


'''simple docstring'''




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

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


enable_full_determinism()

@skip_mps
class 						UpperCAmelCase_ (    __lowercase			,      __lowercase			,      unittest.TestCase						):
     lowerCamelCase							:      Dict            =	StableDiffusionPanoramaPipeline
     lowerCamelCase							:      Union[str, Any]            =	TEXT_TO_IMAGE_PARAMS
     lowerCamelCase							:      List[Any]            =	TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCamelCase							:      List[str]            =	TEXT_TO_IMAGE_IMAGE_PARAMS
     lowerCamelCase							:      int            =	TEXT_TO_IMAGE_IMAGE_PARAMS





     def 				__UpperCAmelCase (					self			: str       )							->    int:
        torch.manual_seed(0       )
        lowerCAmelCase	=     UNetaDConditionModel(
            block_out_channels=(3_2, 6_4)       , layers_per_block=1       , 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       , )
        lowerCAmelCase	=     DDIMScheduler()
        torch.manual_seed(0       )
        lowerCAmelCase	=     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       )
        lowerCAmelCase	=     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       , )
        lowerCAmelCase	=     CLIPTextModel(UpperCAmelCase__       )
        lowerCAmelCase	=     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

        lowerCAmelCase	=     {
            "unet": unet,
            "scheduler": scheduler,
            "vae": vae,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "safety_checker": None,
            "feature_extractor": None,
        }
        return components





     def 				__UpperCAmelCase (					self			: str       , UpperCAmelCase__			: List[str]       , UpperCAmelCase__			: Dict=0       )							->    Optional[int]:
        lowerCAmelCase	=     torch.manual_seed(UpperCAmelCase__       )
        lowerCAmelCase	=     {
            "prompt": "a photo of the dolomites",
            "generator": generator,
            # Setting height and width to None to prevent OOMs on CPU.
            "height": None,
            "width": None,
            "num_inference_steps": 1,
            "guidance_scale": 6.0,
            "output_type": "numpy",
        }
        return inputs





     def 				__UpperCAmelCase (					self			: int       )							->    str:
        lowerCAmelCase	=     "cpu"  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase	=     self.get_dummy_components()
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline(**UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe.to(UpperCAmelCase__       )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

        lowerCAmelCase	=     self.get_dummy_inputs(UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe(**UpperCAmelCase__       ).images
        lowerCAmelCase	=     image[0, -3:, -3:, -1]
        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase	=     np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757]       )

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





     def 				__UpperCAmelCase (					self			: Dict       )							->    Optional[int]:
        super().test_inference_batch_consistent(batch_sizes=[1, 2]       )





     def 				__UpperCAmelCase (					self			: Tuple       )							->    Any:
        super().test_inference_batch_single_identical(batch_size=2       , expected_max_diff=3.25E-3       )





     def 				__UpperCAmelCase (					self			: Any       )							->    Optional[Any]:
        lowerCAmelCase	=     "cpu"  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase	=     self.get_dummy_components()
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline(**UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe.to(UpperCAmelCase__       )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

        lowerCAmelCase	=     self.get_dummy_inputs(UpperCAmelCase__       )
        lowerCAmelCase	=     "french fries"
        lowerCAmelCase	=     sd_pipe(**UpperCAmelCase__       , negative_prompt=UpperCAmelCase__       )
        lowerCAmelCase	=     output.images
        lowerCAmelCase	=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase	=     np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]       )

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





     def 				__UpperCAmelCase (					self			: int       )							->    Dict:
        lowerCAmelCase	=     "cpu"  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase	=     self.get_dummy_components()
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline(**UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe.to(UpperCAmelCase__       )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

        lowerCAmelCase	=     self.get_dummy_inputs(UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe(**UpperCAmelCase__       , view_batch_size=2       )
        lowerCAmelCase	=     output.images
        lowerCAmelCase	=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase	=     np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]       )

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





     def 				__UpperCAmelCase (					self			: Optional[Any]       )							->    str:
        lowerCAmelCase	=     "cpu"  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase	=     self.get_dummy_components()
        lowerCAmelCase	=     EulerAncestralDiscreteScheduler(
            beta_start=0.00_085       , beta_end=0.012       , beta_schedule='scaled_linear'       )
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline(**UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe.to(UpperCAmelCase__       )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

        lowerCAmelCase	=     self.get_dummy_inputs(UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe(**UpperCAmelCase__       ).images
        lowerCAmelCase	=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase	=     np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952]       )

