

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__								=		logging.get_logger(__name__)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]							,       UpperCAmelCase_							:     Tuple	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Union[str, 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 					lowerCamelCase_     (							UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     int	)						->				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   :		List[Any]			=						in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		_UpperCamelCase   :		Optional[Any]			=						in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		_UpperCamelCase   :		Union[str, Any]			=						in_proj_weight[
		    -encoder_config.hidden_size :, :
		]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int							,       UpperCAmelCase_							:     Any							,       UpperCAmelCase_							:     int	)						->				Union[str, Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						dct.pop(UpperCAmelCase_	)
	_UpperCamelCase   :		int			=						val






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Dict	)						->				str:




	'''simple docstring'''




	if "handwritten" in checkpoint_url:
		_UpperCamelCase   :		str			=						'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[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   :		Optional[Any]			=						Image.open(requests.get(UpperCAmelCase_							,       stream=UpperCAmelCase_	).raw	).convert('RGB'	)
	return im






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     int	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						ViTConfig(image_size=3_8_4							,       qkv_bias=UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						TrOCRConfig()

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

	# load HuggingFace model
	_UpperCamelCase   :		str			=						ViTModel(UpperCAmelCase_							,       add_pooling_layer=UpperCAmelCase_	)
	_UpperCamelCase   :		Union[str, Any]			=						TrOCRForCausalLM(UpperCAmelCase_	)
	_UpperCamelCase   :		Dict			=						VisionEncoderDecoderModel(encoder=UpperCAmelCase_							,       decoder=UpperCAmelCase_	)
	model.eval()

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

	_UpperCamelCase   :		List[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   :		List[Any]			=						state_dict.pop(UpperCAmelCase_	)
		if key.startswith('decoder'	) and "output_projection" not in key:
			_UpperCamelCase   :		List[str]			=						val
		else:
			_UpperCamelCase   :		Optional[Any]			=						val

    # load state dict
	model.load_state_dict(UpperCAmelCase_	)

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

	_UpperCamelCase   :		Dict			=						processor(images=prepare_img(UpperCAmelCase_	)							,       return_tensors='pt'	).pixel_values

	# verify logits
	_UpperCamelCase   :		int			=						torch.tensor([[model.config.decoder.decoder_start_token_id]]	)
	_UpperCamelCase   :		Tuple			=						model(pixel_values=UpperCAmelCase_							,       decoder_input_ids=UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						outputs.logits

	_UpperCamelCase   :		Optional[Any]			=						torch.Size([1, 1, 5_0_2_6_5]	)
	if "trocr-base-handwritten" in checkpoint_url:
		_UpperCamelCase   :		Any			=						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   :		Dict			=						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   :		List[str]			=						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[Any]			=						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, :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__":
							lowerCAmelCase__								=		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__								=		parser.parse_args()
							convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


lowerCAmelCase__								=		["""bert-base-uncased""", """bert-base-cased"""]
lowerCAmelCase__								=		"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():







							class 	lowercase    (				tf.keras.Model							):


									"""simple docstring"""




									def __init__(				self	,       __snake_case):
										super().__init__()
										_UpperCamelCase   :		List[Any]			=						tokenizer
										_UpperCamelCase   :		List[Any]			=						AutoConfig.from_pretrained(__snake_case)
										_UpperCamelCase   :		Dict			=						TFAutoModel.from_config(__snake_case)






									def 		A__					(				self	,       __snake_case):
										_UpperCamelCase   :		Any			=						self.tokenizer(__snake_case)
										_UpperCamelCase   :		Dict			=						self.bert(**__snake_case)
										return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self):
			super().setUp()

			_UpperCamelCase   :		Optional[Any]			=						[
			    BertTokenizer.from_pretrained(__snake_case) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
			]  # repeat for when fast_bert_tokenizer=false
			_UpperCamelCase   :		Optional[Any]			=						[TFBertTokenizer.from_pretrained(__snake_case) for checkpoint in TOKENIZER_CHECKPOINTS] + [
			    TFBertTokenizer.from_pretrained(__snake_case	,       use_fast_bert_tokenizer=__snake_case)
			    for checkpoint in TOKENIZER_CHECKPOINTS
			]
			assert len(self.tokenizers) == len(self.tf_tokenizers)

			_UpperCamelCase   :		Optional[Any]			=						[
			    'This is a straightforward English test sentence.',
			    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
			    'Now we\'re going to add some Chinese: 一 二 三 一二三',
			    'And some much more rare Chinese: 齉 堃 齉堃',
			    'Je vais aussi écrire en français pour tester les accents',
			    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
			]
			_UpperCamelCase   :		Dict			=						list(zip(self.test_sentences	,       self.test_sentences[::-1]))


		def 		A__					(				self):
			for tokenizer, tf_tokenizer in zip(self.tokenizers	,       self.tf_tokenizers):
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		List[str]			=						tokenizer(__snake_case	,       return_tensors='tf'	,       padding='longest')
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					for key in python_outputs.keys():
						self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
						self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,       tf.intaa) == tf_outputs[key]))


		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf_tokenizer(self.paired_sentences)
				_UpperCamelCase   :		Optional[Any]			=						tf_tokenizer(
				    text=[sentence[0] for sentence in self.paired_sentences]	,       text_pair=[sentence[1] for sentence in self.paired_sentences]	,       )
				for key in merged_outputs.keys():
					self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,       tf.intaa) == separated_outputs[key]))


		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf.function(__snake_case)
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		Optional[int]			=						tf.constant(__snake_case)
					_UpperCamelCase   :		Union[str, Any]			=						compiled_tokenizer(__snake_case)
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					for key in eager_outputs.keys():
						self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))






		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Any			=						ModelToSave(tokenizer=__snake_case)
				_UpperCamelCase   :		Any			=						tf.convert_to_tensor(self.test_sentences)
				_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)  # Build model with some sample inputs
				with TemporaryDirectory() as tempdir:
					_UpperCamelCase   :		int			=						Path(__snake_case) / 'saved.model'
					model.save(__snake_case)
					_UpperCamelCase   :		Optional[int]			=						tf.keras.models.load_model(__snake_case)
				_UpperCamelCase   :		int			=						loaded_model(__snake_case)
				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output))	,       1e-5)




