from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""levit"""







   def __init__(     self						:						Any   ,   lowerCamelCase_						:						Any=224   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						List[Any]=3   ,   lowerCamelCase_						:						str=2   ,   lowerCamelCase_						:						List[str]=1   ,   lowerCamelCase_						:						int=16   ,   lowerCamelCase_						:						str=[128, 256, 384]   ,   lowerCamelCase_						:						Any=[4, 8, 12]   ,   lowerCamelCase_						:						Optional[int]=[4, 4, 4]   ,   lowerCamelCase_						:						Tuple=[16, 16, 16]   ,   lowerCamelCase_						:						Optional[Any]=0   ,   lowerCamelCase_						:						Any=[2, 2, 2]   ,   lowerCamelCase_						:						Any=[2, 2, 2]   ,   lowerCamelCase_						:						Optional[int]=0.0_2   ,   **lowerCamelCase_						:						Optional[Any]   ,   ):





    """simple docstring"""


    super().__init__(**UpperCamelCase_				)
    UpperCamelCase			  =      image_size
    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      kernel_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      num_attention_heads
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      key_dim
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      attention_ratio
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      initializer_range
    UpperCamelCase			  =      [
        ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
        ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
    ]





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    return 1E-4


"""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     ,	)