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





     def 				__UpperCAmelCase (					self			: Optional[int]       )							->    List[str]:
        lowerCAmelCase	=     "cpu"  # ensure determinism for the device-dependent torch.Generator
        lowerCAmelCase	=     self.get_dummy_components()
        lowerCAmelCase	=     PNDMScheduler(
            beta_start=0.00_085       , beta_end=0.012       , beta_schedule='scaled_linear'       , skip_prk_steps=UpperCAmelCase__       )
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline(**UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe.to(UpperCAmelCase__       )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__       )

        lowerCAmelCase	=     self.get_dummy_inputs(UpperCAmelCase__       )
        lowerCAmelCase	=     sd_pipe(**UpperCAmelCase__       ).images
        lowerCAmelCase	=     image[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase	=     np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539]       )

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




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





     def 				__UpperCAmelCase (					self			: str       )							->    Optional[Any]:
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





     def 				__UpperCAmelCase (					self			: int       , UpperCAmelCase__			: Union[str, Any]=0       )							->    Any:
        lowerCAmelCase	=     torch.manual_seed(UpperCAmelCase__       )
        lowerCAmelCase	=     {
            "prompt": "a photo of the dolomites",
            "generator": generator,
            "num_inference_steps": 3,
            "guidance_scale": 7.5,
            "output_type": "numpy",
        }
        return inputs





     def 				__UpperCAmelCase (					self			: str       )							->    Any:
        lowerCAmelCase	=     "stabilityai/stable-diffusion-2-base"
        lowerCAmelCase	=     DDIMScheduler.from_pretrained(UpperCAmelCase__       , subfolder='scheduler'       )
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__       , scheduler=UpperCAmelCase__       , safety_checker=UpperCAmelCase__       )
        pipe.to(UpperCAmelCase__       )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__       )
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

        lowerCAmelCase	=     np.array(
            [
                0.36_968_392,
                0.27_025_372,
                0.32_446_766,
                0.28_379_387,
                0.36_363_274,
                0.30_733_347,
                0.27_100_027,
                0.27_054_125,
                0.25_536_096,
            ]       )

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





     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    Tuple:
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-base'       , safety_checker=UpperCAmelCase__       )
        lowerCAmelCase	=     LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
        pipe.to(UpperCAmelCase__       )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__       )
        pipe.enable_attention_slicing()

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

        assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

        lowerCAmelCase	=     np.array(
            [
                [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                ]
            ]       )

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





     def 				__UpperCAmelCase (					self			: Optional[Any]       )							->    Optional[Any]:
        lowerCAmelCase	=     0

        def callback_fn(UpperCAmelCase__			: int       , UpperCAmelCase__			: int       , UpperCAmelCase__			: torch.FloatTensor       ) -> None:
           lowerCAmelCase	=     True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
              lowerCAmelCase	=     latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 6_4, 2_5_6)
              lowerCAmelCase	=     latents[0, -3:, -3:, -1]

              lowerCAmelCase	=     np.array(
                  [
                      0.18_681_869,
                      0.33_907_816,
                      0.5_361_276,
                      0.14_432_865,
                      -0.02_856_611,
                      -0.73_941_123,
                      0.23_397_987,
                      0.47_322_682,
                      -0.37_823_164,
                  ]       )
              assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
           elif step == 2:
              lowerCAmelCase	=     latents.detach().cpu().numpy()
              assert latents.shape == (1, 4, 6_4, 2_5_6)
              lowerCAmelCase	=     latents[0, -3:, -3:, -1]

              lowerCAmelCase	=     np.array(
                  [
                      0.18_539_645,
                      0.33_987_248,
                      0.5_378_559,
                      0.14_437_142,
                      -0.02_455_261,
                      -0.7_338_317,
                      0.23_990_755,
                      0.47_356_272,
                      -0.3_786_505,
                  ]       )

              assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

        lowerCAmelCase	=     False

        lowerCAmelCase	=     "stabilityai/stable-diffusion-2-base"
        lowerCAmelCase	=     DDIMScheduler.from_pretrained(UpperCAmelCase__       , subfolder='scheduler'       )
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__       , scheduler=UpperCAmelCase__       , safety_checker=UpperCAmelCase__       )
        lowerCAmelCase	=     pipe.to(UpperCAmelCase__       )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__       )
        pipe.enable_attention_slicing()

        lowerCAmelCase	=     self.get_inputs()
        pipe(**UpperCAmelCase__       , callback=UpperCAmelCase__       , callback_steps=1       )
        assert callback_fn.has_been_called
        assert number_of_steps == 3





