

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       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   'https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png'
      lowerCamelCase_							=   Image.open(requests.get(lowercase     ,							stream=lowercase					).raw					).convert('RGB'					)

      return image

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]					):


      '''simple docstring'''



      lowerCamelCase_							=   []
      # 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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	Optional[int]     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



      lowerCamelCase_							=   dct.pop(lowercase					)
      lowerCamelCase_							=   val

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Optional[Any]     ,							lowercase     :	Tuple					):


      '''simple docstring'''



      for i in range(config.vision_config.num_hidden_layers					):
            # read in original q and v biases
            lowerCamelCase_							=   state_dict.pop(f"""visual_encoder.blocks.{i}.attn.q_bias"""					)
            lowerCamelCase_							=   state_dict.pop(f"""visual_encoder.blocks.{i}.attn.v_bias"""					)

            # next, set bias in the state dict
            lowerCamelCase_							=   torch.cat((q_bias, torch.zeros_like(lowercase     ,							requires_grad=lowercase					), v_bias)					)
            lowerCamelCase_							=   qkv_bias

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]     ,							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   3_64 if 'coco' in model_name else 2_24
      lowerCamelCase_							=   BlipaVisionConfig(image_size=lowercase					).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_							=   OPTConfig.from_pretrained('facebook/opt-2.7b'     ,							eos_token_id=lowercase					).to_dict()
      elif "opt-6.7b" in model_name:
            lowerCamelCase_							=   OPTConfig.from_pretrained('facebook/opt-6.7b'     ,							eos_token_id=lowercase					).to_dict()
      elif "t5-xl" in model_name:
            lowerCamelCase_							=   TaConfig.from_pretrained('google/flan-t5-xl'     ,							dense_act_fn='gelu'     ,							bos_token_id=1					).to_dict()
      elif "t5-xxl" in model_name:
            lowerCamelCase_							=   TaConfig.from_pretrained('google/flan-t5-xxl'     ,							dense_act_fn='gelu'     ,							bos_token_id=1					).to_dict()

      lowerCamelCase_							=   BlipaConfig(vision_config=lowercase     ,							text_config=lowercase					)

      return config, image_size

@torch.no_grad()
def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	Optional[Any]=None     ,							lowercase     :	Dict=False					):


      '''simple docstring'''



      lowerCamelCase_							=   (
          AutoTokenizer.from_pretrained('facebook/opt-2.7b'					)
          if 'opt' in model_name
          else AutoTokenizer.from_pretrained('google/flan-t5-xl'					)
      )
      lowerCamelCase_							=   tokenizer('\n'     ,							add_special_tokens=lowercase					).input_ids[0]
      lowerCamelCase_					,    lowerCamelCase_							=   get_blipa_config(lowercase     ,							eos_token_id=lowercase					)

      lowerCamelCase_							=   BlipaForConditionalGeneration(lowercase					).eval()

      lowerCamelCase_							=   {
          '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_							=   model_name_to_original[model_name]

      # load original model
      print('Loading original model...'					)
      lowerCamelCase_							=   'cuda' if torch.cuda.is_available() else 'cpu'
      lowerCamelCase_					,    lowerCamelCase_					,    lowerCamelCase_							=   load_model_and_preprocess(
          name=lowercase     ,							model_type=lowercase     ,							is_eval=lowercase     ,							device=lowercase					)
      original_model.eval()
      print('Done!'					)

      # update state dict keys
      lowerCamelCase_							=   original_model.state_dict()
      lowerCamelCase_							=   create_rename_keys(lowercase					)
      for src, dest in rename_keys:
            rename_key(lowercase     ,							lowercase     ,							lowercase					)

      # some keys can be renamed efficiently
      for key, val in state_dict.copy().items():
            lowerCamelCase_							=   state_dict.pop(lowercase					)
            if key.startswith('Qformer.bert'					):
                  lowerCamelCase_							=   key.replace('Qformer.bert'     ,							'qformer'					)
            if "attention.self" in key:
                  lowerCamelCase_							=   key.replace('self'     ,							'attention'					)
            if "opt_proj" in key:
                  lowerCamelCase_							=   key.replace('opt_proj'     ,							'language_projection'					)
            if "t5_proj" in key:
                  lowerCamelCase_							=   key.replace('t5_proj'     ,							'language_projection'					)
            if key.startswith('opt'					):
                  lowerCamelCase_							=   key.replace('opt'     ,							'language'					)
            if key.startswith('t5'					):
                  lowerCamelCase_							=   key.replace('t5'     ,							'language'					)
            lowerCamelCase_							=   val

