



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__  :  Dict								=					logging.get_logger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		str ):
		lowerCAmelCase_			:     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 			UpperCamelCase(  __UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		Union[str, Any] ):
		for i in range(encoder_config.num_hidden_layers ):
				# queries, keys and values (only weights, no biases)
				lowerCAmelCase_			:     Optional[int]									= state_dict.pop(f"""encoder.deit.blocks.{i}.attn.qkv.weight""" )

				lowerCAmelCase_			:     Optional[int]									= in_proj_weight[
				    : encoder_config.hidden_size, :
				]
				lowerCAmelCase_			:     int									= in_proj_weight[
				    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
				]
				lowerCAmelCase_			:     Tuple									= in_proj_weight[
				    -encoder_config.hidden_size :, :
				]


def 			UpperCamelCase(  __UpperCamelCase							:		Dict     ,__UpperCamelCase							:		List[str]     ,__UpperCamelCase							:		int ):
		lowerCAmelCase_			:     Dict									= dct.pop(__UpperCamelCase )
		lowerCAmelCase_			:     Optional[Any]									= val


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		if "handwritten" in checkpoint_url:
				lowerCAmelCase_			:     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:
				lowerCAmelCase_			:     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_			:     Optional[int]									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw ).convert('''RGB''' )
		return im


@torch.no_grad()
def 			UpperCamelCase(  __UpperCamelCase							:		Tuple     ,__UpperCamelCase							:		List[Any] ):
		lowerCAmelCase_			:     List[str]									= ViTConfig(image_size=384     ,qkv_bias=__UpperCamelCase )
		lowerCAmelCase_			:     Dict									= TrOCRConfig()

		# size of the architecture
		if "base" in checkpoint_url:
				lowerCAmelCase_			:     Tuple									= 768
		elif "large" in checkpoint_url:
				# use ViT-large encoder
				lowerCAmelCase_			:     Tuple									= 1024
				lowerCAmelCase_			:     str									= 4096
				lowerCAmelCase_			:     Dict									= 24
				lowerCAmelCase_			:     str									= 16
				lowerCAmelCase_			:     int									= 1024
		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_			:     Dict									= False
				lowerCAmelCase_			:     List[Any]									= '''relu'''
				lowerCAmelCase_			:     Optional[Any]									= 1024
				lowerCAmelCase_			:     Any									= True
				lowerCAmelCase_			:     int									= False
				lowerCAmelCase_			:     Tuple									= False

		# load HuggingFace model
		lowerCAmelCase_			:     Optional[Any]									= ViTModel(__UpperCamelCase     ,add_pooling_layer=__UpperCamelCase )
		lowerCAmelCase_			:     Any									= TrOCRForCausalLM(__UpperCamelCase )
		lowerCAmelCase_			:     List[str]									= VisionEncoderDecoderModel(encoder=__UpperCamelCase     ,decoder=__UpperCamelCase )
		model.eval()

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

		lowerCAmelCase_			:     Tuple									= 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():
				lowerCAmelCase_			:     List[Any]									= state_dict.pop(__UpperCamelCase )
				if key.startswith('''decoder''' ) and "output_projection" not in key:
						lowerCAmelCase_			:     Optional[int]									= val
				else:
						lowerCAmelCase_			:     Optional[Any]									= val

    # load state dict
		model.load_state_dict(__UpperCamelCase )

		# Check outputs on an image
		lowerCAmelCase_			:     Tuple									= ViTImageProcessor(size=encoder_config.image_size )
		lowerCAmelCase_			:     int									= RobertaTokenizer.from_pretrained('''roberta-large''' )
		lowerCAmelCase_			:     str									= TrOCRProcessor(__UpperCamelCase     ,__UpperCamelCase )

		lowerCAmelCase_			:     str									= processor(images=prepare_img(__UpperCamelCase )     ,return_tensors='''pt''' ).pixel_values

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

		lowerCAmelCase_			:     Optional[int]									= torch.Size([1, 1, 50265] )
		if "trocr-base-handwritten" in checkpoint_url:
				lowerCAmelCase_			:     str									= 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:
				lowerCAmelCase_			:     Union[str, Any]									= 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:
				lowerCAmelCase_			:     Dict									= 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:
				lowerCAmelCase_			:     str									= 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, :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__":
						A__  :  Optional[int]								=					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__  :  Dict								=					parser.parse_args()
						convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)









"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=None    ,   __a=True    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			config_class
										__lowerCAmelCase				    =			has_text_modality
										__lowerCAmelCase				    =			kwargs
										__lowerCAmelCase				    =			common_properties

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			(
										    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
										    if self.common_properties is None
										    else self.common_properties
										)

										# Add common fields for text models
										if self.has_text_modality:
													common_properties.extend(["vocab_size"]	)

										# Test that config has the common properties as getters
										for prop in common_properties:
													self.parent.assertTrue(hasattr(__a    ,   __a	)    ,   msg=f"`{prop}` does not exist"	)

										# Test that config has the common properties as setter
										for idx, name in enumerate(__a	):
													try:
																setattr(__a    ,   __a    ,   __a	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

        # Test if config class can be called with Config(prop_name=..)
										for idx, name in enumerate(__a	):
													try:
																__lowerCAmelCase				    =			self.config_class(**{name: idx}	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			json.loads(config.to_json_string()	)
										for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   "config.json"	)
													config_first.to_json_file(__a	)
													__lowerCAmelCase				    =			self.config_class.from_json_file(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										__lowerCAmelCase				    =			"test"
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   __a	)
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a    ,   subfolder=__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict    ,   num_labels=5	)
										self.parent.assertEqual(len(config.idalabel	)    ,   5	)
										self.parent.assertEqual(len(config.labelaid	)    ,   5	)

										__lowerCAmelCase				    =			3
										self.parent.assertEqual(len(config.idalabel	)    ,   3	)
										self.parent.assertEqual(len(config.labelaid	)    ,   3	)

							def 			snake_case				(     self	):
										if self.config_class.is_composition:
													return
										__lowerCAmelCase				    =			self.config_class()
										self.parent.assertIsNotNone(__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			copy.deepcopy(__a	)
										__lowerCAmelCase				    =			self.config_class(**__a	)
										__lowerCAmelCase				    =			[]
										for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																if not is_torch_available():
																			continue
																else:
																			import torch

																			if config.torch_dtype != torch.floataa:
																						wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)	)
													elif getattr(__a    ,   __a	) != value:
																wrong_values.append((key, getattr(__a    ,   __a	), value)	)

										if len(__a	) > 0:
													__lowerCAmelCase				    =			"\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values]	)
													raise ValueError(f"The following keys were not properly set in the config:\n{errors}"	)



							def 			snake_case				(     self	):
										self.create_and_test_config_common_properties()
										self.create_and_test_config_to_json_string()
										self.create_and_test_config_to_json_file()
										self.create_and_test_config_from_and_save_pretrained()
										self.create_and_test_config_from_and_save_pretrained_subfolder()
										self.create_and_test_config_with_num_labels()
										self.check_config_can_be_init_without_params()
										self.check_config_arguments_init()




