





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					,							_UpperCamelCase					)						->		str:






				"""simple docstring"""


				snake_case_		:       int           =    []
				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					,							_UpperCamelCase					)						->		int:






				"""simple docstring"""


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

								snake_case_		:       List[Any]           =    in_proj_weight[
								    : encoder_config.hidden_size, :
								]
								snake_case_		:       List[str]           =    in_proj_weight[
								    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
								]
								snake_case_		:       Union[str, Any]           =    in_proj_weight[
								    -encoder_config.hidden_size :, :
								]






def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					,							_UpperCamelCase					)						->		Dict:






				"""simple docstring"""


				snake_case_		:       Tuple           =    dct.pop(_UpperCamelCase					)
				snake_case_		:       Any           =    val






def 	lowerCamelCase_		(							_UpperCamelCase					)						->		int:






				"""simple docstring"""


				if "handwritten" in checkpoint_url:
								snake_case_		:       Tuple           =    '''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:
								snake_case_		:       Tuple           =    '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
				snake_case_		:       Optional[int]           =    Image.open(requests.get(_UpperCamelCase					,							stream=_UpperCamelCase					).raw					).convert('''RGB'''					)
				return im






@torch.no_grad()
def 	lowerCamelCase_		(							_UpperCamelCase					,							_UpperCamelCase					)						->		Tuple:






				"""simple docstring"""


				snake_case_		:       List[Any]           =    ViTConfig(image_size=384					,							qkv_bias=_UpperCamelCase					)
				snake_case_		:       str           =    TrOCRConfig()

				# size of the architecture
				if "base" in checkpoint_url:
								snake_case_		:       Optional[int]           =    768
				elif "large" in checkpoint_url:
								# use ViT-large encoder
								snake_case_		:       str           =    1_024
								snake_case_		:       Optional[Any]           =    4_096
								snake_case_		:       Union[str, Any]           =    24
								snake_case_		:       Tuple           =    16
								snake_case_		:       Any           =    1_024
				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:
								snake_case_		:       List[Any]           =    False
								snake_case_		:       Union[str, Any]           =    '''relu'''
								snake_case_		:       Dict           =    1_024
								snake_case_		:       List[str]           =    True
								snake_case_		:       List[Any]           =    False
								snake_case_		:       List[Any]           =    False

				# load HuggingFace model
				snake_case_		:       Any           =    ViTModel(_UpperCamelCase					,							add_pooling_layer=_UpperCamelCase					)
				snake_case_		:       Any           =    TrOCRForCausalLM(_UpperCamelCase					)
				snake_case_		:       int           =    VisionEncoderDecoderModel(encoder=_UpperCamelCase					,							decoder=_UpperCamelCase					)
				model.eval()

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

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

    # load state dict
				model.load_state_dict(_UpperCamelCase					)

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

				snake_case_		:       Optional[int]           =    processor(images=prepare_img(_UpperCamelCase					)					,							return_tensors='''pt'''					).pixel_values

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

				snake_case_		:       Union[str, Any]           =    torch.Size([1, 1, 50_265]					)
				if "trocr-base-handwritten" in checkpoint_url:
								snake_case_		:       Any           =    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:
								snake_case_		:       Tuple           =    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:
								snake_case_		:       str           =    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:
								snake_case_		:       List[str]           =    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]					,							_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)







"""simple docstring"""




import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CTRLForSequenceClassification,
		    CTRLLMHeadModel,
		    CTRLModel,
		)


class 			__UpperCamelCase							:






							def __init__(  self ,   lowerCAmelCase__ ,   lowerCAmelCase__=14 ,   lowerCAmelCase__=7 ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=True ,   lowerCAmelCase__=99 ,   lowerCAmelCase__=32 ,   lowerCAmelCase__=5 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=37 ,   lowerCAmelCase__="gelu" ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=0.1 ,   lowerCAmelCase__=512 ,   lowerCAmelCase__=16 ,   lowerCAmelCase__=2 ,   lowerCAmelCase__=0.02 ,   lowerCAmelCase__=3 ,   lowerCAmelCase__=4 ,   lowerCAmelCase__=None ,   )      ->					Optional[int]:
								a :						Optional[int]      =							parent
								a :						Optional[int]      =							batch_size
								a :						str      =							seq_length
								a :						int      =							is_training
								a :						Union[str, Any]      =							use_token_type_ids
								a :						Tuple      =							use_input_mask
								a :						Optional[Any]      =							use_labels
								a :						str      =							use_mc_token_ids
								a :						str      =							vocab_size
								a :						str      =							hidden_size
								a :						List[Any]      =							num_hidden_layers
								a :						str      =							num_attention_heads
								a :						Tuple      =							intermediate_size
								a :						List[Any]      =							hidden_act
								a :						List[Any]      =							hidden_dropout_prob
								a :						List[str]      =							attention_probs_dropout_prob
								a :						Dict      =							max_position_embeddings
								a :						int      =							type_vocab_size
								a :						Any      =							type_sequence_label_size
								a :						List[Any]      =							initializer_range
								a :						List[str]      =							num_labels
								a :						Optional[int]      =							num_choices
								a :						int      =							scope
								a :						Tuple      =							self.vocab_size - 1






							def        __a  (  self )      ->					int:
								a :						Optional[Any]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size )

								a :						str      =							None
								if self.use_input_mask:
									a :						Dict      =							random_attention_mask([self.batch_size, self.seq_length] )

								a :						Tuple      =							None
								if self.use_token_type_ids:
									a :						int      =							ids_tensor([self.batch_size, self.seq_length] ,   self.type_vocab_size )

								a :						int      =							None
								if self.use_mc_token_ids:
									a :						Dict      =							ids_tensor([self.batch_size, self.num_choices] ,   self.seq_length )

