





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


logging.set_verbosity_info()
__UpperCAmelCase					:     Union[str, Any]	      =	logging.get_logger(__name__)




def 						lowerCamelCase_	(		UpperCamelCase_			,					UpperCamelCase_=False			,					UpperCamelCase_=False       ):
      _a							:  int				= '''backbone.''' if is_semantic else ''''''

      _a							:  str				= []
      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'''),
              (f"""{prefix}pos_embed""", '''beit.embeddings.position_embeddings'''),
          ]       )

      if has_lm_head:
            # mask token + layernorm
            rename_keys.extend(
                [
                    ('''mask_token''', '''beit.embeddings.mask_token'''),
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.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 						lowerCamelCase_	(		UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_=False			,					UpperCamelCase_=False       ):







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

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

            # gamma_1 and gamma_2
            # we call them lambda because otherwise they are renamed when using .from_pretrained
            _a							:  str				= state_dict.pop(f"""{prefix}blocks.{i}.gamma_1"""       )
            _a							:  str				= state_dict.pop(f"""{prefix}blocks.{i}.gamma_2"""       )

            _a							:  Any				= gamma_a
            _a							:  Optional[Any]				= gamma_a




def 						lowerCamelCase_	(		UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_       ):
      _a							:  Any				= dct.pop(snake_case_       )
      _a							:  Optional[Any]				= val




def 						lowerCamelCase_	(		):
      _a							:  Optional[int]				= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      _a							:  Optional[int]				= Image.open(requests.get(snake_case_			,					stream=snake_case_       ).raw       )
      return im




@torch.no_grad()
def 						lowerCamelCase_	(		UpperCamelCase_			,					UpperCamelCase_			,					UpperCamelCase_=False       ):
      _a							:  List[str]				= False if '''rvlcdip''' in checkpoint_url else True
      _a							:  List[str]				= BeitConfig(use_absolute_position_embeddings=snake_case_			,					use_mask_token=snake_case_       )

      # size of the architecture
      if "large" in checkpoint_url or "dit-l" in checkpoint_url:
            _a							:  int				= 1024
            _a							:  int				= 4096
            _a							:  Union[str, Any]				= 24
            _a							:  Dict				= 16

      # labels
      if "rvlcdip" in checkpoint_url:
            _a							:  Any				= 16
            _a							:  Any				= '''huggingface/label-files'''
            _a							:  Tuple				= '''rvlcdip-id2label.json'''
            _a							:  str				= json.load(open(hf_hub_download(snake_case_			,					snake_case_			,					repo_type='''dataset'''       )			,					'''r'''       )       )
            _a							:  Dict				= {int(snake_case_       ): v for k, v in idalabel.items()}
            _a							:  Optional[int]				= idalabel
            _a							:  Dict				= {v: k for k, v in idalabel.items()}

      # load state_dict of original model, remove and rename some keys
      _a							:  Any				= torch.hub.load_state_dict_from_url(snake_case_			,					map_location='''cpu'''       )['''model''']

      _a							:  Any				= create_rename_keys(snake_case_			,					has_lm_head=snake_case_       )
      for src, dest in rename_keys:
            rename_key(snake_case_			,					snake_case_			,					snake_case_       )
      read_in_q_k_v(snake_case_			,					snake_case_			,					has_lm_head=snake_case_       )

      # load HuggingFace model
      _a							:  Optional[int]				= BeitForMaskedImageModeling(snake_case_       ) if has_lm_head else BeitForImageClassification(snake_case_       )
      model.eval()
      model.load_state_dict(snake_case_       )

      # Check outputs on an image
      _a							:  Optional[Any]				= BeitImageProcessor(
          size=config.image_size			,					resample=PILImageResampling.BILINEAR			,					do_center_crop=snake_case_       )
      _a							:  List[str]				= prepare_img()

      _a							:  str				= image_processor(images=snake_case_			,					return_tensors='''pt'''       )
      _a							:  List[str]				= encoding['''pixel_values''']

      _a							:  str				= model(snake_case_       )
      _a							:  Optional[Any]				= outputs.logits

      # verify logits
      _a							:  Dict				= [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
      assert logits.shape == torch.Size(snake_case_       ), "Shape of logits not as expected"

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

      if push_to_hub:
            if has_lm_head:
                  _a							:  str				= '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
            else:
                  _a							:  Dict				= '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
            image_processor.push_to_hub(
                repo_path_or_name=Path(snake_case_			,					snake_case_       )			,					organization='''nielsr'''			,					commit_message='''Add image processor'''			,					use_temp_dir=snake_case_			,					)
            model.push_to_hub(
                repo_path_or_name=Path(snake_case_			,					snake_case_       )			,					organization='''nielsr'''			,					commit_message='''Add model'''			,					use_temp_dir=snake_case_			,					)


if __name__ == "__main__":
    __UpperCAmelCase					:     Dict	      =	argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_url',
        default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.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.'
    )
    parser.add_argument(
        '--push_to_hub',
        action='store_true',
    )
    __UpperCAmelCase					:     Any	      =	parser.parse_args()
    convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)












