


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

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


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


def      a (     __a					,   __a  )    ->      List[Any]:



					'''simple docstring'''






					UpperCamelCase__  :Optional[Any]				   =					[]
					for i in range(encoder_config.num_hidden_layers  ):
										# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
										rename_keys.append(
										    (f'''encoder.deit.blocks.{i}.norm1.weight''', f'''encoder.encoder.layer.{i}.layernorm_before.weight''')  )
										rename_keys.append((f'''encoder.deit.blocks.{i}.norm1.bias''', f'''encoder.encoder.layer.{i}.layernorm_before.bias''')  )
										rename_keys.append(
										    (f'''encoder.deit.blocks.{i}.attn.proj.weight''', f'''encoder.encoder.layer.{i}.attention.output.dense.weight''')  )
										rename_keys.append(
										    (f'''encoder.deit.blocks.{i}.attn.proj.bias''', f'''encoder.encoder.layer.{i}.attention.output.dense.bias''')  )
										rename_keys.append(
										    (f'''encoder.deit.blocks.{i}.norm2.weight''', f'''encoder.encoder.layer.{i}.layernorm_after.weight''')  )
										rename_keys.append((f'''encoder.deit.blocks.{i}.norm2.bias''', f'''encoder.encoder.layer.{i}.layernorm_after.bias''')  )
										rename_keys.append(
										    (f'''encoder.deit.blocks.{i}.mlp.fc1.weight''', f'''encoder.encoder.layer.{i}.intermediate.dense.weight''')  )
										rename_keys.append(
										    (f'''encoder.deit.blocks.{i}.mlp.fc1.bias''', f'''encoder.encoder.layer.{i}.intermediate.dense.bias''')  )
										rename_keys.append(
										    (f'''encoder.deit.blocks.{i}.mlp.fc2.weight''', f'''encoder.encoder.layer.{i}.output.dense.weight''')  )
										rename_keys.append((f'''encoder.deit.blocks.{i}.mlp.fc2.bias''', f'''encoder.encoder.layer.{i}.output.dense.bias''')  )

					# cls token, position embeddings and patch embeddings of encoder
					rename_keys.extend(
					    [
					        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
					        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
					        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
					        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
					        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
					        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
					    ]  )

					return rename_keys


def      a (     __a					,   __a  )    ->      Tuple:



					'''simple docstring'''






					for i in range(encoder_config.num_hidden_layers  ):
										# queries, keys and values (only weights, no biases)
										UpperCamelCase__  :Dict				   =					state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight'''  )

										UpperCamelCase__  :Optional[Any]				   =					in_proj_weight[
										    : encoder_config.hidden_size, :
										]
										UpperCamelCase__  :Optional[int]				   =					in_proj_weight[
										    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
										]
										UpperCamelCase__  :Optional[int]				   =					in_proj_weight[
										    -encoder_config.hidden_size :, :
										]


def      a (     __a					,   __a					,   __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					dct.pop(__a  )
					UpperCamelCase__  :Union[str, Any]				   =					val


def      a (     __a  )    ->      Dict:



					'''simple docstring'''






					if "handwritten" in checkpoint_url:
										UpperCamelCase__  :Optional[int]				   =					'''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
										# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
										# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
										# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
										# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
					elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
										UpperCamelCase__  :Any				   =					'''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
					UpperCamelCase__  :List[Any]				   =					Image.open(requests.get(__a					,   stream=__a  ).raw  ).convert('''RGB'''  )
					return im


@torch.no_grad()
def      a (     __a					,   __a  )    ->      Tuple:



					'''simple docstring'''






					UpperCamelCase__  :Union[str, Any]				   =					ViTConfig(image_size=384					,   qkv_bias=__a  )
					UpperCamelCase__  :int				   =					TrOCRConfig()

					# size of the architecture
					if "base" in checkpoint_url:
										UpperCamelCase__  :Tuple				   =					768
					elif "large" in checkpoint_url:
										# use ViT-large encoder
										UpperCamelCase__  :List[Any]				   =					1024
										UpperCamelCase__  :str				   =					4096
										UpperCamelCase__  :str				   =					24
										UpperCamelCase__  :Union[str, Any]				   =					16
										UpperCamelCase__  :Any				   =					1024
					else:
										raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''  )

					# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
					if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
										UpperCamelCase__  :int				   =					False
										UpperCamelCase__  :List[str]				   =					'''relu'''
										UpperCamelCase__  :List[Any]				   =					1024
										UpperCamelCase__  :Tuple				   =					True
										UpperCamelCase__  :Tuple				   =					False
										UpperCamelCase__  :Any				   =					False

					# load HuggingFace model
					UpperCamelCase__  :List[str]				   =					ViTModel(__a					,   add_pooling_layer=__a  )
					UpperCamelCase__  :Tuple				   =					TrOCRForCausalLM(__a  )
					UpperCamelCase__  :Tuple				   =					VisionEncoderDecoderModel(encoder=__a					,   decoder=__a  )
					model.eval()

