




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_					(				UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   BlenderbotSmallTokenizer
    UpperCAmelCase__													=   False


    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->int:





           '''simple docstring'''

           super().setUp()

           A__   				=					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
           A__   				=					dict(zip(UpperCAmelCase__      ,				range(len(UpperCAmelCase__))))

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

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict      ,				**UpperCAmelCase__   :			Optional[Any])		->Union[str, Any]:





           '''simple docstring'''

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


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			str)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					'''adapt act apte'''
           A__   				=					'''adapt act apte'''
           return input_text, output_text


    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Dict:





           '''simple docstring'''

           A__   				=					BlenderbotSmallTokenizer(self.vocab_file      ,				self.merges_file      ,				**self.special_tokens_map)
           A__   				=					'''adapt act apte'''
           A__   				=					['''adapt''', '''act''', '''ap@@''', '''te''']
           A__   				=					tokenizer.tokenize(UpperCAmelCase__)
           self.assertListEqual(UpperCAmelCase__      ,				UpperCAmelCase__)

           A__   				=					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

           A__   				=					[0, 1, 2, 3, 4, 5]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__)      ,				UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->int:





           '''simple docstring'''

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





    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Union[str, Any]:





           '''simple docstring'''

           A__   				=					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''')
           A__   				=					'''I am a small frog .'''
           A__   				=					'''.'''
           A__   				=					tok(UpperCAmelCase__)['''input_ids''']
           A__   				=					tok(UpperCAmelCase__)['''input_ids''']

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








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class        UpperCamelCase_					(				UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase  ):

    '''simple docstring'''






    UpperCAmelCase__													=   IFInpaintingPipeline
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
    UpperCAmelCase__													=   TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase__													=   PipelineTesterMixin.required_optional_params - {'''latents'''}


    def  SCREAMING_SNAKE_CASE							(    self   :			Any)		->Dict:





           '''simple docstring'''

           return self._get_dummy_components()


    def  SCREAMING_SNAKE_CASE							(    self   :			int      ,				UpperCAmelCase__   :			Dict      ,				UpperCAmelCase__   :			int=0)		->int:





           '''simple docstring'''

           if str(UpperCAmelCase__).startswith('''mps'''):
                  A__   				=					torch.manual_seed(UpperCAmelCase__)
           else:
                  A__   				=					torch.Generator(device=UpperCAmelCase__).manual_seed(UpperCAmelCase__)

           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)
           A__   				=					floats_tensor((1, 3, 32, 32)      ,				rng=random.Random(UpperCAmelCase__)).to(UpperCAmelCase__)

           A__   				=					{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''image''': image,
               '''mask_image''': mask_image,
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''output_type''': '''numpy''',
           }

           return inputs


    @unittest.skipIf(
        torch_device != '''cuda''' or not is_xformers_available()      ,				reason='''XFormers attention is only available with CUDA and `xformers` installed'''      ,				)
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->List[Any]:





           '''simple docstring'''

           self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3)


    def  SCREAMING_SNAKE_CASE							(    self   :			Dict)		->int:





           '''simple docstring'''

           self._test_save_load_optional_components()


    @unittest.skipIf(torch_device != '''cuda'''      ,				reason='''float16 requires CUDA''')
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->List[str]:





           '''simple docstring'''

           super().test_save_load_floataa(expected_max_diff=1e-1)


    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->List[Any]:





           '''simple docstring'''

           self._test_attention_slicing_forward_pass(expected_max_diff=1e-2)


    def  SCREAMING_SNAKE_CASE							(    self   :			Union[str, Any])		->Any:





           '''simple docstring'''

           self._test_save_load_local()





    def  SCREAMING_SNAKE_CASE							(    self   :			int)		->Union[str, Any]:





           '''simple docstring'''

           self._test_inference_batch_single_identical(
               expected_max_diff=1e-2      ,				)


