





'''simple docstring'''






from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
    from .features import FeatureType
@dataclass
class 					__snake_case  :






   '''simple docstring'''





   lowerCamelCase__    							=      42
   lowerCamelCase__    							=      None
   # Automatically constructed
   lowerCamelCase__    							=      "dict"
   lowerCamelCase__    							=      None
   lowerCamelCase__    							=      field(default='''Translation'''		,				init=__SCREAMING_SNAKE_CASE		,				repr=__SCREAMING_SNAKE_CASE							)

   def __call__(							self       ):
    return pa.struct({lang: pa.string() for lang in sorted(self.languages       )}       )


   def 		__UpperCamelCase						(							self       ):
    from .features import Value

    return {k: Value("""string"""       ) for k in sorted(self.languages       )}



@dataclass
class 					__snake_case  :






   '''simple docstring'''





   lowerCamelCase__    							=      None
   lowerCamelCase__    							=      None
   lowerCamelCase__    							=      None
   # Automatically constructed
   lowerCamelCase__    							=      "dict"
   lowerCamelCase__    							=      None
   lowerCamelCase__    							=      field(default='''TranslationVariableLanguages'''		,				init=__SCREAMING_SNAKE_CASE		,				repr=__SCREAMING_SNAKE_CASE							)

   def 		__UpperCamelCase						(							self       ):
    snake_case__     :					str    =  sorted(set(self.languages       )       ) if self.languages else None
    snake_case__     :					int    =  len(self.languages       ) if self.languages else None

   def __call__(							self       ):
    return pa.struct({"""language""": pa.list_(pa.string()       ), """translation""": pa.list_(pa.string()       )}       )

   def 		__UpperCamelCase						(							self	,		__SCREAMING_SNAKE_CASE       ):
    snake_case__     :					List[str]    =  set(self.languages       )
    if self.languages and set(__SCREAMING_SNAKE_CASE       ) - lang_set:
     raise ValueError(
         f"Some languages in example ({', '.join(sorted(set(__SCREAMING_SNAKE_CASE       ) - lang_set       )       )}) are not in valid set ({', '.join(__SCREAMING_SNAKE_CASE       )})."       )

    # Convert dictionary into tuples, splitting out cases where there are
    # multiple translations for a single language.
    snake_case__     :					Optional[int]    =  []
    for lang, text in translation_dict.items():
     if isinstance(__SCREAMING_SNAKE_CASE	,		__SCREAMING_SNAKE_CASE       ):
      translation_tuples.append((lang, text)       )
     else:
      translation_tuples.extend([(lang, el) for el in text]       )

        # Ensure translations are in ascending order by language code.
    snake_case__  ,							snake_case__     :					Any    =  zip(*sorted(__SCREAMING_SNAKE_CASE       )       )

    return {"language": languages, "translation": translations}


   def 		__UpperCamelCase						(							self       ):
    from .features import Sequence, Value

    return {
        "language": Sequence(Value("""string"""       )       ),
        "translation": Sequence(Value("""string"""       )       ),
    }






"""simple docstring"""



import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
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			(					lowercase						,		lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   int           =     IFImgaImgSuperResolutionPipeline
      UpperCamelCase							:   Union[str, Any]           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""width""", """height"""}
      UpperCamelCase							:   Tuple           =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"""original_image"""}      )
      UpperCamelCase							:   Union[str, Any]           =     PipelineTesterMixin.required_optional_params - {"""latents"""}



      def  __snake_case							(   self      ):
          return self._get_superresolution_dummy_components()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_=0      ):
          if str(UpperCamelCase_      ).startswith('mps'      ):
              UpperCAmelCase__	:      Optional[int]     							=					torch.manual_seed(UpperCamelCase_      )
          else:
              UpperCAmelCase__	:      int     							=					torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )

          UpperCAmelCase__	:      Union[str, Any]     							=					floats_tensor((1, 3, 32, 32)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )
          UpperCAmelCase__	:      Tuple     							=					floats_tensor((1, 3, 16, 16)     ,	rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

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



      def  __snake_case							(   self      ):
          self._test_save_load_optional_components()



      @unittest.skipIf(torch_device != 'cuda'     ,	reason='float16 requires CUDA'      )
      def  __snake_case							(   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      )



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



      def  __snake_case							(   self      ):
          self._test_save_load_local()



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

