



"""simple docstring"""
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np
class 					a	:



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Optional[int]:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: int     			=TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: str     			=UNetaDConditionModel(
         sample_size=32       ,		layers_per_block=1       ,		block_out_channels=[32, 64]       ,		down_block_types=[
             """ResnetDownsampleBlock2D""",
             """SimpleCrossAttnDownBlock2D""",
         ]       ,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""       ,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]       ,		in_channels=3       ,		out_channels=6       ,		cross_attention_dim=32       ,		encoder_hid_dim=32       ,		attention_head_dim=8       ,		addition_embed_type="""text"""       ,		addition_embed_type_num_heads=2       ,		cross_attention_norm="""group_norm"""       ,		resnet_time_scale_shift="""scale_shift"""       ,		act_fn="""gelu"""       ,		)
     unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: List[str]     			=DDPMScheduler(
         num_train_timesteps=1000       ,		beta_schedule="""squaredcos_cap_v2"""       ,		beta_start=0.0_0_0_1       ,		beta_end=0.0_2       ,		thresholding=lowerCAmelCase       ,		dynamic_thresholding_ratio=0.9_5       ,		sample_max_value=1.0       ,		prediction_type="""epsilon"""       ,		variance_type="""learned_range"""       ,		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }



  def 	lowerCamelCase__       (						self :					str		)     ->					Any:


     '''simple docstring'''




     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Any     			=TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Dict     			=AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5"""		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=UNetaDConditionModel(
         sample_size=32       ,		layers_per_block=[1, 2]       ,		block_out_channels=[32, 64]       ,		down_block_types=[
             """ResnetDownsampleBlock2D""",
             """SimpleCrossAttnDownBlock2D""",
         ]       ,		mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""       ,		up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]       ,		in_channels=6       ,		out_channels=6       ,		cross_attention_dim=32       ,		encoder_hid_dim=32       ,		attention_head_dim=8       ,		addition_embed_type="""text"""       ,		addition_embed_type_num_heads=2       ,		cross_attention_norm="""group_norm"""       ,		resnet_time_scale_shift="""scale_shift"""       ,		act_fn="""gelu"""       ,		class_embed_type="""timestep"""       ,		mid_block_scale_factor=1.4_1_4       ,		time_embedding_act_fn="""gelu"""       ,		time_embedding_dim=32       ,		)
     unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: str     			=DDPMScheduler(
         num_train_timesteps=1000       ,		beta_schedule="""squaredcos_cap_v2"""       ,		beta_start=0.0_0_0_1       ,		beta_end=0.0_2       ,		thresholding=lowerCAmelCase       ,		dynamic_thresholding_ratio=0.9_5       ,		sample_max_value=1.0       ,		prediction_type="""epsilon"""       ,		variance_type="""learned_range"""       ,		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=DDPMScheduler(
         num_train_timesteps=1000       ,		beta_schedule="""squaredcos_cap_v2"""       ,		beta_start=0.0_0_0_1       ,		beta_end=0.0_2       ,		)

     torch.manual_seed(0		)
     SCREAMING_SNAKE_CASE_: Tuple     			=IFWatermarker()

     return {
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "unet": unet,
         "scheduler": scheduler,
         "image_noising_scheduler": image_noising_scheduler,
         "watermarker": watermarker,
         "safety_checker": None,
         "feature_extractor": None,
     }



  def 	lowerCamelCase__       (						self :					int		)     ->					str:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=self.get_dummy_components()
     SCREAMING_SNAKE_CASE_: str     			=self.pipeline_class(**lowerCAmelCase		)
     pipe.to(lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Any     			=self.get_dummy_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Dict     			=inputs["""prompt"""]
     SCREAMING_SNAKE_CASE_: Tuple     			=inputs["""generator"""]
     SCREAMING_SNAKE_CASE_: Tuple     			=inputs["""num_inference_steps"""]
     SCREAMING_SNAKE_CASE_: Tuple     			=inputs["""output_type"""]

     if "image" in inputs:
        SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""image"""]
     else:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None

     if "mask_image" in inputs:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=inputs["""mask_image"""]
     else:
        SCREAMING_SNAKE_CASE_: Tuple     			=None

     if "original_image" in inputs:
        SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""original_image"""]
     else:
        SCREAMING_SNAKE_CASE_: Optional[int]     			=None

     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: str     			=pipe.encode_prompt(lowerCAmelCase		)

     # inputs with prompt converted to embeddings
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			={
         """prompt_embeds""": prompt_embeds,
         """negative_prompt_embeds""": negative_prompt_embeds,
         """generator""": generator,
         """num_inference_steps""": num_inference_steps,
         """output_type""": output_type,
     }

     if image is not None:
        SCREAMING_SNAKE_CASE_: int     			=image

     if mask_image is not None:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=mask_image

     if original_image is not None:
        SCREAMING_SNAKE_CASE_: str     			=original_image

