




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

						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    : encoder_config.hidden_size, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    -encoder_config.hidden_size :, :
						]
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					dct.pop(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					val
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if "handwritten" in checkpoint_url:
						SCREAMING_SNAKE_CASE           =					"""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:
						SCREAMING_SNAKE_CASE           =					"""https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg"""
			SCREAMING_SNAKE_CASE           =					Image.open(requests.get(SCREAMING_SNAKE_CASE					,	stream=SCREAMING_SNAKE_CASE   ).raw   ).convert("""RGB"""   )
			return im
@torch.no_grad()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					ViTConfig(image_size=3_84					,	qkv_bias=SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					TrOCRConfig()

			# size of the architecture
			if "base" in checkpoint_url:
						SCREAMING_SNAKE_CASE           =					7_68
			elif "large" in checkpoint_url:
						# use ViT-large encoder
						SCREAMING_SNAKE_CASE           =					10_24
						SCREAMING_SNAKE_CASE           =					40_96
						SCREAMING_SNAKE_CASE           =					24
						SCREAMING_SNAKE_CASE           =					16
						SCREAMING_SNAKE_CASE           =					10_24
			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:
						SCREAMING_SNAKE_CASE           =					False
						SCREAMING_SNAKE_CASE           =					"""relu"""
						SCREAMING_SNAKE_CASE           =					10_24
						SCREAMING_SNAKE_CASE           =					True
						SCREAMING_SNAKE_CASE           =					False
						SCREAMING_SNAKE_CASE           =					False

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

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

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

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

    # load state dict
			model.load_state_dict(SCREAMING_SNAKE_CASE   )

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

			SCREAMING_SNAKE_CASE           =					processor(images=prepare_img(SCREAMING_SNAKE_CASE   )					,	return_tensors="""pt"""   ).pixel_values

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

			SCREAMING_SNAKE_CASE           =					torch.Size([1, 1, 5_02_65]   )
			if "trocr-base-handwritten" in checkpoint_url:
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [-1.4_502, -4.6_683, -0.5_347, -2.9_291, 9.1_435, -3.0_571, 8.9_764, 1.7_560, 8.7_358, -1.5_311]   )
			elif "trocr-large-handwritten" in checkpoint_url:
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [-2.6_437, -1.3_129, -2.2_596, -5.3_455, 6.3_539, 1.7_604, 5.4_991, 1.4_702, 5.6_113, 2.0_170]   )
			elif "trocr-base-printed" in checkpoint_url:
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [-5.6_816, -5.8_388, 1.1_398, -6.9_034, 6.8_505, -2.4_393, 1.2_284, -1.0_232, -1.9_661, -3.9_210]   )
			elif "trocr-large-printed" in checkpoint_url:
						SCREAMING_SNAKE_CASE           =					torch.tensor(
						    [-6.0_162, -7.0_959, 4.4_155, -5.1_063, 7.0_468, -3.1_631, 2.6_466, -0.3_081, -0.8_106, -1.7_535]   )

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

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


if __name__ == "__main__":
		__A						:							Optional[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."""
		)
		__A						:							Dict							=  parser.parse_args()
		convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)












import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''image_processor''', '''tokenizer''']
							a__						  =     '''ViTImageProcessor'''
							a__						  =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')





							def __init__(					self       :	List[str]       ,     a       :	Dict=None       ,     a       :	List[str]=None       ,     **a       :	List[str]			)			->					Optional[Any]:
										SCREAMING_SNAKE_CASE           =					None
										if "feature_extractor" in kwargs:
													warnings.warn(
													    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
													    """ instead."""       ,     a       ,     )
													SCREAMING_SNAKE_CASE           =					kwargs.pop("""feature_extractor"""			)

										SCREAMING_SNAKE_CASE           =					image_processor if image_processor is not None else feature_extractor
										if image_processor is None:
													raise ValueError("""You need to specify an `image_processor`."""			)
										if tokenizer is None:
													raise ValueError("""You need to specify a `tokenizer`."""			)

										super().__init__(a       ,     a			)





							def __call__(					self       :	List[Any]       ,     a       :	int=None       ,     a       :	Dict=None       ,     a       :	int=None       ,     a       :	Tuple=None       ,     **a       :	Any			)			->					Any:
										if text is None and visual_prompt is None and images is None:
													raise ValueError("""You have to specify either text, visual prompt or images."""			)

										if text is not None and visual_prompt is not None:
													raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""			)

										if text is not None:
													SCREAMING_SNAKE_CASE           =					self.tokenizer(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if images is not None:
													SCREAMING_SNAKE_CASE           =					self.image_processor(a       ,     return_tensors=a       ,     **a			)

										if visual_prompt is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """pixel_values""": image_features.pixel_values,
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										elif text is not None and images is not None:
													SCREAMING_SNAKE_CASE           =					image_features.pixel_values
													return encoding
										elif text is not None:
													return encoding
										elif visual_prompt is not None:
													SCREAMING_SNAKE_CASE           =					{
													    """conditional_pixel_values""": prompt_features.pixel_values,
													}
													return encoding
										else:
													return BatchEncoding(data=dict(**a			)       ,     tensor_type=a			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     *a       :	Dict       ,     **a       :	Union[str, Any]			)			->					Any:
										return self.tokenizer.batch_decode(*a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Tuple       ,     *a       :	List[Any]       ,     **a       :	Union[str, Any]			)			->					Dict:
										return self.tokenizer.decode(*a       ,     **a			)





							@property
							def    _UpperCAmelCase     (					self       :	Any			)			->					Optional[Any]:
										warnings.warn(
										    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,     a       ,     )
										return self.image_processor_class







							@property
							def    _UpperCAmelCase     (					self       :	Optional[int]			)			->					str:
										warnings.warn(
										    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,     a       ,     )
										return self.image_processor






