



'''simple docstring'''




import collections
import inspect
import unittest

from transformers import DonutSwinConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import DonutSwinModel
						from transformers.models.donut.modeling_donut_swin import DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Any							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Dict=1_3							,					lowerCAmelCase__					:			str=3_2							,					lowerCAmelCase__					:			Union[str, Any]=2							,					lowerCAmelCase__					:			Tuple=3							,					lowerCAmelCase__					:			Optional[int]=1_6							,					lowerCAmelCase__					:			List[Any]=[1, 2, 1]							,					lowerCAmelCase__					:			List[str]=[2, 2, 4]							,					lowerCAmelCase__					:			Union[str, Any]=2							,					lowerCAmelCase__					:			Optional[Any]=2.0							,					lowerCAmelCase__					:			int=True							,					lowerCAmelCase__					:			Union[str, Any]=0.0							,					lowerCAmelCase__					:			Optional[Any]=0.0							,					lowerCAmelCase__					:			Optional[int]=0.1							,					lowerCAmelCase__					:			List[Any]="gelu"							,					lowerCAmelCase__					:			List[str]=False							,					lowerCAmelCase__					:			Union[str, Any]=True							,					lowerCAmelCase__					:			Optional[int]=0.02							,					lowerCAmelCase__					:			Optional[Any]=1E-5							,					lowerCAmelCase__					:			int=True							,					lowerCAmelCase__					:			List[str]=None							,					lowerCAmelCase__					:			List[str]=True							,					lowerCAmelCase__					:			List[Any]=1_0							,					lowerCAmelCase__					:			Optional[Any]=8							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		parent
					__SCREAMING_SNAKE_CASE	:		Any   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		image_size
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		patch_size
					__SCREAMING_SNAKE_CASE	:		Any   	=		num_channels
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		embed_dim
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		depths
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		num_heads
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		window_size
					__SCREAMING_SNAKE_CASE	:		int   	=		mlp_ratio
					__SCREAMING_SNAKE_CASE	:		Dict   	=		qkv_bias
					__SCREAMING_SNAKE_CASE	:		Any   	=		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE	:		Dict   	=		drop_path_rate
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		hidden_act
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		use_absolute_embeddings
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		patch_norm
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		layer_norm_eps
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		Dict   	=		is_training
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		scope
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		type_sequence_label_size
					__SCREAMING_SNAKE_CASE	:		int   	=		encoder_stride

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

					__SCREAMING_SNAKE_CASE	:		str   	=		None
					if self.use_labels:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		ids_tensor([self.batch_size]							,					self.type_sequence_label_size						)

					__SCREAMING_SNAKE_CASE	:		str   	=		self.get_config()

					return config, pixel_values, labels

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					return DonutSwinConfig(
					    image_size=self.image_size							,					patch_size=self.patch_size							,					num_channels=self.num_channels							,					embed_dim=self.embed_dim							,					depths=self.depths							,					num_heads=self.num_heads							,					window_size=self.window_size							,					mlp_ratio=self.mlp_ratio							,					qkv_bias=self.qkv_bias							,					hidden_dropout_prob=self.hidden_dropout_prob							,					attention_probs_dropout_prob=self.attention_probs_dropout_prob							,					drop_path_rate=self.drop_path_rate							,					hidden_act=self.hidden_act							,					use_absolute_embeddings=self.use_absolute_embeddings							,					path_norm=self.patch_norm							,					layer_norm_eps=self.layer_norm_eps							,					initializer_range=self.initializer_range							,					encoder_stride=self.encoder_stride							,					)

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[str]   	=		DonutSwinModel(config=lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()
					__SCREAMING_SNAKE_CASE	:		Any   	=		model(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

					self.parent.assertEqual(result.last_hidden_state.shape							,					(self.batch_size, expected_seq_len, expected_dim)						)



			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.prepare_config_and_inputs()
					(
					    (
					    __SCREAMING_SNAKE_CASE
					)	,						(
					    __SCREAMING_SNAKE_CASE
					)	,						(
					    __SCREAMING_SNAKE_CASE
					)	,						
					)	:		Dict   	=		config_and_inputs
					__SCREAMING_SNAKE_CASE	:		int   	=		{"""pixel_values""": pixel_values}
					return config, inputs_dict






@require_torch
class 						_UpperCamelCase  (							lowerCamelCase__   , lowerCamelCase__   , unittest.TestCase   ):


