





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  _snake_case				= """platform"""

  import jax
  import jax.numpy as jnp

  from transformers import AutoTokenizer
  from transformers.models.mbart.modeling_flax_mbart import (
      FlaxMBartForConditionalGeneration,
      FlaxMBartForQuestionAnswering,
      FlaxMBartForSequenceClassification,
      FlaxMBartModel,
      shift_tokens_right,
  )


def _A   (    __magic_name__  ,					__magic_name__  ,					__magic_name__=None  ,					__magic_name__=None  ,					__magic_name__=None  ,					__magic_name__=None  ,					__magic_name__=None  ,					__magic_name__=None  ,					):
   if attention_mask is None:
      lowercase__    =  np.where(input_ids != config.pad_token_id  ,					1  ,					0       )
   if decoder_attention_mask is None:
      lowercase__    =  np.where(decoder_input_ids != config.pad_token_id  ,					1  ,					0       )
   if head_mask is None:
      lowercase__    =  np.ones((config.encoder_layers, config.encoder_attention_heads)       )
   if decoder_head_mask is None:
      lowercase__    =  np.ones((config.decoder_layers, config.decoder_attention_heads)       )
   if cross_attn_head_mask is None:
      lowercase__    =  np.ones((config.decoder_layers, config.decoder_attention_heads)       )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": decoder_attention_mask,
   }





class 					lowerCAmelCase							(   unittest.TestCase					):







  def __init__(			self  :str    ,							_lowercase  :List[Any]    ,							_lowercase  :Dict=13    ,							_lowercase  :Tuple=7    ,							_lowercase  :Optional[int]=True    ,							_lowercase  :List[Any]=False    ,							_lowercase  :List[str]=99    ,							_lowercase  :Union[str, Any]=16    ,							_lowercase  :Any=2    ,							_lowercase  :Optional[Any]=4    ,							_lowercase  :Any=4    ,							_lowercase  :Dict="gelu"    ,							_lowercase  :Any=0.1    ,							_lowercase  :Union[str, Any]=0.1    ,							_lowercase  :str=32    ,							_lowercase  :int=2    ,							_lowercase  :Optional[Any]=1    ,							_lowercase  :str=0    ,							_lowercase  :int=2    ,							_lowercase  :Union[str, Any]=0.02    ,							):






     '''simple docstring'''





     lowercase__    =  parent
     lowercase__    =  batch_size
     lowercase__    =  seq_length
     lowercase__    =  is_training
     lowercase__    =  use_labels
     lowercase__    =  vocab_size
     lowercase__    =  hidden_size
     lowercase__    =  num_hidden_layers
     lowercase__    =  num_attention_heads
     lowercase__    =  intermediate_size
     lowercase__    =  hidden_act
     lowercase__    =  hidden_dropout_prob
     lowercase__    =  attention_probs_dropout_prob
     lowercase__    =  max_position_embeddings
     lowercase__    =  eos_token_id
     lowercase__    =  pad_token_id
     lowercase__    =  bos_token_id
     lowercase__    =  decoder_start_token_id
     lowercase__    =  initializer_range







  def   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     lowercase__    =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,							self.vocab_size					)    ,							3    ,							self.vocab_size					)
     lowercase__    =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)    ,							dtype=np.intaa					))    ,							-1					)

     lowercase__    =  shift_tokens_right(_lowercase    ,							1					)

     lowercase__    =  MBartConfig(
         vocab_size=self.vocab_size    ,							d_model=self.hidden_size    ,							encoder_layers=self.num_hidden_layers    ,							decoder_layers=self.num_hidden_layers    ,							encoder_attention_heads=self.num_attention_heads    ,							decoder_attention_heads=self.num_attention_heads    ,							encoder_ffn_dim=self.intermediate_size    ,							decoder_ffn_dim=self.intermediate_size    ,							dropout=self.hidden_dropout_prob    ,							attention_dropout=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							eos_token_id=self.eos_token_id    ,							bos_token_id=self.bos_token_id    ,							pad_token_id=self.pad_token_id    ,							decoder_start_token_id=self.decoder_start_token_id    ,							initializer_range=self.initializer_range    ,							use_cache=_lowercase    ,							)
     lowercase__    =  prepare_mbart_inputs_dict(_lowercase    ,							_lowercase    ,							_lowercase					)
     return config, inputs_dict







