"""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()
lowerCamelCase_    :		str							=	logging.get_logger(__name__)
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       []
					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 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					for i in range(encoder_config.num_hidden_layers						):
										# queries, keys and values (only weights, no biases)
										__a		 =       state_dict.pop(f'''encoder.deit.blocks.{i}.attn.qkv.weight'''						)

										__a		 =       in_proj_weight[
										    : encoder_config.hidden_size, :
										]
										__a		 =       in_proj_weight[
										    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
										]
										__a		 =       in_proj_weight[
										    -encoder_config.hidden_size :, :
										]
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       dct.pop(__lowerCamelCase						)
					__a		 =       val
def 							lowerCAmelCase(     __lowerCamelCase						):
					if "handwritten" in checkpoint_url:
										__a		 =       '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:
										__a		 =       'https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'
					__a		 =       Image.open(requests.get(__lowerCamelCase				,    stream=__lowerCamelCase						).raw						).convert('RGB'						)
					return im
@torch.no_grad()
def 							lowerCAmelCase(     __lowerCamelCase				,    __lowerCamelCase						):
					__a		 =       ViTConfig(image_size=384				,    qkv_bias=__lowerCamelCase						)
					__a		 =       TrOCRConfig()

					# size of the architecture
					if "base" in checkpoint_url:
										__a		 =       768
					elif "large" in checkpoint_url:
										# use ViT-large encoder
										__a		 =       1024
										__a		 =       4096
										__a		 =       24
										__a		 =       16
										__a		 =       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:
										__a		 =       False
										__a		 =       'relu'
										__a		 =       1024
										__a		 =       True
										__a		 =       False
										__a		 =       False

					# load HuggingFace model
					__a		 =       ViTModel(__lowerCamelCase				,    add_pooling_layer=__lowerCamelCase						)
					__a		 =       TrOCRForCausalLM(__lowerCamelCase						)
					__a		 =       VisionEncoderDecoderModel(encoder=__lowerCamelCase				,    decoder=__lowerCamelCase						)
					model.eval()

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

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

					# 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():
										__a		 =       state_dict.pop(__lowerCamelCase						)
										if key.startswith('decoder'						) and "output_projection" not in key:
															__a		 =       val
										else:
															__a		 =       val

										# load state dict
					model.load_state_dict(__lowerCamelCase						)

					# Check outputs on an image
					__a		 =       ViTImageProcessor(size=encoder_config.image_size						)
					__a		 =       RobertaTokenizer.from_pretrained('roberta-large'						)
					__a		 =       TrOCRProcessor(__lowerCamelCase				,    __lowerCamelCase						)

					__a		 =       processor(images=prepare_img(__lowerCamelCase						)				,    return_tensors='pt'						).pixel_values

					# verify logits
					__a		 =       torch.tensor([[model.config.decoder.decoder_start_token_id]]						)
					__a		 =       model(pixel_values=__lowerCamelCase				,    decoder_input_ids=__lowerCamelCase						)
					__a		 =       outputs.logits

					__a		 =       torch.Size([1, 1, 5_0265]						)
					if "trocr-base-handwritten" in checkpoint_url:
										__a		 =       torch.tensor(
										    [-1.45_02, -4.66_83, -0.53_47, -2.92_91, 9.14_35, -3.05_71, 8.97_64, 1.75_60, 8.73_58, -1.53_11]						)
					elif "trocr-large-handwritten" in checkpoint_url:
										__a		 =       torch.tensor(
										    [-2.64_37, -1.31_29, -2.25_96, -5.34_55, 6.35_39, 1.76_04, 5.49_91, 1.47_02, 5.61_13, 2.01_70]						)
					elif "trocr-base-printed" in checkpoint_url:
										__a		 =       torch.tensor(
										    [-5.68_16, -5.83_88, 1.13_98, -6.90_34, 6.85_05, -2.43_93, 1.22_84, -1.02_32, -1.96_61, -3.92_10]						)
					elif "trocr-large-printed" in checkpoint_url:
										__a		 =       torch.tensor(
										    [-6.01_62, -7.09_59, 4.41_55, -5.10_63, 7.04_68, -3.16_31, 2.64_66, -0.30_81, -0.81_06, -1.75_35]						)

					if "stage1" not in checkpoint_url:
										assert logits.shape == expected_shape, "Shape of logits not as expected"
										assert torch.allclose(logits[0, 0, :10]				,    __lowerCamelCase				,    atol=1e-3						), "First elements of logits not as expected"

					Path(__lowerCamelCase						).mkdir(exist_ok=__lowerCamelCase						)
					print(f'''Saving model to {pytorch_dump_folder_path}'''						)
					model.save_pretrained(__lowerCamelCase						)
					print(f'''Saving processor to {pytorch_dump_folder_path}'''						)
					processor.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
	lowerCamelCase_    :		Any							=	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."""
	)
	lowerCamelCase_    :		Union[str, Any]							=	parser.parse_args()
	convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





from typing import TYPE_CHECKING

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


lowerCamelCase_    :		Optional[Any]							=	{
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Dict							=	[
	    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """RobertaPreLayerNormForCausalLM""",
	    """RobertaPreLayerNormForMaskedLM""",
	    """RobertaPreLayerNormForMultipleChoice""",
	    """RobertaPreLayerNormForQuestionAnswering""",
	    """RobertaPreLayerNormForSequenceClassification""",
	    """RobertaPreLayerNormForTokenClassification""",
	    """RobertaPreLayerNormModel""",
	    """RobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFRobertaPreLayerNormForCausalLM""",
	    """TFRobertaPreLayerNormForMaskedLM""",
	    """TFRobertaPreLayerNormForMultipleChoice""",
	    """TFRobertaPreLayerNormForQuestionAnswering""",
	    """TFRobertaPreLayerNormForSequenceClassification""",
	    """TFRobertaPreLayerNormForTokenClassification""",
	    """TFRobertaPreLayerNormMainLayer""",
	    """TFRobertaPreLayerNormModel""",
	    """TFRobertaPreLayerNormPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCamelCase_    :		Any							=	[
	    """FlaxRobertaPreLayerNormForCausalLM""",
	    """FlaxRobertaPreLayerNormForMaskedLM""",
	    """FlaxRobertaPreLayerNormForMultipleChoice""",
	    """FlaxRobertaPreLayerNormForQuestionAnswering""",
	    """FlaxRobertaPreLayerNormForSequenceClassification""",
	    """FlaxRobertaPreLayerNormForTokenClassification""",
	    """FlaxRobertaPreLayerNormModel""",
	    """FlaxRobertaPreLayerNormPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	lowerCamelCase_    :		Optional[int]							=	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




