import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__						      =			logging.get_logger(__name__)
def    __magic_name__(	__UpperCAmelCase		)    ->     int:



      '''simple docstring'''

      _lowerCamelCase           =					ConvNextConfig()

      if "tiny" in checkpoint_url:
            _lowerCamelCase           =					[3, 3, 9, 3]
            _lowerCamelCase           =					[96, 192, 384, 768]
      if "small" in checkpoint_url:
            _lowerCamelCase           =					[3, 3, 27, 3]
            _lowerCamelCase           =					[96, 192, 384, 768]
      if "base" in checkpoint_url:
            _lowerCamelCase           =					[3, 3, 27, 3]
            _lowerCamelCase           =					[128, 256, 512, 1024]
      if "large" in checkpoint_url:
            _lowerCamelCase           =					[3, 3, 27, 3]
            _lowerCamelCase           =					[192, 384, 768, 1536]
      if "xlarge" in checkpoint_url:
            _lowerCamelCase           =					[3, 3, 27, 3]
            _lowerCamelCase           =					[256, 512, 1024, 2048]

      if "1k" in checkpoint_url:
            _lowerCamelCase           =					1000
            _lowerCamelCase           =					'''imagenet-1k-id2label.json'''
            _lowerCamelCase           =					(1, 1000)
      else:
            _lowerCamelCase           =					2_1841
            _lowerCamelCase           =					'''imagenet-22k-id2label.json'''
            _lowerCamelCase           =					(1, 2_1841)

      _lowerCamelCase           =					'''huggingface/label-files'''
      _lowerCamelCase           =					num_labels
      _lowerCamelCase           =					json.load(open(hf_hub_download(__UpperCAmelCase			,    __UpperCAmelCase			,    repo_type='''dataset'''		)			,    '''r'''		)		)
      _lowerCamelCase           =					{int(__UpperCAmelCase		): v for k, v in idalabel.items()}
      if "1k" not in checkpoint_url:
            # this dataset contains 21843 labels but the model only has 21841
            # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
            del idalabel[9205]
            del idalabel[1_5027]
      _lowerCamelCase           =					idalabel
      _lowerCamelCase           =					{v: k for k, v in idalabel.items()}
      _lowerCamelCase           =					hidden_sizes
      _lowerCamelCase           =					depths

      return config, expected_shape
def    __magic_name__(	__UpperCAmelCase		)    ->     Tuple:



      '''simple docstring'''

      if "downsample_layers.0.0" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.0.0'''			,    '''embeddings.patch_embeddings'''		)
      if "downsample_layers.0.1" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.0.1'''			,    '''embeddings.norm'''		)  # we rename to layernorm later on
      if "downsample_layers.1.0" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.1.0'''			,    '''stages.1.downsampling_layer.0'''		)
      if "downsample_layers.1.1" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.1.1'''			,    '''stages.1.downsampling_layer.1'''		)
      if "downsample_layers.2.0" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.2.0'''			,    '''stages.2.downsampling_layer.0'''		)
      if "downsample_layers.2.1" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.2.1'''			,    '''stages.2.downsampling_layer.1'''		)
      if "downsample_layers.3.0" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.3.0'''			,    '''stages.3.downsampling_layer.0'''		)
      if "downsample_layers.3.1" in name:
            _lowerCamelCase           =					name.replace('''downsample_layers.3.1'''			,    '''stages.3.downsampling_layer.1'''		)
      if "stages" in name and "downsampling_layer" not in name:
            # stages.0.0. for instance should be renamed to stages.0.layers.0.
            _lowerCamelCase           =					name[: len('''stages.0'''		)] + '''.layers''' + name[len('''stages.0'''		) :]
      if "stages" in name:
            _lowerCamelCase           =					name.replace('''stages'''			,    '''encoder.stages'''		)
      if "norm" in name:
            _lowerCamelCase           =					name.replace('''norm'''			,    '''layernorm'''		)
      if "gamma" in name:
            _lowerCamelCase           =					name.replace('''gamma'''			,    '''layer_scale_parameter'''		)
      if "head" in name:
            _lowerCamelCase           =					name.replace('''head'''			,    '''classifier'''		)

      return name
def    __magic_name__(	)    ->     Dict:



      '''simple docstring'''

      _lowerCamelCase           =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _lowerCamelCase           =					Image.open(requests.get(__UpperCAmelCase			,    stream=__UpperCAmelCase		).raw		)
      return im
@torch.no_grad()
def    __magic_name__(	__UpperCAmelCase			,    __UpperCAmelCase		)    ->     Union[str, Any]:



      '''simple docstring'''

      _lowerCamelCase     ,       _lowerCamelCase           =					get_convnext_config(__UpperCAmelCase		)
      # load original state_dict from URL
      _lowerCamelCase           =					torch.hub.load_state_dict_from_url(__UpperCAmelCase		)['''model''']
      # rename keys
      for key in state_dict.copy().keys():
            _lowerCamelCase           =					state_dict.pop(__UpperCAmelCase		)
            _lowerCamelCase           =					val
      # add prefix to all keys expect classifier head
      for key in state_dict.copy().keys():
            _lowerCamelCase           =					state_dict.pop(__UpperCAmelCase		)
            if not key.startswith('''classifier'''		):
                  _lowerCamelCase           =					'''convnext.''' + key
            _lowerCamelCase           =					val

