"""simple docstring"""






import argparse
import os

import align
import numpy as np
import requests
import tensorflow as tf
import torch
from PIL import Image
from tokenizer import Tokenizer

from transformers import (
    AlignConfig,
    AlignModel,
    AlignProcessor,
    BertConfig,
    BertTokenizer,
    EfficientNetConfig,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__  :  Tuple											=     logging.get_logger(__name__)



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							tf.image.resize(lowerCamelCase ,      (3_4_6, 3_4_6)	)
				UpperCAmelCase__					=							tf.image.crop_to_bounding_box(lowerCamelCase ,      (3_4_6 - 2_8_9) // 2 ,      (3_4_6 - 2_8_9) // 2 ,      2_8_9 ,      2_8_9	)
				return image



def   a_						( ):
				UpperCAmelCase__					=							EfficientNetConfig.from_pretrained('google/efficientnet-b7'	)
				UpperCAmelCase__					=							2_8_9
				UpperCAmelCase__					=							6_4_0
				UpperCAmelCase__					=							{'0': 'LABEL_0', '1': 'LABEL_1'}
				UpperCAmelCase__					=							{'LABEL_0': 0, 'LABEL_1': 1}
				UpperCAmelCase__					=							[]

				UpperCAmelCase__					=							BertConfig()
				UpperCAmelCase__					=							AlignConfig.from_text_vision_configs(
				    text_config=lowerCamelCase ,      vision_config=lowerCamelCase ,      projection_dim=6_4_0	)
				return config



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



def   a_						( ):
				UpperCAmelCase__					=							EfficientNetImageProcessor(
				    do_center_crop=lowerCamelCase ,      rescale_factor=1 / 127.5 ,      rescale_offset=lowerCamelCase ,      do_normalize=lowerCamelCase ,      include_top=lowerCamelCase ,      resample=Image.BILINEAR ,      )
				UpperCAmelCase__					=							BertTokenizer.from_pretrained('bert-base-uncased'	)
				UpperCAmelCase__					=							6_4
				UpperCAmelCase__					=							AlignProcessor(image_processor=lowerCamelCase ,      tokenizer=lowerCamelCase	)
				return processor



def   a_						( lowerCamelCase	):
				# EfficientNet image encoder
				UpperCAmelCase__					=							[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
				UpperCAmelCase__					=							list(set(lowerCamelCase	)	)
				UpperCAmelCase__					=							sorted(lowerCamelCase	)
				UpperCAmelCase__					=							len(lowerCamelCase	)
				UpperCAmelCase__					=							{b: str(lowerCamelCase	) for b, i in zip(lowerCamelCase ,      range(lowerCamelCase	)	)}

				UpperCAmelCase__					=							[]
				rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
				rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
				rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
				rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
				rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

				for b in block_names:
								UpperCAmelCase__					=							block_name_mapping[b]
								rename_keys.append((f'''block{b}_expand_conv/kernel:0''', f'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')	)
								rename_keys.append((f'''block{b}_expand_bn/gamma:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')	)
								rename_keys.append((f'''block{b}_expand_bn/beta:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')	)
								rename_keys.append(
								    (f'''block{b}_expand_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')	)
								rename_keys.append(
								    (f'''block{b}_expand_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')	)
								rename_keys.append(
								    (f'''block{b}_dwconv/depthwise_kernel:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')	)
								rename_keys.append((f'''block{b}_bn/gamma:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')	)
								rename_keys.append((f'''block{b}_bn/beta:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')	)
								rename_keys.append(
								    (f'''block{b}_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')	)
								rename_keys.append(
								    (f'''block{b}_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')	)

								rename_keys.append((f'''block{b}_se_reduce/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')	)
								rename_keys.append((f'''block{b}_se_reduce/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')	)
								rename_keys.append((f'''block{b}_se_expand/kernel:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')	)
								rename_keys.append((f'''block{b}_se_expand/bias:0''', f'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')	)
								rename_keys.append(
								    (f'''block{b}_project_conv/kernel:0''', f'''encoder.blocks.{hf_b}.projection.project_conv.weight''')	)
								rename_keys.append((f'''block{b}_project_bn/gamma:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.weight''')	)
								rename_keys.append((f'''block{b}_project_bn/beta:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.bias''')	)
								rename_keys.append(
								    (f'''block{b}_project_bn/moving_mean:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')	)
								rename_keys.append(
								    (f'''block{b}_project_bn/moving_variance:0''', f'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')	)

