



import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     Tuple									= '''https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'''
		lowerCAmelCase_			:     Optional[Any]									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw ).convert('''RGB''' )

		return image


def 			UpperCamelCase(  __UpperCamelCase							:		str ):
		lowerCAmelCase_			:     str									= []
		# fmt: off

		# vision encoder
		rename_keys.append(('''visual_encoder.cls_token''', '''vision_model.embeddings.class_embedding''') )
		rename_keys.append(('''visual_encoder.pos_embed''', '''vision_model.embeddings.position_embedding''') )
		rename_keys.append(('''visual_encoder.patch_embed.proj.weight''', '''vision_model.embeddings.patch_embedding.weight''') )
		rename_keys.append(('''visual_encoder.patch_embed.proj.bias''', '''vision_model.embeddings.patch_embedding.bias''') )
		rename_keys.append(('''ln_vision.weight''', '''vision_model.post_layernorm.weight''') )
		rename_keys.append(('''ln_vision.bias''', '''vision_model.post_layernorm.bias''') )

		for i in range(config.vision_config.num_hidden_layers ):
				rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.weight""", f"""vision_model.encoder.layers.{i}.layer_norm1.weight""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.norm1.bias""", f"""vision_model.encoder.layers.{i}.layer_norm1.bias""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.weight""", f"""vision_model.encoder.layers.{i}.layer_norm2.weight""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.norm2.bias""", f"""vision_model.encoder.layers.{i}.layer_norm2.bias""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.attn.qkv.weight""", f"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.weight""", f"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",) )
				rename_keys.append((f"""visual_encoder.blocks.{i}.attn.proj.bias""", f"""vision_model.encoder.layers.{i}.self_attn.projection.bias""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc1.weight""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc1.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc1.bias""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.weight""", f"""vision_model.encoder.layers.{i}.mlp.fc2.weight""") )
				rename_keys.append((f"""visual_encoder.blocks.{i}.mlp.fc2.bias""", f"""vision_model.encoder.layers.{i}.mlp.fc2.bias""") )

		# QFormer
		rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.weight''', '''qformer.layernorm.weight''') )
		rename_keys.append(('''Qformer.bert.embeddings.LayerNorm.bias''', '''qformer.layernorm.bias''') )

		# fmt: on
		return rename_keys


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		Dict     ,__UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     int									= dct.pop(__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= val


def 			UpperCamelCase(  __UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		Optional[Any] ):
		for i in range(config.vision_config.num_hidden_layers ):
				# read in original q and v biases
				lowerCAmelCase_			:     str									= state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias""" )
				lowerCAmelCase_			:     int									= state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias""" )

				# next, set bias in the state dict
				lowerCAmelCase_			:     Dict									= torch.cat((q_bias, torch.zeros_like(__UpperCamelCase     ,requires_grad=__UpperCamelCase ), v_bias) )
				lowerCAmelCase_			:     Tuple									= qkv_bias


def 			UpperCamelCase(  __UpperCamelCase							:		Union[str, Any]     ,__UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Dict									= 364 if '''coco''' in model_name else 224
		lowerCAmelCase_			:     Any									= BlipaVisionConfig(image_size=__UpperCamelCase ).to_dict()

		# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
		# seems like flan-T5 models don't have bos_token_id properly set?
		if "opt-2.7b" in model_name:
				lowerCAmelCase_			:     Union[str, Any]									= OPTConfig.from_pretrained('''facebook/opt-2.7b'''     ,eos_token_id=__UpperCamelCase ).to_dict()
		elif "opt-6.7b" in model_name:
				lowerCAmelCase_			:     Dict									= OPTConfig.from_pretrained('''facebook/opt-6.7b'''     ,eos_token_id=__UpperCamelCase ).to_dict()
		elif "t5-xl" in model_name:
				lowerCAmelCase_			:     int									= TaConfig.from_pretrained('''google/flan-t5-xl'''     ,dense_act_fn='''gelu'''     ,bos_token_id=1 ).to_dict()
		elif "t5-xxl" in model_name:
				lowerCAmelCase_			:     List[Any]									= TaConfig.from_pretrained('''google/flan-t5-xxl'''     ,dense_act_fn='''gelu'''     ,bos_token_id=1 ).to_dict()

		lowerCAmelCase_			:     Optional[Any]									= BlipaConfig(vision_config=__UpperCamelCase     ,text_config=__UpperCamelCase )

