



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 			__snake_case   ( SCREAMING_SNAKE_CASE      ,SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   int       =       IFImgaImgPipeline
							UpperCamelCase__					:   Optional[int]       =       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
							UpperCamelCase__					:   int       =       TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							UpperCamelCase__					:   Tuple       =       PipelineTesterMixin.required_optional_params - {"""latents"""}


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


							def _a   (				self	,	a_	,	a_=0		):
											if str(a_		).startswith("""mps"""		):
															a__					   =		torch.manual_seed(a_		)
											else:
															a__					   =		torch.Generator(device=a_		).manual_seed(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,
											    """generator""": generator,
											    """num_inference_steps""": 2,
											    """output_type""": """numpy""",
											}

											return inputs


							def _a   (				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 _a   (				self		):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3		)


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


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


							def _a   (				self		):
											self._test_save_load_local()







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


'''simple docstring'''




from typing import Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_openai import OpenAIGPTTokenizer


__snake_case  : Union[str, Any]		       =    logging.get_logger(__name__)

__snake_case  : Union[str, Any]		       =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

__snake_case  : List[str]		       =    {
    'vocab_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/vocab.json'},
    'merges_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/merges.txt'},
    'tokenizer_file': {'openai-gpt': 'https://huggingface.co/openai-gpt/resolve/main/tokenizer.json'},
}

__snake_case  : Optional[int]		       =    {
    'openai-gpt': 512,
}




class       __UpperCAmelCase							(							_UpperCamelCase	):





       '''simple docstring'''



       __lowercase   :   int  					=							VOCAB_FILES_NAMES
       __lowercase   :   int  					=							PRETRAINED_VOCAB_FILES_MAP
       __lowercase   :   str  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __lowercase   :   Optional[Any]  					=							['input_ids', 'attention_mask']
       __lowercase   :   List[Any]  					=							OpenAIGPTTokenizer

       def __init__( self       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE=None       ,	_SCREAMING_SNAKE_CASE="<unk>"       ,	**_SCREAMING_SNAKE_CASE					)						->	Optional[Any]:
           super().__init__(_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE       ,	tokenizer_file=_SCREAMING_SNAKE_CASE       ,	unk_token=_SCREAMING_SNAKE_CASE       ,	**_SCREAMING_SNAKE_CASE					)

       @property
       def    __A			( self					)						->	Optional[Any]:
           return True

       def    __A			( self       ,	_SCREAMING_SNAKE_CASE       ,	_SCREAMING_SNAKE_CASE = None					)						->	Tuple[str]:
           A_      =  self._tokenizer.model.save(_SCREAMING_SNAKE_CASE       ,	name=_SCREAMING_SNAKE_CASE					)
           return tuple(_SCREAMING_SNAKE_CASE					)






