import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


UpperCamelCase  							=						False



class   snake_case_       (		unittest.TestCase       ):
   pass



@nightly
@require_torch_gpu
class   snake_case_       (		unittest.TestCase       ):






   def       __UpperCamelCase     (      self				:     Optional[Any]  )					->						Tuple:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






   def       __UpperCamelCase     (      self				:     Any  )					->						Union[str, Any]:
       lowercase__ :   List[str]    					=				VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,						torch_dtype=torch.floataa  )
       pipe.to(lowercase_  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       lowercase__ :   Optional[int]    					=				load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )

       lowercase__ :   Tuple    					=				torch.manual_seed(0  )
       lowercase__ :   List[Any]    					=				pipe.dual_guided(
           prompt="first prompt"	,						image=lowercase_	,						text_to_image_strength=0.75	,						generator=lowercase_	,						guidance_scale=7.5	,						num_inference_steps=2	,						output_type="numpy"	,						).images

       with tempfile.TemporaryDirectory() as tmpdirname:
           pipe.save_pretrained(lowercase_  )
           lowercase__ :   List[str]    					=				VersatileDiffusionPipeline.from_pretrained(lowercase_	,						torch_dtype=torch.floataa  )
       pipe.to(lowercase_  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       lowercase__ :   Any    					=				generator.manual_seed(0  )
       lowercase__ :   Optional[Any]    					=				pipe.dual_guided(
           prompt="first prompt"	,						image=lowercase_	,						text_to_image_strength=0.75	,						generator=lowercase_	,						guidance_scale=7.5	,						num_inference_steps=2	,						output_type="numpy"	,						).images

       assert np.abs(image - new_image  ).sum() < 1E-5, "Models don't have the same forward pass"







   def       __UpperCamelCase     (      self				:     Union[str, Any]  )					->						int:
       lowercase__ :   str    					=				VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"	,						torch_dtype=torch.floataa  )
       pipe.to(lowercase_  )
       pipe.set_progress_bar_config(disable=lowercase_  )

       lowercase__ :   Tuple    					=				"cyberpunk 2077"
       lowercase__ :   str    					=				load_image(
           "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"  )
       lowercase__ :   Any    					=				torch.manual_seed(0  )
       lowercase__ :   Any    					=				pipe.dual_guided(
           prompt=lowercase_	,						image=lowercase_	,						text_to_image_strength=0.75	,						generator=lowercase_	,						guidance_scale=7.5	,						num_inference_steps=50	,						output_type="numpy"	,						).images

       lowercase__ :   Optional[int]    					=				image[0, 2_53:2_56, 2_53:2_56, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       lowercase__ :   Optional[int]    					=				np.array([0.14_48, 0.16_19, 0.17_41, 0.10_86, 0.11_47, 0.11_28, 0.11_99, 0.11_65, 0.10_01]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-1

       lowercase__ :   str    					=				"A painting of a squirrel eating a burger "
       lowercase__ :   Any    					=				torch.manual_seed(0  )
       lowercase__ :   List[str]    					=				pipe.text_to_image(
           prompt=lowercase_	,						generator=lowercase_	,						guidance_scale=7.5	,						num_inference_steps=50	,						output_type="numpy"  ).images

       lowercase__ :   Any    					=				image[0, 2_53:2_56, 2_53:2_56, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       lowercase__ :   Optional[int]    					=				np.array([0.33_67, 0.31_69, 0.26_56, 0.38_70, 0.47_90, 0.37_96, 0.40_09, 0.48_78, 0.47_78]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-1

       lowercase__ :   Union[str, Any]    					=				pipe.image_variation(lowercase_	,						generator=lowercase_	,						output_type="numpy"  ).images

       lowercase__ :   Optional[Any]    					=				image[0, 2_53:2_56, 2_53:2_56, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       lowercase__ :   int    					=				np.array([0.30_76, 0.31_23, 0.32_84, 0.37_82, 0.37_70, 0.38_94, 0.42_97, 0.43_31, 0.44_56]  )

       assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-1




'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 				_lowercase     (				UpperCAmelCase__			):


 '''simple docstring'''






 def a					(      self   :	int							)  ->    Optional[Any]:
  return [
      {"col_1": 3, "col_2": "a"},
      {"col_1": 2, "col_2": "b"},
      {"col_1": 1, "col_2": "c"},
      {"col_1": 0, "col_2": "d"},
  ]
 def a					(      self   :	List[Any]							)  ->    Any:
  __lowerCAmelCase											=						{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
  return Dataset.from_dict(SCREAMING_SNAKE_CASE__							)
 def a					(      self   :	List[Any]							)  ->    Tuple:
  __lowerCAmelCase											=						self._create_example_records()
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  self.assertListEqual(dset.column_names      ,       ["""col_1""", """col_2"""]							)
  for i, r in enumerate(SCREAMING_SNAKE_CASE__							):
   self.assertDictEqual(SCREAMING_SNAKE_CASE__      ,       example_records[i]							)
 def a					(      self   :	Tuple							)  ->    List[str]:
  __lowerCAmelCase											=						self._create_example_records()
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase											=						Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}							)
  self.assertEqual(dset.info      ,       dset_from_dict.info							)
 def a					(      self   :	List[str]							)  ->    List[str]:  # checks what happens with missing columns
  __lowerCAmelCase											=						[{"""col_1""": 1}, {"""col_2""": """x"""}]
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  self.assertDictEqual(dset[0]      ,       {"""col_1""": 1}							)
  self.assertDictEqual(dset[1]      ,       {"""col_1""": None}							)  # NB: first record is used for columns
 def a					(      self   :	Dict							)  ->    Optional[int]:  # checks if the type can be inferred from the second record
  __lowerCAmelCase											=						[{"""col_1""": []}, {"""col_1""": [1, 2]}]
  __lowerCAmelCase											=						Dataset.from_list(SCREAMING_SNAKE_CASE__							)
  self.assertEqual(dset.info.features["""col_1"""]      ,       Sequence(Value("""int64"""							)							)							)





 def a					(      self   :	Optional[Any]							)  ->    Tuple:
  __lowerCAmelCase											=						Dataset.from_list([]							)
  self.assertEqual(len(SCREAMING_SNAKE_CASE__							)      ,       0							)
  self.assertListEqual(dset.column_names      ,       []							)






