



from typing import Any, Callable, Dict, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker


_snake_case		=  "CompVis/stable-diffusion-v1-1"
_snake_case		=  "CompVis/stable-diffusion-v1-2"
_snake_case		=  "CompVis/stable-diffusion-v1-3"
_snake_case		=  "CompVis/stable-diffusion-v1-4"



class   UpperCAmelCase_    (       a):







							def __init__(   self,			__a,			__a,			__a,			__a,			__a,			__a,			__a,			__a = True,			):

														'''simple docstring'''

														super()._init_()

														_lowerCAmelCase     :							List[Any]    =    StableDiffusionPipeline.from_pretrained(__a)
														_lowerCAmelCase     :							List[Any]    =    StableDiffusionPipeline.from_pretrained(__a)
														_lowerCAmelCase     :							str    =    StableDiffusionPipeline.from_pretrained(__a)
														_lowerCAmelCase     :							str    =    StableDiffusionPipeline(
														    vae=__a,			text_encoder=__a,			tokenizer=__a,			unet=__a,			scheduler=__a,			safety_checker=__a,			feature_extractor=__a,			requires_safety_checker=__a,			)

														self.register_modules(pipelinea=self.pipea,			pipelinea=self.pipea,			pipelinea=self.pipea,			pipelinea=self.pipea)







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return {k: getattr(self,			__a) for k in self.config.keys() if not k.startswith("_")}







							def    snake_case__      (   self,			__a = "auto"):

														'''simple docstring'''

														if slice_size == "auto":
																					# half the attention head size is usually a good trade-off between
																					# speed and memory
																					_lowerCAmelCase     :							Union[str, Any]    =    self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														self.enable_attention_slicing(__a)







							@torch.no_grad()
							def    snake_case__      (   self,			__a,			__a = 512,			__a = 512,			__a = 50,			__a = 7.5,			__a = None,			__a = 1,			__a = 0.0,			__a = None,			__a = None,			__a = "pil",			__a = True,			__a = None,			__a = 1,			**__a,			):

														'''simple docstring'''

														return self.pipea(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)







							@torch.no_grad()
							def    snake_case__      (   self,			__a,			__a = 512,			__a = 512,			__a = 50,			__a = 7.5,			__a = None,			__a = 1,			__a = 0.0,			__a = None,			__a = None,			__a = "pil",			__a = True,			__a = None,			__a = 1,			**__a,			):

														'''simple docstring'''

														return self.pipea(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)







							@torch.no_grad()
							def    snake_case__      (   self,			__a,			__a = 512,			__a = 512,			__a = 50,			__a = 7.5,			__a = None,			__a = 1,			__a = 0.0,			__a = None,			__a = None,			__a = "pil",			__a = True,			__a = None,			__a = 1,			**__a,			):

														'''simple docstring'''

														return self.pipea(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)







							@torch.no_grad()
							def    snake_case__      (   self,			__a,			__a = 512,			__a = 512,			__a = 50,			__a = 7.5,			__a = None,			__a = 1,			__a = 0.0,			__a = None,			__a = None,			__a = "pil",			__a = True,			__a = None,			__a = 1,			**__a,			):

														'''simple docstring'''

														return self.pipea(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)







							@torch.no_grad()
							def    snake_case__      (   self,			__a,			__a = 512,			__a = 512,			__a = 50,			__a = 7.5,			__a = None,			__a = 1,			__a = 0.0,			__a = None,			__a = None,			__a = "pil",			__a = True,			__a = None,			__a = 1,			**__a,			):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    "cuda" if torch.cuda.is_available() else "cpu"
														self.to(__a)

														# Checks if the height and width are divisible by 8 or not
														if height % 8 != 0 or width % 8 != 0:
																					raise ValueError(f"`height` and `width` must be divisible by 8 but are {height} and {width}.")

														# Get first result from Stable Diffusion Checkpoint v1.1
														_lowerCAmelCase     :							Any    =    self.textaimg_sda_a(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)

														# Get first result from Stable Diffusion Checkpoint v1.2
														_lowerCAmelCase     :							int    =    self.textaimg_sda_a(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)

														# Get first result from Stable Diffusion Checkpoint v1.3
														_lowerCAmelCase     :							str    =    self.textaimg_sda_a(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)

														# Get first result from Stable Diffusion Checkpoint v1.4
														_lowerCAmelCase     :							Union[str, Any]    =    self.textaimg_sda_a(
														    prompt=__a,			height=__a,			width=__a,			num_inference_steps=__a,			guidance_scale=__a,			negative_prompt=__a,			num_images_per_prompt=__a,			eta=__a,			generator=__a,			latents=__a,			output_type=__a,			return_dict=__a,			callback=__a,			callback_steps=__a,			**__a,			)

														# Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
														return StableDiffusionPipelineOutput([resa[0], resa[0], resa[0], resa[0]])





"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
__a      =				"src/transformers"


