





from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image


snake_case_           =      ['text', 'image', 'audio']






def lowerCamelCase__   (							snake_case_     :  List[str]	)				->    Dict:
   __snake_case								=       []

   for input_type in input_types:
      if input_type == "text":
         inputs.append('''Text input'''	)
      elif input_type == "image":
         inputs.append(
             Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''	)	) / '''000000039769.png'''	).resize((512, 512)	)	)
      elif input_type == "audio":
         inputs.append(torch.ones(3000	)	)
      elif isinstance(snake_case_	,				snake_case_	):
         inputs.append(create_inputs(snake_case_	)	)
      else:
         raise ValueError(f"""Invalid type requested: {input_type}"""	)

   return inputs






def lowerCamelCase__   (							snake_case_     :  List	)				->    Any:
   __snake_case								=       []

   for output in outputs:
      if isinstance(snake_case_	,				(str, AgentText)	):
         output_types.append('''text'''	)
      elif isinstance(snake_case_	,				(Image.Image, AgentImage)	):
         output_types.append('''image'''	)
      elif isinstance(snake_case_	,				(torch.Tensor, AgentAudio)	):
         output_types.append('''audio'''	)
      else:
         raise ValueError(f"""Invalid output: {output}"""	)

   return output_types







@is_tool_test
class 		SCREAMING_SNAKE_CASE__    :


      def        a			(self	:				Dict				):





         """simple docstring"""



         self.assertTrue(hasattr(self.tool    ,      '''inputs'''				)				)
         self.assertTrue(hasattr(self.tool    ,      '''outputs'''				)				)

         __snake_case								=       self.tool.inputs
         for _input in inputs:
            if isinstance(_input    ,      a__				):
               for __input in _input:
                  self.assertTrue(__input in authorized_types				)
            else:
               self.assertTrue(_input in authorized_types				)

         __snake_case								=       self.tool.outputs
         for _output in outputs:
            self.assertTrue(_output in authorized_types				)


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       create_inputs(self.tool.inputs				)
         __snake_case								=       self.tool(*a__				)

         # There is a single output
         if len(self.tool.outputs				) == 1:
            __snake_case								=       [outputs]

         self.assertListEqual(output_types(a__				)    ,      self.tool.outputs				)


      def        a			(self	:				str				):





         """simple docstring"""



         self.assertTrue(hasattr(self.tool    ,      '''description'''				)				)
         self.assertTrue(hasattr(self.tool    ,      '''default_checkpoint'''				)				)
         self.assertTrue(self.tool.description.startswith('''This is a tool that'''				)				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       create_inputs(self.tool.inputs				)
         __snake_case								=       self.tool(*a__				)

         if not isinstance(a__    ,      a__				):
            __snake_case								=       [outputs]

         self.assertEqual(len(a__				)    ,      len(self.tool.outputs				)				)

         for output, output_type in zip(a__    ,      self.tool.outputs				):
            __snake_case								=       AGENT_TYPE_MAPPING[output_type]
            self.assertTrue(isinstance(a__    ,      a__				)				)


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         __snake_case								=       create_inputs(self.tool.inputs				)

         __snake_case								=       []

         for _input, input_type in zip(a__    ,      self.tool.inputs				):
            if isinstance(a__    ,      a__				):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
            else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
         __snake_case								=       self.tool(*a__				)

         if not isinstance(a__    ,      a__				):
            __snake_case								=       [outputs]

         self.assertEqual(len(a__				)    ,      len(self.tool.outputs				)				)











import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase						,				_UpperCAmelCase						,				unittest.TestCase  ):
      A_						:    Optional[int]							=   CycleDiffusionPipeline
      A_						:    List[Any]							=   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
          'negative_prompt',
          'height',
          'width',
          'negative_prompt_embeds',
      }
      A_						:    List[str]							=   PipelineTesterMixin.required_optional_params - {'latents'}
      A_						:    List[str]							=   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
      A_						:    List[str]							=   IMAGE_TO_IMAGE_IMAGE_PARAMS
      A_						:    Any							=   IMAGE_TO_IMAGE_IMAGE_PARAMS


      def        a			(self	:				Dict				):





