






"""simple docstring"""







import datasets

from .evaluate import evaluate


__a       :	Optional[int]             = """\
@article{hendrycks2021cuad,
      title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},
      author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},
      journal={arXiv preprint arXiv:2103.06268},
      year={2021}
}
"""

__a       :	int             = """
This metric wrap the official scoring script for version 1 of the Contract
Understanding Atticus Dataset (CUAD).
Contract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510
commercial legal contracts that have been manually labeled to identify 41 categories of important
clauses that lawyers look for when reviewing contracts in connection with corporate transactions.
"""

__a       :	str             = """
Computes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair as given in the references (see below)
        - 'prediction_text': list of possible texts for the answer, as a list of strings
        depending on a threshold on the confidence probability of each prediction.
    references: List of question-answers dictionaries with the following key-values:
        - 'id': id of the question-answer pair (see above),
        - 'answers': a Dict in the CUAD dataset format
            {
                'text': list of possible texts for the answer, as a list of strings
                'answer_start': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    'exact_match': Exact match (the normalized answer exactly match the gold answer)
    'f1': The F-score of predicted tokens versus the gold answer
    'aupr': Area Under the Precision-Recall curve
    'prec_at_80_recall': Precision at 80% recall
    'prec_at_90_recall': Precision at 90% recall
Examples:
    >>> predictions = [{'prediction_text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.'], 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> references = [{'answers': {'answer_start': [143, 49], 'text': ['The seller:', 'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.']}, 'id': 'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties'}]
    >>> cuad_metric = datasets.load_metric(\"cuad\")
    >>> results = cuad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 100.0, 'f1': 100.0, 'aupr': 0.0, 'prec_at_80_recall': 1.0, 'prec_at_90_recall': 1.0}
"""







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





							"""simple docstring"""





							def  lowercase		(   self:     List[str]    ):

												'''simple docstring'''

												return datasets.MetricInfo(
												    description=_DESCRIPTION				,		citation=_CITATION				,		inputs_description=_KWARGS_DESCRIPTION				,		features=datasets.Features(
												        {
												            '''predictions''': {
												                '''id''': datasets.Value('''string'''    ),
												                '''prediction_text''': datasets.features.Sequence(datasets.Value('''string'''    )    ),
												            },
												            '''references''': {
												                '''id''': datasets.Value('''string'''    ),
												                '''answers''': datasets.features.Sequence(
												                    {
												                        '''text''': datasets.Value('''string'''    ),
												                        '''answer_start''': datasets.Value('''int32'''    ),
												                    }    ),
												            },
												        }    )				,		codebase_urls=['''https://www.atticusprojectai.org/cuad''']				,		reference_urls=['''https://www.atticusprojectai.org/cuad''']				,		)





							def  lowercase		(   self:     Union[str, Any]				,		__A:     Any				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  {prediction["id"]: prediction["prediction_text"] for prediction in predictions}
												a__          =  [
												    {
												        "paragraphs": [
												            {
												                "qas": [
												                    {
												                        "answers": [{"text": answer_text} for answer_text in ref["answers"]["text"]],
												                        "id": ref["id"],
												                    }
												                    for ref in references
												                ]
												            }
												        ]
												    }
												]
												a__          =  evaluate(dataset=__A				,		predictions=__A    )
												return score









"""simple docstring"""







import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a       :	Optional[Any]             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										a__          =  [144, 192, 240]
										a__          =  [16, 32, 64, 96, 128, 160, 640]
					elif "mobilevit_xs" in mobilevit_name:
										a__          =  [96, 120, 144]
										a__          =  [16, 32, 48, 64, 80, 96, 384]
					elif "mobilevit_xxs" in mobilevit_name:
										a__          =  [64, 80, 96]
										a__          =  [16, 16, 24, 48, 64, 80, 320]
										a__          =  0.05
										a__          =  2.0

					if mobilevit_name.startswith('''deeplabv3_'''):
										a__          =  512
										a__          =  16
										a__          =  21
										a__          =  '''pascal-voc-id2label.json'''
					else:
										a__          =  1000
										a__          =  '''imagenet-1k-id2label.json'''

					a__          =  '''huggingface/label-files'''
					a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
					a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
					a__          =  idalabel
					a__          =  {v: k for k, v in idalabel.items()}

					return config





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					for i in range(1 ,     6):
										if f'layer_{i}.' in name:
															a__          =  name.replace(f'layer_{i}.' ,     f'encoder.layer.{i - 1}.')

