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__           =							logging.get_logger(__name__)





def  _lowerCAmelCase					(   a_				: Union[str, Any]						,			a_				: Union[str, Any]):
			lowerCamelCase						:Dict						  =     []
			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					(   a_				: Dict						,			a_				: Tuple):
			for i in range(encoder_config.num_hidden_layers):
						# queries, keys and values (only weights, no biases)
						lowerCamelCase						:int						  =     state_dict.pop(F"encoder.deit.blocks.{i}.attn.qkv.weight")

						lowerCamelCase						:Optional[Any]						  =     in_proj_weight[
						    : encoder_config.hidden_size, :
						]
						lowerCamelCase						:Optional[Any]						  =     in_proj_weight[
						    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
						]
						lowerCamelCase						:Any						  =     in_proj_weight[
						    -encoder_config.hidden_size :, :
						]





def  _lowerCAmelCase					(   a_				: Union[str, Any]						,			a_				: Dict						,			a_				: Optional[int]):
			lowerCamelCase						:Optional[int]						  =     dct.pop(a_)
			lowerCamelCase						:List[Any]						  =     val





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





@torch.no_grad()
def  _lowerCAmelCase					(   a_				: List[str]						,			a_				: str):
			lowerCamelCase						:Union[str, Any]						  =     ViTConfig(image_size=3_84						,			qkv_bias=a_)
			lowerCamelCase						:Union[str, Any]						  =     TrOCRConfig()

			# size of the architecture
			if "base" in checkpoint_url:
						lowerCamelCase						:str						  =     7_68
			elif "large" in checkpoint_url:
						# use ViT-large encoder
						lowerCamelCase						:List[Any]						  =     10_24
						lowerCamelCase						:Optional[Any]						  =     40_96
						lowerCamelCase						:Optional[Any]						  =     24
						lowerCamelCase						:int						  =     16
						lowerCamelCase						:Any						  =     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:
						lowerCamelCase						:Optional[Any]						  =     False
						lowerCamelCase						:Tuple						  =     '''relu'''
						lowerCamelCase						:Optional[int]						  =     10_24
						lowerCamelCase						:Dict						  =     True
						lowerCamelCase						:Optional[int]						  =     False
						lowerCamelCase						:List[Any]						  =     False

			# load HuggingFace model
			lowerCamelCase						:Any						  =     ViTModel(a_						,			add_pooling_layer=a_)
			lowerCamelCase						:Optional[Any]						  =     TrOCRForCausalLM(a_)
			lowerCamelCase						:Union[str, Any]						  =     VisionEncoderDecoderModel(encoder=a_						,			decoder=a_)
			model.eval()

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

			lowerCamelCase						:Any						  =     create_rename_keys(a_						,			a_)
			for src, dest in rename_keys:
						rename_key(a_						,			a_						,			a_)
			read_in_q_k_v(a_						,			a_)

			# 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():
						lowerCamelCase						:int						  =     state_dict.pop(a_)
						if key.startswith('''decoder''') and "output_projection" not in key:
									lowerCamelCase						:Optional[int]						  =     val
						else:
									lowerCamelCase						:Any						  =     val

    # load state dict
			model.load_state_dict(a_)

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

			lowerCamelCase						:int						  =     processor(images=prepare_img(a_)						,			return_tensors='''pt''').pixel_values

			# verify logits
			lowerCamelCase						:List[Any]						  =     torch.tensor([[model.config.decoder.decoder_start_token_id]])
			lowerCamelCase						:str						  =     model(pixel_values=a_						,			decoder_input_ids=a_)
			lowerCamelCase						:Optional[Any]						  =     outputs.logits

			lowerCamelCase						:Optional[Any]						  =     torch.Size([1, 1, 5_02_65])
			if "trocr-base-handwritten" in checkpoint_url:
						lowerCamelCase						:Dict						  =     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:
						lowerCamelCase						:Optional[int]						  =     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:
						lowerCamelCase						: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:
						lowerCamelCase						:Tuple						  =     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]						,			a_						,			atol=1e-3), "First elements of logits not as expected"

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


if __name__ == "__main__":
			A__           =							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__           =							parser.parse_args()
			convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




import inspect
import os
import unittest

import torch

import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
    execute_subprocess_async,
    require_cpu,
    require_huggingface_suite,
    require_multi_gpu,
    require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class   _lowerCAmelCase		(     unittest.TestCase				):







							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:List[Any]						  =     inspect.getfile(accelerate.test_utils		)
										lowerCamelCase						:Dict						  =     os.path.sep.join(
										    mod_file.split(os.path.sep		)[:-1] + ['''scripts''', '''external_deps''', '''test_metrics.py''']		)

										from accelerate.test_utils.scripts.external_deps import test_metrics  # noqa: F401

										lowerCamelCase						:Any						  =     test_metrics







							@require_cpu
							def 				snake_case  (				self     :    Dict		):
										debug_launcher(self.test_metrics.main	,					num_processes=1		)







							@require_cpu
							def 				snake_case  (				self     :    int		):
										debug_launcher(self.test_metrics.main		)







							@require_single_gpu
							def 				snake_case  (				self     :    Any		):
										self.test_metrics.main()







							@require_multi_gpu
							def 				snake_case  (				self     :    Optional[int]		):
										print(F"Found {torch.cuda.device_count()} devices."		)
										lowerCamelCase						:Optional[int]						  =     ['''torchrun''', F"--nproc_per_node={torch.cuda.device_count()}", self.test_file_path]
										with patch_environment(omp_num_threads=1		):
													execute_subprocess_async(__snake_case	,					env=os.environ.copy()		)



