
"""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()
_a														=		logging.get_logger(__name__)



def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		List[str]:


					"""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__		(			__snake_case,     __snake_case	)							->		Optional[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__		(			__snake_case,     __snake_case,     __snake_case	)							->		Union[str, Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  dct.pop(__snake_case	)
					_UpperCamelCase							 =  val



def       lowerCamelCase__		(			__snake_case	)							->		Optional[Any]:


					"""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(__snake_case,     stream=__snake_case	).raw	).convert('''RGB'''	)
					return im



@torch.no_grad()
def       lowerCamelCase__		(			__snake_case,     __snake_case	)							->		Optional[int]:


					"""simple docstring"""





					_UpperCamelCase							 =  ViTConfig(image_size=3_84,     qkv_bias=__snake_case	)
					_UpperCamelCase							 =  TrOCRConfig()

					# size of the architecture
					if "base" in checkpoint_url:
										_UpperCamelCase							 =  7_68
					elif "large" in checkpoint_url:
										# use ViT-large encoder
										_UpperCamelCase							 =  10_24
										_UpperCamelCase							 =  40_96
										_UpperCamelCase							 =  24
										_UpperCamelCase							 =  16
										_UpperCamelCase							 =  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:
										_UpperCamelCase							 =  False
										_UpperCamelCase							 =  '''relu'''
										_UpperCamelCase							 =  10_24
										_UpperCamelCase							 =  True
										_UpperCamelCase							 =  False
										_UpperCamelCase							 =  False

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

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

					_UpperCamelCase							 =  create_rename_keys(__snake_case,     __snake_case	)
					for src, dest in rename_keys:
										rename_key(__snake_case,     __snake_case,     __snake_case	)
					read_in_q_k_v(__snake_case,     __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():
										_UpperCamelCase							 =  state_dict.pop(__snake_case	)
										if key.startswith('''decoder'''	) and "output_projection" not in key:
															_UpperCamelCase							 =  val
										else:
															_UpperCamelCase							 =  val

    # load state dict
					model.load_state_dict(__snake_case	)

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

					_UpperCamelCase							 =  processor(images=prepare_img(__snake_case	),     return_tensors='''pt'''	).pixel_values

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

					_UpperCamelCase							 =  torch.Size([1, 1, 5_02_65]	)
					if "trocr-base-handwritten" in checkpoint_url:
										_UpperCamelCase							 =  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:
										_UpperCamelCase							 =  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:
										_UpperCamelCase							 =  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:
										_UpperCamelCase							 =  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],     __snake_case,     atol=1e-3	), "First elements of logits not as expected"

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


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)








"""simple docstring"""





from sklearn.metrics import matthews_corrcoef

import datasets


_a														=		"""
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""

_a														=		"""
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results['matthews_correlation'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results['matthews_correlation'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results['matthews_correlation'], 2))
        -0.25
"""

_a														=		"""\
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,     _KWARGS_DESCRIPTION	)
class     _UpperCAmelCase(    datasets.Metric	):



		def 						UpperCAmelCase							(					self)       ->       List[Any]:






							'''simple docstring'''
							return datasets.MetricInfo(
							    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
							        {
							            '''predictions''': datasets.Value('''int32'''),
							            '''references''': datasets.Value('''int32'''),
							        })				,		reference_urls=[
							        '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
							    ]				,		)




		def 						UpperCAmelCase							(					self				,		__a				,		__a				,		__a=None)       ->       Dict:






							'''simple docstring'''
							return {
							    "matthews_correlation": float(matthews_corrcoef(__a				,		__a				,		sample_weight=__a)),
							}