     # set all optional components to None
     for optional_component in pipe._optional_components:
        setattr(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=pipe(**lowerCAmelCase		)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Any     			=self.pipeline_class.from_pretrained(lowerCAmelCase		)
        pipe_loaded.to(lowerCAmelCase		)
        pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase		)

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

     for optional_component in pipe._optional_components:
        self.assertTrue(
            getattr(lowerCAmelCase       ,		lowerCAmelCase		) is None       ,		f'''`{optional_component}` did not stay set to None after loading.'''       ,		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=self.get_dummy_inputs(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""generator"""]
     SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""num_inference_steps"""]
     SCREAMING_SNAKE_CASE_: List[str]     			=inputs["""output_type"""]

     # inputs with prompt converted to embeddings
     SCREAMING_SNAKE_CASE_: Dict     			={
         """prompt_embeds""": prompt_embeds,
         """negative_prompt_embeds""": negative_prompt_embeds,
         """generator""": generator,
         """num_inference_steps""": num_inference_steps,
         """output_type""": output_type,
     }

     if image is not None:
        SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image

     if mask_image is not None:
        SCREAMING_SNAKE_CASE_: List[str]     			=mask_image

     if original_image is not None:
        SCREAMING_SNAKE_CASE_: int     			=original_image

     SCREAMING_SNAKE_CASE_: List[str]     			=pipe_loaded(**lowerCAmelCase		)[0]

     SCREAMING_SNAKE_CASE_: Optional[int]     			=np.abs(to_np(lowerCAmelCase		) - to_np(lowerCAmelCase		)		).max()
     self.assertLess(lowerCAmelCase       ,		1E-4		)





  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=self.get_dummy_components()
     SCREAMING_SNAKE_CASE_: Any     			=self.pipeline_class(**lowerCAmelCase		)
     pipe.to(lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.get_dummy_inputs(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=pipe(**lowerCAmelCase		)[0]

     with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Tuple     			=self.pipeline_class.from_pretrained(lowerCAmelCase		)
        pipe_loaded.to(lowerCAmelCase		)
        pipe_loaded.set_progress_bar_config(disable=lowerCAmelCase		)

     pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()		)  # For reproducibility tests

     SCREAMING_SNAKE_CASE_: Tuple     			=self.get_dummy_inputs(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=pipe_loaded(**lowerCAmelCase		)[0]

     SCREAMING_SNAKE_CASE_: List[Any]     			=np.abs(to_np(lowerCAmelCase		) - to_np(lowerCAmelCase		)		).max()
     self.assertLess(lowerCAmelCase       ,		1E-4		)








"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 					a	(  yaml.SafeLoader       ):



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE_: Any     			=[tuple(lowerCAmelCase		) if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) else key for key in keys]
     SCREAMING_SNAKE_CASE_: Dict     			=Counter(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[key for key in counter if counter[key] > 1]
     if duplicate_keys:
        raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''		)





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]=False		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=super().construct_mapping(lowerCAmelCase       ,		deep=lowerCAmelCase		)
     self._check_no_duplicates_on_constructed_node(lowerCAmelCase		)
     return mapping







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=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[str]     			="""\n""".join(full_content[1:sep_idx] )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

   return None, "\n".join(lowercase )
class 					a	(  UpperCAmelCase__       ):
  # class attributes
  UpperCamelCase	:				Tuple     						=					{'train_eval_index'}  # train-eval-index in the YAML metadata



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					Path		)     ->					"DatasetMetadata":


     '''simple docstring'''




     with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_split_yaml_from_readme(readme_file.read()		)
     if yaml_string is not None:
        return cls.from_yaml_string(lowerCAmelCase		)
     else:
        return cls()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Path		)     ->					List[str]:


     '''simple docstring'''




     if path.exists():
        with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
           SCREAMING_SNAKE_CASE_: str     			=readme_file.read()
     else:
        SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=self._to_readme(lowerCAmelCase		)
     with open(lowerCAmelCase       ,		"""w"""       ,		encoding="""utf-8"""		) as readme_file:
        readme_file.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[str] = None		)     ->					str:


     '''simple docstring'''




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



  @classmethod
  def 	lowerCamelCase__       (						cls :					Optional[int]       ,		lowerCAmelCase :					str		)     ->					"DatasetMetadata":


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=yaml.load(lowerCAmelCase       ,		Loader=_NoDuplicateSafeLoader		) or {}

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





  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




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







_UpperCAmelCase     =			{
    """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

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

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