         """simple docstring"""



         torch.manual_seed(0				)
         __snake_case								=       UNetaDConditionModel(
             block_out_channels=(32, 64)    ,      layers_per_block=2    ,      sample_size=32    ,      in_channels=4    ,      out_channels=4    ,      down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')    ,      up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')    ,      cross_attention_dim=32    ,      )
         __snake_case								=       DDIMScheduler(
             beta_start=0.0_0_0_8_5    ,      beta_end=0.0_1_2    ,      beta_schedule='''scaled_linear'''    ,      num_train_timesteps=1000    ,      clip_sample=a__    ,      set_alpha_to_one=a__    ,      )
         torch.manual_seed(0				)
         __snake_case								=       AutoencoderKL(
             block_out_channels=[32, 64]    ,      in_channels=3    ,      out_channels=3    ,      down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']    ,      up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']    ,      latent_channels=4    ,      )
         torch.manual_seed(0				)
         __snake_case								=       CLIPTextConfig(
             bos_token_id=0    ,      eos_token_id=2    ,      hidden_size=32    ,      intermediate_size=37    ,      layer_norm_eps=1E-05    ,      num_attention_heads=4    ,      num_hidden_layers=5    ,      pad_token_id=1    ,      vocab_size=1000    ,      )
         __snake_case								=       CLIPTextModel(a__				)
         __snake_case								=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

         __snake_case								=       {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components


      def        a			(self	:				List[str]    ,      a__	:				Tuple    ,      a__	:				Optional[Any]=0				):





         """simple docstring"""



         __snake_case								=       floats_tensor((1, 3, 32, 32)    ,      rng=random.Random(a__				)				).to(a__				)
         __snake_case								=       image / 2 + 0.5
         if str(a__				).startswith('''mps'''				):
            __snake_case								=       torch.manual_seed(a__				)
         else:
            __snake_case								=       torch.Generator(device=a__				).manual_seed(a__				)
         __snake_case								=       {
             '''prompt''': '''An astronaut riding an elephant''',
             '''source_prompt''': '''An astronaut riding a horse''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''eta''': 0.1,
             '''strength''': 0.8,
             '''guidance_scale''': 3,
             '''source_guidance_scale''': 1,
             '''output_type''': '''numpy''',
         }
         return inputs


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator

         __snake_case								=       self.get_dummy_components()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


      @unittest.skipIf(torch_device != '''cuda'''    ,      '''This test requires a GPU'''				)
      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       self.get_dummy_components()
         for name, module in components.items():
            if hasattr(a__    ,      '''half'''				):
               __snake_case								=       module.half()
         __snake_case								=       CycleDiffusionPipeline(**a__				)
         __snake_case								=       pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)

         __snake_case								=       self.get_dummy_inputs(a__				)
         __snake_case								=       pipe(**a__				)
         __snake_case								=       output.images

         __snake_case								=       images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         __snake_case								=       np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_save_load_local()


      @unittest.skip('''non-deterministic pipeline'''				)
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_inference_batch_single_identical()


      @skip_mps
      def        a			(self	:				Any				):





         """simple docstring"""



         return super().test_dict_tuple_outputs_equivalent()


      @skip_mps
      def        a			(self	:				str				):





         """simple docstring"""



         return super().test_save_load_optional_components()


      @skip_mps
      def        a			(self	:				Dict				):





         """simple docstring"""



         return super().test_attention_slicing_forward_pass()






@slow
@require_torch_gpu
class 		SCREAMING_SNAKE_CASE__    (	unittest.TestCase  ):


      def        a			(self	:				Union[str, Any]				):





         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(
             a__    ,      scheduler=a__    ,      safety_checker=a__    ,      torch_dtype=torch.floataa    ,      revision='''fp16'''				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image				).max() < 5E-1


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/cycle-diffusion/black_colored_car.png'''				)
         __snake_case								=       load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
         __snake_case								=       init_image.resize((512, 512)				)

         __snake_case								=       '''CompVis/stable-diffusion-v1-4'''
         __snake_case								=       DDIMScheduler.from_pretrained(a__    ,      subfolder='''scheduler'''				)
         __snake_case								=       CycleDiffusionPipeline.from_pretrained(a__    ,      scheduler=a__    ,      safety_checker=a__				)

         pipe.to(a__				)
         pipe.set_progress_bar_config(disable=a__				)
         pipe.enable_attention_slicing()

         __snake_case								=       '''A black colored car'''
         __snake_case								=       '''A blue colored car'''

         __snake_case								=       torch.manual_seed(0				)
         __snake_case								=       pipe(
             prompt=a__    ,      source_prompt=a__    ,      image=a__    ,      num_inference_steps=100    ,      eta=0.1    ,      strength=0.8_5    ,      guidance_scale=3    ,      source_guidance_scale=1    ,      generator=a__    ,      output_type='''np'''    ,      )
         __snake_case								=       output.images

         assert np.abs(image - expected_image				).max() < 2E-2

