'''simple docstring'''


import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)






class 						A   (   nn.Module   ):





       def __init__(      self				:							str	)							->	Tuple:
        super().__init__()
        __UpperCAmelCase							=			nn.Linear(3     ,		4	)
        __UpperCAmelCase							=			nn.BatchNormad(4	)
        __UpperCAmelCase							=			nn.Linear(4     ,		5	)






       def       snake_case__						(      self				:							Dict     ,		__a				:							int	)							->	List[str]:
        return self.lineara(self.batchnorm(self.lineara(a_	)	)	)






class 						A   (   unittest.TestCase   ):





       def       snake_case__						(      self				:							List[Any]	)							->	int:
        __UpperCAmelCase							=			ModelForTest()
        with TemporaryDirectory() as tmp_dir:
         offload_state_dict(a_     ,		model.state_dict()	)
         __UpperCAmelCase							=			os.path.join(a_     ,		'''index.json'''	)
         self.assertTrue(os.path.isfile(a_	)	)
         # TODO: add tests on what is inside the index

         for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
          __UpperCAmelCase							=			os.path.join(a_     ,		f"""{key}.dat"""	)
          self.assertTrue(os.path.isfile(a_	)	)
          # TODO: add tests on the fact weights are properly loaded





       def       snake_case__						(      self				:							Tuple	)							->	Optional[int]:
        __UpperCAmelCase							=			[torch.floataa, torch.floataa, torch.bfloataa]

        for dtype in dtypes:
         __UpperCAmelCase							=			torch.randn(2     ,		3     ,		dtype=a_	)
         with TemporaryDirectory() as tmp_dir:
          __UpperCAmelCase							=			offload_weight(a_     ,		'''weight'''     ,		a_     ,		{}	)
          __UpperCAmelCase							=			os.path.join(a_     ,		'''weight.dat'''	)
          self.assertTrue(os.path.isfile(a_	)	)
          self.assertDictEqual(a_     ,		{'''weight''': {'''shape''': [2, 3], '''dtype''': str(a_	).split('''.'''	)[1]}}	)

          __UpperCAmelCase							=			load_offloaded_weight(a_     ,		index['''weight''']	)
          self.assertTrue(torch.equal(a_     ,		a_	)	)





       def       snake_case__						(      self				:							Optional[int]	)							->	List[Any]:
        __UpperCAmelCase							=			ModelForTest()
        __UpperCAmelCase							=			model.state_dict()
        __UpperCAmelCase							=			{k: v for k, v in state_dict.items() if """linear2""" not in k}
        __UpperCAmelCase							=			{k: v for k, v in state_dict.items() if """linear2""" in k}

        with TemporaryDirectory() as tmp_dir:
         offload_state_dict(a_     ,		a_	)
         __UpperCAmelCase							=			OffloadedWeightsLoader(state_dict=a_     ,		save_folder=a_	)

         # Every key is there with the right value
         self.assertEqual(sorted(a_	)     ,		sorted(state_dict.keys()	)	)
         for key, param in state_dict.items():
          self.assertTrue(torch.allclose(a_     ,		weight_map[key]	)	)

        __UpperCAmelCase							=			{k: v for k, v in state_dict.items() if """weight""" in k}
        __UpperCAmelCase							=			{k: v for k, v in state_dict.items() if """weight""" not in k}

        with TemporaryDirectory() as tmp_dir:
         offload_state_dict(a_     ,		a_	)
         __UpperCAmelCase							=			OffloadedWeightsLoader(state_dict=a_     ,		save_folder=a_	)

         # Every key is there with the right value
         self.assertEqual(sorted(a_	)     ,		sorted(state_dict.keys()	)	)
         for key, param in state_dict.items():
          self.assertTrue(torch.allclose(a_     ,		weight_map[key]	)	)

        with TemporaryDirectory() as tmp_dir:
         offload_state_dict(a_     ,		a_	)
         # Duplicates are removed
         __UpperCAmelCase							=			OffloadedWeightsLoader(state_dict=a_     ,		save_folder=a_	)

         # Every key is there with the right value
         self.assertEqual(sorted(a_	)     ,		sorted(state_dict.keys()	)	)
         for key, param in state_dict.items():
          self.assertTrue(torch.allclose(a_     ,		weight_map[key]	)	)






       def       snake_case__						(      self				:							Optional[Any]	)							->	List[str]:
        __UpperCAmelCase							=			{"""a.1""": 0, """a.10""": 1, """a.2""": 2}
        __UpperCAmelCase							=			extract_submodules_state_dict(a_     ,		['''a.1''', '''a.2''']	)
        self.assertDictEqual(a_     ,		{'''a.1''': 0, '''a.2''': 2}	)

        __UpperCAmelCase							=			{"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
        __UpperCAmelCase							=			extract_submodules_state_dict(a_     ,		['''a.1''', '''a.2''']	)
        self.assertDictEqual(a_     ,		{'''a.1.a''': 0, '''a.2.a''': 2}	)

'''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()
__lowerCAmelCase	:       Dict						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__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       lowerCAmelCase					(		UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[Any]       ):


	"""simple docstring"""
	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       lowerCAmelCase					(		UpperCamelCase__				:   Any    ,					UpperCamelCase__				:   Optional[Any]    ,					UpperCamelCase__				:   List[str]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			dct.pop(UpperCamelCase__       )
	__UpperCAmelCase							=			val





def       lowerCAmelCase					(		UpperCamelCase__				:   Dict       ):


	"""simple docstring"""
	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(UpperCamelCase__    ,					stream=UpperCamelCase__       ).raw       ).convert('''RGB'''       )
	return im





@torch.no_grad()
def       lowerCAmelCase					(		UpperCamelCase__				:   Dict    ,					UpperCamelCase__				:   Union[str, Any]       ):


	"""simple docstring"""
	__UpperCAmelCase							=			ViTConfig(image_size=3_8_4    ,					qkv_bias=UpperCamelCase__       )
	__UpperCAmelCase							=			TrOCRConfig()

	# size of the architecture
	if "base" in checkpoint_url:
		__UpperCAmelCase							=			7_6_8
	elif "large" in checkpoint_url:
		# use ViT-large encoder
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			4_0_9_6
		__UpperCAmelCase							=			2_4
		__UpperCAmelCase							=			1_6
		__UpperCAmelCase							=			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							=			False
		__UpperCAmelCase							=			'''relu'''
		__UpperCAmelCase							=			1_0_2_4
		__UpperCAmelCase							=			True
		__UpperCAmelCase							=			False
		__UpperCAmelCase							=			False

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

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

	__UpperCAmelCase							=			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							=			state_dict.pop(UpperCamelCase__       )
		if key.startswith('''decoder'''       ) and "output_projection" not in key:
			__UpperCAmelCase							=			val
		else:
			__UpperCAmelCase							=			val

    # load state dict
	model.load_state_dict(UpperCamelCase__       )

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

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

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

	__UpperCAmelCase							=			torch.Size([1, 1, 5_0_2_6_5]       )
	if "trocr-base-handwritten" in checkpoint_url:
		__UpperCAmelCase							=			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							=			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							=			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							=			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__":
					__lowerCAmelCase	:       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."
					)
					__lowerCAmelCase	:       Optional[int]						       =	parser.parse_args()
					convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