					# load state_dict of original model, rename some keys
					UpperCamelCase__  :Optional[int]				   =					torch.hub.load_state_dict_from_url(__a					,   map_location='''cpu'''					,   check_hash=__a  )['''model''']

					UpperCamelCase__  :Any				   =					create_rename_keys(__a					,   __a  )
					for src, dest in rename_keys:
										rename_key(__a					,   __a					,   __a  )
					read_in_q_k_v(__a					,   __a  )

					# remove parameters we don't need
					del state_dict["encoder.deit.head.weight"]
					del state_dict["encoder.deit.head.bias"]
					del state_dict["decoder.version"]

					# add prefix to decoder keys
					for key, val in state_dict.copy().items():
										UpperCamelCase__  :List[Any]				   =					state_dict.pop(__a  )
										if key.startswith('''decoder'''  ) and "output_projection" not in key:
															UpperCamelCase__  :List[Any]				   =					val
										else:
															UpperCamelCase__  :Union[str, Any]				   =					val

    # load state dict
					model.load_state_dict(__a  )

					# Check outputs on an image
					UpperCamelCase__  :Any				   =					ViTImageProcessor(size=encoder_config.image_size  )
					UpperCamelCase__  :List[Any]				   =					RobertaTokenizer.from_pretrained('''roberta-large'''  )
					UpperCamelCase__  :List[Any]				   =					TrOCRProcessor(__a					,   __a  )

					UpperCamelCase__  :Tuple				   =					processor(images=prepare_img(__a  )					,   return_tensors='''pt'''  ).pixel_values

					# verify logits
					UpperCamelCase__  :Optional[Any]				   =					torch.tensor([[model.config.decoder.decoder_start_token_id]]  )
					UpperCamelCase__  :Any				   =					model(pixel_values=__a					,   decoder_input_ids=__a  )
					UpperCamelCase__  :Optional[int]				   =					outputs.logits

					UpperCamelCase__  :str				   =					torch.Size([1, 1, 50265]  )
					if "trocr-base-handwritten" in checkpoint_url:
										UpperCamelCase__  :int				   =					torch.tensor(
										    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]  )
					elif "trocr-large-handwritten" in checkpoint_url:
										UpperCamelCase__  :Any				   =					torch.tensor(
										    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]  )
					elif "trocr-base-printed" in checkpoint_url:
										UpperCamelCase__  :Optional[int]				   =					torch.tensor(
										    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]  )
					elif "trocr-large-printed" in checkpoint_url:
										UpperCamelCase__  :Optional[int]				   =					torch.tensor(
										    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]  )

					if "stage1" not in checkpoint_url:
										assert logits.shape == expected_shape, "Shape of logits not as expected"
										assert torch.allclose(logits[0, 0, :10]					,   __a					,   atol=1e-3  ), "First elements 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 processor to {pytorch_dump_folder_path}'''  )
					processor.save_pretrained(__a  )


if __name__ == "__main__":
		__snake_case				 =	argparse.ArgumentParser()

		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt''',
		    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.'''
		)
		__snake_case				 =	parser.parse_args()
		convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)



'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case				 =	{
    '''configuration_convbert''': ['''CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvBertConfig''', '''ConvBertOnnxConfig'''],
    '''tokenization_convbert''': ['''ConvBertTokenizer'''],
}

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ConvBertForMaskedLM''',
		    '''ConvBertForMultipleChoice''',
		    '''ConvBertForQuestionAnswering''',
		    '''ConvBertForSequenceClassification''',
		    '''ConvBertForTokenClassification''',
		    '''ConvBertLayer''',
		    '''ConvBertModel''',
		    '''ConvBertPreTrainedModel''',
		    '''load_tf_weights_in_convbert''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case				 =	[
		    '''TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFConvBertForMaskedLM''',
		    '''TFConvBertForMultipleChoice''',
		    '''TFConvBertForQuestionAnswering''',
		    '''TFConvBertForSequenceClassification''',
		    '''TFConvBertForTokenClassification''',
		    '''TFConvBertLayer''',
		    '''TFConvBertModel''',
		    '''TFConvBertPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
		from .tokenization_convbert import ConvBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_convbert_fast import ConvBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_convbert import (
				    CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ConvBertForMaskedLM,
				    ConvBertForMultipleChoice,
				    ConvBertForQuestionAnswering,
				    ConvBertForSequenceClassification,
				    ConvBertForTokenClassification,
				    ConvBertLayer,
				    ConvBertModel,
				    ConvBertPreTrainedModel,
				    load_tf_weights_in_convbert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_convbert import (
				    TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFConvBertForMaskedLM,
				    TFConvBertForMultipleChoice,
				    TFConvBertForQuestionAnswering,
				    TFConvBertForSequenceClassification,
				    TFConvBertForTokenClassification,
				    TFConvBertLayer,
				    TFConvBertModel,
				    TFConvBertPreTrainedModel,
				)


else:
		import sys

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