


'''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()
__A		=logging.get_logger(__name__)







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Dict       				=       []
			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       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			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__					:   List[Any]       				=       in_proj_weight[
						    : encoder_config.hidden_size, :
						]
						UpperCAmelCase__					:   str       				=       in_proj_weight[
						    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
						]
						UpperCAmelCase__					:   Optional[int]       				=       in_proj_weight[
						    -encoder_config.hidden_size :, :
						]







def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   int       				=       dct.pop(UpperCamelCase__   )
			UpperCAmelCase__					:   str       				=       val







def       _UpperCamelCase (						UpperCamelCase__   ):
			if "handwritten" in checkpoint_url:
						UpperCAmelCase__					:   Optional[Any]       				=       """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__					:   List[str]       				=       """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__					:   Union[str, Any]       				=       Image.open(requests.get(UpperCamelCase__	, stream=UpperCamelCase__   ).raw   ).convert("""RGB"""   )
			return im







@torch.no_grad()
def       _UpperCamelCase (						UpperCamelCase__	, UpperCamelCase__   ):
			UpperCAmelCase__					:   Optional[Any]       				=       ViTConfig(image_size=3_8_4	, qkv_bias=UpperCamelCase__   )
			UpperCAmelCase__					:   Optional[Any]       				=       TrOCRConfig()

			# size of the architecture
			if "base" in checkpoint_url:
						UpperCAmelCase__					:   str       				=       7_6_8
			elif "large" in checkpoint_url:
						# use ViT-large encoder
						UpperCAmelCase__					:   List[str]       				=       1_0_2_4
						UpperCAmelCase__					:   Union[str, Any]       				=       4_0_9_6
						UpperCAmelCase__					:   str       				=       2_4
						UpperCAmelCase__					:   Dict       				=       1_6
						UpperCAmelCase__					:   Any       				=       1_0_2_4
			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__					:   List[Any]       				=       False
						UpperCAmelCase__					:   List[Any]       				=       """relu"""
						UpperCAmelCase__					:   Tuple       				=       1_0_2_4
						UpperCAmelCase__					:   Any       				=       True
						UpperCAmelCase__					:   Union[str, Any]       				=       False
						UpperCAmelCase__					:   List[Any]       				=       False

			# load HuggingFace model
			UpperCAmelCase__					:   Dict       				=       ViTModel(UpperCamelCase__	, add_pooling_layer=UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       TrOCRForCausalLM(UpperCamelCase__   )
			UpperCAmelCase__					:   int       				=       VisionEncoderDecoderModel(encoder=UpperCamelCase__	, decoder=UpperCamelCase__   )
			model.eval()

			# load state_dict of original model, rename some keys
			UpperCAmelCase__					:   List[str]       				=       torch.hub.load_state_dict_from_url(UpperCamelCase__	, map_location="""cpu"""	, check_hash=UpperCamelCase__   )["""model"""]

			UpperCAmelCase__					:   Optional[Any]       				=       create_rename_keys(UpperCamelCase__	, UpperCamelCase__   )
			for src, dest in rename_keys:
						rename_key(UpperCamelCase__	, UpperCamelCase__	, UpperCamelCase__   )
			read_in_q_k_v(UpperCamelCase__	, UpperCamelCase__   )

			# 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__					:   Tuple       				=       state_dict.pop(UpperCamelCase__   )
						if key.startswith("""decoder"""   ) and "output_projection" not in key:
									UpperCAmelCase__					:   Optional[int]       				=       val
						else:
									UpperCAmelCase__					:   Optional[int]       				=       val

    # load state dict
			model.load_state_dict(UpperCamelCase__   )

			# Check outputs on an image
			UpperCAmelCase__					:   Dict       				=       ViTImageProcessor(size=encoder_config.image_size   )
			UpperCAmelCase__					:   Tuple       				=       RobertaTokenizer.from_pretrained("""roberta-large"""   )
			UpperCAmelCase__					:   str       				=       TrOCRProcessor(UpperCamelCase__	, UpperCamelCase__   )

			UpperCAmelCase__					:   List[Any]       				=       processor(images=prepare_img(UpperCamelCase__   )	, return_tensors="""pt"""   ).pixel_values

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

			UpperCAmelCase__					:   Tuple       				=       torch.Size([1, 1, 5_0_2_6_5]   )
			if "trocr-base-handwritten" in checkpoint_url:
						UpperCAmelCase__					:   Union[str, Any]       				=       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:
						UpperCAmelCase__					:   Optional[int]       				=       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:
						UpperCAmelCase__					:   Any       				=       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:
						UpperCAmelCase__					:   List[str]       				=       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, :1_0]	, UpperCamelCase__	, atol=1e-3   ), "First elements 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 processor to {pytorch_dump_folder_path}'''   )
			processor.save_pretrained(UpperCamelCase__   )


if __name__ == "__main__":
		__A		=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.'
		)
		__A		=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_tokenizers_available, is_torch_available


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

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