





'''simple docstring'''




import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()







class   A_	(  lowerCAmelCase_							,						unittest.TestCase			):
 _lowerCamelCase  : Union[str, Any]           =							LDMTextToImagePipeline
 _lowerCamelCase  : List[Any]           =							TEXT_TO_IMAGE_PARAMS - {
     """negative_prompt""",
     """negative_prompt_embeds""",
     """cross_attention_kwargs""",
     """prompt_embeds""",
 }
 _lowerCamelCase  : List[str]           =							PipelineTesterMixin.required_optional_params - {
     """num_images_per_prompt""",
     """callback""",
     """callback_steps""",
 }
 _lowerCamelCase  : str           =							TEXT_TO_IMAGE_BATCH_PARAMS
 _lowerCamelCase  : List[Any]           =							False




 def      lowercase	(					self       :    Union[str, Any]    ):
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   UNetaDConditionModel(
          block_out_channels=(3_2, 6_4)	,			layers_per_block=2	,			sample_size=3_2	,			in_channels=4	,			out_channels=4	,			down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")	,			up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")	,			cross_attention_dim=3_2	,			)
      _UpperCAmelCase     							=   DDIMScheduler(
          beta_start=0.0_0_0_8_5	,			beta_end=0.0_1_2	,			beta_schedule="scaled_linear"	,			clip_sample=snake_case_	,			set_alpha_to_one=snake_case_	,			)
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   AutoencoderKL(
          block_out_channels=(3_2, 6_4)	,			in_channels=3	,			out_channels=3	,			down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D")	,			up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D")	,			latent_channels=4	,			)
      torch.manual_seed(0    )
      _UpperCAmelCase     							=   CLIPTextConfig(
          bos_token_id=0	,			eos_token_id=2	,			hidden_size=3_2	,			intermediate_size=3_7	,			layer_norm_eps=1e-05	,			num_attention_heads=4	,			num_hidden_layers=5	,			pad_token_id=1	,			vocab_size=1_0_0_0	,			)
      _UpperCAmelCase     							=   CLIPTextModel(snake_case_    )
      _UpperCAmelCase     							=   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"    )

      _UpperCAmelCase     							=   {
          "unet": unet,
          "scheduler": scheduler,
          "vqvae": vae,
          "bert": text_encoder,
          "tokenizer": tokenizer,
      }
      return components




 def      lowercase	(					self       :    int	,			snake_case_       :    str	,			snake_case_       :    Tuple=0    ):
      if str(snake_case_    ).startswith("mps"    ):
           _UpperCAmelCase     							=   torch.manual_seed(snake_case_    )
      else:
           _UpperCAmelCase     							=   torch.Generator(device=snake_case_    ).manual_seed(snake_case_    )
      _UpperCAmelCase     							=   {
          "prompt": "A painting of a squirrel eating a burger",
          "generator": generator,
          "num_inference_steps": 2,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs




 def      lowercase	(					self       :    Dict    ):
      _UpperCAmelCase     							=   "cpu"  # ensure determinism for the device-dependent torch.Generator

      _UpperCAmelCase     							=   self.get_dummy_components()
      _UpperCAmelCase     							=   LDMTextToImagePipeline(**snake_case_    )
      pipe.to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   self.get_dummy_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe(**snake_case_    ).images
      _UpperCAmelCase     							=   image[0, -3:, -3:, -1]

      assert image.shape == (1, 1_6, 1_6, 3)
      _UpperCAmelCase     							=   np.array([0.6_1_0_1, 0.6_1_5_6, 0.5_6_2_2, 0.4_8_9_5, 0.6_6_6_1, 0.3_8_0_4, 0.5_7_4_8, 0.6_1_3_6, 0.5_0_1_4]    )

      assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3







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




 def      lowercase	(					self       :    int    ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    int=torch.floataa	,			snake_case_       :    Optional[Any]=0    ):
      _UpperCAmelCase     							=   torch.manual_seed(snake_case_    )
      _UpperCAmelCase     							=   np.random.RandomState(snake_case_    ).standard_normal((1, 4, 3_2, 3_2)    )
      _UpperCAmelCase     							=   torch.from_numpy(snake_case_    ).to(device=snake_case_	,			dtype=snake_case_    )
      _UpperCAmelCase     							=   {
          "prompt": "A painting of a squirrel eating a burger",
          "latents": latents,
          "generator": generator,
          "num_inference_steps": 3,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs




 def      lowercase	(					self       :    List[Any]    ):
      _UpperCAmelCase     							=   LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"    ).to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   self.get_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe(**snake_case_    ).images
      _UpperCAmelCase     							=   image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 2_5_6, 2_5_6, 3)
      _UpperCAmelCase     							=   np.array([0.5_1_8_2_5, 0.5_2_8_5_0, 0.5_2_5_4_3, 0.5_4_2_5_8, 0.5_2_3_0_4, 0.5_2_5_6_9, 0.5_4_3_6_3, 0.5_5_2_7_6, 0.5_6_8_7_8]    )
      _UpperCAmelCase     							=   np.abs(expected_slice - image_slice    ).max()
      assert max_diff < 1e-3







