
'''simple docstring'''
import argparse
import json
from pathlib import Path

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

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
A__ :		str					=logging.get_logger(__name__)







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]=False      ,  __SCREAMING_SNAKE_CASE    :	int=False			)					->							Tuple:





  """simple docstring"""

  __A		:  Optional[Any]       				=		"""backbone.""" if is_semantic else """"""

  __A		:  int       				=		[]
  for i in range(config.num_hidden_layers			):
    # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
    rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")			)
    rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")			)
    rename_keys.append(
        (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")			)
    rename_keys.append(
        (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")			)
    rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")			)
    rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")			)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")			)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")			)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")			)
    rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")			)

  # projection layer + position embeddings
  rename_keys.extend(
      [
          (F"{prefix}cls_token", """beit.embeddings.cls_token"""),
          (F"{prefix}patch_embed.proj.weight", """beit.embeddings.patch_embeddings.projection.weight"""),
          (F"{prefix}patch_embed.proj.bias", """beit.embeddings.patch_embeddings.projection.bias"""),
      ]			)

  if has_lm_head:
    # mask token + shared relative position bias + layernorm
    rename_keys.extend(
        [
            ("""mask_token""", """beit.embeddings.mask_token"""),
            (
                """rel_pos_bias.relative_position_bias_table""",
                """beit.encoder.relative_position_bias.relative_position_bias_table""",
            ),
            (
                """rel_pos_bias.relative_position_index""",
                """beit.encoder.relative_position_bias.relative_position_index""",
            ),
            ("""norm.weight""", """layernorm.weight"""),
            ("""norm.bias""", """layernorm.bias"""),
        ]			)
  elif is_semantic:
    # semantic segmentation classification heads
    rename_keys.extend(
        [
            ("""decode_head.conv_seg.weight""", """decode_head.classifier.weight"""),
            ("""decode_head.conv_seg.bias""", """decode_head.classifier.bias"""),
            ("""auxiliary_head.conv_seg.weight""", """auxiliary_head.classifier.weight"""),
            ("""auxiliary_head.conv_seg.bias""", """auxiliary_head.classifier.bias"""),
        ]			)
  else:
    # layernorm + classification head
    rename_keys.extend(
        [
            ("""fc_norm.weight""", """beit.pooler.layernorm.weight"""),
            ("""fc_norm.bias""", """beit.pooler.layernorm.bias"""),
            ("""head.weight""", """classifier.weight"""),
            ("""head.bias""", """classifier.bias"""),
        ]			)

  return rename_keys







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Union[str, Any]      ,  __SCREAMING_SNAKE_CASE    :	Optional[int]      ,  __SCREAMING_SNAKE_CASE    :	Tuple=False      ,  __SCREAMING_SNAKE_CASE    :	Tuple=False			)					->							Optional[int]:





  """simple docstring"""

  for i in range(config.num_hidden_layers			):
    __A		:  Optional[Any]       				=		"""backbone.""" if is_semantic else """"""
    # queries, keys and values
    __A		:  Tuple       				=		state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"			)
    __A		:  Optional[int]       				=		state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"			)
    __A		:  Union[str, Any]       				=		state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"			)

    __A		:  int       				=		in_proj_weight[
        : config.hidden_size, :
    ]
    __A		:  int       				=		q_bias
    __A		:  List[Any]       				=		in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    __A		:  int       				=		in_proj_weight[
        -config.hidden_size :, :
    ]
    __A		:  str       				=		v_bias

    # gamma_1 and gamma_2
    # we call them lambda because otherwise they are renamed when using .from_pretrained
    __A		:  Optional[Any]       				=		state_dict.pop(F"{prefix}blocks.{i}.gamma_1"			)
    __A		:  Optional[Any]       				=		state_dict.pop(F"{prefix}blocks.{i}.gamma_2"			)

    __A		:  int       				=		gamma_a
    __A		:  Any       				=		gamma_a

    # relative_position bias table + index
    if not has_lm_head:
      # each layer has its own relative position bias
      __A		:  Union[str, Any]       				=		state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_bias_table"			)
      __A		:  str       				=		state_dict.pop(F"{prefix}blocks.{i}.attn.relative_position_index"			)

