_pretrained(self.tmpdirname      ,    **a__     )



       def        __lowercase    (				self      ,    a__     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :Any 							=    """lower newer"""
            return input_text, output_text



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    GPTaTokenizer(self.vocab_file      ,    self.merges_file      ,    **self.special_tokens_map     )
            __snake_case      :List[Any] 							=    """lower newer"""
            __snake_case      :int 							=    ["""\u0120low""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
            __snake_case      :Any 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            self.assertListEqual(a__      ,    a__     )

            __snake_case      :List[Any] 							=    tokens + [tokenizer.unk_token]
            __snake_case      :List[Any] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self     )			->   Dict:






            '''simple docstring'''





            if not self.test_rust_tokenizer:
                 return

            __snake_case      :Union[str, Any] 							=    self.get_tokenizer()
            __snake_case      :Optional[Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )

            __snake_case      :Optional[int] 							=    """lower newer"""

            # Testing tokenization
            __snake_case      :List[Any] 							=    tokenizer.tokenize(a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.tokenize(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids without special tokens
            __snake_case      :List[str] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__      ,    add_prefix_space=a__     )
            __snake_case      :int 							=    rust_tokenizer.encode(a__      ,    add_special_tokens=a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing conversion to ids with special tokens
            __snake_case      :Union[str, Any] 							=    self.get_rust_tokenizer(add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    tokenizer.encode(a__      ,    add_prefix_space=a__     )
            __snake_case      :Optional[int] 							=    rust_tokenizer.encode(a__     )
            self.assertListEqual(a__      ,    a__     )

            # Testing the unknown token
            __snake_case      :Dict 							=    tokens + [rust_tokenizer.unk_token]
            __snake_case      :Optional[int] 							=    [14, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(a__     )      ,    a__     )



       def        __lowercase    (				self      ,    *a__      ,    **a__     )			->   Any:






            '''simple docstring'''





            pass



       def        __lowercase    (				self      ,    a__=15     )			->   Optional[Any]:






            '''simple docstring'''





            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''     ):
                      __snake_case      :Optional[int] 							=    self.rust_tokenizer_class.from_pretrained(a__      ,    **a__     )

                      # Simple input
                      __snake_case      :List[str] 							=    """This is a simple input"""
                      __snake_case      :List[str] 							=    ["""This is a simple input 1""", """This is a simple input 2"""]
                      __snake_case      :Tuple 							=    ("""This is a simple input""", """This is a pair""")
                      __snake_case      :Union[str, Any] 							=    [
                          ("""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        __lowercase    (				self     )			->   int:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    pad_token="""<pad>"""     )

            # Simple input
            __snake_case      :Tuple 							=    """This is a simple input"""
            __snake_case      :Optional[int] 							=    ["""This is a simple input looooooooong""", """This is a simple input"""]
            __snake_case      :List[str] 							=    ("""This is a simple input""", """This is a pair""")
            __snake_case      :List[str] 							=    [
                ("""This is a simple input loooooong""", """This is a simple input"""),
                ("""This is a simple pair loooooong""", """This is a simple pair"""),
            ]

            __snake_case      :Tuple 							=    tokenizer.pad_token_id

            __snake_case      :int 							=    tokenizer(a__      ,    padding="""max_length"""      ,    max_length=30      ,    return_tensors="""np"""     )
            __snake_case      :Optional[Any] 							=    tokenizer(a__      ,    padding=a__      ,    truncate=a__      ,    return_tensors="""np"""     )
            __snake_case      :List[Any] 							=    tokenizer(*a__      ,    padding="""max_length"""      ,    max_length=60      ,    return_tensors="""np"""     )
            __snake_case      :Optional[int] 							=    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        __lowercase    (				self     )			->   List[str]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    """$$$"""
            __snake_case      :Optional[Any] 							=    GPTaTokenizer.from_pretrained(self.tmpdirname      ,    bos_token=a__      ,    add_bos_token=a__     )

            __snake_case      :List[Any] 							=    """This is a simple input"""
            __snake_case      :Tuple 							=    ["""This is a simple input 1""", """This is a simple input 2"""]

            __snake_case      :Tuple 							=    tokenizer.bos_token_id

            __snake_case      :List[str] 							=    tokenizer(a__     )
            __snake_case      :Any 							=    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     )     )

            __snake_case      :int 							=    tokenizer.decode(out_s.input_ids     )
            __snake_case      :Tuple 							=    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        __lowercase    (				self     )			->   str:






            '''simple docstring'''





            pass



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :List[str] 							=    [self.get_tokenizer(do_lower_case=a__      ,    add_bos_token=a__     )]
            for tokenizer in tokenizers:
                 with self.subTest(F'''{tokenizer.__class__.__name__}'''     ):
                      __snake_case      :Tuple 							=    """Encode this."""
                      __snake_case      :Tuple 							=    """This one too please."""
                      __snake_case      :Union[str, Any] 							=    tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      encoded_sequence += tokenizer.encode(a__      ,    add_special_tokens=a__     )
                      __snake_case      :List[str] 							=    tokenizer.encode_plus(
                          a__      ,    a__      ,    add_special_tokens=a__      ,    return_special_tokens_mask=a__      ,    )
                      __snake_case      :Union[str, Any] 							=    encoded_sequence_dict["""input_ids"""]
                      __snake_case      :Optional[Any] 							=    encoded_sequence_dict["""special_tokens_mask"""]
                      self.assertEqual(len(a__     )      ,    len(a__     )     )

                      __snake_case      :Optional[int] 							=    [
                          (x if not special_tokens_mask[i] else None) for i, x in enumerate(a__     )
                      ]
                      __snake_case      :str 							=    [x for x in filtered_sequence if x is not None]
                      self.assertEqual(a__      ,    a__     )




@require_tokenizers
class       snake_case__ (				unittest.TestCase):

       '''simple docstring'''



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )
            __snake_case      :Union[str, Any] 							=    """A photo of a cat"""

            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""test_opt"""     )

            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""./test_opt"""     )
            __snake_case      :Tuple 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       def        __lowercase    (				self     )			->   Any:






            '''simple docstring'''





            __snake_case      :Tuple 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    use_slow=a__     )
            __snake_case      :str 							=    """A photo of a cat"""

            __snake_case      :List[str] 							=    tokenizer.encode(
                a__      ,    )
            # Same as above
            self.assertEqual(a__      ,    [2, 2_50, 13_45, 9, 10, 47_58]     )



       @unittest.skip("""This test is failing because of a bug in the fast tokenizer"""     )
       def        __lowercase    (				self     )			->   List[Any]:






            '''simple docstring'''





            __snake_case      :int 							=    AutoTokenizer.from_pretrained("""facebook/opt-350m"""      ,    from_slow=a__     )

            __snake_case      :Optional[Any] 							=    """bos"""
            __snake_case      :int 							=    tokenizer.get_vocab()["""bos"""]

            __snake_case      :str 							=    """A photo of a cat"""
            __snake_case      :int 							=    tokenizer.encode(
                a__      ,    )
            # We changed the bos token
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )
            tokenizer.save_pretrained("""./tok"""     )
            __snake_case      :Dict 							=    AutoTokenizer.from_pretrained("""./tok"""     )
            self.assertTrue(tokenizer.is_fast     )
            __snake_case      :List[Any] 							=    tokenizer.encode(
                a__      ,    )
            self.assertEqual(a__      ,    [3_19_57, 2_50, 13_45, 9, 10, 47_58]     )


