





"""simple docstring"""


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        lowercase__ (      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    unittest.TestCase  ):

			"""simple docstring"""
			__lowerCAmelCase    :							str								=     IFImgaImgPipeline
			__lowerCAmelCase    :							Union[str, Any]								=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
			__lowerCAmelCase    :							Tuple								=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
			__lowerCAmelCase    :							List[Any]								=     PipelineTesterMixin.required_optional_params - {"""latents"""}







			def      _a			(			self						):






									'''simple docstring'''







									return self._get_dummy_components()







			def      _a			(			self	,      _A	,      _A=0						):






									'''simple docstring'''







									if str(_A						).startswith("""mps"""						):
															UpperCamelCase							:		int							      = torch.manual_seed(_A						)
									else:
															UpperCamelCase							:		List[str]							      = torch.Generator(device=_A						).manual_seed(_A						)

									UpperCamelCase							:		Any							      = floats_tensor((1, 3, 3_2, 3_2)	,      rng=random.Random(_A						)						).to(_A						)

									UpperCamelCase							:		Any							      = {
									    """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						):






									'''simple docstring'''







									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						):






									'''simple docstring'''







									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3						)







			@unittest.skipIf(torch_device != """cuda"""	,      reason="""float16 requires CUDA"""						)
			def      _a			(			self						):






									'''simple docstring'''







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







			@unittest.skipIf(torch_device != """cuda"""	,      reason="""float16 requires CUDA"""						)
			def      _a			(			self						):






									'''simple docstring'''







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







			def      _a			(			self						):






									'''simple docstring'''







									self._test_attention_slicing_forward_pass(expected_max_diff=1e-2						)







			def      _a			(			self						):






									'''simple docstring'''







									self._test_save_load_local()







			def      _a			(			self						):






									'''simple docstring'''







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










"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__					:				Any	= logging.get_logger(__name__)

__magic_name__					:				Dict	= {
    """uw-madison/yoso-4096""": """https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json""",
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}






class        lowercase__ (      __SCREAMING_SNAKE_CASE  ):

			"""simple docstring"""
			__lowerCAmelCase    :							Optional[int]								=     """yoso"""







			def __init__(			self	,      _A=5_0_2_6_5	,      _A=7_6_8	,      _A=1_2	,      _A=1_2	,      _A=3_0_7_2	,      _A="gelu"	,      _A=0.1	,      _A=0.1	,      _A=4_0_9_6	,      _A=1	,      _A=0.02	,      _A=1e-1_2	,      _A="absolute"	,      _A=True	,      _A=9	,      _A=6_4	,      _A=None	,      _A=True	,      _A=True	,      _A=1	,      _A=0	,      _A=2	,      **_A	,      ):






									'''simple docstring'''







									super().__init__(pad_token_id=_A	,      bos_token_id=_A	,      eos_token_id=_A	,      **_A						)

									UpperCamelCase							:		Any							      = vocab_size
									UpperCamelCase							:		Any							      = max_position_embeddings
									UpperCamelCase							:		int							      = hidden_size
									UpperCamelCase							:		List[Any]							      = num_hidden_layers
									UpperCamelCase							:		Any							      = num_attention_heads
									UpperCamelCase							:		Optional[Any]							      = intermediate_size
									UpperCamelCase							:		Optional[int]							      = hidden_act
									UpperCamelCase							:		List[Any]							      = hidden_dropout_prob
									UpperCamelCase							:		int							      = attention_probs_dropout_prob
									UpperCamelCase							:		Any							      = initializer_range
									UpperCamelCase							:		Optional[Any]							      = type_vocab_size
									UpperCamelCase							:		Any							      = layer_norm_eps
									UpperCamelCase							:		List[Any]							      = position_embedding_type
									UpperCamelCase							:		Optional[Any]							      = use_expectation
									UpperCamelCase							:		Any							      = hash_code_len
									UpperCamelCase							:		Optional[int]							      = num_hash
									UpperCamelCase							:		List[Any]							      = conv_window
									UpperCamelCase							:		Tuple							      = use_fast_hash
									UpperCamelCase							:		Dict							      = lsh_backward