      # read in qv biases
      read_in_q_v_bias(lowercase     ,							lowercase					)

      lowerCamelCase_					,    lowerCamelCase_							=   hf_model.load_state_dict(lowercase     ,							strict=lowercase					)
      assert len(lowercase					) == 0
      assert unexpected_keys == ["qformer.embeddings.position_ids"]

      lowerCamelCase_							=   load_demo_image()
      lowerCamelCase_							=   vis_processors['eval'](lowercase					).unsqueeze(0					).to(lowercase					)
      lowerCamelCase_							=   tokenizer(['\n']     ,							return_tensors='pt'					).input_ids.to(lowercase					)

      # create processor
      lowerCamelCase_							=   BlipImageProcessor(
          size={'height': image_size, 'width': image_size}     ,							image_mean=lowercase     ,							image_std=lowercase					)
      lowerCamelCase_							=   BlipaProcessor(image_processor=lowercase     ,							tokenizer=lowercase					)
      lowerCamelCase_							=   processor(images=lowercase     ,							return_tensors='pt'					).pixel_values.to(lowercase					)

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

      original_model.to(lowercase					)
      hf_model.to(lowercase					)
      with torch.no_grad():
            if "opt" in model_name:
                  lowerCamelCase_							=   original_model({'image': original_pixel_values, 'text_input': ['']}					).logits
                  lowerCamelCase_							=   hf_model(lowercase     ,							lowercase					).logits
            else:
                  lowerCamelCase_							=   original_model(
                      {'image': original_pixel_values, 'text_input': ['\n'], 'text_output': ['\n']}					).logits
                  lowerCamelCase_							=   input_ids.masked_fill(input_ids == tokenizer.pad_token_id     ,							-1_00					)
                  lowerCamelCase_							=   hf_model(lowercase     ,							lowercase     ,							labels=lowercase					).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_							=   torch.tensor(
                [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]     ,							device=lowercase					)
            assert torch.allclose(logits[0, :3, :3]     ,							lowercase     ,							atol=1e-4					)
      elif model_name == "blip2-flan-t5-xl-coco":
            lowerCamelCase_							=   torch.tensor(
                [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]     ,							device=lowercase					)
      else:
            # cast to same type
            lowerCamelCase_							=   logits.dtype
            assert torch.allclose(original_logits.to(lowercase					)     ,							lowercase     ,							atol=1e-2					)
      print('Looks ok!'					)

      print('Generating a caption...'					)
      lowerCamelCase_							=   ''
      lowerCamelCase_							=   tokenizer(lowercase     ,							return_tensors='pt'					).input_ids.to(lowercase					)

      lowerCamelCase_							=   original_model.generate({'image': original_pixel_values}					)
      lowerCamelCase_							=   hf_model.generate(
          lowercase     ,							lowercase     ,							do_sample=lowercase     ,							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:'     ,							lowercase					)
      lowerCamelCase_							=   input_ids.shape[1]
      lowerCamelCase_							=   processor.batch_decode(outputs[:, prompt_length:]     ,							skip_special_tokens=lowercase					)
      lowerCamelCase_							=   [text.strip() for text in output_text]
      print('HF generation:'     ,							lowercase					)

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

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


if __name__ == "__main__":
 lowerCamelCase    :		str			   =       argparse.ArgumentParser()
 lowerCamelCase    :		Optional[Any]			   =       [
     "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",
 )

 lowerCamelCase    :		List[Any]			   =       parser.parse_args()

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









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
lowerCamelCase    :		List[Any]			   =       "src/transformers"