					if "conv_1." in name:
										a__          =  name.replace('''conv_1.''' ,     '''conv_stem.''')
					if ".block." in name:
										a__          =  name.replace('''.block.''' ,     '''.''')

					if "exp_1x1" in name:
										a__          =  name.replace('''exp_1x1''' ,     '''expand_1x1''')
					if "red_1x1" in name:
										a__          =  name.replace('''red_1x1''' ,     '''reduce_1x1''')
					if ".local_rep.conv_3x3." in name:
										a__          =  name.replace('''.local_rep.conv_3x3.''' ,     '''.conv_kxk.''')
					if ".local_rep.conv_1x1." in name:
										a__          =  name.replace('''.local_rep.conv_1x1.''' ,     '''.conv_1x1.''')
					if ".norm." in name:
										a__          =  name.replace('''.norm.''' ,     '''.normalization.''')
					if ".conv." in name:
										a__          =  name.replace('''.conv.''' ,     '''.convolution.''')
					if ".conv_proj." in name:
										a__          =  name.replace('''.conv_proj.''' ,     '''.conv_projection.''')

					for i in range(0 ,     2):
										for j in range(0 ,     4):
															if f'.{i}.{j}.' in name:
																				a__          =  name.replace(f'.{i}.{j}.' ,     f'.{i}.layer.{j}.')

					for i in range(2 ,     6):
										for j in range(0 ,     4):
															if f'.{i}.{j}.' in name:
																				a__          =  name.replace(f'.{i}.{j}.' ,     f'.{i}.')
																				if "expand_1x1" in name:
																									a__          =  name.replace('''expand_1x1''' ,     '''downsampling_layer.expand_1x1''')
																				if "conv_3x3" in name:
																									a__          =  name.replace('''conv_3x3''' ,     '''downsampling_layer.conv_3x3''')
																				if "reduce_1x1" in name:
																									a__          =  name.replace('''reduce_1x1''' ,     '''downsampling_layer.reduce_1x1''')

					for i in range(2 ,     5):
										if f'.global_rep.{i}.weight' in name:
															a__          =  name.replace(f'.global_rep.{i}.weight' ,     '''.layernorm.weight''')
										if f'.global_rep.{i}.bias' in name:
															a__          =  name.replace(f'.global_rep.{i}.bias' ,     '''.layernorm.bias''')

					if ".global_rep." in name:
										a__          =  name.replace('''.global_rep.''' ,     '''.transformer.''')
					if ".pre_norm_mha.0." in name:
										a__          =  name.replace('''.pre_norm_mha.0.''' ,     '''.layernorm_before.''')
					if ".pre_norm_mha.1.out_proj." in name:
										a__          =  name.replace('''.pre_norm_mha.1.out_proj.''' ,     '''.attention.output.dense.''')
					if ".pre_norm_ffn.0." in name:
										a__          =  name.replace('''.pre_norm_ffn.0.''' ,     '''.layernorm_after.''')
					if ".pre_norm_ffn.1." in name:
										a__          =  name.replace('''.pre_norm_ffn.1.''' ,     '''.intermediate.dense.''')
					if ".pre_norm_ffn.4." in name:
										a__          =  name.replace('''.pre_norm_ffn.4.''' ,     '''.output.dense.''')
					if ".transformer." in name:
										a__          =  name.replace('''.transformer.''' ,     '''.transformer.layer.''')

					if ".aspp_layer." in name:
										a__          =  name.replace('''.aspp_layer.''' ,     '''.''')
					if ".aspp_pool." in name:
										a__          =  name.replace('''.aspp_pool.''' ,     '''.''')
					if "seg_head." in name:
										a__          =  name.replace('''seg_head.''' ,     '''segmentation_head.''')
					if "segmentation_head.classifier.classifier." in name:
										a__          =  name.replace('''segmentation_head.classifier.classifier.''' ,     '''segmentation_head.classifier.''')

