



'''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
UpperCamelCase__ :			Tuple												=				'''src/transformers'''


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


# Regexes that match TF/Flax/PT model names.
UpperCamelCase__ :			Tuple												=				re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
UpperCamelCase__ :			Dict												=				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.
UpperCamelCase__ :			List[str]												=				re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
UpperCamelCase__ :			List[Any]												=				[
    ('''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        lowerCAmelCase_     (			_lowerCamelCase:     Union[str, Any]      ):
		__SCREAMING_SNAKE_CASE :	Any					 =	re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" ,   _lowerCamelCase      )
		return [m.group(0      ) for m in matches]


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
		__SCREAMING_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.
		__SCREAMING_SNAKE_CASE :	List[str]					 =	collections.defaultdict(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	Any					 =	collections.defaultdict(_lowerCamelCase      )
		__SCREAMING_SNAKE_CASE :	int					 =	collections.defaultdict(_lowerCamelCase      )

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

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

		__SCREAMING_SNAKE_CASE :	List[Any]					 =	set(list(pt_models.keys()      ) + list(tf_models.keys()      ) + list(flax_models.keys()      )      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	list(_lowerCamelCase      )
		all_models.sort()

		__SCREAMING_SNAKE_CASE :	int					 =	{"""model_type""": all_models}
		__SCREAMING_SNAKE_CASE :	Dict					 =	[pt_models[t] for t in all_models]
		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[tf_models[t] for t in all_models]
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	[flax_models[t] for t in all_models]

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

		__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[processors[t] for t in all_models]

		return pd.DataFrame(_lowerCamelCase      )


def        lowerCAmelCase_     (			_lowerCamelCase:     Any      ):
		__SCREAMING_SNAKE_CASE :	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:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	[model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
				__SCREAMING_SNAKE_CASE :	Tuple					 =	[auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
				# Loop through all three frameworks
				for module, cls, mapping in zip(_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase      ):
						# The type of pipeline may not exist in this framework
						if not hasattr(_lowerCamelCase ,   _lowerCamelCase      ):
								continue
						# First extract all model_names
						__SCREAMING_SNAKE_CASE :	Tuple					 =	[]
						for name in getattr(_lowerCamelCase ,   _lowerCamelCase      ).values():
								if isinstance(_lowerCamelCase ,   _lowerCamelCase      ):
										model_names.append(_lowerCamelCase      )
								else:
										model_names.extend(list(_lowerCamelCase      )      )

            # 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        lowerCAmelCase_     (			_lowerCamelCase:     Any ,   _lowerCamelCase:     Tuple      ):
		__SCREAMING_SNAKE_CASE :	Tuple					 =	get_frameworks_table()
		__SCREAMING_SNAKE_CASE :	List[str]					 =	Dataset.from_pandas(_lowerCamelCase      )

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

		# Sort the model classes to avoid some nondeterministic updates to create false update commits.
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	sorted(table.keys()      )
		__SCREAMING_SNAKE_CASE :	Any					 =	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],
		    }      )
		__SCREAMING_SNAKE_CASE :	str					 =	Dataset.from_pandas(_lowerCamelCase      )

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

				if commit_sha is not None:
						__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	(
						    F"Update with commit {commit_sha}\n\nSee: "
						    F"https://github.com/huggingface/transformers/commit/{commit_sha}"
						)
				else:
						__SCREAMING_SNAKE_CASE :	Any					 =	"""Update"""

				upload_folder(
				    repo_id="""huggingface/transformers-metadata""" ,   folder_path=_lowerCamelCase ,   repo_type="""dataset""" ,   token=_lowerCamelCase ,   commit_message=_lowerCamelCase ,   )


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

		if len(_lowerCamelCase      ) > 0:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	""", """.join(_lowerCamelCase      )
				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__":
						UpperCamelCase__ :			Optional[int]												=				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.''')
						UpperCamelCase__ :			List[Any]												=				parser.parse_args()

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




'''simple docstring'''




import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)

os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)


def        lowerCAmelCase_     (			_lowerCamelCase:     int      ):
		if hor == 1_28:
				__SCREAMING_SNAKE_CASE :	Any					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	List[Any]					 =	(32, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	str					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""")

		elif hor == 32:
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
				__SCREAMING_SNAKE_CASE :	str					 =	(32, 64, 1_28, 2_56)
				__SCREAMING_SNAKE_CASE :	Tuple					 =	("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	torch.load(F"/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"      )
		__SCREAMING_SNAKE_CASE :	Any					 =	model.state_dict()
		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	{
		    """down_block_types""": down_block_types,
		    """block_out_channels""": block_out_channels,
		    """up_block_types""": up_block_types,
		    """layers_per_block""": 1,
		    """use_timestep_embedding""": True,
		    """out_block_type""": """OutConv1DBlock""",
		    """norm_num_groups""": 8,
		    """downsample_each_block""": False,
		    """in_channels""": 14,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """sample_size""": 6_55_36,
		    """mid_block_type""": """MidResTemporalBlock1D""",
		    """act_fn""": """mish""",
		}
		__SCREAMING_SNAKE_CASE :	int					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )
		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	dict(zip(model.state_dict().keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	Union[str, Any]					 =	state_dict.pop(_lowerCamelCase      )
		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   F"hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"      )
		with open(F"hub/hopper-medium-v2/unet/hor{hor}/config.json" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


def        lowerCAmelCase_     (			):
		__SCREAMING_SNAKE_CASE :	Dict					 =	{
		    """in_channels""": 14,
		    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
		    """up_block_types""": (),
		    """out_block_type""": """ValueFunction""",
		    """mid_block_type""": """ValueFunctionMidBlock1D""",
		    """block_out_channels""": (32, 64, 1_28, 2_56),
		    """layers_per_block""": 1,
		    """downsample_each_block""": True,
		    """sample_size""": 6_55_36,
		    """out_channels""": 14,
		    """extra_in_channels""": 0,
		    """time_embedding_type""": """positional""",
		    """use_timestep_embedding""": True,
		    """flip_sin_to_cos""": False,
		    """freq_shift""": 1,
		    """norm_num_groups""": 8,
		    """act_fn""": """mish""",
		}

		__SCREAMING_SNAKE_CASE :	Optional[int]					 =	torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""      )
		__SCREAMING_SNAKE_CASE :	Dict					 =	model
		__SCREAMING_SNAKE_CASE :	List[Any]					 =	UNetaDModel(**_lowerCamelCase      )
		print(F"length of state dict: {len(state_dict.keys()      )}"      )
		print(F"length of value function dict: {len(hf_value_function.state_dict().keys()      )}"      )

		__SCREAMING_SNAKE_CASE :	Optional[Any]					 =	dict(zip(state_dict.keys() ,   hf_value_function.state_dict().keys()      )      )
		for k, v in mapping.items():
				__SCREAMING_SNAKE_CASE :	str					 =	state_dict.pop(_lowerCamelCase      )

		hf_value_function.load_state_dict(_lowerCamelCase      )

		torch.save(hf_value_function.state_dict() ,   """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""      )
		with open("""hub/hopper-medium-v2/value_function/config.json""" ,   """w"""      ) as f:
				json.dump(_lowerCamelCase ,   _lowerCamelCase      )


if __name__ == "__main__":
						unet(32)
						# unet(128)
						value_function()