				UpperCAmelCase__					=							{}
				for item in rename_keys:
								if item[0] in original_param_names:
												UpperCAmelCase__					=							'vision_model.' + item[1]

    # BERT text encoder
				UpperCAmelCase__					=							[]
				UpperCAmelCase__					=							'tf_bert_model/bert'
				UpperCAmelCase__					=							'text_model'
				for i in range(1_2	):
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/query/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.query.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/query/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.query.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/key/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.key.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/key/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.key.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/value/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.value.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/self/value/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.self.value.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/dense/kernel:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.dense.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/dense/bias:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.dense.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/gamma:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.LayerNorm.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/attention/output/LayerNorm/beta:0''',
								        f'''{new}.encoder.layer.{i}.attention.output.LayerNorm.bias''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/intermediate/dense/kernel:0''',
								        f'''{new}.encoder.layer.{i}.intermediate.dense.weight''',
								    )	)
								rename_keys.append(
								    (
								        f'''{old}/encoder/layer_._{i}/intermediate/dense/bias:0''',
								        f'''{new}.encoder.layer.{i}.intermediate.dense.bias''',
								    )	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/dense/kernel:0''', f'''{new}.encoder.layer.{i}.output.dense.weight''')	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/dense/bias:0''', f'''{new}.encoder.layer.{i}.output.dense.bias''')	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/LayerNorm/gamma:0''', f'''{new}.encoder.layer.{i}.output.LayerNorm.weight''')	)
								rename_keys.append(
								    (f'''{old}/encoder/layer_._{i}/output/LayerNorm/beta:0''', f'''{new}.encoder.layer.{i}.output.LayerNorm.bias''')	)

				rename_keys.append((f'''{old}/embeddings/word_embeddings/weight:0''', f'''{new}.embeddings.word_embeddings.weight''')	)
				rename_keys.append(
				    (f'''{old}/embeddings/position_embeddings/embeddings:0''', f'''{new}.embeddings.position_embeddings.weight''')	)
				rename_keys.append(
				    (f'''{old}/embeddings/token_type_embeddings/embeddings:0''', f'''{new}.embeddings.token_type_embeddings.weight''')	)
				rename_keys.append((f'''{old}/embeddings/LayerNorm/gamma:0''', f'''{new}.embeddings.LayerNorm.weight''')	)
				rename_keys.append((f'''{old}/embeddings/LayerNorm/beta:0''', f'''{new}.embeddings.LayerNorm.bias''')	)

				rename_keys.append((f'''{old}/pooler/dense/kernel:0''', f'''{new}.pooler.dense.weight''')	)
				rename_keys.append((f'''{old}/pooler/dense/bias:0''', f'''{new}.pooler.dense.bias''')	)
				rename_keys.append(('dense/kernel:0', 'text_projection.weight')	)
				rename_keys.append(('dense/bias:0', 'text_projection.bias')	)
				rename_keys.append(('dense/bias:0', 'text_projection.bias')	)
				rename_keys.append(('temperature:0', 'temperature')	)

				for item in rename_keys:
								if item[0] in original_param_names:
												UpperCAmelCase__					=							item[1]
				return key_mapping



def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				list(hf_params.keys()	)

				for key, value in tf_params.items():
								if key not in key_mapping:
												continue

								UpperCAmelCase__					=							key_mapping[key]
								if "_conv" in key and "kernel" in key:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	).permute(3 ,      2 ,      0 ,      1	)
								elif "embeddings" in key:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	)
								elif "depthwise_kernel" in key:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	).permute(2 ,      3 ,      0 ,      1	)
								elif "kernel" in key:
												UpperCAmelCase__					=							torch.from_numpy(np.transpose(lowerCamelCase	)	)
								elif "temperature" in key:
												UpperCAmelCase__					=							value
								elif "bn/gamma" or "bn/beta" in key:
												UpperCAmelCase__					=							torch.from_numpy(np.transpose(lowerCamelCase	)	).squeeze()
								else:
												UpperCAmelCase__					=							torch.from_numpy(lowerCamelCase	)