      __A		:  Optional[Any]       				=		table
      __A		:  Optional[int]       				=		index







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	List[str]      ,  __SCREAMING_SNAKE_CASE    :	int			)					->							Tuple:





  """simple docstring"""

  __A		:  Optional[Any]       				=		dct.pop(SCREAMING_SNAKE_CASE__			)
  __A		:  Dict       				=		val







def 							A_  (    )					->							Any:





  """simple docstring"""

  __A		:  Optional[int]       				=		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __A		:  str       				=		Image.open(requests.get(SCREAMING_SNAKE_CASE__      ,  stream=SCREAMING_SNAKE_CASE__			).raw			)
  return im







@torch.no_grad()
def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	Union[str, Any]			)					->							Optional[int]:





  """simple docstring"""

  __A		:  str       				=		BeitConfig()
  __A		:  Any       				=		False
  __A		:  List[str]       				=		False
  __A		:  Any       				=		"""huggingface/label-files"""
  # set config parameters based on URL
  if checkpoint_url[-9:-4] == "pt22k":
    # masked image modeling
    __A		:  Union[str, Any]       				=		True
    __A		:  str       				=		True
    __A		:  Tuple       				=		True
  elif checkpoint_url[-9:-4] == "ft22k":
    # intermediate fine-tuning on ImageNet-22k
    __A		:  Any       				=		True
    __A		:  Union[str, Any]       				=		2_1841
    __A		:  List[Any]       				=		"""imagenet-22k-id2label.json"""
    __A		:  List[Any]       				=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__      ,  repo_type="""dataset"""			)      ,  """r"""			)			)
    __A		:  str       				=		{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
    # 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]
    __A		:  Union[str, Any]       				=		idalabel
    __A		:  List[Any]       				=		{v: k for k, v in idalabel.items()}
  elif checkpoint_url[-8:-4] == "to1k":
    # fine-tuning on ImageNet-1k
    __A		:  Optional[Any]       				=		True
    __A		:  Dict       				=		1000
    __A		:  str       				=		"""imagenet-1k-id2label.json"""
    __A		:  Tuple       				=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__      ,  repo_type="""dataset"""			)      ,  """r"""			)			)
    __A		:  List[str]       				=		{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
    __A		:  Optional[int]       				=		idalabel
    __A		:  int       				=		{v: k for k, v in idalabel.items()}
    if "384" in checkpoint_url:
      __A		:  Tuple       				=		384
    if "512" in checkpoint_url:
      __A		:  int       				=		512
  elif "ade20k" in checkpoint_url:
    # fine-tuning
    __A		:  str       				=		True
    __A		:  Tuple       				=		150
    __A		:  str       				=		"""ade20k-id2label.json"""
    __A		:  Optional[Any]       				=		json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__      ,  repo_type="""dataset"""			)      ,  """r"""			)			)
    __A		:  List[Any]       				=		{int(SCREAMING_SNAKE_CASE__			): v for k, v in idalabel.items()}
    __A		:  Any       				=		idalabel
    __A		:  Dict       				=		{v: k for k, v in idalabel.items()}
    __A		:  str       				=		640
    __A		:  Dict       				=		True
  else:
    raise ValueError("""Checkpoint not supported, URL should either end with 'pt22k', 'ft22k', 'to1k' or 'ade20k'"""			)

  # size of the architecture
  if "base" in checkpoint_url:
    pass
  elif "large" in checkpoint_url:
    __A		:  List[str]       				=		1024
    __A		:  Any       				=		4096
    __A		:  Optional[int]       				=		24
    __A		:  List[Any]       				=		16
    if "ade20k" in checkpoint_url:
      __A		:  Union[str, Any]       				=		640
      __A		:  Optional[Any]       				=		[7, 11, 15, 23]
  else:
    raise ValueError("""Should either find 'base' or 'large' in checkpoint URL"""			)

