





'''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  a_					(    snake_case_				,   snake_case_				,   unittest.TestCase	):

    '''simple docstring'''
    UpperCamelCase			   =			IFImgaImgPipeline
    UpperCamelCase			   =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''width''', '''height'''}
    UpperCamelCase			   =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    UpperCamelCase			   =			PipelineTesterMixin.required_optional_params - {'''latents'''}


    def       snake_case_(		self	)		->       List[str]:
        return self._get_dummy_components()


    def       snake_case_(		self			,		A			,		A=0	)		->       Union[str, Any]:
        if str(A	).startswith("""mps"""	):
            _SCREAMING_SNAKE_CASE     =      torch.manual_seed(A	)
        else:
            _SCREAMING_SNAKE_CASE     =      torch.Generator(device=A	).manual_seed(A	)

        _SCREAMING_SNAKE_CASE     =      floats_tensor((1, 3, 32, 32)			,		rng=random.Random(A	)	).to(A	)

        _SCREAMING_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       snake_case_(		self	)		->       Tuple:
        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       snake_case_(		self	)		->       Any:
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3	)


    @unittest.skipIf(torch_device != """cuda"""			,		reason="""float16 requires CUDA"""	)
    def       snake_case_(		self	)		->       Dict:
        # 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       snake_case_(		self	)		->       Union[str, Any]:
        super().test_floataa_inference(expected_max_diff=1e-1	)


    def       snake_case_(		self	)		->       str:
        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2	)


    def       snake_case_(		self	)		->       Union[str, Any]:
        self._test_save_load_local()





    def       snake_case_(		self	)		->       Optional[int]:
        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2			,		)











'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowercase_         =			{
    """configuration_esm""": ["""ESM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """EsmConfig"""],
    """tokenization_esm""": ["""EsmTokenizer"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """EsmForMaskedLM""",
          """EsmForSequenceClassification""",
          """EsmForTokenClassification""",
          """EsmModel""",
          """EsmPreTrainedModel""",
      ]
      lowercase_         =			["""EsmForProteinFolding""", """EsmFoldPreTrainedModel"""]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_         =			[
          """TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFEsmForMaskedLM""",
          """TFEsmForSequenceClassification""",
          """TFEsmForTokenClassification""",
          """TFEsmModel""",
          """TFEsmPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      lowercase_         =			_LazyModule(__name__, globals()["""__file__"""], _import_structure)



