




'''simple docstring'''







import functools
import operator

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


A									=logging.get_logger(__name__)

A									={
    "asapp/sew-tiny-100k": "https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json",
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class     _a						(			_lowerCamelCase				):
       __a       :  Tuple            =      """sew"""
       def __init__(			self		:			Any     ,       lowercase		:			Union[str, Any]=32     ,       lowercase		:			int=768     ,       lowercase		:			Union[str, Any]=12     ,       lowercase		:			Dict=12     ,       lowercase		:			Optional[int]=3_072     ,       lowercase		:			str=2     ,       lowercase		:			List[Any]="gelu"     ,       lowercase		:			str=0.1     ,       lowercase		:			Optional[Any]=0.1     ,       lowercase		:			Union[str, Any]=0.1     ,       lowercase		:			Optional[int]=0.0     ,       lowercase		:			Optional[Any]=0.1     ,       lowercase		:			Dict=0.1     ,       lowercase		:			Optional[Any]=0.02     ,       lowercase		:			Optional[Any]=1E-5     ,       lowercase		:			Any="group"     ,       lowercase		:			List[str]="gelu"     ,       lowercase		:			List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,       lowercase		:			Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,       lowercase		:			List[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,       lowercase		:			Tuple=False     ,       lowercase		:			Optional[Any]=128     ,       lowercase		:			Dict=16     ,       lowercase		:			Any=True     ,       lowercase		:			Union[str, Any]=0.05     ,       lowercase		:			Dict=10     ,       lowercase		:			Dict=2     ,       lowercase		:			str=0.0     ,       lowercase		:			Any=10     ,       lowercase		:			int=0     ,       lowercase		:			List[str]="mean"     ,       lowercase		:			str=False     ,       lowercase		:			Union[str, Any]=False     ,       lowercase		:			Any=256     ,       lowercase		:			Any=0     ,       lowercase		:			List[str]=1     ,       lowercase		:			Optional[int]=2     ,       **lowercase		:			str     ,       ):



           '''simple docstring'''

           super().__init__(**A__     ,       pad_token_id=A__     ,       bos_token_id=A__     ,       eos_token_id=A__       )
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	feat_extract_norm
           UpperCAmelCase     =	feat_extract_activation
           UpperCAmelCase     =	list(A__       )
           UpperCAmelCase     =	list(A__       )
           UpperCAmelCase     =	list(A__       )
           UpperCAmelCase     =	conv_bias
           UpperCAmelCase     =	num_conv_pos_embeddings
           UpperCAmelCase     =	num_conv_pos_embedding_groups
           UpperCAmelCase     =	len(self.conv_dim       )
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	squeeze_factor
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	hidden_dropout
           UpperCAmelCase     =	attention_dropout
           UpperCAmelCase     =	activation_dropout
           UpperCAmelCase     =	feat_proj_dropout
           UpperCAmelCase     =	final_dropout
           UpperCAmelCase     =	layerdrop
           UpperCAmelCase     =	layer_norm_eps
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	vocab_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)`,'''
                   f"but is `len(config.conv_dim) = {len(self.conv_dim       )}`, `len(config.conv_stride)"
                   f"= {len(self.conv_stride       )}`, `len(config.conv_kernel) = {len(self.conv_kernel       )}`."       )

           # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
           UpperCAmelCase     =	apply_spec_augment
           UpperCAmelCase     =	mask_time_prob
           UpperCAmelCase     =	mask_time_length
           UpperCAmelCase     =	mask_time_min_masks
           UpperCAmelCase     =	mask_feature_prob
           UpperCAmelCase     =	mask_feature_length
           UpperCAmelCase     =	mask_feature_min_masks

           # ctc loss
           UpperCAmelCase     =	ctc_loss_reduction
           UpperCAmelCase     =	ctc_zero_infinity

           # sequence classification
           UpperCAmelCase     =	use_weighted_layer_sum
           UpperCAmelCase     =	classifier_proj_size







       @property
       def 					A   (			self		:			Any       ):



           '''simple docstring'''

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







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


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





def 		snake_case_							(_a			:							Tuple					,     _a			:							Any=False					,     _a			:							str=False  ):
    UpperCAmelCase     =	'''backbone.''' if is_semantic else ''''''

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

        UpperCAmelCase     =	in_proj_weight[
            : config.hidden_size, :
        ]
        UpperCAmelCase     =	q_bias
        UpperCAmelCase     =	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase     =	in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase     =	v_bias

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

        UpperCAmelCase     =	gamma_a
        UpperCAmelCase     =	gamma_a





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							str					,     _a			:							Optional[int]  ):
    UpperCAmelCase     =	dct.pop(_a  )
    UpperCAmelCase     =	val





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





@torch.no_grad()
def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Optional[Any]					,     _a			:							Optional[Any]=False  ):
    UpperCAmelCase     =	False if '''rvlcdip''' in checkpoint_url else True
    UpperCAmelCase     =	BeitConfig(use_absolute_position_embeddings=_a					,     use_mask_token=_a  )

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        UpperCAmelCase     =	1_0_2_4
        UpperCAmelCase     =	4_0_9_6
        UpperCAmelCase     =	2_4
        UpperCAmelCase     =	1_6

    # labels
    if "rvlcdip" in checkpoint_url:
        UpperCAmelCase     =	1_6
        UpperCAmelCase     =	'''huggingface/label-files'''
        UpperCAmelCase     =	'''rvlcdip-id2label.json'''
        UpperCAmelCase     =	json.load(open(hf_hub_download(_a					,     _a					,     repo_type='''dataset'''  )					,     '''r'''  )  )
        UpperCAmelCase     =	{int(_a  ): v for k, v in idalabel.items()}
        UpperCAmelCase     =	idalabel
        UpperCAmelCase     =	{v: k for k, v in idalabel.items()}

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

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

    # load HuggingFace model
    UpperCAmelCase     =	BeitForMaskedImageModeling(_a  ) if has_lm_head else BeitForImageClassification(_a  )
    model.eval()
    model.load_state_dict(_a  )

    # Check outputs on an image
    UpperCAmelCase     =	BeitImageProcessor(
        size=config.image_size					,     resample=PILImageResampling.BILINEAR					,     do_center_crop=_a  )
    UpperCAmelCase     =	prepare_img()

    UpperCAmelCase     =	image_processor(images=_a					,     return_tensors='''pt'''  )
    UpperCAmelCase     =	encoding['''pixel_values''']

    UpperCAmelCase     =	model(_a  )
    UpperCAmelCase     =	outputs.logits

    # verify logits
    UpperCAmelCase     =	[1, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
    assert logits.shape == torch.Size(_a  ), "Shape of logits not as expected"

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

    if push_to_hub:
        if has_lm_head:
            UpperCAmelCase     =	'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            UpperCAmelCase     =	'''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(_a					,     _a  )					,     organization='''nielsr'''					,     commit_message='''Add image processor'''					,     use_temp_dir=_a					,     )
        model.push_to_hub(
            repo_path_or_name=Path(_a					,     _a  )					,     organization='''nielsr'''					,     commit_message='''Add model'''					,     use_temp_dir=_a					,     )


if __name__ == "__main__":
   A									=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',
   )
   A									=parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