  def   UpperCAmelCase     (			self  :List[Any]					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  self.prepare_config_and_inputs()
     return config, inputs_dict







  def   UpperCAmelCase     (			self  :int    ,							_lowercase  :str    ,							_lowercase  :int    ,							_lowercase  :List[Any]					):






     '''simple docstring'''





     lowercase__    =  20
     lowercase__    =  model_class_name(_lowercase					)

     lowercase__    =  model.encode(inputs_dict["input_ids"]					)

     lowercase__   ,							lowercase__    =  (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     lowercase__    =  model.init_cache(decoder_input_ids.shape[0]    ,							_lowercase    ,							_lowercase					)
     lowercase__    =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,							dtype="i4"					)

     lowercase__    =  jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]    ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,							)
     lowercase__    =  model.decode(
         decoder_input_ids[:, :-1]    ,							_lowercase    ,							decoder_attention_mask=_lowercase    ,							past_key_values=_lowercase    ,							decoder_position_ids=_lowercase    ,							)

     lowercase__    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,							dtype="i4"					)
     lowercase__    =  model.decode(
         decoder_input_ids[:, -1:]    ,							_lowercase    ,							decoder_attention_mask=_lowercase    ,							past_key_values=outputs_cache.past_key_values    ,							decoder_position_ids=_lowercase    ,							)

     lowercase__    =  model.decode(_lowercase    ,							_lowercase					)

     lowercase__    =  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  :Dict    ,							_lowercase  :str    ,							_lowercase  :List[Any]    ,							_lowercase  :Optional[Any]					):






     '''simple docstring'''





     lowercase__    =  20
     lowercase__    =  model_class_name(_lowercase					)

     lowercase__    =  model.encode(inputs_dict["input_ids"]					)

     lowercase__   ,							lowercase__    =  (
         inputs_dict["decoder_input_ids"],
         inputs_dict["decoder_attention_mask"],
     )

     lowercase__    =  jnp.concatenate(
         [
             decoder_attention_mask,
             jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])					),
         ]    ,							axis=-1    ,							)

     lowercase__    =  model.init_cache(decoder_input_ids.shape[0]    ,							_lowercase    ,							_lowercase					)
     lowercase__    =  jnp.broadcast_to(
         jnp.arange(decoder_input_ids.shape[-1] - 1					)[None, :]    ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,							)

     lowercase__    =  model.decode(
         decoder_input_ids[:, :-1]    ,							_lowercase    ,							decoder_attention_mask=_lowercase    ,							past_key_values=_lowercase    ,							decoder_position_ids=_lowercase    ,							)
     lowercase__    =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,							dtype="i4"					)
     lowercase__    =  model.decode(
         decoder_input_ids[:, -1:]    ,							_lowercase    ,							past_key_values=outputs_cache.past_key_values    ,							decoder_attention_mask=_lowercase    ,							decoder_position_ids=_lowercase    ,							)

     lowercase__    =  model.decode(_lowercase    ,							_lowercase    ,							decoder_attention_mask=_lowercase					)

     lowercase__    =  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 					lowerCAmelCase							(   unittest.TestCase					):
  __lowerCamelCase		=			99







  def   UpperCAmelCase     (			self  :Optional[Any]					):