								# Replace HF parameters with original TF model parameters
								hf_params[hf_key].copy_(lowerCamelCase	)



@torch.no_grad()
def   a_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				UpperCAmelCase__					=							6_4
				UpperCAmelCase__					=							Tokenizer(lowerCamelCase	)
				UpperCAmelCase__					=							align.Align('efficientnet-b7' ,      'bert-base' ,      6_4_0 ,      lowerCamelCase ,      tok.get_vocab_size()	)
				original_model.compile()
				original_model.load_weights(lowerCamelCase	)

				UpperCAmelCase__					=							original_model.trainable_variables
				UpperCAmelCase__					=							original_model.non_trainable_variables
				UpperCAmelCase__					=							{param.name: param.numpy() for param in tf_params}
				for param in tf_non_train_params:
								UpperCAmelCase__					=							param.numpy()
				UpperCAmelCase__					=							list(tf_params.keys()	)

				# Load HuggingFace model
				UpperCAmelCase__					=							get_align_config()
				UpperCAmelCase__					=							AlignModel(lowerCamelCase	).eval()
				UpperCAmelCase__					=							hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print('Converting parameters...'	)
				UpperCAmelCase__					=							rename_keys(lowerCamelCase	)
				replace_params(lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	)

				# Initialize processor
				UpperCAmelCase__					=							get_processor()
				UpperCAmelCase__					=							processor(
				    images=prepare_img() ,      text='A picture of a cat' ,      padding='max_length' ,      max_length=6_4 ,      return_tensors='pt'	)

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								UpperCAmelCase__					=							hf_model(**lowerCamelCase	)

				UpperCAmelCase__					=							outputs.image_embeds.detach().numpy()
				UpperCAmelCase__					=							outputs.text_embeds.detach().numpy()

				# Original model inference
				UpperCAmelCase__					=							False
				UpperCAmelCase__					=							EfficientNetImageProcessor(
				    do_center_crop=lowerCamelCase ,      do_rescale=lowerCamelCase ,      do_normalize=lowerCamelCase ,      include_top=lowerCamelCase ,      resample=Image.BILINEAR ,      )
				UpperCAmelCase__					=							tf_image_processor(images=prepare_img() ,      return_tensors='tf' ,      data_format='channels_last'	)['pixel_values']
				UpperCAmelCase__					=							tok(tf.constant(['A picture of a cat']	)	)

				UpperCAmelCase__					=							original_model.image_encoder(lowerCamelCase ,      training=lowerCamelCase	)
				UpperCAmelCase__					=							original_model.text_encoder(lowerCamelCase ,      training=lowerCamelCase	)

				UpperCAmelCase__					=							tf.nn.la_normalize(lowerCamelCase ,      axis=-1	)
				UpperCAmelCase__					=							tf.nn.la_normalize(lowerCamelCase ,      axis=-1	)

				# Check whether original and HF model outputs match  -> np.allclose
				if not np.allclose(lowerCamelCase ,      lowerCamelCase ,      atol=1e-3	):
								raise ValueError('The predicted image features are not the same.'	)
				if not np.allclose(lowerCamelCase ,      lowerCamelCase ,      atol=1e-3	):
								raise ValueError('The predicted text features are not the same.'	)
				print('Model outputs match!'	)

				if save_model:
								# Create folder to save model
								if not os.path.isdir(lowerCamelCase	):
												os.mkdir(lowerCamelCase	)
								# Save converted model and image processor
								hf_model.save_pretrained(lowerCamelCase	)
								processor.save_pretrained(lowerCamelCase	)

				if push_to_hub:
								# Push model and image processor to hub
								print('Pushing converted ALIGN to the hub...'	)
								processor.push_to_hub('align-base'	)
								hf_model.push_to_hub('align-base'	)


if __name__ == "__main__":
						lowerCAmelCase__  :  List[Any]											=     argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_path',
						    default='./weights/model-weights',
						    type=str,
						    help='Path to the pretrained TF ALIGN checkpoint.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path',
						    default='hf_model',
						    type=str,
						    help='Path to the output PyTorch model directory.',
						)
						parser.add_argument('--save_model', action='store_true', help='Save model to local')
						parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

						lowerCAmelCase__  :  Dict											=     parser.parse_args()
						convert_align_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




