



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()
SCREAMING_SNAKE_CASE__   			=   logging.get_logger(__name__)


def A    (							__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Dict:
			A__	  =     'backbone.' if is_semantic else ''

			A__	  =     []
			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 A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False			,						__UpperCamelCase=False       )    ->   Optional[Any]:
			for i in range(config.num_hidden_layers       ):
						A__	  =     'backbone.' if is_semantic else ''
						# queries, keys and values
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''       )
						A__	  =     state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''       )

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

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

						A__	  =     gamma_a
						A__	  =     gamma_a


def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )    ->   Union[str, Any]:
			A__	  =     dct.pop(__UpperCamelCase       )
			A__	  =     val


def A    (							)    ->   Dict:
			A__	  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
			A__	  =     Image.open(requests.get(__UpperCamelCase			,						stream=__UpperCamelCase       ).raw       )
			return im


@torch.no_grad()
def A    (							__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase=False       )    ->   str:
			A__	  =     False if 'rvlcdip' in checkpoint_url else True
			A__	  =     BeitConfig(use_absolute_position_embeddings=__UpperCamelCase			,						use_mask_token=__UpperCamelCase       )

			# size of the architecture
			if "large" in checkpoint_url or "dit-l" in checkpoint_url:
						A__	  =     1_024
						A__	  =     4_096
						A__	  =     24
						A__	  =     16

			# labels
			if "rvlcdip" in checkpoint_url:
						A__	  =     16
						A__	  =     'huggingface/label-files'
						A__	  =     'rvlcdip-id2label.json'
						A__	  =     json.load(open(hf_hub_download(__UpperCamelCase			,						__UpperCamelCase			,						repo_type='dataset'       )			,						'r'       )       )
						A__	  =     {int(__UpperCamelCase       ): v for k, v in idalabel.items()}
						A__	  =     idalabel
						A__	  =     {v: k for k, v in idalabel.items()}

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

			A__	  =     create_rename_keys(__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )
			for src, dest in rename_keys:
						rename_key(__UpperCamelCase			,						__UpperCamelCase			,						__UpperCamelCase       )
			read_in_q_k_v(__UpperCamelCase			,						__UpperCamelCase			,						has_lm_head=__UpperCamelCase       )

			# load HuggingFace model
			A__	  =     BeitForMaskedImageModeling(__UpperCamelCase       ) if has_lm_head else BeitForImageClassification(__UpperCamelCase       )
			model.eval()
			model.load_state_dict(__UpperCamelCase       )

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

			A__	  =     image_processor(images=__UpperCamelCase			,						return_tensors='pt'       )
			A__	  =     encoding['pixel_values']

			A__	  =     model(__UpperCamelCase       )
			A__	  =     outputs.logits

			# verify logits
			A__	  =     [1, 16] if 'rvlcdip' in checkpoint_url else [1, 196, 8_192]
			assert logits.shape == torch.Size(__UpperCamelCase       ), "Shape of logits not as expected"

			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       )

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


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






"""simple docstring"""






from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE							=     {
    'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WhisperConfig', 'WhisperOnnxConfig'],
    'feature_extraction_whisper': ['WhisperFeatureExtractor'],
    'processing_whisper': ['WhisperProcessor'],
    'tokenization_whisper': ['WhisperTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     ['WhisperTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'WhisperForConditionalGeneration',
     'WhisperModel',
     'WhisperPreTrainedModel',
     'WhisperForAudioClassification',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFWhisperForConditionalGeneration',
     'TFWhisperModel',
     'TFWhisperPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __SCREAMING_SNAKE_CASE							=     [
     'FlaxWhisperForConditionalGeneration',
     'FlaxWhisperModel',
     'FlaxWhisperPreTrainedModel',
     'FlaxWhisperForAudioClassification',
 ]


if TYPE_CHECKING:
 from .configuration_whisper import WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP, WhisperConfig, WhisperOnnxConfig
 from .feature_extraction_whisper import WhisperFeatureExtractor
 from .processing_whisper import WhisperProcessor
 from .tokenization_whisper import WhisperTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_whisper_fast import WhisperTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_whisper import (
      WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      WhisperForAudioClassification,
      WhisperForConditionalGeneration,
      WhisperModel,
      WhisperPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_whisper import (
      TF_WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFWhisperForConditionalGeneration,
      TFWhisperModel,
      TFWhisperPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_whisper import (
      FlaxWhisperForAudioClassification,
      FlaxWhisperForConditionalGeneration,
      FlaxWhisperModel,
      FlaxWhisperPreTrainedModel,
  )

else:
 import sys

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