     '''simple docstring'''





     lowercase__    =  np.array(
         [
             [71, 82, 18, 33, 46, 91, 2],
             [68, 34, 26, 58, 30, 82, 2],
             [5, 97, 17, 39, 94, 40, 2],
             [76, 83, 94, 25, 70, 78, 2],
             [87, 59, 41, 35, 48, 66, 2],
             [55, 13, 16, 58, 5, 2, 1],  # note padding
             [64, 27, 31, 51, 12, 75, 2],
             [52, 64, 86, 17, 83, 39, 2],
             [48, 61, 9, 24, 71, 82, 2],
             [26, 1, 60, 48, 22, 13, 2],
             [21, 5, 62, 28, 14, 76, 2],
             [45, 98, 37, 86, 59, 48, 2],
             [70, 70, 50, 9, 28, 0, 2],
         ]    ,							dtype=np.intaa    ,							)

     lowercase__    =  input_ids.shape[0]
     lowercase__    =  MBartConfig(
         vocab_size=self.vocab_size    ,							d_model=24    ,							encoder_layers=2    ,							decoder_layers=2    ,							encoder_attention_heads=2    ,							decoder_attention_heads=2    ,							encoder_ffn_dim=32    ,							decoder_ffn_dim=32    ,							max_position_embeddings=48    ,							eos_token_id=2    ,							pad_token_id=1    ,							bos_token_id=0    ,							)
     return config, input_ids, batch_size







  def   UpperCAmelCase     (			self  :Any					):






     '''simple docstring'''





     lowercase__   ,							lowercase__   ,							lowercase__    =  self._get_config_and_data()
     lowercase__    =  FlaxMBartForSequenceClassification(_lowercase					)
     lowercase__    =  model(input_ids=_lowercase    ,							decoder_input_ids=_lowercase					)
     lowercase__    =  (batch_size, config.num_labels)
     self.assertEqual(outputs["logits"].shape    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :Dict					):






     '''simple docstring'''





     lowercase__   ,							lowercase__   ,							lowercase__    =  self._get_config_and_data()
     lowercase__    =  FlaxMBartForQuestionAnswering(_lowercase					)
     lowercase__    =  model(input_ids=_lowercase					)

     self.assertEqual(outputs["start_logits"].shape    ,							input_ids.shape					)
     self.assertEqual(outputs["end_logits"].shape    ,							input_ids.shape					)







  def   UpperCAmelCase     (			self  :List[Any]					):






     '''simple docstring'''





     lowercase__   ,							lowercase__   ,							lowercase__    =  self._get_config_and_data()
     lowercase__    =  FlaxMBartForConditionalGeneration(_lowercase					)
     lowercase__    =  lm_model(input_ids=_lowercase					)
     lowercase__    =  (batch_size, input_ids.shape[1], config.vocab_size)
     self.assertEqual(outputs["logits"].shape    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :Optional[int]					):






     '''simple docstring'''





     lowercase__    =  MBartConfig(
         vocab_size=self.vocab_size    ,							d_model=14    ,							encoder_layers=2    ,							decoder_layers=2    ,							encoder_attention_heads=2    ,							decoder_attention_heads=2    ,							encoder_ffn_dim=8    ,							decoder_ffn_dim=8    ,							max_position_embeddings=48    ,							)
     lowercase__    =  FlaxMBartForConditionalGeneration(_lowercase					)
     lowercase__    =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,							dtype=np.intaa					)
     lowercase__    =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,							dtype=np.intaa					)
     lowercase__    =  lm_model(input_ids=_lowercase    ,							decoder_input_ids=_lowercase					)
     lowercase__    =  (*summary.shape, config.vocab_size)
     self.assertEqual(outputs["logits"].shape    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     lowercase__    =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]    ,							dtype=np.intaa					)
     lowercase__    =  shift_tokens_right(_lowercase    ,							1					)
     lowercase__    =  np.equal(_lowercase    ,							1					).astype(np.floataa					).sum()
     lowercase__    =  np.equal(_lowercase    ,							1					).astype(np.floataa					).sum()
     self.assertEqual(shifted.shape    ,							input_ids.shape					)
     self.assertEqual(_lowercase    ,							n_pad_before - 1					)
     self.assertTrue(np.equal(shifted[:, 0]    ,							2					).all()					)





