



'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
 import jax.numpy as jnp

if is_tf_available():
 import tensorflow as tf

if is_torch_available():
 import torch






class 	lowerCAmelCase_(    unittest.TestCase					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Optional[Any]											= {
          """task_specific_params""": {
              """summarization""": {"""length_penalty""": 1.0, """max_length""": 128, """min_length""": 12, """num_beams""": 4},
              """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 142, """min_length""": 56, """num_beams""": 4},
              """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 62, """min_length""": 11, """num_beams""": 6},
          }
      }
      lowerCAmelCase__			:  int											= {
          """task_specific_params.summarization.length_penalty""": 1.0,
          """task_specific_params.summarization.max_length""": 128,
          """task_specific_params.summarization.min_length""": 12,
          """task_specific_params.summarization.num_beams""": 4,
          """task_specific_params.summarization_cnn.length_penalty""": 2.0,
          """task_specific_params.summarization_cnn.max_length""": 142,
          """task_specific_params.summarization_cnn.min_length""": 56,
          """task_specific_params.summarization_cnn.num_beams""": 4,
          """task_specific_params.summarization_xsum.length_penalty""": 1.0,
          """task_specific_params.summarization_xsum.max_length""": 62,
          """task_specific_params.summarization_xsum.min_length""": 11,
          """task_specific_params.summarization_xsum.num_beams""": 6,
      }

      self.assertEqual(flatten_dict(__UpperCAmelCase				)  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Union[str, Any]											= np.random.randn(3  ,4				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase				)  ,x.transpose()				)				)

      lowerCAmelCase__			:  List[str]											= np.random.randn(3  ,4  ,5				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase  ,axes=(1, 2, 0)				)  ,x.transpose((1, 2, 0)				)				)				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  List[Any]											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  Union[str, Any]											= torch.tensor(__UpperCAmelCase				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase				)  ,transpose(__UpperCAmelCase				).numpy()				)				)

      lowerCAmelCase__			:  Tuple											= np.random.randn(3  ,4  ,5				)
      lowerCAmelCase__			:  str											= torch.tensor(__UpperCAmelCase				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase  ,axes=(1, 2, 0)				)  ,transpose(__UpperCAmelCase  ,axes=(1, 2, 0)				).numpy()				)				)




  @require_tf
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Union[str, Any]											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  int											= tf.constant(__UpperCAmelCase				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase				)  ,transpose(__UpperCAmelCase				).numpy()				)				)

      lowerCAmelCase__			:  List[str]											= np.random.randn(3  ,4  ,5				)
      lowerCAmelCase__			:  Optional[Any]											= tf.constant(__UpperCAmelCase				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase  ,axes=(1, 2, 0)				)  ,transpose(__UpperCAmelCase  ,axes=(1, 2, 0)				).numpy()				)				)




  @require_flax
  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Union[str, Any]											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  Dict											= jnp.array(__UpperCAmelCase				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase				)  ,np.asarray(transpose(__UpperCAmelCase				)				)				)				)

      lowerCAmelCase__			:  str											= np.random.randn(3  ,4  ,5				)
      lowerCAmelCase__			:  Optional[Any]											= jnp.array(__UpperCAmelCase				)
      self.assertTrue(np.allclose(transpose(__UpperCAmelCase  ,axes=(1, 2, 0)				)  ,np.asarray(transpose(__UpperCAmelCase  ,axes=(1, 2, 0)				)				)				)				)




  def 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  Dict											= np.random.randn(3  ,4				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(4, 3)				)  ,np.reshape(__UpperCAmelCase  ,(4, 3)				)				)				)

      lowerCAmelCase__			:  int											= np.random.randn(3  ,4  ,5				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(12, 5)				)  ,np.reshape(__UpperCAmelCase  ,(12, 5)				)				)				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  Union[str, Any]											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  str											= torch.tensor(__UpperCAmelCase				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(4, 3)				)  ,reshape(__UpperCAmelCase  ,(4, 3)				).numpy()				)				)

      lowerCAmelCase__			:  Any											= np.random.randn(3  ,4  ,5				)
      lowerCAmelCase__			:  int											= torch.tensor(__UpperCAmelCase				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(12, 5)				)  ,reshape(__UpperCAmelCase  ,(12, 5)				).numpy()				)				)




  @require_tf
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      lowerCAmelCase__			:  Optional[int]											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  Any											= tf.constant(__UpperCAmelCase				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(4, 3)				)  ,reshape(__UpperCAmelCase  ,(4, 3)				).numpy()				)				)

      lowerCAmelCase__			:  Optional[int]											= np.random.randn(3  ,4  ,5				)
      lowerCAmelCase__			:  Union[str, Any]											= tf.constant(__UpperCAmelCase				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(12, 5)				)  ,reshape(__UpperCAmelCase  ,(12, 5)				).numpy()				)				)