     def 				__UpperCAmelCase (					self			: str       )							->    Union[str, Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        lowerCAmelCase	=     "stabilityai/stable-diffusion-2-base"
        lowerCAmelCase	=     DDIMScheduler.from_pretrained(UpperCAmelCase__       , subfolder='scheduler'       )
        lowerCAmelCase	=     StableDiffusionPanoramaPipeline.from_pretrained(UpperCAmelCase__       , scheduler=UpperCAmelCase__       , safety_checker=UpperCAmelCase__       )
        lowerCAmelCase	=     pipe.to(UpperCAmelCase__       )
        pipe.set_progress_bar_config(disable=UpperCAmelCase__       )
        pipe.enable_attention_slicing(1       )
        pipe.enable_sequential_cpu_offload()

        lowerCAmelCase	=     self.get_inputs()
        lowerCAmelCase	=     pipe(**UpperCAmelCase__       )

        lowerCAmelCase	=     torch.cuda.max_memory_allocated()
        # make sure that less than 5.2 GB is allocated
        assert mem_bytes < 5.5 * 1_0**9









from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml



class 						_UpperCAmelCase      (     yaml.SafeLoader							):






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						[self.constructed_objects[key_node] for key_node, _ in node.value]
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						[tuple(UpperCAmelCase) if isinstance(UpperCAmelCase   ,  UpperCAmelCase) else key for key in keys]
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						Counter(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						[key for key in counter if counter[key] > 1]
				if duplicate_keys:
						raise TypeError(F"Got duplicate yaml keys: {duplicate_keys}")







		def _snake_case		(						self				:     int   ,  UpperCAmelCase				:     Union[str, Any]   ,  UpperCAmelCase				:     Union[str, Any]=False):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						super().construct_mapping(UpperCAmelCase   ,  deep=UpperCAmelCase)
				self._check_no_duplicates_on_constructed_node(UpperCAmelCase)
				return mapping




def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Any				     =						list(readme_content.splitlines() )
		if full_content and full_content[0] == "---" and "---" in full_content[1:]:
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						full_content[1:].index("---" ) + 1
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						"\n".join(full_content[1:sep_idx] )
				return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

		return None, "\n".join(a )



class 						_UpperCAmelCase      (     lowercase							):
		# class attributes
		lowerCamelCase_	:			List[Any]       			=  {"""train_eval_index"""}  # train-eval-index in the YAML metadata






		@classmethod
		def _snake_case		(						cls				:     Optional[int]   ,  UpperCAmelCase				:     Path):
				with open(UpperCAmelCase   ,  encoding="utf-8") as readme_file:
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						_split_yaml_from_readme(readme_file.read())
				if yaml_string is not None:
						return cls.from_yaml_string(UpperCAmelCase)
				else:
						return cls()






		def _snake_case		(						self				:     Dict   ,  UpperCAmelCase				:     Path):
				if path.exists():
						with open(UpperCAmelCase   ,  encoding="utf-8") as readme_file:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						readme_file.read()
				else:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						None
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self._to_readme(UpperCAmelCase)
				with open(UpperCAmelCase   ,  "w"   ,  encoding="utf-8") as readme_file:
						readme_file.write(UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     Optional[str] = None):
				if readme_content is not None:
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:List[str]				     =						_split_yaml_from_readme(UpperCAmelCase)
						SCREAMING_SNAKE_CASE_				:int				     =						"---\n" + self.to_yaml_string() + "---\n" + content
				else:
						SCREAMING_SNAKE_CASE_				:Any				     =						"---\n" + self.to_yaml_string() + "---\n"
				return full_content






		@classmethod
		def _snake_case		(						cls				:     List[str]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						yaml.load(UpperCAmelCase   ,  Loader=_NoDuplicateSafeLoader) or {}

				# Convert the YAML keys to DatasetMetadata fields
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						{
				    (key.replace("-"   ,  "_") if key.replace("-"   ,  "_") in cls._FIELDS_WITH_DASHES else key): value
				    for key, value in metadata_dict.items()
				}
				return cls(**UpperCAmelCase)







		def _snake_case		(						self				:     str):
				return yaml.safe_dump(
				    {
				        (key.replace("_"   ,  "-") if key in self._FIELDS_WITH_DASHES else key): value
				        for key, value in self.items()
				    }   ,  sort_keys=UpperCAmelCase   ,  allow_unicode=UpperCAmelCase   ,  encoding="utf-8"   ,  ).decode("utf-8")




SCREAMING_SNAKE_CASE__     					=			{
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
		from argparse import ArgumentParser

		SCREAMING_SNAKE_CASE__     					=			ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
		ap.add_argument("readme_filepath")
		SCREAMING_SNAKE_CASE__     					=			ap.parse_args()

		SCREAMING_SNAKE_CASE__     					=			Path(args.readme_filepath)
		SCREAMING_SNAKE_CASE__     					=			DatasetMetadata.from_readme(readme_filepath)
		print(dataset_metadata)
		dataset_metadata.to_readme(readme_filepath)