			'''simple docstring'''

			_A				:		int         =		(DonutSwinModel,) if is_torch_available() else ()
			_A				:		List[Any]         =		{'''feature-extraction''': DonutSwinModel} if is_torch_available() else {}
			_A				:		str         =		True

			_A				:		Tuple         =		False
			_A				:		List[Any]         =		False
			_A				:		int         =		False

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		DonutSwinModelTester(self						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		ConfigTester(self							,					config_class=lowerCAmelCase__							,					embed_dim=3_7						)

			def UpperCamelCase__ (							self					:			Dict						):





					"""simple docstring"""






					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					return

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					pass

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		str   	=		model_class(lowerCAmelCase__						)
							self.assertIsInstance(model.get_input_embeddings()							,					(nn.Module)						)
							__SCREAMING_SNAKE_CASE	:		int   	=		model.get_output_embeddings()
							self.assertTrue(x is None or isinstance(lowerCAmelCase__							,					nn.Linear						)						)

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model_class(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		inspect.signature(model.forward						)
							# signature.parameters is an OrderedDict => so arg_names order is deterministic
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		[*signature.parameters.keys()]

							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		["""pixel_values"""]
							self.assertListEqual(arg_names[:1]							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		int   	=		True

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		True
							__SCREAMING_SNAKE_CASE	:		Dict   	=		False
							__SCREAMING_SNAKE_CASE	:		int   	=		True
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		Any   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		outputs.attentions
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		len(self.model_tester.depths						)
							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

							# check that output_attentions also work using config
							del inputs_dict["output_attentions"]
							__SCREAMING_SNAKE_CASE	:		int   	=		True
							__SCREAMING_SNAKE_CASE	:		Any   	=		config.window_size**2
							__SCREAMING_SNAKE_CASE	:		int   	=		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		str   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		outputs.attentions
							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

							self.assertListEqual(
							    list(attentions[0].shape[-3:]						)							,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,					)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		len(lowerCAmelCase__						)

							# Check attention is always last and order is fine
							__SCREAMING_SNAKE_CASE	:		Dict   	=		True
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		True
							__SCREAMING_SNAKE_CASE	:		Dict   	=		model_class(lowerCAmelCase__						)
							model.to(lowerCAmelCase__						)
							model.eval()
							with torch.no_grad():
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)

							if hasattr(self.model_tester							,					"""num_hidden_states_types"""						):
									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.model_tester.num_hidden_states_types
							else:
									# also another +1 for reshaped_hidden_states
									__SCREAMING_SNAKE_CASE	:		int   	=		2
							self.assertEqual(out_len + added_hidden_states							,					len(lowerCAmelCase__						)						)

							__SCREAMING_SNAKE_CASE	:		List[str]   	=		outputs.attentions

							self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

							self.assertListEqual(
							    list(self_attentions[0].shape[-3:]						)							,					[self.model_tester.num_heads[0], window_size_squared, window_size_squared]							,					)

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			Dict							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			List[str]							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		model_class(lowerCAmelCase__						)
					model.to(lowerCAmelCase__						)
					model.eval()

					with torch.no_grad():
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(**self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		outputs.hidden_states

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		getattr(
					    self.model_tester							,					"""expected_num_hidden_layers"""							,					len(self.model_tester.depths						) + 1						)
					self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

					# DonutSwin has a different seq_length
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    config.patch_size
					    if isinstance(config.patch_size							,					collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]						)							,					[num_patches, self.model_tester.embed_dim]							,					)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		outputs.reshaped_hidden_states
					self.assertEqual(len(lowerCAmelCase__						)							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		reshaped_hidden_states[0].shape
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    reshaped_hidden_states[0].view(lowerCAmelCase__							,					lowerCAmelCase__							,					height * width						).permute(0							,					2							,					1						)
					)
					self.assertListEqual(
					    list(reshaped_hidden_states.shape[-2:]						)							,					[num_patches, self.model_tester.embed_dim]							,					)

			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size							,					collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		True

							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		str   	=		self.model_tester.prepare_config_and_inputs_for_common()
					__SCREAMING_SNAKE_CASE	:		int   	=		3

					__SCREAMING_SNAKE_CASE	:		int   	=		(
					    self.model_tester.image_size
					    if isinstance(self.model_tester.image_size							,					collections.abc.Iterable						)
					    else (self.model_tester.image_size, self.model_tester.image_size)
					)
					__SCREAMING_SNAKE_CASE	:		str   	=		(
					    config.patch_size
					    if isinstance(config.patch_size							,					collections.abc.Iterable						)
					    else (config.patch_size, config.patch_size)
					)