# This is to make sure the transformers module imported is the one in the repo.
__a      =				direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
__a      =				re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
__a      =				re.compile(r"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
__a      =				re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
__a      =				[
    ("pretraining", "MODEL_FOR_PRETRAINING_MAPPING_NAMES", "AutoModelForPreTraining"),
    ("feature-extraction", "MODEL_MAPPING_NAMES", "AutoModel"),
    ("audio-classification", "MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForAudioClassification"),
    ("text-generation", "MODEL_FOR_CAUSAL_LM_MAPPING_NAMES", "AutoModelForCausalLM"),
    ("automatic-speech-recognition", "MODEL_FOR_CTC_MAPPING_NAMES", "AutoModelForCTC"),
    ("image-classification", "MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForImageClassification"),
    ("image-segmentation", "MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES", "AutoModelForImageSegmentation"),
    ("fill-mask", "MODEL_FOR_MASKED_LM_MAPPING_NAMES", "AutoModelForMaskedLM"),
    ("object-detection", "MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES", "AutoModelForObjectDetection"),
    (
        "zero-shot-object-detection",
        "MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES",
        "AutoModelForZeroShotObjectDetection",
    ),
    ("question-answering", "MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES", "AutoModelForQuestionAnswering"),
    ("text2text-generation", "MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES", "AutoModelForSeq2SeqLM"),
    ("text-classification", "MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES", "AutoModelForSequenceClassification"),
    ("automatic-speech-recognition", "MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES", "AutoModelForSpeechSeq2Seq"),
    (
        "table-question-answering",
        "MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForTableQuestionAnswering",
    ),
    ("token-classification", "MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES", "AutoModelForTokenClassification"),
    ("multiple-choice", "MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES", "AutoModelForMultipleChoice"),
    (
        "next-sentence-prediction",
        "MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES",
        "AutoModelForNextSentencePrediction",
    ),
    (
        "audio-frame-classification",
        "MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForAudioFrameClassification",
    ),
    ("audio-xvector", "MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES", "AutoModelForAudioXVector"),
    (
        "document-question-answering",
        "MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForDocumentQuestionAnswering",
    ),
    (
        "visual-question-answering",
        "MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES",
        "AutoModelForVisualQuestionAnswering",
    ),
    ("image-to-text", "MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES", "AutoModelForVision2Seq"),
    (
        "zero-shot-image-classification",
        "MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES",
        "AutoModelForZeroShotImageClassification",
    ),
    ("depth-estimation", "MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES", "AutoModelForDepthEstimation"),
    ("video-classification", "MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES", "AutoModelForVideoClassification"),
    ("mask-generation", "MODEL_FOR_MASK_GENERATION_MAPPING_NAMES", "AutoModelForMaskGeneration"),
]
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :Any						=			re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""",					_lowercase	)
						return [m.group(0	) for m in matches]
def    A_  (			):







						'''simple docstring'''


						snake_case_ :int						=			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
						snake_case_ :Dict						=			{
						    config.replace("""Config""",					""""""	): model_type for model_type, config in config_maping_names.items()
						}

						# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
						snake_case_ :Optional[Any]						=			collections.defaultdict(_lowercase	)
						snake_case_ :int						=			collections.defaultdict(_lowercase	)
						snake_case_ :List[str]						=			collections.defaultdict(_lowercase	)

						# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
						for attr_name in dir(_lowercase	):
												snake_case_ :int						=			None
												if _re_tf_models.match(_lowercase	) is not None:
																		snake_case_ :int						=			tf_models
																		snake_case_ :List[str]						=			_re_tf_models.match(_lowercase	).groups()[0]
												elif _re_flax_models.match(_lowercase	) is not None:
																		snake_case_ :List[Any]						=			flax_models
																		snake_case_ :Any						=			_re_flax_models.match(_lowercase	).groups()[0]
												elif _re_pt_models.match(_lowercase	) is not None:
																		snake_case_ :Optional[Any]						=			pt_models
																		snake_case_ :int						=			_re_pt_models.match(_lowercase	).groups()[0]

												if lookup_dict is not None:
																		while len(_lowercase	) > 0:
																								if attr_name in model_prefix_to_model_type:
																														snake_case_ :Optional[int]						=			True
																														break
																								# Try again after removing the last word in the name
																								snake_case_ :Optional[Any]						=			"""""".join(camel_case_split(_lowercase	)[:-1]	)

						snake_case_ :Optional[int]						=			set(list(pt_models.keys()	) + list(tf_models.keys()	) + list(flax_models.keys()	)	)
						snake_case_ :Optional[Any]						=			list(_lowercase	)
						all_models.sort()

						snake_case_ :Optional[int]						=			{"""model_type""": all_models}
						snake_case_ :Optional[int]						=			[pt_models[t] for t in all_models]
						snake_case_ :Any						=			[tf_models[t] for t in all_models]
						snake_case_ :Dict						=			[flax_models[t] for t in all_models]

