


import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class    UpperCAmelCase_	(							_a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     List[Any]							=					BlenderbotSmallTokenizer
       __UpperCamelCase      :     str							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              super().setUp()

              UpperCamelCase    :	Optional[Any]   	=		['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
              UpperCamelCase    :	Any   	=		dict(zip(__SCREAMING_SNAKE_CASE ,					range(len(__SCREAMING_SNAKE_CASE						)						)						)						)

              UpperCamelCase    :	Any   	=		['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
              UpperCamelCase    :	Tuple   	=		{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

              UpperCamelCase    :	Optional[int]   	=		os.path.join(self.tmpdirname ,					VOCAB_FILES_NAMES['''vocab_file''']						)
              UpperCamelCase    :	int   	=		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(__SCREAMING_SNAKE_CASE						) + '''\n'''						)
              with open(self.merges_file ,					'''w''' ,					encoding='''utf-8'''						) as fp:
                     fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE						)						)






       def  _lowercase   (     self ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              kwargs.update(self.special_tokens_map						)
              return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		'''adapt act apte'''
              UpperCamelCase    :	List[Any]   	=		'''adapt act apte'''
              return input_text, output_text






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		BlenderbotSmallTokenizer(self.vocab_file ,					self.merges_file ,					**self.special_tokens_map						)
              UpperCamelCase    :	List[str]   	=		'''adapt act apte'''
              UpperCamelCase    :	List[str]   	=		['''adapt''', '''act''', '''ap@@''', '''te''']
              UpperCamelCase    :	str   	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE						)
              self.assertListEqual(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	str   	=		[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

              UpperCamelCase    :	Dict   	=		[0, 1, 2, 3, 4, 5]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Tuple   	=		BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''						)
              assert tok('''sam'''						).input_ids == [1_384]
              UpperCamelCase    :	Dict   	=		'''I am a small frog.'''
              UpperCamelCase    :	Tuple   	=		tok([src_text] ,					padding=__SCREAMING_SNAKE_CASE ,					truncation=__SCREAMING_SNAKE_CASE						)['''input_ids''']
              UpperCamelCase    :	Tuple   	=		tok.batch_decode(__SCREAMING_SNAKE_CASE ,					skip_special_tokens=__SCREAMING_SNAKE_CASE ,					clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE						)[0]
              assert src_text != decoded  # I wish it did!
              assert decoded == "i am a small frog ."






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''						)
              UpperCamelCase    :	List[Any]   	=		'''I am a small frog .'''
              UpperCamelCase    :	Union[str, Any]   	=		'''.'''
              UpperCamelCase    :	Dict   	=		tok(__SCREAMING_SNAKE_CASE						)['''input_ids''']
              UpperCamelCase    :	Optional[int]   	=		tok(__SCREAMING_SNAKE_CASE						)['''input_ids''']

              assert encoded[-1] == encoded_dot[0]





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


__UpperCAmelCase : Dict 			=				False



class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''
       pass




@slow
@require_torch_gpu
class    UpperCAmelCase_	(							unittest.TestCase):
       '''simple docstring'''






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Dict   	=		VersatileDiffusionImageVariationPipeline.from_pretrained('''shi-labs/versatile-diffusion'''						)
              pipe.to(__SCREAMING_SNAKE_CASE						)
              pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		load_image(
                  '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''						)
              UpperCamelCase    :	str   	=		torch.manual_seed(0						)
              UpperCamelCase    :	Union[str, Any]   	=		pipe(
                  image=__SCREAMING_SNAKE_CASE ,					generator=__SCREAMING_SNAKE_CASE ,					guidance_scale=7.5 ,					num_inference_steps=50 ,					output_type='''numpy''' ,					).images

              UpperCamelCase    :	List[Any]   	=		image[0, 253:256, 253:256, -1]

              assert image.shape == (1, 512, 512, 3)
              UpperCamelCase    :	Dict   	=		np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]						)

              assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2