@require_flax
class 					lowerCAmelCase							(   lowercase_							,							unittest.TestCase							,							lowercase_					):
  __lowerCamelCase		=			True
  __lowerCamelCase		=			(
      (
          FlaxMBartModel,
          FlaxMBartForConditionalGeneration,
          FlaxMBartForSequenceClassification,
          FlaxMBartForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )
  __lowerCamelCase		=			(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()







  def   UpperCAmelCase     (			self  :str					):






     '''simple docstring'''





     lowercase__    =  FlaxMBartModelTester(self					)







  def   UpperCAmelCase     (			self  :Any					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward(_lowercase    ,							_lowercase    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :Optional[Any]					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  self.model_tester.prepare_config_and_inputs()
     for model_class in self.all_model_classes:
        self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase    ,							_lowercase    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :Optional[Any]					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__					):
           lowercase__    =  self._prepare_for_class(_lowercase    ,							_lowercase					)
           lowercase__    =  model_class(_lowercase					)

           @jax.jit
           def encode_jitted(_lowercase  :Optional[Any]    ,							_lowercase  :Tuple=None    ,							**_lowercase  :Dict					):
              return model.encode(input_ids=_lowercase    ,							attention_mask=_lowercase					)

           with self.subTest("JIT Enabled"					):
              lowercase__    =  encode_jitted(**_lowercase					).to_tuple()

           with self.subTest("JIT Disabled"					):
              with jax.disable_jit():
                 lowercase__    =  encode_jitted(**_lowercase					).to_tuple()

           self.assertEqual(len(_lowercase					)    ,							len(_lowercase					)					)
           for jitted_output, output in zip(_lowercase    ,							_lowercase					):
              self.assertEqual(jitted_output.shape    ,							output.shape					)







  def   UpperCAmelCase     (			self  :Optional[int]					):






     '''simple docstring'''





     lowercase__   ,							lowercase__    =  self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__					):
           lowercase__    =  model_class(_lowercase					)
           lowercase__    =  model.encode(inputs_dict["input_ids"]    ,							inputs_dict["attention_mask"]					)

           lowercase__    =  {
               "decoder_input_ids": inputs_dict["decoder_input_ids"],
               "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
               "encoder_outputs": encoder_outputs,
           }

           @jax.jit
           def decode_jitted(_lowercase  :Optional[int]    ,							_lowercase  :Any    ,							_lowercase  :List[str]					):
              return model.decode(
                  decoder_input_ids=_lowercase    ,							decoder_attention_mask=_lowercase    ,							encoder_outputs=_lowercase    ,							)

           with self.subTest("JIT Enabled"					):
              lowercase__    =  decode_jitted(**_lowercase					).to_tuple()

           with self.subTest("JIT Disabled"					):
              with jax.disable_jit():
                 lowercase__    =  decode_jitted(**_lowercase					).to_tuple()

           self.assertEqual(len(_lowercase					)    ,							len(_lowercase					)					)
           for jitted_output, output in zip(_lowercase    ,							_lowercase					):
              self.assertEqual(jitted_output.shape    ,							output.shape					)







  @slow
  def   UpperCAmelCase     (			self  :Tuple					):






     '''simple docstring'''





     for model_class_name in self.all_model_classes:
        lowercase__    =  model_class_name.from_pretrained("facebook/mbart-large-cc25"    ,							from_pt=_lowercase					)
        # FlaxMBartForSequenceClassification expects eos token in input_ids
        lowercase__    =  np.ones((1, 1)					) * model.config.eos_token_id
        lowercase__    =  model(_lowercase					)
        self.assertIsNotNone(_lowercase					)





@require_flax
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase							(   unittest.TestCase					):
  __lowerCamelCase		=			[
      ' UN Chief Says There Is No Military Solution in Syria',
  ]
  __lowerCamelCase		=			[
      'Şeful ONU declară că nu există o soluţie militară în Siria',
  ]
  __lowerCamelCase		=			'facebook/mbart-large-en-ro'







  @cached_property
  def   UpperCAmelCase     (			self  :Optional[Any]					):






     '''simple docstring'''





     return AutoTokenizer.from_pretrained(self.model_name					)