		return config, image_size


@torch.no_grad()
def 			UpperCamelCase(  __UpperCamelCase							:		List[Any]     ,__UpperCamelCase							:		Optional[Any]=None     ,__UpperCamelCase							:		Any=False ):
		lowerCAmelCase_			:     Any									= (
		    AutoTokenizer.from_pretrained('''facebook/opt-2.7b''' )
		    if '''opt''' in model_name
		    else AutoTokenizer.from_pretrained('''google/flan-t5-xl''' )
		)
		lowerCAmelCase_			:     Optional[int]									= tokenizer('''\n'''     ,add_special_tokens=__UpperCamelCase ).input_ids[0]
		lowerCAmelCase_					,  lowerCAmelCase_			:     List[str]									= get_blipa_config(__UpperCamelCase     ,eos_token_id=__UpperCamelCase )

		lowerCAmelCase_			:     Union[str, Any]									= BlipaForConditionalGeneration(__UpperCamelCase ).eval()

		lowerCAmelCase_			:     Tuple									= {
		    '''blip2-opt-2.7b''': ('''blip2_opt''', '''pretrain_opt2.7b'''),
		    '''blip2-opt-6.7b''': ('''blip2_opt''', '''pretrain_opt6.7b'''),
		    '''blip2-opt-2.7b-coco''': ('''blip2_opt''', '''caption_coco_opt2.7b'''),
		    '''blip2-opt-6.7b-coco''': ('''blip2_opt''', '''caption_coco_opt6.7b'''),
		    '''blip2-flan-t5-xl''': ('''blip2_t5''', '''pretrain_flant5xl'''),
		    '''blip2-flan-t5-xl-coco''': ('''blip2_t5''', '''caption_coco_flant5xl'''),
		    '''blip2-flan-t5-xxl''': ('''blip2_t5''', '''pretrain_flant5xxl'''),
		}

		lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= model_name_to_original[model_name]

		# load original model
		print('''Loading original model...''' )
		lowerCAmelCase_			:     Optional[int]									= '''cuda''' if torch.cuda.is_available() else '''cpu'''
		lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Tuple									= load_model_and_preprocess(
		    name=__UpperCamelCase     ,model_type=__UpperCamelCase     ,is_eval=__UpperCamelCase     ,device=__UpperCamelCase )
		original_model.eval()
		print('''Done!''' )

		# update state dict keys
		lowerCAmelCase_			:     Tuple									= original_model.state_dict()
		lowerCAmelCase_			:     int									= create_rename_keys(__UpperCamelCase )
		for src, dest in rename_keys:
				rename_key(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase )

		# some keys can be renamed efficiently
		for key, val in state_dict.copy().items():
				lowerCAmelCase_			:     Optional[Any]									= state_dict.pop(__UpperCamelCase )
				if key.startswith('''Qformer.bert''' ):
						lowerCAmelCase_			:     List[str]									= key.replace('''Qformer.bert'''     ,'''qformer''' )
				if "attention.self" in key:
						lowerCAmelCase_			:     Union[str, Any]									= key.replace('''self'''     ,'''attention''' )
				if "opt_proj" in key:
						lowerCAmelCase_			:     Any									= key.replace('''opt_proj'''     ,'''language_projection''' )
				if "t5_proj" in key:
						lowerCAmelCase_			:     List[Any]									= key.replace('''t5_proj'''     ,'''language_projection''' )
				if key.startswith('''opt''' ):
						lowerCAmelCase_			:     Union[str, Any]									= key.replace('''opt'''     ,'''language''' )
				if key.startswith('''t5''' ):
						lowerCAmelCase_			:     Optional[int]									= key.replace('''t5'''     ,'''language''' )
				lowerCAmelCase_			:     List[str]									= val

		# read in qv biases
		read_in_q_v_bias(__UpperCamelCase     ,__UpperCamelCase )

		lowerCAmelCase_					,  lowerCAmelCase_			:     Dict									= hf_model.load_state_dict(__UpperCamelCase     ,strict=__UpperCamelCase )
		assert len(__UpperCamelCase ) == 0
		assert unexpected_keys == ["qformer.embeddings.position_ids"]

		lowerCAmelCase_			:     List[str]									= load_demo_image()
		lowerCAmelCase_			:     Any									= vis_processors['''eval'''](__UpperCamelCase ).unsqueeze(0 ).to(__UpperCamelCase )
		lowerCAmelCase_			:     Union[str, Any]									= tokenizer(['''\n''']     ,return_tensors='''pt''' ).input_ids.to(__UpperCamelCase )

		# create processor
		lowerCAmelCase_			:     Optional[int]									= BlipImageProcessor(
		    size={'''height''': image_size, '''width''': image_size}     ,image_mean=__UpperCamelCase     ,image_std=__UpperCamelCase )
		lowerCAmelCase_			:     Optional[Any]									= BlipaProcessor(image_processor=__UpperCamelCase     ,tokenizer=__UpperCamelCase )
		lowerCAmelCase_			:     int									= processor(images=__UpperCamelCase     ,return_tensors='''pt''' ).pixel_values.to(__UpperCamelCase )

