



import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
						import torch






class 						_UpperCamelCase  :


			'''simple docstring'''

			def __init__(							self					:			Tuple							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			Tuple=1_4							,					lowerCAmelCase__					:			Tuple=7							,					lowerCAmelCase__					:			Tuple=True							,					lowerCAmelCase__					:			int=True							,					lowerCAmelCase__					:			Optional[Any]=False							,					lowerCAmelCase__					:			Any=True							,					lowerCAmelCase__					:			Union[str, Any]=9_9							,					lowerCAmelCase__					:			Union[str, Any]=3_2							,					lowerCAmelCase__					:			List[Any]=5							,					lowerCAmelCase__					:			Optional[int]=4							,					lowerCAmelCase__					:			Union[str, Any]=3_7							,					lowerCAmelCase__					:			List[Any]="gelu"							,					lowerCAmelCase__					:			Union[str, Any]=0.1							,					lowerCAmelCase__					:			List[Any]=0.1							,					lowerCAmelCase__					:			Optional[Any]=5_1_2							,					lowerCAmelCase__					:			Dict=0.02							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		parent
					__SCREAMING_SNAKE_CASE	:		Dict   	=		batch_size
					__SCREAMING_SNAKE_CASE	:		int   	=		seq_length
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		is_training
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		use_input_mask
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		use_token_type_ids
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		use_labels
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		vocab_size
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		hidden_size
					__SCREAMING_SNAKE_CASE	:		Any   	=		num_hidden_layers
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		num_attention_heads
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		intermediate_size
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		hidden_act
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		hidden_dropout_prob
					__SCREAMING_SNAKE_CASE	:		str   	=		attention_probs_dropout_prob
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		max_position_embeddings
					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		initializer_range
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		vocab_size - 1
					__SCREAMING_SNAKE_CASE	:		int   	=		vocab_size - 1
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		vocab_size - 1

			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		ids_tensor([self.batch_size, self.seq_length]							,					self.vocab_size						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		None
					if self.use_input_mask:
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		random_attention_mask([self.batch_size, self.seq_length]						)

					__SCREAMING_SNAKE_CASE	:		str   	=		GPTaConfig(
					    vocab_size=self.vocab_size							,					n_embd=self.hidden_size							,					n_layer=self.num_hidden_layers							,					n_head=self.num_attention_heads							,					n_positions=self.max_position_embeddings							,					use_cache=lowerCAmelCase__							,					bos_token_id=self.bos_token_id							,					eos_token_id=self.eos_token_id							,					pad_token_id=self.pad_token_id							,					)

					return (config, input_ids, input_mask)

			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		self.prepare_config_and_inputs()
					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		config_and_inputs
					__SCREAMING_SNAKE_CASE	:		str   	=		{"""input_ids""": input_ids, """attention_mask""": attention_mask}
					return config, inputs_dict

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
					__SCREAMING_SNAKE_CASE	:		int   	=		ids_tensor([self.batch_size, self.seq_length]							,					vocab_size=2						)

					return (
					    config,
					    input_ids,
					    attention_mask,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		2_0
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class_name(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		int   	=		model.init_cache(input_ids.shape[0]							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		jnp.ones((input_ids.shape[0], max_decoder_length)							,					dtype="""i4"""						)

					__SCREAMING_SNAKE_CASE	:		int   	=		jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(
					    input_ids[:, :-1]							,					attention_mask=lowerCAmelCase__							,					past_key_values=lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					)

					__SCREAMING_SNAKE_CASE	:		Any   	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,					dtype="""i4"""						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(
					    input_ids[:, -1:]							,					attention_mask=lowerCAmelCase__							,					past_key_values=outputs_cache.past_key_values							,					position_ids=lowerCAmelCase__							,					)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
					self.parent.assertTrue(diff < 1E-3							,					msg=F"Max diff is {diff}"						)



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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		2_0
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class_name(lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		jnp.concatenate(
					    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]							,					axis=-1							,					)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model.init_cache(input_ids.shape[0]							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Dict   	=		jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1						)[None, :]							,					(input_ids.shape[0], input_ids.shape[-1] - 1)						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		model(
					    input_ids[:, :-1]							,					attention_mask=lowerCAmelCase__							,					past_key_values=lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,					dtype="""i4"""						)
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		model(
					    input_ids[:, -1:]							,					past_key_values=outputs_cache.past_key_values							,					attention_mask=lowerCAmelCase__							,					position_ids=lowerCAmelCase__							,					)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model(lowerCAmelCase__							,					attention_mask=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		int   	=		np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
					self.parent.assertTrue(diff < 1E-3							,					msg=F"Max diff is {diff}"						)






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


			'''simple docstring'''

			_A				:		str         =		(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
			_A				:		Optional[Any]         =		(FlaxGPTaLMHeadModel,) if is_flax_available() else ()

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





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		FlaxGPTaModelTester(self						)

			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		List[str]   	=		self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

			@slow
			def UpperCamelCase__ (							self					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		GPTaTokenizer.from_pretrained("""gpt2"""							,					pad_token="""</s>"""							,					padding_side="""left"""						)
					__SCREAMING_SNAKE_CASE	:		int   	=		tokenizer(["""Hello this is a long string""", """Hey"""]							,					return_tensors="""np"""							,					padding=lowerCAmelCase__							,					truncation=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Dict   	=		FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		False
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		model.config.eos_token_id

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		jax.jit(model.generate						)