  @cached_property
  def   UpperCAmelCase     (			self  :Optional[int]					):






     '''simple docstring'''





     lowercase__    =  FlaxMBartForConditionalGeneration.from_pretrained(self.model_name    ,							from_pt=_lowercase					)
     return model







  def   UpperCAmelCase     (			self  :str    ,							**_lowercase  :List[str]					):






     '''simple docstring'''





     lowercase__    =  self.translate_src_text(**_lowercase					)
     self.assertListEqual(self.expected_text    ,							_lowercase					)







  def   UpperCAmelCase     (			self  :List[str]    ,							**_lowercase  :Union[str, Any]					):






     '''simple docstring'''





     lowercase__    =  self.tokenizer(self.src_text    ,							**_lowercase    ,							return_tensors="np"					)
     lowercase__    =  self.model.generate(
         model_inputs.input_ids    ,							attention_mask=model_inputs.attention_mask    ,							decoder_start_token_id=self.tokenizer.lang_code_to_id["ro_RO"]    ,							early_stopping=_lowercase    ,							num_beams=2    ,							).sequences
     lowercase__    =  self.tokenizer.batch_decode(_lowercase    ,							skip_special_tokens=_lowercase					)
     return generated_words







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






     '''simple docstring'''





     self._assert_generated_batch_equal_expected()













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


_snake_case				= ["""CoLA""", """SST""", """MRPC""", """QQP""", """STS""", """MNLI""", """SNLI""", """QNLI""", """RTE""", """WNLI""", """diagnostic"""]
_snake_case				= {
    """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""",
}

_snake_case				= """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"""
_snake_case				= """https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"""


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


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

   lowercase__    =  []
   with open(os.path.join(__magic_name__  ,					"dev_ids.tsv"       )  ,					encoding="utf8"       ) as ids_fh:
      for row in ids_fh:
         dev_ids.append(row.strip().split("\t"       )       )

   with open(__magic_name__  ,					encoding="utf8"       ) as data_fh, open(
       os.path.join(__magic_name__  ,					"train.tsv"       )  ,					"w"  ,					encoding="utf8"       ) as train_fh, open(os.path.join(__magic_name__  ,					"dev.tsv"       )  ,					"w"  ,					encoding="utf8"       ) as dev_fh:
      lowercase__    =  data_fh.readline()
      train_fh.write(__magic_name__       )
      dev_fh.write(__magic_name__       )
      for row in data_fh:
         lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__    =  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(__magic_name__  ,					encoding="utf8"       ) as data_fh, open(
       os.path.join(__magic_name__  ,					"test.tsv"       )  ,					"w"  ,					encoding="utf8"       ) as test_fh:
      lowercase__    =  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(__magic_name__       ):
         lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__   ,							lowercase__    =  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   (    __magic_name__       ):
   print("Downloading and extracting diagnostic..."       )
   if not os.path.isdir(os.path.join(__magic_name__  ,					"diagnostic"       )       ):
      os.mkdir(os.path.join(__magic_name__  ,					"diagnostic"       )       )
   lowercase__    =  os.path.join(__magic_name__  ,					"diagnostic"  ,					"diagnostic.tsv"       )
   urllib.request.urlretrieve(TASKaPATH["diagnostic"]  ,					__magic_name__       )
   print("\tCompleted!"       )
   return


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


def _A   (    __magic_name__       ):
   lowercase__    =  argparse.ArgumentParser()
   parser.add_argument("--data_dir"  ,					help="directory to save data to"  ,					type=__magic_name__  ,					default="glue_data"       )
   parser.add_argument(
       "--tasks"  ,					help="tasks to download data for as a comma separated string"  ,					type=__magic_name__  ,					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=__magic_name__  ,					default=""  ,					)
   lowercase__    =  parser.parse_args(__magic_name__       )

   if not os.path.isdir(args.data_dir       ):
      os.mkdir(args.data_dir       )
   lowercase__    =  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(__magic_name__  ,					args.data_dir       )


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