"""simple docstring"""






import argparse
import os
import sys
import urllib.request
import zipfile


lowerCAmelCase__  :  Optional[Any]											=     ['CoLA', 'SST', 'MRPC', 'QQP', 'STS', 'MNLI', 'SNLI', 'QNLI', 'RTE', 'WNLI', 'diagnostic']
lowerCAmelCase__  :  Optional[Any]											=     {
    'CoLA': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FCoLA.zip?alt=media&token=46d5e637-3411-4188-bc44-5809b5bfb5f4',
    'SST': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSST-2.zip?alt=media&token=aabc5f6b-e466-44a2-b9b4-cf6337f84ac8',
    'MRPC': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2Fmrpc_dev_ids.tsv?alt=media&token=ec5c0836-31d5-48f4-b431-7480817f1adc',
    'QQP': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQQP.zip?alt=media&token=700c6acf-160d-4d89-81d1-de4191d02cb5',
    'STS': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSTS-B.zip?alt=media&token=bddb94a7-8706-4e0d-a694-1109e12273b5',
    'MNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FMNLI.zip?alt=media&token=50329ea1-e339-40e2-809c-10c40afff3ce',
    'SNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FSNLI.zip?alt=media&token=4afcfbb2-ff0c-4b2d-a09a-dbf07926f4df',
    'QNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FQNLIv2.zip?alt=media&token=6fdcf570-0fc5-4631-8456-9505272d1601',
    'RTE': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FRTE.zip?alt=media&token=5efa7e85-a0bb-4f19-8ea2-9e1840f077fb',
    'WNLI': 'https://firebasestorage.googleapis.com/v0/b/mtl-sentence-representations.appspot.com/o/data%2FWNLI.zip?alt=media&token=068ad0a0-ded7-4bd7-99a5-5e00222e0faf',
    'diagnostic': 'https://storage.googleapis.com/mtl-sentence-representations.appspot.com/tsvsWithoutLabels%2FAX.tsv?GoogleAccessId=firebase-adminsdk-0khhl@mtl-sentence-representations.iam.gserviceaccount.com&Expires=2498860800&Signature=DuQ2CSPt2Yfre0C%2BiISrVYrIFaZH1Lc7hBVZDD4ZyR7fZYOMNOUGpi8QxBmTNOrNPjR3z1cggo7WXFfrgECP6FBJSsURv8Ybrue8Ypt%2FTPxbuJ0Xc2FhDi%2BarnecCBFO77RSbfuz%2Bs95hRrYhTnByqu3U%2FYZPaj3tZt5QdfpH2IUROY8LiBXoXS46LE%2FgOQc%2FKN%2BA9SoscRDYsnxHfG0IjXGwHN%2Bf88q6hOmAxeNPx6moDulUF6XMUAaXCSFU%2BnRO2RDL9CapWxj%2BDl7syNyHhB7987hZ80B%2FwFkQ3MEs8auvt5XW1%2Bd4aCU7ytgM69r8JDCwibfhZxpaa4gd50QXQ%3D%3D',
}

lowerCAmelCase__  :  Dict											=     'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'
lowerCAmelCase__  :  List[str]											=     'https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				print(f'''Downloading and extracting {task}...'''	)
				UpperCAmelCase__					=							f'''{task}.zip'''
				urllib.request.urlretrieve(TASKaPATH[task] ,      lowerCamelCase	)
				with zipfile.ZipFile(lowerCamelCase	) as zip_ref:
								zip_ref.extractall(lowerCamelCase	)
				os.remove(lowerCamelCase	)
				print('\tCompleted!'	)



def   a_						( lowerCamelCase ,      lowerCamelCase	):
				print('Processing MRPC...'	)
				UpperCAmelCase__					=							os.path.join(lowerCamelCase ,      'MRPC'	)
				if not os.path.isdir(lowerCamelCase	):
								os.mkdir(lowerCamelCase	)
				if path_to_data:
								UpperCAmelCase__					=							os.path.join(lowerCamelCase ,      'msr_paraphrase_train.txt'	)
								UpperCAmelCase__					=							os.path.join(lowerCamelCase ,      'msr_paraphrase_test.txt'	)
				else:
								print('Local MRPC data not specified, downloading data from %s' % MRPC_TRAIN	)
								UpperCAmelCase__					=							os.path.join(lowerCamelCase ,      'msr_paraphrase_train.txt'	)
								UpperCAmelCase__					=							os.path.join(lowerCamelCase ,      'msr_paraphrase_test.txt'	)
								urllib.request.urlretrieve(lowerCamelCase ,      lowerCamelCase	)
								urllib.request.urlretrieve(lowerCamelCase ,      lowerCamelCase	)
				if not os.path.isfile(lowerCamelCase	):
								raise ValueError(f'''Train data not found at {mrpc_train_file}'''	)
				if not os.path.isfile(lowerCamelCase	):
								raise ValueError(f'''Test data not found at {mrpc_test_file}'''	)
				urllib.request.urlretrieve(TASKaPATH['MRPC'] ,      os.path.join(lowerCamelCase ,      'dev_ids.tsv'	)	)

