

"""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()
_lowercase   :	List[Any]												=						logging.get_logger(__name__)





def       lowercase__						(       snake_case_   :Union[str, Any]			,		snake_case_   :Optional[Any]    ):
	__UpperCAmelCase      		= []
	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       lowercase__						(       snake_case_   :int			,		snake_case_   :Any    ):
	for i in range(encoder_config.num_hidden_layers    ):
		# queries, keys and values (only weights, no biases)
		__UpperCAmelCase      		= state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''    )

		__UpperCAmelCase      		= in_proj_weight[
		    : encoder_config.hidden_size, :
		]
		__UpperCAmelCase      		= in_proj_weight[
		    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
		]
		__UpperCAmelCase      		= in_proj_weight[
		    -encoder_config.hidden_size :, :
		]





def       lowercase__						(       snake_case_   :Dict			,		snake_case_   :Tuple			,		snake_case_   :Tuple    ):
	__UpperCAmelCase      		= dct.pop(snake_case_    )
	__UpperCAmelCase      		= val





def       lowercase__						(       snake_case_   :Optional[Any]    ):
	if "handwritten" in checkpoint_url:
		__UpperCAmelCase      		= '''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      		= '''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      		= Image.open(requests.get(snake_case_			,		stream=snake_case_    ).raw    ).convert('''RGB'''    )
	return im





@torch.no_grad()
def       lowercase__						(       snake_case_   :Tuple			,		snake_case_   :str    ):
	__UpperCAmelCase      		= ViTConfig(image_size=384			,		qkv_bias=snake_case_    )
	__UpperCAmelCase      		= TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__UpperCAmelCase      		= 768
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__UpperCAmelCase      		= 1_024
		__UpperCAmelCase      		= 4_096
		__UpperCAmelCase      		= 24
		__UpperCAmelCase      		= 16
		__UpperCAmelCase      		= 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:
		__UpperCAmelCase      		= False
		__UpperCAmelCase      		= '''relu'''
		__UpperCAmelCase      		= 1_024
		__UpperCAmelCase      		= True
		__UpperCAmelCase      		= False
		__UpperCAmelCase      		= False

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

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

	__UpperCAmelCase      		= create_rename_keys(snake_case_			,		snake_case_    )
	for src, dest in rename_keys:
		rename_key(snake_case_			,		snake_case_			,		snake_case_    )
	read_in_q_k_v(snake_case_			,		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():
		__UpperCAmelCase      		= state_dict.pop(snake_case_    )
		if key.startswith('''decoder'''    ) and "output_projection" not in key:
			__UpperCAmelCase      		= val
		else:
			__UpperCAmelCase      		= val

    # load state dict
	model.load_state_dict(snake_case_    )

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

	__UpperCAmelCase      		= processor(images=prepare_img(snake_case_    )			,		return_tensors='''pt'''    ).pixel_values

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

	__UpperCAmelCase      		= torch.Size([1, 1, 50_265]    )
	if "trocr-base-handwritten" in checkpoint_url:
		__UpperCAmelCase      		= torch.tensor(
		    [-1.4502, -4.6683, -0.5347, -2.9291, 9.1435, -3.0571, 8.9764, 1.7560, 8.7358, -1.5311]    )
	elif "trocr-large-handwritten" in checkpoint_url:
		__UpperCAmelCase      		= torch.tensor(
		    [-2.6437, -1.3129, -2.2596, -5.3455, 6.3539, 1.7604, 5.4991, 1.4702, 5.6113, 2.0170]    )
	elif "trocr-base-printed" in checkpoint_url:
		__UpperCAmelCase      		= torch.tensor(
		    [-5.6816, -5.8388, 1.1398, -6.9034, 6.8505, -2.4393, 1.2284, -1.0232, -1.9661, -3.9210]    )
	elif "trocr-large-printed" in checkpoint_url:
		__UpperCAmelCase      		= torch.tensor(
		    [-6.0162, -7.0959, 4.4155, -5.1063, 7.0468, -3.1631, 2.6466, -0.3081, -0.8106, -1.7535]    )

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

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


if __name__ == "__main__":
				_lowercase   :	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.'
				)
				_lowercase   :	Any												=						parser.parse_args()
				convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)


'''simple docstring'''
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler


@dataclass
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							0
				lowerCAmelCase_     			=							False
				lowerCAmelCase_     			=							3.0


class 			SCREAMING_SNAKE_CASE__      (     unittest.TestCase):







				def 						UpperCAmelCase_    (       self     )->		int:







											'''simple docstring'''






											self.assertDictEqual(MockClass().to_kwargs()						,				{}     )
											self.assertDictEqual(MockClass(a=2     ).to_kwargs()						,				{'a': 2}     )
											self.assertDictEqual(MockClass(a=2						,				b=A_     ).to_kwargs()						,				{'a': 2, 'b': True}     )
											self.assertDictEqual(MockClass(a=2						,				c=2.25     ).to_kwargs()						,				{'a': 2, 'c': 2.25}     )







				@require_cuda
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     GradScalerKwargs(init_scale=1024						,				growth_factor=2     )
											AcceleratorState._reset_state()
											UpperCamelCase   			=     Accelerator(mixed_precision='fp16'						,				kwargs_handlers=[scaler_handler]     )
											print(accelerator.use_fpaa     )
											UpperCamelCase   			=     accelerator.scaler

											# Check the kwargs have been applied
											self.assertEqual(scaler._init_scale						,				1_024.0     )
											self.assertEqual(scaler._growth_factor						,				2.0     )

											# Check the other values are at the default
											self.assertEqual(scaler._backoff_factor						,				0.5     )
											self.assertEqual(scaler._growth_interval						,				2000     )
											self.assertEqual(scaler._enabled						,				A_     )







				@require_multi_gpu
				def 						UpperCAmelCase_    (       self     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     ['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__     )]
											execute_subprocess_async(A_						,				env=os.environ.copy()     )


if __name__ == "__main__":
						lowerCAmelCase					:    Tuple           = DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
						lowerCAmelCase					:    List[str]           = Accelerator(kwargs_handlers=[ddp_scaler])
						lowerCAmelCase					:    List[Any]           = torch.nn.Linear(1_00, 2_00)
						lowerCAmelCase					:    int           = accelerator.prepare(model)

						# Check the values changed in kwargs
						lowerCAmelCase					:    Dict           = ''
						lowerCAmelCase					:    Dict           = model.bucket_bytes_cap // (10_24 * 10_24)
						if observed_bucket_cap_map != 15:
												error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
						if model.find_unused_parameters is not True:
												error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

						# Check the values of the defaults
						if model.dim != 0:
												error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
						if model.broadcast_buffers is not True:
												error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
						if model.gradient_as_bucket_view is not False:
												error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

						# Raise error at the end to make sure we don't stop at the first failure.
						if len(error_msg) > 0:
												raise ValueError(error_msg)




