



'''simple docstring'''

import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case_			=     logging.getLogger()






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Path    ,	_SCREAMING_SNAKE_CASE      :list       ):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''\n'''.join(_SCREAMING_SNAKE_CASE       )
				Path(_SCREAMING_SNAKE_CASE       ).open('''w'''       ).writelines(_SCREAMING_SNAKE_CASE       )


snake_case_			=     """patrickvonplaten/t5-tiny-random"""
snake_case_			=     """sshleifer/bart-tiny-random"""
snake_case_			=     """sshleifer/tiny-mbart"""

snake_case_			=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks




class 	a__     (				_lowercase	):


	def 				lowercase__  (self : str,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= Path(self.get_auto_remove_tmp_dir()      ) / '''utest_input.source'''
					SCREAMING_SNAKE_CASE  :						Dict      					= input_file_name.parent / '''utest_output.txt'''
					assert not output_file_name.exists()
					SCREAMING_SNAKE_CASE  :						List[str]      					= [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
					_dump_articles(__UpperCAmelCase,					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= str(Path(self.get_auto_remove_tmp_dir()      ) / '''scores.json'''      )
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					SCREAMING_SNAKE_CASE  :						Tuple      					= F'''
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            '''.split()

					with patch.object(__UpperCAmelCase,					'''argv''',					__UpperCAmelCase      ):
									run_generate()
									assert Path(__UpperCAmelCase      ).exists()
									# os.remove(Path(output_file_name))


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

					"""simple docstring"""

					self.run_eval_tester(__UpperCAmelCase      )


	@parameterized.expand([BART_TINY, MBART_TINY]      )
	@slow
	def 				lowercase__  (self : List[str],					__UpperCAmelCase : Dict      )     ->			str:

					"""simple docstring"""

					self.run_eval_tester(__UpperCAmelCase      )






	@parameterized.expand([T5_TINY, MBART_TINY]      )
	@slow
	def 				lowercase__  (self : Dict,					__UpperCAmelCase : List[Any]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= Path(self.get_auto_remove_tmp_dir()      ) / '''utest_input.source'''
					SCREAMING_SNAKE_CASE  :						int      					= input_file_name.parent / '''utest_output.txt'''
					assert not output_file_name.exists()

					SCREAMING_SNAKE_CASE  :						Tuple      					= {
					    '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
					    '''de''': [
					        '''Maschinelles Lernen ist großartig, oder?''',
					        '''Ich esse gerne Bananen''',
					        '''Morgen ist wieder ein toller Tag!''',
					    ],
					}

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= Path(self.get_auto_remove_tmp_dir()      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= str(tmp_dir / '''scores.json'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= str(tmp_dir / '''val.target'''      )
					_dump_articles(__UpperCAmelCase,					text['''en''']      )
					_dump_articles(__UpperCAmelCase,					text['''de''']      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
					SCREAMING_SNAKE_CASE  :						str      					= F'''
            run_eval_search.py
            {model}
            {str(__UpperCAmelCase      )}
            {str(__UpperCAmelCase      )}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            '''.split()
					testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0''']      )

					with patch.object(__UpperCAmelCase,					'''argv''',					__UpperCAmelCase      ):
									with CaptureStdout() as cs:
													run_search()
									SCREAMING_SNAKE_CASE  :						Any      					= [''' num_beams | length_penalty''', model, '''Best score args''']
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ['''Info''']
									if "translation" in task:
													expected_strings.append('''bleu'''      )
									else:
													expected_strings.extend(__UpperCAmelCase      )
									for w in expected_strings:
													assert w in cs.out
									for w in un_expected_strings:
													assert w not in cs.out
									assert Path(__UpperCAmelCase      ).exists()
									os.remove(Path(__UpperCAmelCase      )      )









'''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()
snake_case_			=     logging.get_logger(__name__)






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

								SCREAMING_SNAKE_CASE  :						Any      					= in_proj_weight[
								    : encoder_config.hidden_size, :
								]
								SCREAMING_SNAKE_CASE  :						List[Any]      					= in_proj_weight[
								    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
								]
								SCREAMING_SNAKE_CASE  :						Dict      					= in_proj_weight[
								    -encoder_config.hidden_size :, :
								]






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						List[str]      					= dct.pop(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= val






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






@torch.no_grad()
def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]       ):
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ViTConfig(image_size=3_84    ,	qkv_bias=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= TrOCRConfig()

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

				# load HuggingFace model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= ViTModel(_SCREAMING_SNAKE_CASE    ,	add_pooling_layer=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= TrOCRForCausalLM(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Any      					= VisionEncoderDecoderModel(encoder=_SCREAMING_SNAKE_CASE    ,	decoder=_SCREAMING_SNAKE_CASE       )
				model.eval()

				# load state_dict of original model, rename some keys
				SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE    ,	map_location='''cpu'''    ,	check_hash=_SCREAMING_SNAKE_CASE       )['''model''']

				SCREAMING_SNAKE_CASE  :						int      					= create_rename_keys(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				for src, dest in rename_keys:
								rename_key(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				read_in_q_k_v(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_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():
								SCREAMING_SNAKE_CASE  :						List[Any]      					= state_dict.pop(_SCREAMING_SNAKE_CASE       )
								if key.startswith('''decoder'''       ) and "output_projection" not in key:
												SCREAMING_SNAKE_CASE  :						Tuple      					= val
								else:
												SCREAMING_SNAKE_CASE  :						int      					= val

    # load state dict
				model.load_state_dict(_SCREAMING_SNAKE_CASE       )

				# Check outputs on an image
				SCREAMING_SNAKE_CASE  :						List[Any]      					= ViTImageProcessor(size=encoder_config.image_size       )
				SCREAMING_SNAKE_CASE  :						Tuple      					= RobertaTokenizer.from_pretrained('''roberta-large'''       )
				SCREAMING_SNAKE_CASE  :						Dict      					= TrOCRProcessor(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Tuple      					= processor(images=prepare_img(_SCREAMING_SNAKE_CASE       )    ,	return_tensors='''pt'''       ).pixel_values

				# verify logits
				SCREAMING_SNAKE_CASE  :						str      					= torch.tensor([[model.config.decoder.decoder_start_token_id]]       )
				SCREAMING_SNAKE_CASE  :						Dict      					= model(pixel_values=_SCREAMING_SNAKE_CASE    ,	decoder_input_ids=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						int      					= outputs.logits

				SCREAMING_SNAKE_CASE  :						Any      					= torch.Size([1, 1, 5_02_65]       )
				if "trocr-base-handwritten" in checkpoint_url:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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:
								SCREAMING_SNAKE_CASE  :						Any      					= 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:
								SCREAMING_SNAKE_CASE  :						str      					= 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:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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]    ,	_SCREAMING_SNAKE_CASE    ,	atol=1E-3       ), "First elements of logits not as expected"

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


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