				UpperCAmelCase__					=							[]
				with open(os.path.join(lowerCamelCase ,      'dev_ids.tsv'	) ,      encoding='utf8'	) as ids_fh:
								for row in ids_fh:
												dev_ids.append(row.strip().split('\t'	)	)

				with open(lowerCamelCase ,      encoding='utf8'	) as data_fh, open(
				    os.path.join(lowerCamelCase ,      'train.tsv'	) ,      'w' ,      encoding='utf8'	) as train_fh, open(os.path.join(lowerCamelCase ,      'dev.tsv'	) ,      'w' ,      encoding='utf8'	) as dev_fh:
								UpperCAmelCase__					=							data_fh.readline()
								train_fh.write(lowerCamelCase	)
								dev_fh.write(lowerCamelCase	)
								for row in data_fh:
												UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							row.strip().split('\t'	)
												if [ida, ida] in dev_ids:
																dev_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)	)
												else:
																train_fh.write('%s\t%s\t%s\t%s\t%s\n' % (label, ida, ida, sa, sa)	)

				with open(lowerCamelCase ,      encoding='utf8'	) as data_fh, open(
				    os.path.join(lowerCamelCase ,      'test.tsv'	) ,      'w' ,      encoding='utf8'	) as test_fh:
								UpperCAmelCase__					=							data_fh.readline()
								test_fh.write('index\t#1 ID\t#2 ID\t#1 String\t#2 String\n'	)
								for idx, row in enumerate(lowerCamelCase	):
												UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__				,     UpperCAmelCase__					=							row.strip().split('\t'	)
												test_fh.write('%d\t%s\t%s\t%s\t%s\n' % (idx, ida, ida, sa, sa)	)
				print('\tCompleted!'	)



def   a_						( lowerCamelCase	):
				print('Downloading and extracting diagnostic...'	)
				if not os.path.isdir(os.path.join(lowerCamelCase ,      'diagnostic'	)	):
								os.mkdir(os.path.join(lowerCamelCase ,      'diagnostic'	)	)
				UpperCAmelCase__					=							os.path.join(lowerCamelCase ,      'diagnostic' ,      'diagnostic.tsv'	)
				urllib.request.urlretrieve(TASKaPATH['diagnostic'] ,      lowerCamelCase	)
				print('\tCompleted!'	)
				return



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							task_names.split(','	)
				if "all" in task_names:
								UpperCAmelCase__					=							TASKS
				else:
								UpperCAmelCase__					=							[]
								for task_name in task_names:
												if task_name not in TASKS:
																raise ValueError(f'''Task {task_name} not found!'''	)
												tasks.append(lowerCamelCase	)
				return tasks



def   a_						( lowerCamelCase	):
				UpperCAmelCase__					=							argparse.ArgumentParser()
				parser.add_argument('--data_dir' ,      help='directory to save data to' ,      type=lowerCamelCase ,      default='glue_data'	)
				parser.add_argument(
				    '--tasks' ,      help='tasks to download data for as a comma separated string' ,      type=lowerCamelCase ,      default='all'	)
				parser.add_argument(
				    '--path_to_mrpc' ,      help='path to directory containing extracted MRPC data, msr_paraphrase_train.txt and msr_paraphrase_text.txt' ,      type=lowerCamelCase ,      default='' ,      )
				UpperCAmelCase__					=							parser.parse_args(lowerCamelCase	)

				if not os.path.isdir(args.data_dir	):
								os.mkdir(args.data_dir	)
				UpperCAmelCase__					=							get_tasks(args.tasks	)

				for task in tasks:
								if task == "MRPC":
												format_mrpc(args.data_dir ,      args.path_to_mrpc	)
								elif task == "diagnostic":
												download_diagnostic(args.data_dir	)
								else:
												download_and_extract(lowerCamelCase ,      args.data_dir	)


if __name__ == "__main__":
						sys.exit(main(sys.argv[1:]))