						# Now let's use the auto-mapping names to make sure
						snake_case_ :Dict						=			{}
						for t in all_models:
												if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
																		snake_case_ :Optional[Any]						=			"""AutoProcessor"""
												elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
																		snake_case_ :Tuple						=			"""AutoTokenizer"""
												elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
																		snake_case_ :Tuple						=			"""AutoFeatureExtractor"""
												else:
																		# Default to AutoTokenizer if a model has nothing, for backward compatibility.
																		snake_case_ :str						=			"""AutoTokenizer"""

						snake_case_ :int						=			[processors[t] for t in all_models]

						return pd.DataFrame(_lowercase	)
def    A_  (			_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			[
						    transformers_module.models.auto.modeling_auto,
						    transformers_module.models.auto.modeling_tf_auto,
						    transformers_module.models.auto.modeling_flax_auto,
						]
						for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
												snake_case_ :Optional[int]						=			[model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
												snake_case_ :List[str]						=			[auto_class, f"""TF_{auto_class}""", f"""Flax_{auto_class}"""]
												# Loop through all three frameworks
												for module, cls, mapping in zip(_lowercase,					_lowercase,					_lowercase	):
																		# The type of pipeline may not exist in this framework
																		if not hasattr(_lowercase,					_lowercase	):
																								continue
																		# First extract all model_names
																		snake_case_ :Tuple						=			[]
																		for name in getattr(_lowercase,					_lowercase	).values():
																								if isinstance(_lowercase,					_lowercase	):
																														model_names.append(_lowercase	)
																								else:
																														model_names.extend(list(_lowercase	)	)

            # Add pipeline tag and auto model class for those models
																		table.update({model_name: (pipeline_tag, cls) for model_name in model_names}	)

						return table
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :List[Any]						=			get_frameworks_table()
						snake_case_ :str						=			Dataset.from_pandas(_lowercase	)

						snake_case_ :List[Any]						=			hf_hub_download(
						    """huggingface/transformers-metadata""",					"""pipeline_tags.json""",					repo_type="""dataset""",					token=_lowercase	)
						snake_case_ :List[str]						=			Dataset.from_json(_lowercase	)
						snake_case_ :int						=			{
						    tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
						    for i in range(len(_lowercase	)	)
						}
						snake_case_ :Optional[int]						=			update_pipeline_and_auto_class_table(_lowercase	)

						# Sort the model classes to avoid some nondeterministic updates to create false update commits.
						snake_case_ :Tuple						=			sorted(table.keys()	)
						snake_case_ :Tuple						=			pd.DataFrame(
						    {
						        """model_class""": model_classes,
						        """pipeline_tag""": [table[m][0] for m in model_classes],
						        """auto_class""": [table[m][1] for m in model_classes],
						    }	)
						snake_case_ :Union[str, Any]						=			Dataset.from_pandas(_lowercase	)

						with tempfile.TemporaryDirectory() as tmp_dir:
												frameworks_dataset.to_json(os.path.join(_lowercase,					"""frameworks.json"""	)	)
												tags_dataset.to_json(os.path.join(_lowercase,					"""pipeline_tags.json"""	)	)

												if commit_sha is not None:
																		snake_case_ :Union[str, Any]						=			(
																		    f"""Update with commit {commit_sha}\n\nSee: """
																		    f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
																		)
												else:
																		snake_case_ :List[Any]						=			"""Update"""

												upload_folder(
												    repo_id="""huggingface/transformers-metadata""",					folder_path=_lowercase,					repo_type="""dataset""",					token=_lowercase,					commit_message=_lowercase,					)
def    A_  (			):







						'''simple docstring'''


						snake_case_ :List[Any]						=			{tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
						snake_case_ :Dict						=			transformers_module.pipelines.SUPPORTED_TASKS
						snake_case_ :List[str]						=			[]
						for key in pipeline_tasks:
												if key not in in_table:
																		snake_case_ :int						=			pipeline_tasks[key]["""pt"""]
																		if isinstance(_lowercase,					(list, tuple)	):
																								snake_case_ :Any						=			model[0]
																		snake_case_ :str						=			model.__name__
																		if model not in in_table.values():
																								missing.append(_lowercase	)

						if len(_lowercase	) > 0:
												snake_case_ :Optional[int]						=			""", """.join(_lowercase	)
												raise ValueError(
												    """The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
												    f"""`utils/update_metadata.py`: {msg}. Please add them!"""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						parser.add_argument("--token", type=str, help="The token to use to push to the transformers-metadata dataset.")
						parser.add_argument("--commit_sha", type=str, help="The sha of the commit going with this update.")
						parser.add_argument("--check-only", action="store_true", help="Activate to just check all pipelines are present.")
						__a      =				parser.parse_args()

						if args.check_only:
												check_pipeline_tags()
						else:
												update_metadata(args.token, args.commit_sha)