								a :						List[Any]      =							None
								a :						Optional[int]      =							None
								a :						str      =							None
								if self.use_labels:
									a :						List[str]      =							ids_tensor([self.batch_size] ,   self.type_sequence_label_size )
									a :						List[str]      =							ids_tensor([self.batch_size, self.seq_length] ,   self.num_labels )
									a :						Any      =							ids_tensor([self.batch_size] ,   self.num_choices )

								a :						Tuple      =							self.get_config()

								a :						int      =							ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,   2 )

								return (
								    config,
								    input_ids,
								    input_mask,
								    head_mask,
								    token_type_ids,
								    mc_token_ids,
								    sequence_labels,
								    token_labels,
								    choice_labels,
								)






							def        __a  (  self )      ->					Tuple:
								return CTRLConfig(
								    vocab_size=self.vocab_size ,   n_embd=self.hidden_size ,   n_layer=self.num_hidden_layers ,   n_head=self.num_attention_heads ,   n_positions=self.max_position_embeddings ,   pad_token_id=self.pad_token_id ,   )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					str:
								a :						Union[str, Any]      =							CTRLModel(config=lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()

								model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ ,   head_mask=lowerCAmelCase__ )
								model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ )
								a :						List[Any]      =							model(lowerCAmelCase__ )
								self.parent.assertEqual(result.last_hidden_state.shape ,   (self.batch_size, self.seq_length, self.hidden_size) )
								self.parent.assertEqual(len(result.past_key_values ) ,   config.n_layer )






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					Optional[int]:
								a :						Tuple      =							CTRLLMHeadModel(lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()

								a :						List[str]      =							model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ ,   labels=lowerCAmelCase__ )
								self.parent.assertEqual(result.loss.shape ,   () )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.seq_length, self.vocab_size) )






							def        __a  (  self )      ->					Optional[int]:
								a :						Tuple      =							self.prepare_config_and_inputs()

								(
								    (
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			(
								    a
								),			
								) :						Optional[Any]      =							config_and_inputs

								a :						List[Any]      =							{"input_ids": input_ids, "token_type_ids": token_type_ids, "head_mask": head_mask}

								return config, inputs_dict






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   *lowerCAmelCase__ )      ->					Optional[Any]:
								a :						List[str]      =							self.num_labels
								a :						Union[str, Any]      =							CTRLForSequenceClassification(lowerCAmelCase__ )
								model.to(lowerCAmelCase__ )
								model.eval()
								a :						Optional[int]      =							ids_tensor([self.batch_size] ,   self.type_sequence_label_size )
								a :						Dict      =							model(lowerCAmelCase__ ,   token_type_ids=lowerCAmelCase__ ,   labels=lowerCAmelCase__ )
								self.parent.assertEqual(result.logits.shape ,   (self.batch_size, self.num_labels) )


@require_torch
class 			__UpperCamelCase							(		a__						,			a__						,			a__						,			unittest.TestCase		):
							lowerCamelCase  :   Tuple    =(CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
							lowerCamelCase  :   List[Any]    =(CTRLLMHeadModel,) if is_torch_available() else ()
							lowerCamelCase  :   List[Any]    =(
							    {
							        """feature-extraction""": CTRLModel,
							        """text-classification""": CTRLForSequenceClassification,
							        """text-generation""": CTRLLMHeadModel,
							        """zero-shot""": CTRLForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							lowerCamelCase  :   Optional[int]    =True
							lowerCamelCase  :   List[str]    =False
							lowerCamelCase  :   Optional[Any]    =False






							def        __a  (  self ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ ,   lowerCAmelCase__ )      ->					Optional[int]:
								if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
									# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
									# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
									# config could not be created.
									return True

								return False






							def        __a  (  self )      ->					str:
								a :						str      =							CTRLModelTester(self )
								a :						Dict      =							ConfigTester(self ,   config_class=lowerCAmelCase__ ,   n_embd=37 )






							def        __a  (  self )      ->					Tuple:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()






							def        __a  (  self )      ->					Optional[Any]:
								self.config_tester.run_common_tests()






							def        __a  (  self )      ->					Union[str, Any]:
								a :						Optional[int]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCAmelCase__ )






							def        __a  (  self )      ->					int:
								a :						List[str]      =							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCAmelCase__ )






							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
							def        __a  (  self )      ->					Tuple:
								pass






							@slow
							def        __a  (  self )      ->					Optional[Any]:
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									a :						Union[str, Any]      =							CTRLModel.from_pretrained(lowerCAmelCase__ )
									self.assertIsNotNone(lowerCAmelCase__ )






							@unittest.skip("The model doesn't support left padding" )  # and it's not used enough to be worth fixing :)
							def        __a  (  self )      ->					Tuple:
								pass


@require_torch
class 			__UpperCamelCase							(		unittest.TestCase		):






							def        __a  (  self )      ->					int:
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()






							@slow
							def        __a  (  self )      ->					Dict:
								a :						Dict      =							CTRLLMHeadModel.from_pretrained("ctrl" )
								model.to(lowerCAmelCase__ )
								a :						List[str]      =							torch.tensor(
								    [[1_1859, 0, 1611, 8]] ,   dtype=torch.long ,   device=lowerCAmelCase__ )  # Legal the president is
								a :						Any      =							[
								    1_1859,
								    0,
								    1611,
								    8,
								    5,
								    150,
								    2_6449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2595,
								    48,
								    2_0740,
								    24_6533,
								    24_6533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								a :						str      =							model.generate(lowerCAmelCase__ ,   do_sample=lowerCAmelCase__ )
								self.assertListEqual(output_ids[0].tolist() ,   lowerCAmelCase__ )