  # load state_dict of original model, remove and rename some keys
  __A		:  Any       				=		torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__      ,  map_location="""cpu"""      ,  check_hash=SCREAMING_SNAKE_CASE__			)
  __A		:  Dict       				=		state_dict["""model"""] if """ade20k""" not in checkpoint_url else state_dict["""state_dict"""]

  __A		:  Union[str, Any]       				=		create_rename_keys(SCREAMING_SNAKE_CASE__      ,  has_lm_head=SCREAMING_SNAKE_CASE__      ,  is_semantic=SCREAMING_SNAKE_CASE__			)
  for src, dest in rename_keys:
    rename_key(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__			)
  read_in_q_k_v(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__      ,  has_lm_head=SCREAMING_SNAKE_CASE__      ,  is_semantic=SCREAMING_SNAKE_CASE__			)
  if is_semantic:
    # add prefix to decoder keys
    for key, val in state_dict.copy().items():
      __A		:  Dict       				=		state_dict.pop(SCREAMING_SNAKE_CASE__			)
      if key.startswith("""backbone.fpn"""			):
        __A		:  Tuple       				=		key.replace("""backbone.fpn"""      ,  """fpn"""			)
      __A		:  Dict       				=		val

    # load HuggingFace model
  if checkpoint_url[-9:-4] == "pt22k":
    __A		:  Dict       				=		BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE__			)
  elif "ade20k" in checkpoint_url:
    __A		:  str       				=		BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE__			)
  else:
    __A		:  int       				=		BeitForImageClassification(SCREAMING_SNAKE_CASE__			)
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE__			)

  # Check outputs on an image
  if is_semantic:
    __A		:  Union[str, Any]       				=		BeitImageProcessor(size=config.image_size      ,  do_center_crop=SCREAMING_SNAKE_CASE__			)
    __A		:  Optional[int]       				=		load_dataset("""hf-internal-testing/fixtures_ade20k"""      ,  split="""test"""			)
    __A		:  List[str]       				=		Image.open(ds[0]["""file"""]			)
  else:
    __A		:  Tuple       				=		BeitImageProcessor(
        size=config.image_size      ,  resample=PILImageResampling.BILINEAR      ,  do_center_crop=SCREAMING_SNAKE_CASE__			)
    __A		:  int       				=		prepare_img()

  __A		:  Dict       				=		image_processor(images=SCREAMING_SNAKE_CASE__      ,  return_tensors="""pt"""			)
  __A		:  Any       				=		encoding["""pixel_values"""]

