


import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin







class lowerCAmelCase_		(					lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    BertweetTokenizer
 __snake_case				=    False



 def 		UpperCamelCase__	(      self   ):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       snake_case_			= ['''I''', '''m''', '''V@@''', '''R@@''', '''r''', '''e@@''']
       snake_case_			= dict(zip(_UpperCAmelCase						, range(len(_UpperCAmelCase   )   )   )   )
       snake_case_			= ['''#version: 0.2''', '''a m</w>''']
       snake_case_			= {'''unk_token''': '''<unk>'''}

       snake_case_			= os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''vocab_file''']   )
       snake_case_			= os.path.join(self.tmpdirname						, VOCAB_FILES_NAMES['''merges_file''']   )
       with open(self.vocab_file						, '''w'''						, encoding='''utf-8'''   ) as fp:
             for token in vocab_tokens:
                   fp.write(F'''{token} {vocab_tokens[token]}\n'''   )
       with open(self.merges_file						, '''w'''						, encoding='''utf-8'''   ) as fp:
             fp.write('''\n'''.join(_UpperCAmelCase   )   )



 def 		UpperCamelCase__	(      self						, **_UpperCAmelCase   ):
       kwargs.update(self.special_tokens_map   )
       return BertweetTokenizer.from_pretrained(self.tmpdirname						, **_UpperCAmelCase   )



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase   ):
       snake_case_			= '''I am VinAI Research'''
       snake_case_			= '''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
       return input_text, output_text





 def 		UpperCamelCase__	(      self   ):
       snake_case_			= BertweetTokenizer(self.vocab_file						, self.merges_file						, **self.special_tokens_map   )
       snake_case_			= '''I am VinAI Research'''
       snake_case_			= '''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
       snake_case_			= tokenizer.tokenize(_UpperCAmelCase   )
       self.assertListEqual(_UpperCAmelCase						, _UpperCAmelCase   )

       snake_case_			= tokens + [tokenizer.unk_token]

       snake_case_			= [4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )						, _UpperCAmelCase   )



import random
import unittest

import torch

from diffusers import IFImgaImgPipeline
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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class lowerCAmelCase_		(					lowerCamelCase__   ,		lowerCamelCase__   ,		unittest.TestCase			):

 '''simple docstring'''

 __snake_case				=    IFImgaImgPipeline
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
 __snake_case				=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __snake_case				=    PipelineTesterMixin.required_optional_params - {"latents"}



 def 		UpperCamelCase__	(      self   ):
       return self._get_dummy_components()



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase=0   ):
       if str(_UpperCAmelCase   ).startswith('''mps'''   ):
             snake_case_			= torch.manual_seed(_UpperCAmelCase   )
       else:
             snake_case_			= torch.Generator(device=_UpperCAmelCase   ).manual_seed(_UpperCAmelCase   )

       snake_case_			= floats_tensor((1, 3, 32, 32)						, rng=random.Random(_UpperCAmelCase   )   ).to(_UpperCAmelCase   )

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

       return inputs



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_optional_components()



 @unittest.skipIf(
     torch_device != '''cuda''' or not is_xformers_available()						, reason='''XFormers attention is only available with CUDA and `xformers` installed'''						, )
 def 		UpperCamelCase__	(      self   ):
       self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       # Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
       super().test_save_load_floataa(expected_max_diff=1E-1   )



 @unittest.skipIf(torch_device != '''cuda'''						, reason='''float16 requires CUDA'''   )
 def 		UpperCamelCase__	(      self   ):
       super().test_floataa_inference(expected_max_diff=1E-1   )



 def 		UpperCamelCase__	(      self   ):
       self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )



 def 		UpperCamelCase__	(      self   ):
       self._test_save_load_local()





 def 		UpperCamelCase__	(      self   ):
       self._test_inference_batch_single_identical(
           expected_max_diff=1E-2						, )