		# make sure processor creates exact same pixel values
		assert torch.allclose(__UpperCamelCase     ,__UpperCamelCase )

		original_model.to(__UpperCamelCase )
		hf_model.to(__UpperCamelCase )
		with torch.no_grad():
				if "opt" in model_name:
						lowerCAmelCase_			:     List[str]									= original_model({'''image''': original_pixel_values, '''text_input''': ['''''']} ).logits
						lowerCAmelCase_			:     str									= hf_model(__UpperCamelCase     ,__UpperCamelCase ).logits
				else:
						lowerCAmelCase_			:     str									= original_model(
						    {'''image''': original_pixel_values, '''text_input''': ['''\n'''], '''text_output''': ['''\n''']} ).logits
						lowerCAmelCase_			:     Union[str, Any]									= input_ids.masked_fill(input_ids == tokenizer.pad_token_id     ,-100 )
						lowerCAmelCase_			:     Tuple									= hf_model(__UpperCamelCase     ,__UpperCamelCase     ,labels=__UpperCamelCase ).logits

		assert original_logits.shape == logits.shape
		print('''First values of original logits:'''     ,original_logits[0, :3, :3] )
		print('''First values of HF logits:'''     ,logits[0, :3, :3] )

		# assert values
		if model_name == "blip2-flan-t5-xl":
				lowerCAmelCase_			:     Union[str, Any]									= torch.tensor(
				    [[-4_1.5_8_5_0, -4.4_4_4_0, -8.9_9_2_2], [-4_7.4_3_2_2, -5.9_1_4_3, -1.7_3_4_0]]     ,device=__UpperCamelCase )
				assert torch.allclose(logits[0, :3, :3]     ,__UpperCamelCase     ,atol=1e-4 )
		elif model_name == "blip2-flan-t5-xl-coco":
				lowerCAmelCase_			:     List[str]									= torch.tensor(
				    [[-5_7.0_1_0_9, -9.8_9_6_7, -1_2.6_2_8_0], [-6_8.6_5_7_8, -1_2.7_1_9_1, -1_0.5_0_6_5]]     ,device=__UpperCamelCase )
		else:
				# cast to same type
				lowerCAmelCase_			:     Dict									= logits.dtype
				assert torch.allclose(original_logits.to(__UpperCamelCase )     ,__UpperCamelCase     ,atol=1e-2 )
		print('''Looks ok!''' )

		print('''Generating a caption...''' )
		lowerCAmelCase_			:     Union[str, Any]									= ''''''
		lowerCAmelCase_			:     List[str]									= tokenizer(__UpperCamelCase     ,return_tensors='''pt''' ).input_ids.to(__UpperCamelCase )

		lowerCAmelCase_			:     Any									= original_model.generate({'''image''': original_pixel_values} )
		lowerCAmelCase_			:     Dict									= hf_model.generate(
		    __UpperCamelCase     ,__UpperCamelCase     ,do_sample=__UpperCamelCase     ,num_beams=5     ,max_length=30     ,min_length=1     ,top_p=0.9     ,repetition_penalty=1.0     ,length_penalty=1.0     ,temperature=1     ,)
		print('''Original generation:'''     ,__UpperCamelCase )
		lowerCAmelCase_			:     str									= input_ids.shape[1]
		lowerCAmelCase_			:     Optional[int]									= processor.batch_decode(outputs[:, prompt_length:]     ,skip_special_tokens=__UpperCamelCase )
		lowerCAmelCase_			:     Optional[int]									= [text.strip() for text in output_text]
		print('''HF generation:'''     ,__UpperCamelCase )

		if pytorch_dump_folder_path is not None:
				processor.save_pretrained(__UpperCamelCase )
				hf_model.save_pretrained(__UpperCamelCase )

		if push_to_hub:
				processor.push_to_hub(f"""nielsr/{model_name}""" )
				hf_model.push_to_hub(f"""nielsr/{model_name}""" )


if __name__ == "__main__":
						A__  :  int								=					argparse.ArgumentParser()
						A__  :  Tuple								=					[
						    '''blip2-opt-2.7b''',
						    '''blip2-opt-6.7b''',
						    '''blip2-opt-2.7b-coco''',
						    '''blip2-opt-6.7b-coco''',
						    '''blip2-flan-t5-xl''',
						    '''blip2-flan-t5-xl-coco''',
						    '''blip2-flan-t5-xxl''',
						]
						parser.add_argument(
						    '''--model_name''',
						    default='''blip2-opt-2.7b''',
						    choices=choices,
						    type=str,
						    help='''Path to hf config.json of model to convert''',
						)
						parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						    help='''Whether to push the model and processor to the hub after converting''',
						)

						A__  :  Optional[int]								=					parser.parse_args()

						convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










"""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)