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




 def      lowercase	(					self       :    Optional[int]    ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Dict	,			snake_case_       :    int=torch.floataa	,			snake_case_       :    Dict=0    ):
      _UpperCAmelCase     							=   torch.manual_seed(snake_case_    )
      _UpperCAmelCase     							=   np.random.RandomState(snake_case_    ).standard_normal((1, 4, 3_2, 3_2)    )
      _UpperCAmelCase     							=   torch.from_numpy(snake_case_    ).to(device=snake_case_	,			dtype=snake_case_    )
      _UpperCAmelCase     							=   {
          "prompt": "A painting of a squirrel eating a burger",
          "latents": latents,
          "generator": generator,
          "num_inference_steps": 5_0,
          "guidance_scale": 6.0,
          "output_type": "numpy",
      }
      return inputs




 def      lowercase	(					self       :    Tuple    ):
      _UpperCAmelCase     							=   LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256"    ).to(snake_case_    )
      pipe.set_progress_bar_config(disable=snake_case_    )

      _UpperCAmelCase     							=   self.get_inputs(snake_case_    )
      _UpperCAmelCase     							=   pipe(**snake_case_    ).images[0]

      _UpperCAmelCase     							=   load_numpy(
          "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy"    )
      _UpperCAmelCase     							=   np.abs(expected_image - image    ).max()
      assert max_diff < 1e-3











'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
    "files"			,     [
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Any			,     __lowercase							:      int       )		->       int:


     '''simple docstring'''



     _UpperCAmelCase     							=   tmp_path_factory.mktemp("dset_infos_dir"       )
     if "full:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write("---\ndataset_info:\n  dataset_size: 42\n---"       )
     if "empty:README.md" in files:
          with open(dataset_infos_dir / "README.md"			,     "w"       ) as f:
               f.write(""       )
    # we want to support dataset_infos.json for backward compatibility
     if "dataset_infos.json" in files:
          with open(dataset_infos_dir / "dataset_infos.json"			,     "w"       ) as f:
               f.write("{\"default\": {\"dataset_size\": 42}}"       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )
     assert dataset_infos
     assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
    "dataset_info"			,     [
        DatasetInfo(),
        DatasetInfo(
            description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      Tuple			,     __lowercase							:      DatasetInfo       )		->       Any:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_info.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfo.from_directory(__lowercase       )
     assert dataset_info == reloaded
     assert os.path.exists(os.path.join(__lowercase			,     "dataset_info.json"       )       )
def 	UpperCAmelCase_							(					)		->       Optional[Any]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo(
         description="foo"			,     citation="bar"			,     homepage="https://foo.bar"			,     license="CC0"			,     features=Features({"a": Value("int32"       )}       )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train", "num_examples": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert sorted(__lowercase       ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML       )
     for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
          assert key in dataset_info_yaml_dict
          assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str)       )
     _UpperCAmelCase     							=   yaml.safe_dump(__lowercase       )
     _UpperCAmelCase     							=   yaml.safe_load(__lowercase       )
     assert dataset_info_yaml_dict == reloaded
def 	UpperCAmelCase_							(					)		->       Optional[int]:


     '''simple docstring'''



     _UpperCAmelCase     							=   DatasetInfo()
     _UpperCAmelCase     							=   dataset_info._to_yaml_dict()
     assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    "dataset_infos_dict"			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}       ),
        DatasetInfosDict({"my_config_name": DatasetInfo()}       ),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"			,     features=Features({"a": Value("int32"       )}       )			,     builder_name="builder"			,     config_name="config"			,     version="1.0.0"			,     splits=[{"name": "train"}]			,     download_size=42			,     )
            }       ),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42       ),
                "v2": DatasetInfo(dataset_size=1337       ),
            }       ),
    ]			,     )
def 	UpperCAmelCase_							(					__lowercase							:      int			,     __lowercase							:      DatasetInfosDict       )		->       Dict:


     '''simple docstring'''



     _UpperCAmelCase     							=   str(__lowercase       )
     dataset_infos_dict.write_to_directory(__lowercase       )
     _UpperCAmelCase     							=   DatasetInfosDict.from_directory(__lowercase       )

     # the config_name of the dataset_infos_dict take over the attribute
     for config_name, dataset_info in dataset_infos_dict.items():
          _UpperCAmelCase     							=   config_name
          # the yaml representation doesn't include fields like description or citation
          # so we just test that we can recover what we can from the yaml
          _UpperCAmelCase     							=   DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()       )
     assert dataset_infos_dict == reloaded

     if dataset_infos_dict:
          assert os.path.exists(os.path.join(__lowercase			,     "README.md"       )       )