					__SCREAMING_SNAKE_CASE	:		int   	=		jit_generate(inputs["""input_ids"""]							,					attention_mask=inputs["""attention_mask"""]						).sequences

					__SCREAMING_SNAKE_CASE	:		int   	=		tokenizer.batch_decode(lowerCAmelCase__							,					skip_special_tokens=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		[
					    """Hello this is a long string of words. I\'m going to start with the first one.\n""",
					    """Hey, I\'m not sure if I\'m going to be able to do""",
					]

					self.assertListEqual(lowerCAmelCase__							,					lowerCAmelCase__						)

			@is_pt_flax_cross_test
			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()

					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__						):
									# prepare inputs
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		str   	=		{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									__SCREAMING_SNAKE_CASE	:		str   	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
									__SCREAMING_SNAKE_CASE	:		Dict   	=		getattr(lowerCAmelCase__							,					lowerCAmelCase__						)

									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		pt_inputs["""input_ids"""].shape
									__SCREAMING_SNAKE_CASE	:		int   	=		np.random.randint(0							,					seq_length - 1							,					size=(batch_size,)						)
									for batch_idx, start_index in enumerate(lowerCAmelCase__						):
											__SCREAMING_SNAKE_CASE	:		List[str]   	=		0
											__SCREAMING_SNAKE_CASE	:		Any   	=		1
											__SCREAMING_SNAKE_CASE	:		str   	=		0
											__SCREAMING_SNAKE_CASE	:		List[str]   	=		1
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		pt_model_class(lowerCAmelCase__						).eval()
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class(lowerCAmelCase__							,					dtype=jnp.floataa						)

									__SCREAMING_SNAKE_CASE	:		str   	=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,					lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		fx_state

									with torch.no_grad():
											__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		pt_model(**lowerCAmelCase__						).to_tuple()

									__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		fx_model(**lowerCAmelCase__						).to_tuple()
									self.assertEqual(len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)

									with tempfile.TemporaryDirectory() as tmpdirname:
											pt_model.save_pretrained(lowerCAmelCase__						)
											__SCREAMING_SNAKE_CASE	:		Tuple   	=		model_class.from_pretrained(lowerCAmelCase__							,					from_pt=lowerCAmelCase__						)

									__SCREAMING_SNAKE_CASE	:		int   	=		fx_model_loaded(**lowerCAmelCase__						).to_tuple()
									self.assertEqual(
									    len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output_loaded, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output_loaded[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)

			@is_pt_flax_cross_test
			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
							with self.subTest(model_class.__name__						):
									# prepare inputs
									__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self._prepare_for_class(lowerCAmelCase__							,					lowerCAmelCase__						)
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									__SCREAMING_SNAKE_CASE	:		List[str]   	=		model_class.__name__[4:]  # Skip the "Flax" at the beginning
									__SCREAMING_SNAKE_CASE	:		Dict   	=		getattr(lowerCAmelCase__							,					lowerCAmelCase__						)

									__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		pt_model_class(lowerCAmelCase__						).eval()
									__SCREAMING_SNAKE_CASE	:		List[Any]   	=		model_class(lowerCAmelCase__							,					dtype=jnp.floataa						)

									__SCREAMING_SNAKE_CASE	:		Dict   	=		load_flax_weights_in_pytorch_model(lowerCAmelCase__							,					fx_model.params						)
									__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		int   	=		pt_inputs["""input_ids"""].shape
									__SCREAMING_SNAKE_CASE	:		int   	=		np.random.randint(0							,					seq_length - 1							,					size=(batch_size,)						)
									for batch_idx, start_index in enumerate(lowerCAmelCase__						):
											__SCREAMING_SNAKE_CASE	:		Tuple   	=		0
											__SCREAMING_SNAKE_CASE	:		int   	=		1
											__SCREAMING_SNAKE_CASE	:		List[Any]   	=		0
											__SCREAMING_SNAKE_CASE	:		int   	=		1

									# make sure weights are tied in PyTorch
									pt_model.tie_weights()

									with torch.no_grad():
											__SCREAMING_SNAKE_CASE	:		Any   	=		pt_model(**lowerCAmelCase__						).to_tuple()

									__SCREAMING_SNAKE_CASE	:		str   	=		fx_model(**lowerCAmelCase__						).to_tuple()
									self.assertEqual(len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)

									with tempfile.TemporaryDirectory() as tmpdirname:
											fx_model.save_pretrained(lowerCAmelCase__						)
											__SCREAMING_SNAKE_CASE	:		int   	=		pt_model_class.from_pretrained(lowerCAmelCase__							,					from_flax=lowerCAmelCase__						)

									with torch.no_grad():
											__SCREAMING_SNAKE_CASE	:		str   	=		pt_model_loaded(**lowerCAmelCase__						).to_tuple()

									self.assertEqual(
									    len(lowerCAmelCase__						)							,					len(lowerCAmelCase__						)							,					"""Output lengths differ between Flax and PyTorch"""						)
									for fx_output, pt_output in zip(lowerCAmelCase__							,					lowerCAmelCase__						):
											self.assert_almost_equals(fx_output[:, -1]							,					pt_output[:, -1].numpy()							,					4E-2						)



			@slow
			def UpperCamelCase__ (							self					:			Optional[Any]						):





					"""simple docstring"""






					for model_class_name in self.all_model_classes:
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		model_class_name.from_pretrained("""gpt2"""							,					from_pt=lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE	:		Dict   	=		model(np.ones((1, 1)						)						)
							self.assertIsNotNone(lowerCAmelCase__						)




'''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:]))