






'''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
SCREAMING_SNAKE_CASE__           =  'src/transformers'


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


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__           =  re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
SCREAMING_SNAKE_CASE__           =  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.
SCREAMING_SNAKE_CASE__           =  re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__           =  [
    ('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        lowercase__					( __UpperCamelCase       )->					Optional[int]:
      UpperCamelCase							    =   re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"""						,		__a       )
      return [m.group(0       ) for m in matches]







def        lowercase__					( )->					List[Any]:
      UpperCamelCase							    =   transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      UpperCamelCase							    =   {
          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.
      UpperCamelCase							    =   collections.defaultdict(__a       )
      UpperCamelCase							    =   collections.defaultdict(__a       )
      UpperCamelCase							    =   collections.defaultdict(__a       )

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

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

      UpperCamelCase							    =   set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
      UpperCamelCase							    =   list(__a       )
      all_models.sort()

      UpperCamelCase							    =   {"""model_type""": all_models}
      UpperCamelCase							    =   [pt_models[t] for t in all_models]
      UpperCamelCase							    =   [tf_models[t] for t in all_models]
      UpperCamelCase							    =   [flax_models[t] for t in all_models]

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

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

      return pd.DataFrame(__a       )







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

            # 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        lowercase__					( __UpperCamelCase						,		__UpperCamelCase       )->					Union[str, Any]:
      UpperCamelCase							    =   get_frameworks_table()
      UpperCamelCase							    =   Dataset.from_pandas(__a       )

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

      # Sort the model classes to avoid some nondeterministic updates to create false update commits.
      UpperCamelCase							    =   sorted(table.keys()       )
      UpperCamelCase							    =   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],
          }       )
      UpperCamelCase							    =   Dataset.from_pandas(__a       )

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

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

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







def        lowercase__					( )->					Tuple:
      UpperCamelCase							    =   {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
      UpperCamelCase							    =   transformers_module.pipelines.SUPPORTED_TASKS
      UpperCamelCase							    =   []
      for key in pipeline_tasks:
            if key not in in_table:
                  UpperCamelCase							    =   pipeline_tasks[key]["""pt"""]
                  if isinstance(__a						,		(list, tuple)       ):
                        UpperCamelCase							    =   model[0]
                  UpperCamelCase							    =   model.__name__
                  if model not in in_table.values():
                        missing.append(__a       )

      if len(__a       ) > 0:
            UpperCamelCase							    =   """, """.join(__a       )
            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__":
     SCREAMING_SNAKE_CASE__           =  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.')
     SCREAMING_SNAKE_CASE__           =  parser.parse_args()

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


"""simple docstring"""




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__				=					logging.get_logger(__name__)



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''huggingface/label-files'''
		SCREAMING_SNAKE_CASE_    			=			'''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE_    			=			json.load(open(hf_hub_download(__a,				__a,				repo_type='''dataset'''     ),				'''r'''     )     )
		SCREAMING_SNAKE_CASE_    			=			{int(__a     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_    			=			{v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_    			=			'''std_conv''' if '''bit''' in model_name else False

		# note that when using BiT as backbone for ViT-hybrid checkpoints,
		# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
		# config.conv_layer = "std_conv_same"
		SCREAMING_SNAKE_CASE_    			=			BitConfig(
		    conv_layer=__a,				num_labels=1_000,				idalabel=__a,				labelaid=__a,				)

		return config



def 							_lowerCamelCase			(			__a     ):
		if "stem.conv" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''stem.conv''',				'''bit.embedder.convolution'''     )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''blocks''',				'''layers'''     )
		if "head.fc" in name:
				SCREAMING_SNAKE_CASE_    			=			name.replace('''head.fc''',				'''classifier.1'''     )
		if name.startswith('''norm'''     ):
				SCREAMING_SNAKE_CASE_    			=			'''bit.''' + name
		if "bit" not in name and "classifier" not in name:
				SCREAMING_SNAKE_CASE_    			=			'''bit.encoder.''' + name

		return name



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			get_config(__a     )

		# load original model from timm
		SCREAMING_SNAKE_CASE_    			=			create_model(__a,				pretrained=__a     )
		timm_model.eval()

		# load state_dict of original model
		SCREAMING_SNAKE_CASE_    			=			timm_model.state_dict()
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_    			=			BitForImageClassification(__a     )
		model.eval()
		model.load_state_dict(__a     )

		# create image processor
		SCREAMING_SNAKE_CASE_    			=			create_transform(**resolve_data_config({},				model=__a     )     )
		SCREAMING_SNAKE_CASE_    			=			transform.transforms

		SCREAMING_SNAKE_CASE_    			=			{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

		SCREAMING_SNAKE_CASE_    			=			BitImageProcessor(
		    do_resize=__a,				size={'''shortest_edge''': timm_transforms[0].size},				resample=pillow_resamplings[timm_transforms[0].interpolation.value],				do_center_crop=__a,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]},				do_normalize=__a,				image_mean=timm_transforms[-1].mean.tolist(),				image_std=timm_transforms[-1].std.tolist(),				)

		SCREAMING_SNAKE_CASE_    			=			prepare_img()
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     )
		SCREAMING_SNAKE_CASE_    			=			processor(__a,				return_tensors='''pt'''     ).pixel_values

		# verify pixel values
		assert torch.allclose(__a,				__a     )

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_    			=			model(__a     )
				SCREAMING_SNAKE_CASE_    			=			outputs.logits

		print('''Logits:''',				logits[0, :3]     )
		print('''Predicted class:''',				model.config.idalabel[logits.argmax(-1     ).item()]     )
		SCREAMING_SNAKE_CASE_    			=			timm_model(__a     )
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(__a,				outputs.logits,				atol=1E-3     )
		print('''Looks ok!'''     )

		if pytorch_dump_folder_path is not None:
				Path(__a     ).mkdir(exist_ok=__a     )
				print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'     )
				model.save_pretrained(__a     )
				processor.save_pretrained(__a     )

		if push_to_hub:
				print(F'Pushing model {model_name} and processor to the hub'     )
				model.push_to_hub(F'ybelkada/{model_name}'     )
				processor.push_to_hub(F'ybelkada/{model_name}'     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='resnetv2_50x1_bitm',
						    type=str,
						    help='Name of the BiT timm model you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model to the hub.',
						)

						lowerCAmelCase__				=					parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)