					__SCREAMING_SNAKE_CASE	:		int   	=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
					__SCREAMING_SNAKE_CASE	:		str   	=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		int   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					(padded_height, padded_width)						)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							__SCREAMING_SNAKE_CASE	:		Dict   	=		True
							self.check_hidden_states_output(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					(padded_height, padded_width)						)

			@slow
			def UpperCamelCase__ (							self					:			Union[str, Any]						):





					"""simple docstring"""






					for model_name in DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__SCREAMING_SNAKE_CASE	:		str   	=		DonutSwinModel.from_pretrained(lowerCAmelCase__						)
							self.assertIsNotNone(lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		_config_zero_init(lowerCAmelCase__						)
					for model_class in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class(config=lowerCAmelCase__						)
							for name, param in model.named_parameters():
									if "embeddings" not in name and param.requires_grad:
											self.assertIn(
											    ((param.data.mean() * 1E9).round() / 1E9).item()							,					[0.0, 1.0]							,					msg=F"Parameter {name} of model {model_class} seems not properly initialized"							,					)




'''simple docstring'''




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


UpperCamelCase__	:     str    =			['''CoLA''', '''SST''', '''MRPC''', '''QQP''', '''STS''', '''MNLI''', '''SNLI''', '''QNLI''', '''RTE''', '''WNLI''', '''diagnostic''']
UpperCamelCase__	:     List[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''',
}

UpperCamelCase__	:     Optional[int]    =			'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt'''
UpperCamelCase__	:     Any    =			'''https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt'''


def     lowerCAmelCase_       (     _lowerCamelCase:       Union[str, Any]	,				_lowerCamelCase:       Dict ):
		print(F"Downloading and extracting {task}..." )
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		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     lowerCAmelCase_       (     _lowerCamelCase:       str	,				_lowerCamelCase:       Optional[int] ):
		print("""Processing MRPC...""" )
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.join(_lowerCamelCase	,				"""MRPC""" )
		if not os.path.isdir(_lowerCamelCase ):
				os.mkdir(_lowerCamelCase )
		if path_to_data:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_train.txt""" )
				__SCREAMING_SNAKE_CASE	:		Tuple   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_test.txt""" )
		else:
				print("""Local MRPC data not specified, downloading data from %s""" % MRPC_TRAIN )
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		os.path.join(_lowerCamelCase	,				"""msr_paraphrase_train.txt""" )
				__SCREAMING_SNAKE_CASE	:		List[str]   	=		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""" ) )

		__SCREAMING_SNAKE_CASE	:		Tuple   	=		[]
		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:
				__SCREAMING_SNAKE_CASE	:		Any   	=		data_fh.readline()
				train_fh.write(_lowerCamelCase )
				dev_fh.write(_lowerCamelCase )
				for row in data_fh:
						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		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:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		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 ):
						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Dict   	=		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     lowerCAmelCase_       (     _lowerCamelCase:       Dict ):
		print("""Downloading and extracting diagnostic...""" )
		if not os.path.isdir(os.path.join(_lowerCamelCase	,				"""diagnostic""" ) ):
				os.mkdir(os.path.join(_lowerCamelCase	,				"""diagnostic""" ) )
		__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		os.path.join(_lowerCamelCase	,				"""diagnostic"""	,				"""diagnostic.tsv""" )
		urllib.request.urlretrieve(TASKaPATH["""diagnostic"""]	,				_lowerCamelCase )
		print("""\tCompleted!""" )
		return


def     lowerCAmelCase_       (     _lowerCamelCase:       int ):
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		task_names.split(""",""" )
		if "all" in task_names:
				__SCREAMING_SNAKE_CASE	:		List[Any]   	=		TASKS
		else:
				__SCREAMING_SNAKE_CASE	:		Dict   	=		[]
				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     lowerCAmelCase_       (     _lowerCamelCase:       Tuple ):
		__SCREAMING_SNAKE_CASE	:		int   	=		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=""""""	,				)
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		parser.parse_args(_lowerCamelCase )

		if not os.path.isdir(args.data_dir ):
				os.mkdir(args.data_dir )
		__SCREAMING_SNAKE_CASE	:		List[str]   	=		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:]))