'''simple docstring'''


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						A   (   UpperCAmelCase       ,	unittest.TestCase   ):
       a_        =      RobertaTokenizer
       a_        =      RobertaTokenizerFast
       a_        =      True
       a_        =      {"cls_token": "<s>"}





       def       snake_case__						(      self				:							int	)							->	Optional[int]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        __UpperCAmelCase							=			[
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        __UpperCAmelCase							=			dict(zip(__a     ,		range(len(__a	)	)	)	)
        __UpperCAmelCase							=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        __UpperCAmelCase							=			{'''unk_token''': '''<unk>'''}

        __UpperCAmelCase							=			os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES['''vocab_file''']	)
        __UpperCAmelCase							=			os.path.join(self.tmpdirname     ,		VOCAB_FILES_NAMES['''merges_file''']	)
        with open(self.vocab_file     ,		'''w'''     ,		encoding='''utf-8'''	) as fp:
         fp.write(json.dumps(__a	) + '''\n'''	)
        with open(self.merges_file     ,		'''w'''     ,		encoding='''utf-8'''	) as fp:
         fp.write('''\n'''.join(__a	)	)





       def       snake_case__						(      self				:							Tuple     ,		**__a				:							Optional[Any]	)							->	Union[str, Any]:
        kwargs.update(self.special_tokens_map	)
        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,		**__a	)





       def       snake_case__						(      self				:							Union[str, Any]     ,		**__a				:							List[Any]	)							->	List[str]:
        kwargs.update(self.special_tokens_map	)
        return RobertaTokenizerFast.from_pretrained(self.tmpdirname     ,		**__a	)





       def       snake_case__						(      self				:							str     ,		__a				:							Union[str, Any]	)							->	List[str]:
        __UpperCAmelCase							=			'''lower newer'''
        __UpperCAmelCase							=			'''lower newer'''
        return input_text, output_text





       def       snake_case__						(      self				:							Tuple	)							->	Optional[int]:
        __UpperCAmelCase							=			self.tokenizer_class(self.vocab_file     ,		self.merges_file     ,		**self.special_tokens_map	)
        __UpperCAmelCase							=			'''lower newer'''
        __UpperCAmelCase							=			['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
        __UpperCAmelCase							=			tokenizer.tokenize(__a	)  # , add_prefix_space=True)
        self.assertListEqual(__a     ,		__a	)

        __UpperCAmelCase							=			tokens + [tokenizer.unk_token]
        __UpperCAmelCase							=			[0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__a	)     ,		__a	)





       def       snake_case__						(      self				:							Tuple	)							->	str:
        __UpperCAmelCase							=			self.get_tokenizer()

        self.assertListEqual(tokenizer.encode('''Hello world!'''     ,		add_special_tokens=__a	)     ,		[0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]	)
        self.assertListEqual(
            tokenizer.encode('''Hello world! cécé herlolip 418'''     ,		add_special_tokens=__a	)     ,		[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]     ,		)





       @slow
       def       snake_case__						(      self				:							List[Any]	)							->	Optional[Any]:
        __UpperCAmelCase							=			self.tokenizer_class.from_pretrained('''roberta-base'''	)

        __UpperCAmelCase							=			tokenizer.encode('''sequence builders'''     ,		add_special_tokens=__a	)
        __UpperCAmelCase							=			tokenizer.encode('''multi-sequence build'''     ,		add_special_tokens=__a	)

        __UpperCAmelCase							=			tokenizer.encode(
            '''sequence builders'''     ,		add_special_tokens=__a     ,		add_prefix_space=__a	)
        __UpperCAmelCase							=			tokenizer.encode(
            '''sequence builders'''     ,		'''multi-sequence build'''     ,		add_special_tokens=__a     ,		add_prefix_space=__a	)

        __UpperCAmelCase							=			tokenizer.build_inputs_with_special_tokens(__a	)
        __UpperCAmelCase							=			tokenizer.build_inputs_with_special_tokens(__a     ,		__a	)

        assert encoded_sentence == encoded_text_from_decode
        assert encoded_pair == encoded_pair_from_decode





       def       snake_case__						(      self				:							Union[str, Any]	)							->	Tuple:
        __UpperCAmelCase							=			self.get_tokenizer()