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


# Regexes that match TF/Flax/PT model names.
lowerCamelCase    :		Optional[Any]			   =       re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCamelCase    :		List[Any]			   =       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.
lowerCamelCase    :		Union[str, Any]			   =       re.compile(r"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCamelCase    :		Tuple			   =       [
    ("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       _SCREAMING_SNAKE_CASE      (							lowercase     :	int					):


      '''simple docstring'''



      lowerCamelCase_							=   re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'     ,							lowercase					)
      return [m.group(0					) for m in matches]

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      lowerCamelCase_							=   {
          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.
      lowerCamelCase_							=   collections.defaultdict(lowercase					)
      lowerCamelCase_							=   collections.defaultdict(lowercase					)
      lowerCamelCase_							=   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					):
            lowerCamelCase_							=   None
            if _re_tf_models.match(lowercase					) is not None:
                  lowerCamelCase_							=   tf_models
                  lowerCamelCase_							=   _re_tf_models.match(lowercase					).groups()[0]
            elif _re_flax_models.match(lowercase					) is not None:
                  lowerCamelCase_							=   flax_models
                  lowerCamelCase_							=   _re_flax_models.match(lowercase					).groups()[0]
            elif _re_pt_models.match(lowercase					) is not None:
                  lowerCamelCase_							=   pt_models
                  lowerCamelCase_							=   _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:
                              lowerCamelCase_							=   True
                              break
                        # Try again after removing the last word in the name
                        lowerCamelCase_							=   ''.join(camel_case_split(lowercase					)[:-1]					)

      lowerCamelCase_							=   set(list(pt_models.keys()					) + list(tf_models.keys()					) + list(flax_models.keys()					)					)
      lowerCamelCase_							=   list(lowercase					)
      all_models.sort()

      lowerCamelCase_							=   {'model_type': all_models}
      lowerCamelCase_							=   [pt_models[t] for t in all_models]
      lowerCamelCase_							=   [tf_models[t] for t in all_models]
      lowerCamelCase_							=   [flax_models[t] for t in all_models]

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

      lowerCamelCase_							=   [processors[t] for t in all_models]

      return pd.DataFrame(lowercase					)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str					):


      '''simple docstring'''



      lowerCamelCase_							=   [
          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:
            lowerCamelCase_							=   [model_mapping, f"""TF_{model_mapping}""", f"""FLAX_{model_mapping}"""]
            lowerCamelCase_							=   [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
                  lowerCamelCase_							=   []
                  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       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Optional[int]					):


      '''simple docstring'''



      lowerCamelCase_							=   get_frameworks_table()
      lowerCamelCase_							=   Dataset.from_pandas(lowercase					)

      lowerCamelCase_							=   hf_hub_download(
          'huggingface/transformers-metadata'     ,							'pipeline_tags.json'     ,							repo_type='dataset'     ,							token=lowercase					)
      lowerCamelCase_							=   Dataset.from_json(lowercase					)
      lowerCamelCase_							=   {
          tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
          for i in range(len(lowercase					)					)
      }
      lowerCamelCase_							=   update_pipeline_and_auto_class_table(lowercase					)

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      lowerCamelCase_							=   sorted(table.keys()					)
      lowerCamelCase_							=   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],
          }					)
      lowerCamelCase_							=   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:
                  lowerCamelCase_							=   (
                      f"""Update with commit {commit_sha}\n\nSee: """
                      f"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
                  )
            else:
                  lowerCamelCase_							=   'Update'

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

def       _SCREAMING_SNAKE_CASE      (							):


      '''simple docstring'''



      lowerCamelCase_							=   {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
      lowerCamelCase_							=   transformers_module.pipelines.SUPPORTED_TASKS
      lowerCamelCase_							=   []
      for key in pipeline_tasks:
            if key not in in_table:
                  lowerCamelCase_							=   pipeline_tasks[key]['pt']
                  if isinstance(lowercase     ,							(list, tuple)					):
                        lowerCamelCase_							=   model[0]
                  lowerCamelCase_							=   model.__name__
                  if model not in in_table.values():
                        missing.append(lowercase					)

      if len(lowercase					) > 0:
            lowerCamelCase_							=   ', '.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__":
 lowerCamelCase    :		List[str]			   =       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.")
 lowerCamelCase    :		Optional[int]			   =       parser.parse_args()

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



