






import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs







class 			UpperCAmelCase_						(					snake_case_ ):



      """simple docstring"""
      def __init__(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=None    ,				SCREAMING_SNAKE_CASE_=True    ,				SCREAMING_SNAKE_CASE_=None    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[Any]:
          UpperCamelCase       :List[str]								=   parent
          UpperCamelCase       :str								=   config_class
          UpperCamelCase       :Tuple								=   has_text_modality
          UpperCamelCase       :str								=   kwargs
          UpperCamelCase       :Optional[int]								=   common_properties
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          UpperCamelCase       :Tuple								=   self.config_class(**self.inputs_dict )
          UpperCamelCase       :Any								=   (
              ["""hidden_size""", """num_attention_heads""", """num_hidden_layers"""]
              if self.common_properties is None
              else self.common_properties
          )

          # Add common fields for text models
          if self.has_text_modality:
              common_properties.extend(['''vocab_size'''] )

          # Test that config has the common properties as getters
          for prop in common_properties:
              self.parent.assertTrue(hasattr(UpperCAmelCase__    ,				UpperCAmelCase__ )    ,				msg=F'''`{prop}` does not exist''' )

          # Test that config has the common properties as setter
          for idx, name in enumerate(UpperCAmelCase__ ):
              try:
                  setattr(UpperCAmelCase__    ,				UpperCAmelCase__    ,				UpperCAmelCase__ )
                  self.parent.assertEqual(
                      getattr(UpperCAmelCase__    ,				UpperCAmelCase__ )    ,				UpperCAmelCase__    ,				msg=F'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase__    ,				UpperCAmelCase__ )}''' )
              except NotImplementedError:
                  # Some models might not be able to implement setters for common_properties
                  # In that case, a NotImplementedError is raised
                  pass

                   # Test if config class can be called with Config(prop_name=..)
          for idx, name in enumerate(UpperCAmelCase__ ):
              try:
                  UpperCamelCase       :str								=   self.config_class(**{name: idx} )
                  self.parent.assertEqual(
                      getattr(UpperCAmelCase__    ,				UpperCAmelCase__ )    ,				UpperCAmelCase__    ,				msg=F'''`{name} value {idx} expected, but was {getattr(UpperCAmelCase__    ,				UpperCAmelCase__ )}''' )
              except NotImplementedError:
                  # Some models might not be able to implement setters for common_properties
                  # In that case, a NotImplementedError is raised
                  pass
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :str								=   self.config_class(**self.inputs_dict )
          UpperCamelCase       :Union[str, Any]								=   json.loads(config.to_json_string() )
          for key, value in self.inputs_dict.items():
              self.parent.assertEqual(obj[key]    ,				UpperCAmelCase__ )
      def     UpperCAmelCase						(				self )				->      int:
          UpperCamelCase       :Dict								=   self.config_class(**self.inputs_dict )

          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCamelCase       :str								=   os.path.join(UpperCAmelCase__    ,				'''config.json''' )
              config_first.to_json_file(UpperCAmelCase__ )
              UpperCamelCase       :Union[str, Any]								=   self.config_class.from_json_file(UpperCAmelCase__ )

          self.parent.assertEqual(config_second.to_dict()    ,				config_first.to_dict() )
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :str								=   self.config_class(**self.inputs_dict )

          with tempfile.TemporaryDirectory() as tmpdirname:
              config_first.save_pretrained(UpperCAmelCase__ )
              UpperCamelCase       :Tuple								=   self.config_class.from_pretrained(UpperCAmelCase__ )

          self.parent.assertEqual(config_second.to_dict()    ,				config_first.to_dict() )
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          UpperCamelCase       :str								=   self.config_class(**self.inputs_dict )

          UpperCamelCase       :int								=   """test"""
          with tempfile.TemporaryDirectory() as tmpdirname:
              UpperCamelCase       :List[Any]								=   os.path.join(UpperCAmelCase__    ,				UpperCAmelCase__ )
              config_first.save_pretrained(UpperCAmelCase__ )
              UpperCamelCase       :str								=   self.config_class.from_pretrained(UpperCAmelCase__    ,				subfolder=UpperCAmelCase__ )

          self.parent.assertEqual(config_second.to_dict()    ,				config_first.to_dict() )
      def     UpperCAmelCase						(				self )				->      List[str]:
          UpperCamelCase       :Union[str, Any]								=   self.config_class(**self.inputs_dict    ,				num_labels=5 )
          self.parent.assertEqual(len(config.idalabel )    ,				5 )
          self.parent.assertEqual(len(config.labelaid )    ,				5 )

          UpperCamelCase       :int								=   3
          self.parent.assertEqual(len(config.idalabel )    ,				3 )
          self.parent.assertEqual(len(config.labelaid )    ,				3 )
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          if self.config_class.is_composition:
              return
          UpperCamelCase       :Union[str, Any]								=   self.config_class()
          self.parent.assertIsNotNone(UpperCAmelCase__ )
      def     UpperCAmelCase						(				self )				->      Union[str, Any]:
          UpperCamelCase       :List[str]								=   copy.deepcopy(UpperCAmelCase__ )
          UpperCamelCase       :str								=   self.config_class(**UpperCAmelCase__ )
          UpperCamelCase       :str								=   []
          for key, value in config_common_kwargs.items():
              if key == "torch_dtype":
                  if not is_torch_available():
                      continue
                  else:
                      import torch

                      if config.torch_dtype != torch.floataa:
                          wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa) )
              elif getattr(UpperCAmelCase__    ,				UpperCAmelCase__ ) != value:
                  wrong_values.append((key, getattr(UpperCAmelCase__    ,				UpperCAmelCase__ ), value) )

          if len(UpperCAmelCase__ ) > 0:
              UpperCamelCase       :List[str]								=   """\n""".join([F'''- {v[0]}: got {v[1]} instead of {v[2]}''' for v in wrong_values] )
              raise ValueError(F'''The following keys were not properly set in the config:\n{errors}''' )




      def     UpperCAmelCase						(				self )				->      Any:
          self.create_and_test_config_common_properties()
          self.create_and_test_config_to_json_string()
          self.create_and_test_config_to_json_file()
          self.create_and_test_config_from_and_save_pretrained()
          self.create_and_test_config_from_and_save_pretrained_subfolder()
          self.create_and_test_config_with_num_labels()
          self.check_config_can_be_init_without_params()
          self.check_config_arguments_init()













"""simple docstring"""




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


_snake_case          =   False






class        UpperCamelCase			(	unittest.TestCase      ):
 pass







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


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


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Tuple             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

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

   _a  :       Optional[Any]             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(UpperCAmelCase__							)
     _a  :       Dict             =	VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase__							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	generator.manual_seed(0							)
   _a  :       str             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						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 _lowercase	(     self		:						Optional[int]							)					->       Optional[int]:
   _a  :       Optional[int]             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       int             =	"""cyberpunk 2077"""
   _a  :       Tuple             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Any             =	pipe.dual_guided(
       prompt=UpperCAmelCase__							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							,						).images

   _a  :       List[str]             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[int]             =	np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]							)

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

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

   _a  :       int             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       int             =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]							)

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

   _a  :       str             =	pipe.image_variation(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       str             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[Any]             =	np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]							)

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