      # load HuggingFace model
      _lowerCamelCase           =					ConvNextForImageClassification(__UpperCAmelCase		)
      model.load_state_dict(__UpperCAmelCase		)
      model.eval()

      # Check outputs on an image, prepared by ConvNextImageProcessor
      _lowerCamelCase           =					224 if '''224''' in checkpoint_url else 384
      _lowerCamelCase           =					ConvNextImageProcessor(size=__UpperCAmelCase		)
      _lowerCamelCase           =					image_processor(images=prepare_img()			,    return_tensors='''pt'''		).pixel_values

      _lowerCamelCase           =					model(__UpperCAmelCase		).logits

      # note: the logits below were obtained without center cropping
      if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
            _lowerCamelCase           =					torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
            _lowerCamelCase           =					torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
            _lowerCamelCase           =					torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
            _lowerCamelCase           =					torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
            _lowerCamelCase           =					torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
            _lowerCamelCase           =					torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
            _lowerCamelCase           =					torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
            _lowerCamelCase           =					torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
            _lowerCamelCase           =					torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
            _lowerCamelCase           =					torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
            _lowerCamelCase           =					torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
            _lowerCamelCase           =					torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
            _lowerCamelCase           =					torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
            _lowerCamelCase           =					torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9]		)
      elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
            _lowerCamelCase           =					torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4]		)
      else:
            raise ValueError(F'Unknown URL: {checkpoint_url}'		)

      assert torch.allclose(logits[0, :3]			,    __UpperCAmelCase			,    atol=1E-3		)
      assert logits.shape == expected_shape

      Path(__UpperCAmelCase		).mkdir(exist_ok=__UpperCAmelCase		)
      print(F'Saving model to {pytorch_dump_folder_path}'		)
      model.save_pretrained(__UpperCAmelCase		)
      print(F'Saving image processor to {pytorch_dump_folder_path}'		)
      image_processor.save_pretrained(__UpperCAmelCase		)

      print('''Pushing model to the hub...'''		)
      _lowerCamelCase           =					'''convnext'''
      if "tiny" in checkpoint_url:
            model_name += "-tiny"
      elif "small" in checkpoint_url:
            model_name += "-small"
      elif "base" in checkpoint_url:
            model_name += "-base"
      elif "xlarge" in checkpoint_url:
            model_name += "-xlarge"
      elif "large" in checkpoint_url:
            model_name += "-large"
      if "224" in checkpoint_url:
            model_name += "-224"
      elif "384" in checkpoint_url:
            model_name += "-384"
      if "22k" in checkpoint_url and "1k" not in checkpoint_url:
            model_name += "-22k"
      if "22k" in checkpoint_url and "1k" in checkpoint_url:
            model_name += "-22k-1k"

      model.push_to_hub(
          repo_path_or_name=Path(__UpperCAmelCase			,    __UpperCAmelCase		)			,    organization='''nielsr'''			,    commit_message='''Add model'''			,    )


if __name__ == "__main__":
     snake_case__						      =			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint_url',
         default='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth',
         type=str,
         help='URL of the original ConvNeXT checkpoint you\'d like to convert.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default=None,
         type=str,
         required=True,
         help='Path to the output PyTorch model directory.',
     )

     snake_case__						      =			parser.parse_args()
     convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__						      =			{
    'configuration_dpr': ['DPR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DPRConfig'],
    'tokenization_dpr': [
        'DPRContextEncoderTokenizer',
        'DPRQuestionEncoderTokenizer',
        'DPRReaderOutput',
        'DPRReaderTokenizer',
    ],
}


try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPRContextEncoderTokenizerFast',
         'DPRQuestionEncoderTokenizerFast',
         'DPRReaderTokenizerFast',
     ]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'DPRContextEncoder',
         'DPRPretrainedContextEncoder',
         'DPRPreTrainedModel',
         'DPRPretrainedQuestionEncoder',
         'DPRPretrainedReader',
         'DPRQuestionEncoder',
         'DPRReader',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__						      =			[
         'TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'TFDPRContextEncoder',
         'TFDPRPretrainedContextEncoder',
         'TFDPRPretrainedQuestionEncoder',
         'TFDPRPretrainedReader',
         'TFDPRQuestionEncoder',
         'TFDPRReader',
     ]


if TYPE_CHECKING:
     from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
     from .tokenization_dpr import (
         DPRContextEncoderTokenizer,
         DPRQuestionEncoderTokenizer,
         DPRReaderOutput,
         DPRReaderTokenizer,
     )

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_dpr_fast import (
              DPRContextEncoderTokenizerFast,
              DPRQuestionEncoderTokenizerFast,
              DPRReaderTokenizerFast,
          )

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_dpr import (
              DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              DPRContextEncoder,
              DPRPretrainedContextEncoder,
              DPRPreTrainedModel,
              DPRPretrainedQuestionEncoder,
              DPRPretrainedReader,
              DPRQuestionEncoder,
              DPRReader,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_dpr import (
              TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFDPRContextEncoder,
              TFDPRPretrainedContextEncoder,
              TFDPRPretrainedQuestionEncoder,
              TFDPRPretrainedReader,
              TFDPRQuestionEncoder,
              TFDPRReader,
          )

else:
     import sys

     snake_case__						      =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)