					if "classifier.fc." in name:
										a__          =  name.replace('''classifier.fc.''' ,     '''classifier.''')
					elif (not base_model) and ("segmentation_head." not in name):
										a__          =  '''mobilevit.''' + name

					return name





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					if base_model:
										a__          =  ''''''
					else:
										a__          =  '''mobilevit.'''

					for key in orig_state_dict.copy().keys():
										a__          =  orig_state_dict.pop(lowerCamelCase_)

										if key[:8] == "encoder.":
															a__          =  key[8:]

										if "qkv" in key:
															a__          =  key.split('''.''')
															a__          =  int(key_split[0][6:]) - 1
															a__          =  int(key_split[3])
															a__          =  model.get_submodule(f'{model_prefix}encoder.layer.{layer_num}')
															a__          =  layer.transformer.layer[transformer_num].attention.attention.all_head_size
															a__          =  (
															    f'{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention.'
															)
															if "weight" in key:
																				a__          =  val[:dim, :]
																				a__          =  val[dim : dim * 2, :]
																				a__          =  val[-dim:, :]
															else:
																				a__          =  val[:dim]
																				a__          =  val[dim : dim * 2]
																				a__          =  val[-dim:]
										else:
															a__          =  val

					return orig_state_dict





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  get_mobilevit_config(lowerCamelCase_)

					# load original state_dict
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')

					# load 🤗 model
					if mobilevit_name.startswith('''deeplabv3_'''):
										a__          =  MobileViTForSemanticSegmentation(lowerCamelCase_).eval()
					else:
										a__          =  MobileViTForImageClassification(lowerCamelCase_).eval()

					a__          =  convert_state_dict(lowerCamelCase_ ,     lowerCamelCase_)
					model.load_state_dict(lowerCamelCase_)

					# Check outputs on an image, prepared by MobileViTImageProcessor
					a__          =  MobileViTImageProcessor(crop_size=config.image_size ,     size=config.image_size + 32)
					a__          =  image_processor(images=prepare_img() ,     return_tensors='''pt''')
					a__          =  model(**lowerCamelCase_)
					a__          =  outputs.logits

					if mobilevit_name.startswith('''deeplabv3_'''):
										assert logits.shape == (1, 21, 32, 32)

										if mobilevit_name == "deeplabv3_mobilevit_s":
															a__          =  torch.tensor(
															    [
															        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
															        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
															        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															a__          =  torch.tensor(
															    [
															        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
															        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
															        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															a__          =  torch.tensor(
															    [
															        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
															        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
															        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
															    ])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

										assert torch.allclose(logits[0, :3, :3, :3] ,     lowerCamelCase_ ,     atol=1E-4)
					else:
										assert logits.shape == (1, 1000)

										if mobilevit_name == "mobilevit_s":
															a__          =  torch.tensor([-0.9866, 0.2392, -1.1241])
										elif mobilevit_name == "mobilevit_xs":
															a__          =  torch.tensor([-2.4761, -0.9399, -1.9587])
										elif mobilevit_name == "mobilevit_xxs":
															a__          =  torch.tensor([-1.9364, -1.2327, -0.4653])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

										assert torch.allclose(logits[0, :3] ,     lowerCamelCase_ ,     atol=1E-4)

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model {mobilevit_name} to {pytorch_dump_folder_path}')
					model.save_pretrained(lowerCamelCase_)
					print(f'Saving image processor to {pytorch_dump_folder_path}')
					image_processor.save_pretrained(lowerCamelCase_)

					if push_to_hub:
										a__          =  {
										    '''mobilevit_s''': '''mobilevit-small''',
										    '''mobilevit_xs''': '''mobilevit-x-small''',
										    '''mobilevit_xxs''': '''mobilevit-xx-small''',
										    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
										    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
										    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
										}

										print('''Pushing to the hub...''')
										a__          =  model_mapping[mobilevit_name]
										image_processor.push_to_hub(lowerCamelCase_ ,     organization='''apple''')
										model.push_to_hub(lowerCamelCase_ ,     organization='''apple''')


if __name__ == "__main__":
	__a       :	Optional[int]             = argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--mobilevit_name',
	    default='mobilevit_s',
	    type=str,
	    help=(
	        'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
	        ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
	    ),
	)
	parser.add_argument(
	    '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt file).'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	__a       :	List[str]             = parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