        __UpperCAmelCase							=			'''Encode this sequence.'''
        __UpperCAmelCase							=			tokenizer.byte_encoder[''' '''.encode('''utf-8'''	)[0]]

        # Testing encoder arguments
        __UpperCAmelCase							=			tokenizer.encode(__a     ,		add_special_tokens=__a     ,		add_prefix_space=__a	)
        __UpperCAmelCase							=			tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
        self.assertNotEqual(__a     ,		__a	)

        __UpperCAmelCase							=			tokenizer.encode(__a     ,		add_special_tokens=__a     ,		add_prefix_space=__a	)
        __UpperCAmelCase							=			tokenizer.convert_ids_to_tokens(encoded[0]	)[0]
        self.assertEqual(__a     ,		__a	)

        tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}	)
        __UpperCAmelCase							=			tokenizer.encode(__a     ,		add_special_tokens=__a	)
        __UpperCAmelCase							=			tokenizer.convert_ids_to_tokens(encoded[1]	)[0]
        self.assertNotEqual(__a     ,		__a	)

        # Testing spaces after special tokens
        __UpperCAmelCase							=			'''<mask>'''
        tokenizer.add_special_tokens(
            {'''mask_token''': AddedToken(__a     ,		lstrip=__a     ,		rstrip=__a	)}	)  # mask token has a left space
        __UpperCAmelCase							=			tokenizer.convert_tokens_to_ids(__a	)

        __UpperCAmelCase							=			'''Encode <mask> sequence'''
        __UpperCAmelCase							=			'''Encode <mask>sequence'''

        __UpperCAmelCase							=			tokenizer.encode(__a	)
        __UpperCAmelCase							=			encoded.index(__a	)
        __UpperCAmelCase							=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
        self.assertEqual(__a     ,		__a	)

        __UpperCAmelCase							=			tokenizer.encode(__a	)
        __UpperCAmelCase							=			encoded.index(__a	)
        __UpperCAmelCase							=			tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]	)[0]
        self.assertNotEqual(__a     ,		__a	)





       def       snake_case__						(      self				:							List[Any]	)							->	int:
        pass





       def       snake_case__						(      self				:							Optional[Any]	)							->	Union[str, Any]:




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(__a     ,		**__a	)
          __UpperCAmelCase							=			self.tokenizer_class.from_pretrained(__a     ,		**__a	)
          __UpperCAmelCase							=			'''A, <mask> AllenNLP sentence.'''
          __UpperCAmelCase							=			tokenizer_r.encode_plus(__a     ,		add_special_tokens=__a     ,		return_token_type_ids=__a	)
          __UpperCAmelCase							=			tokenizer_p.encode_plus(__a     ,		add_special_tokens=__a     ,		return_token_type_ids=__a	)

          # token_type_ids should put 0 everywhere
          self.assertEqual(sum(tokens_r['''token_type_ids''']	)     ,		sum(tokens_p['''token_type_ids''']	)	)

          # attention_mask should put 1 everywhere, so sum over length should be 1
          self.assertEqual(
              sum(tokens_r['''attention_mask''']	) / len(tokens_r['''attention_mask''']	)     ,		sum(tokens_p['''attention_mask''']	) / len(tokens_p['''attention_mask''']	)     ,		)

          __UpperCAmelCase							=			tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']	)
          __UpperCAmelCase							=			tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']	)

          # Rust correctly handles the space before the mask while python doesnt
          self.assertSequenceEqual(tokens_p['''input_ids''']     ,		[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]	)
          self.assertSequenceEqual(tokens_r['''input_ids''']     ,		[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]	)

          self.assertSequenceEqual(
              __a     ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']	)
          self.assertSequenceEqual(
              __a     ,		['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']	)





       def       snake_case__						(      self				:							Dict	)							->	Dict:




        for trim_offsets, add_prefix_space in itertools.product([True, False]     ,		repeat=2	):
         __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
             self.tmpdirname     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)

