


import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 				__A    (	_SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  XLMTokenizer
 __lowerCAmelCase			 =  False


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

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     a					=[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''w</w>''',
         '''r</w>''',
         '''t</w>''',
         '''lo''',
         '''low''',
         '''er</w>''',
         '''low</w>''',
         '''lowest</w>''',
         '''newer</w>''',
         '''wider</w>''',
         '''<unk>''',
     ]
     a					=dict(zip(__A			,   range(len(__A						)						)						)						)
     a					=['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

     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'''						) as fp:
         fp.write(json.dumps(__A						)						)
     with open(self.merges_file			,   '''w'''						) as fp:
         fp.write('''\n'''.join(__A						)						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict:
     a					='''lower newer'''
     a					='''lower newer'''
     return input_text, output_text


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     a					=XLMTokenizer(self.vocab_file			,   self.merges_file						)

     a					='''lower'''
     a					=['''low''', '''er</w>''']
     a					=tokenizer.tokenize(__A						)
     self.assertListEqual(__A			,   __A						)

     a					=tokens + ['''<unk>''']
     a					=[14, 15, 20]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(__A						)			,   __A						)





 @slow
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     a					=XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''						)

     a					=tokenizer.encode('''sequence builders'''			,   add_special_tokens=__A						)
     a					=tokenizer.encode('''multi-sequence build'''			,   add_special_tokens=__A						)

     a					=tokenizer.build_inputs_with_special_tokens(__A						)
     a					=tokenizer.build_inputs_with_special_tokens(__A			,   __A						)

     assert encoded_sentence == [0] + text + [1]
     assert encoded_pair == [0] + text + [1] + text_a + [1]



"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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 				__A    (	_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   unittest.TestCase      ):






 """simple docstring"""




 __lowerCAmelCase			 =  IFInpaintingSuperResolutionPipeline
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
 __lowerCAmelCase			 =  TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"original_image"}      )
 __lowerCAmelCase			 =  PipelineTesterMixin.required_optional_params - {"latents"}


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[int]:
     return self._get_superresolution_dummy_components()


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0						) ->					Optional[int]:
     if str(__A						).startswith('''mps'''						):
         a					=torch.manual_seed(__A						)
     else:
         a					=torch.Generator(device=__A						).manual_seed(__A						)

     a					=floats_tensor((1, 3, 16, 16)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)
     a					=floats_tensor((1, 3, 32, 32)			,   rng=random.Random(__A						)						).to(__A						)

     a					={
         '''prompt''': '''A painting of a squirrel eating a burger''',
         '''image''': image,
         '''original_image''': original_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[Any]:
     self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_save_load_optional_components()


 @unittest.skipIf(torch_device != '''cuda'''			,   reason='''float16 requires CUDA'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     # 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						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Any:
     self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)


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





 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Dict:
     self._test_inference_batch_single_identical(
         expected_max_diff=1E-2			,   )