  @require_flax
  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__			:  Any											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  Dict											= jnp.array(__UpperCAmelCase				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(4, 3)				)  ,np.asarray(reshape(__UpperCAmelCase  ,(4, 3)				)				)				)				)

      lowerCAmelCase__			:  Dict											= np.random.randn(3  ,4  ,5				)
      lowerCAmelCase__			:  Dict											= jnp.array(__UpperCAmelCase				)
      self.assertTrue(np.allclose(reshape(__UpperCAmelCase  ,(12, 5)				)  ,np.asarray(reshape(__UpperCAmelCase  ,(12, 5)				)				)				)				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  Dict											= np.random.randn(1  ,3  ,4				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase				)  ,np.squeeze(__UpperCAmelCase				)				)				)

      lowerCAmelCase__			:  List[Any]											= np.random.randn(1  ,4  ,1  ,5				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase  ,axis=2				)  ,np.squeeze(__UpperCAmelCase  ,axis=2				)				)				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  List[str]											= np.random.randn(1  ,3  ,4				)
      lowerCAmelCase__			:  Tuple											= torch.tensor(__UpperCAmelCase				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase				)  ,squeeze(__UpperCAmelCase				).numpy()				)				)

      lowerCAmelCase__			:  List[str]											= np.random.randn(1  ,4  ,1  ,5				)
      lowerCAmelCase__			:  Dict											= torch.tensor(__UpperCAmelCase				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase  ,axis=2				)  ,squeeze(__UpperCAmelCase  ,axis=2				).numpy()				)				)




  @require_tf
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Union[str, Any]											= np.random.randn(1  ,3  ,4				)
      lowerCAmelCase__			:  Tuple											= tf.constant(__UpperCAmelCase				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase				)  ,squeeze(__UpperCAmelCase				).numpy()				)				)

      lowerCAmelCase__			:  List[Any]											= np.random.randn(1  ,4  ,1  ,5				)
      lowerCAmelCase__			:  Optional[int]											= tf.constant(__UpperCAmelCase				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase  ,axis=2				)  ,squeeze(__UpperCAmelCase  ,axis=2				).numpy()				)				)




  @require_flax
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  Dict											= np.random.randn(1  ,3  ,4				)
      lowerCAmelCase__			:  int											= jnp.array(__UpperCAmelCase				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase				)  ,np.asarray(squeeze(__UpperCAmelCase				)				)				)				)

      lowerCAmelCase__			:  Dict											= np.random.randn(1  ,4  ,1  ,5				)
      lowerCAmelCase__			:  str											= jnp.array(__UpperCAmelCase				)
      self.assertTrue(np.allclose(squeeze(__UpperCAmelCase  ,axis=2				)  ,np.asarray(squeeze(__UpperCAmelCase  ,axis=2				)				)				)				)




  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  int											= np.random.randn(3  ,4				)
      self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase  ,axis=1				)  ,np.expand_dims(__UpperCAmelCase  ,axis=1				)				)				)




  @require_torch
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  List[Any]											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  str											= torch.tensor(__UpperCAmelCase				)
      self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase  ,axis=1				)  ,expand_dims(__UpperCAmelCase  ,axis=1				).numpy()				)				)




  @require_tf
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  Any											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  Union[str, Any]											= tf.constant(__UpperCAmelCase				)
      self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase  ,axis=1				)  ,expand_dims(__UpperCAmelCase  ,axis=1				).numpy()				)				)






  @require_flax
  def 							UpperCAmelCase_     (   self				)   ->		str:
      lowerCAmelCase__			:  int											= np.random.randn(3  ,4				)
      lowerCAmelCase__			:  Dict											= jnp.array(__UpperCAmelCase				)
      self.assertTrue(np.allclose(expand_dims(__UpperCAmelCase  ,axis=1				)  ,np.asarray(expand_dims(__UpperCAmelCase  ,axis=1				)				)				)				)







'''simple docstring'''





import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
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_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin






@skip_mps
class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				List[str]           =				IFInpaintingSuperResolutionPipeline
  __lowercase							:				int           =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
  __lowercase							:				List[str]           =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({'''original_image'''}					)
  __lowercase							:				Optional[Any]           =				PipelineTesterMixin.required_optional_params - {'''latents'''}




  def 							UpperCAmelCase_     (   self				)   ->		Any:
      return self._get_superresolution_dummy_components()




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase=0				)   ->		List[Any]:
      if str(__UpperCAmelCase				).startswith("""mps"""				):
          lowerCAmelCase__			:  Any											= torch.manual_seed(__UpperCAmelCase				)
      else:
          lowerCAmelCase__			:  Dict											= torch.Generator(device=__UpperCAmelCase				).manual_seed(__UpperCAmelCase				)

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

      lowerCAmelCase__			:  Dict											= {
          """prompt""": """A painting of a squirrel eating a burger""",
          """image""": image,
          """original_image""": original_image,
          """mask_image""": mask_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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3				)




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      self._test_save_load_optional_components()




  @unittest.skipIf(torch_device != """cuda"""  ,reason="""float16 requires CUDA"""				)
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      # 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 							UpperCAmelCase_     (   self				)   ->		str:
      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2				)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      self._test_save_load_local()






  def 							UpperCAmelCase_     (   self				)   ->		int:
      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2  ,)