"""simple docstring"""



import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_					=     logging.get_logger(__name__)

a_					=     {
    """microsoft/unispeech-large-1500h-cv""": (
        """https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""unispeech"""

 def __init__(       self   ,		__lowerCamelCase=32   ,		__lowerCamelCase=768   ,		__lowerCamelCase=12   ,		__lowerCamelCase=12   ,		__lowerCamelCase=3072   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=0.0_2   ,		__lowerCamelCase=1e-5   ,		__lowerCamelCase="group"   ,		__lowerCamelCase="gelu"   ,		__lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,		__lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,		__lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,		__lowerCamelCase=False   ,		__lowerCamelCase=128   ,		__lowerCamelCase=16   ,		__lowerCamelCase=False   ,		__lowerCamelCase=True   ,		__lowerCamelCase=0.0_5   ,		__lowerCamelCase=10   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.0   ,		__lowerCamelCase=10   ,		__lowerCamelCase=0   ,		__lowerCamelCase=320   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=100   ,		__lowerCamelCase=256   ,		__lowerCamelCase=256   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase="mean"   ,		__lowerCamelCase=False   ,		__lowerCamelCase=False   ,		__lowerCamelCase=256   ,		__lowerCamelCase=80   ,		__lowerCamelCase=0   ,		__lowerCamelCase=1   ,		__lowerCamelCase=2   ,		__lowerCamelCase=0.5   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase   ,		pad_token_id=__lowerCamelCase   ,		bos_token_id=__lowerCamelCase   ,		eos_token_id=__lowerCamelCase    )
     __A       :		Dict							=	hidden_size
     __A       :		Dict							=	feat_extract_norm
     __A       :		int							=	feat_extract_activation
     __A       :		Dict							=	list(__lowerCamelCase    )
     __A       :		str							=	list(__lowerCamelCase    )
     __A       :		Dict							=	list(__lowerCamelCase    )
     __A       :		str							=	conv_bias
     __A       :		Optional[int]							=	num_conv_pos_embeddings
     __A       :		int							=	num_conv_pos_embedding_groups
     __A       :		Union[str, Any]							=	len(self.conv_dim    )
     __A       :		List[str]							=	num_hidden_layers
     __A       :		Dict							=	intermediate_size
     __A       :		List[str]							=	hidden_act
     __A       :		int							=	num_attention_heads
     __A       :		str							=	hidden_dropout
     __A       :		str							=	attention_dropout
     __A       :		Optional[Any]							=	activation_dropout
     __A       :		Optional[int]							=	feat_proj_dropout
     __A       :		Optional[Any]							=	final_dropout
     __A       :		List[Any]							=	layerdrop
     __A       :		Any							=	layer_norm_eps
     __A       :		str							=	initializer_range
     __A       :		List[str]							=	num_ctc_classes
     __A       :		Dict							=	vocab_size
     __A       :		Dict							=	do_stable_layer_norm
     __A       :		Union[str, Any]							=	use_weighted_layer_sum
     __A       :		Tuple							=	classifier_proj_size

     if (
         (len(self.conv_stride    ) != self.num_feat_extract_layers)
         or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
         or (len(self.conv_dim    ) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
             ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
             F""" {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,"""
             F""" `len(config.conv_kernel) = {len(self.conv_kernel    )}`."""    )

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     __A       :		List[Any]							=	apply_spec_augment
     __A       :		str							=	mask_time_prob
     __A       :		Dict							=	mask_time_length
     __A       :		List[str]							=	mask_time_min_masks
     __A       :		Union[str, Any]							=	mask_feature_prob
     __A       :		Tuple							=	mask_feature_length
     __A       :		Any							=	mask_feature_min_masks

     # parameters for pretraining with codevector quantized representations
     __A       :		int							=	num_codevectors_per_group
     __A       :		Any							=	num_codevector_groups
     __A       :		Optional[Any]							=	contrastive_logits_temperature
     __A       :		Dict							=	feat_quantizer_dropout
     __A       :		Tuple							=	num_negatives
     __A       :		List[str]							=	codevector_dim
     __A       :		str							=	proj_codevector_dim
     __A       :		Union[str, Any]							=	diversity_loss_weight

     # ctc loss
     __A       :		Tuple							=	ctc_loss_reduction
     __A       :		Optional[int]							=	ctc_zero_infinity

     # pretraining loss
     __A       :		str							=	replace_prob







 @property
 def    UpperCamelCase__(       self    ):



     '''simple docstring'''




     return functools.reduce(operator.mul   ,		self.conv_stride   ,		1    )