         __UpperCAmelCase							=			json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()	)
         __UpperCAmelCase							=			json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()	)

         self.assertEqual(pre_tokenizer_state['''add_prefix_space''']     ,		__a	)

         self.assertEqual(post_processor_state['''add_prefix_space''']     ,		__a	)
         self.assertEqual(post_processor_state['''trim_offsets''']     ,		__a	)






       def       snake_case__						(      self				:							Dict	)							->	Union[str, Any]:




        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
         with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""	):
          __UpperCAmelCase							=			'''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
          __UpperCAmelCase							=			f"""{text_of_1_token} {text_of_1_token}"""

          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
              __a     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)
          __UpperCAmelCase							=			tokenizer_r(__a     ,		return_offsets_mapping=__a     ,		add_special_tokens=__a	)
          self.assertEqual(encoding.offset_mapping[0]     ,		(0, len(__a	))	)
          self.assertEqual(
              encoding.offset_mapping[1]     ,		(len(__a	) + 1, len(__a	) + 1 + len(__a	))     ,		)

          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
              __a     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)
          __UpperCAmelCase							=			tokenizer_r(__a     ,		return_offsets_mapping=__a     ,		add_special_tokens=__a	)
          self.assertEqual(encoding.offset_mapping[0]     ,		(0, len(__a	))	)
          self.assertEqual(
              encoding.offset_mapping[1]     ,		(len(__a	) + 1, len(__a	) + 1 + len(__a	))     ,		)

          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
              __a     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)
          __UpperCAmelCase							=			tokenizer_r(__a     ,		return_offsets_mapping=__a     ,		add_special_tokens=__a	)
          self.assertEqual(encoding.offset_mapping[0]     ,		(0, len(__a	))	)
          self.assertEqual(
              encoding.offset_mapping[1]     ,		(len(__a	), len(__a	) + 1 + len(__a	))     ,		)

          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
              __a     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)
          __UpperCAmelCase							=			tokenizer_r(__a     ,		return_offsets_mapping=__a     ,		add_special_tokens=__a	)
          self.assertEqual(encoding.offset_mapping[0]     ,		(0, len(__a	))	)
          self.assertEqual(
              encoding.offset_mapping[1]     ,		(len(__a	), len(__a	) + 1 + len(__a	))     ,		)

          __UpperCAmelCase							=			f""" {text}"""

          # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
          #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
          # )
          # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
          # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
          # self.assertEqual(
          #     encoding.offset_mapping[1],
          #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
          # )

          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
              __a     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)
          __UpperCAmelCase							=			tokenizer_r(__a     ,		return_offsets_mapping=__a     ,		add_special_tokens=__a	)
          self.assertEqual(encoding.offset_mapping[0]     ,		(1, 1 + len(__a	))	)
          self.assertEqual(
              encoding.offset_mapping[1]     ,		(1 + len(__a	) + 1, 1 + len(__a	) + 1 + len(__a	))     ,		)

          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
              __a     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)
          __UpperCAmelCase							=			tokenizer_r(__a     ,		return_offsets_mapping=__a     ,		add_special_tokens=__a	)
          self.assertEqual(encoding.offset_mapping[0]     ,		(0, 1 + len(__a	))	)
          self.assertEqual(
              encoding.offset_mapping[1]     ,		(1 + len(__a	), 1 + len(__a	) + 1 + len(__a	))     ,		)

          __UpperCAmelCase							=			self.rust_tokenizer_class.from_pretrained(
              __a     ,		use_fast=__a     ,		add_prefix_space=__a     ,		trim_offsets=__a	)
          __UpperCAmelCase							=			tokenizer_r(__a     ,		return_offsets_mapping=__a     ,		add_special_tokens=__a	)
          self.assertEqual(encoding.offset_mapping[0]     ,		(0, 1 + len(__a	))	)
          self.assertEqual(
              encoding.offset_mapping[1]     ,		(1 + len(__a	), 1 + len(__a	) + 1 + len(__a	))     ,		)

'''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)