  __A		:  int       				=		model(SCREAMING_SNAKE_CASE__			)
  __A		:  Any       				=		outputs.logits

  # verify logits
  __A		:  Dict       				=		torch.Size([1, 1000]			)
  if checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k"""			):
    __A		:  Optional[int]       				=		torch.Size([1, 196, 8192]			)
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k"""			):
    __A		:  Optional[Any]       				=		torch.Size([1, 196, 8192]			)
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22k"""			):
    __A		:  int       				=		torch.Size([1, 2_1841]			)
    __A		:  Union[str, Any]       				=		torch.tensor([2.2_288, 2.4_671, 0.7_395]			)
    __A		:  List[Any]       				=		2397
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22k"""			):
    __A		:  List[str]       				=		torch.Size([1, 2_1841]			)
    __A		:  List[Any]       				=		torch.tensor([1.6_881, -0.2_787, 0.5_901]			)
    __A		:  Optional[int]       				=		2396
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft1k"""			):
    __A		:  List[str]       				=		torch.tensor([0.1_241, 0.0_798, -0.6_569]			)
    __A		:  Optional[Any]       				=		285
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_224_pt22k_ft22kto1k"""			):
    __A		:  Union[str, Any]       				=		torch.tensor([-1.2_385, -1.0_987, -1.0_108]			)
    __A		:  Dict       				=		281
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_384_pt22k_ft22kto1k"""			):
    __A		:  List[str]       				=		torch.tensor([-1.5_303, -0.9_484, -0.3_147]			)
    __A		:  List[str]       				=		761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft1k"""			):
    __A		:  Optional[int]       				=		torch.tensor([0.4_610, -0.0_928, 0.2_086]			)
    __A		:  Dict       				=		761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_224_pt22k_ft22kto1k"""			):
    __A		:  Union[str, Any]       				=		torch.tensor([-0.4_804, 0.6_257, -0.1_837]			)
    __A		:  int       				=		761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_384_pt22k_ft22kto1k"""			):
    __A		:  int       				=		torch.tensor([[-0.5_122, 0.5_117, -0.2_113]]			)
    __A		:  Tuple       				=		761
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_512_pt22k_ft22kto1k"""			):
    __A		:  List[str]       				=		torch.tensor([-0.3_062, 0.7_261, 0.4_852]			)
    __A		:  Dict       				=		761
  elif checkpoint_url[:-4].endswith("""beit_base_patch16_640_pt22k_ft22ktoade20k"""			):
    __A		:  List[Any]       				=		(1, 150, 160, 160)
    __A		:  Dict       				=		torch.tensor(
        [
            [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
            [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
            [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
        ]			)
  elif checkpoint_url[:-4].endswith("""beit_large_patch16_640_pt22k_ft22ktoade20k"""			):
    __A		:  List[str]       				=		(1, 150, 160, 160)
    __A		:  Tuple       				=		torch.tensor(
        [
            [[-4.3_305, -2.3_049, -3.0_161], [-2.9_591, -1.5_305, -2.2_251], [-3.4_198, -1.8_004, -2.9_062]],
            [[-5.8_922, -3.7_435, -4.3_978], [-4.2_063, -2.7_872, -3.4_755], [-4.2_791, -3.1_874, -4.1_681]],
            [[0.9_895, 4.3_467, 4.7_663], [4.2_476, 5.6_830, 6.1_518], [4.5_550, 6.2_495, 6.5_154]],
        ]			)
  else:
    raise ValueError("""Can't verify logits as model is not supported"""			)

  assert logits.shape == expected_shape, "Shape of logits not as expected"
  if not has_lm_head:
    if is_semantic:
      assert torch.allclose(
          logits[0, :3, :3, :3]      ,  SCREAMING_SNAKE_CASE__      ,  atol=1e-3			), "First elements of logits not as expected"
    else:
      print("""Predicted class idx:"""      ,  logits.argmax(-1			).item()			)
      assert torch.allclose(
          logits[0, :3]      ,  SCREAMING_SNAKE_CASE__      ,  atol=1e-3			), "First elements of logits not as expected"
      assert logits.argmax(-1			).item() == expected_class_idx, "Predicted class index not as expected"

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


if __name__ == "__main__":
     A__ :		int					=argparse.ArgumentParser()

     parser.add_argument(
         '--checkpoint_url',
         default='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth',
         type=str,
         help='URL to the original PyTorch checkpoint (.pth file).',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
     )
     A__ :		List[str]					=parser.parse_args()
     convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)








import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					json.load(open(SCREAMING_SNAKE_CASE__			,"""r"""  )  )
						lowerCAmelCase	:   Tuple					     =					GPTNeoConfig(
						    hidden_size=config_json["""n_embd"""]			,num_layers=config_json["""n_layer"""]			,num_heads=config_json["""n_head"""]			,attention_types=config_json["""attention_types"""]			,max_position_embeddings=config_json["""n_positions"""]			,resid_dropout=config_json["""res_dropout"""]			,embed_dropout=config_json["""embed_dropout"""]			,attention_dropout=config_json["""attn_dropout"""]			,)
						print(F"""Building PyTorch model from configuration: {config}"""  )
						lowerCAmelCase	:   str					     =					GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__  )

						# Load weights from tf checkpoint
						load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}"""  )
						model.save_pretrained(SCREAMING_SNAKE_CASE__  )


if __name__ == "__main__":
		lowerCAmelCase					:				Optional[Any]   	=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained mesh-tf model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowerCAmelCase					:				str   	=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

