

'''simple docstring'''






import json
import logging
import os
import sys
from time import time
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, require_torch_tpu


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__   		=      logging.getLogger()


def 						_A	(       A__ ):






 """simple docstring"""



 __lowercase				    =					{}
 __lowercase				    =					os.path.join(A__	,							'''all_results.json''' )
 if os.path.exists(A__ ):
  with open(A__	,							'''r''' ) as f:
   __lowercase				    =					json.load(A__ )
 else:
  raise ValueError(F"can't find {path}" )
 return results


lowerCAmelCase__   		=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






@require_torch_tpu
class      lowercase_      (lowerCamelCase__ ):




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	Any     ):
   import xla_spawn

   __lowercase				    =					self.get_auto_remove_tmp_dir()
   __lowercase				    =					F"\n            ./examples/pytorch/text-classification/run_glue.py\n            --num_cores=8\n            ./examples/pytorch/text-classification/run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --debug tpu_metrics_debug\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

   with patch.object(lowercase__   ,'''argv'''   ,lowercase__     ):
    __lowercase				    =					time()
    xla_spawn.main()
    __lowercase				    =					time()

    __lowercase				    =					get_results(lowercase__     )
    self.assertGreaterEqual(result['''eval_accuracy''']   ,0.7_5     )

    # Assert that the script takes less than 500 seconds to make sure it doesn't hang.
    self.assertLess(end - start   ,5_0_0     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   import xla_spawn

   __lowercase				    =					'''
            ./tests/test_trainer_tpu.py
            --num_cores=8
            ./tests/test_trainer_tpu.py
            '''.split()
   with patch.object(lowercase__   ,'''argv'''   ,lowercase__     ):
    xla_spawn.main()





import unittest

import numpy as np

from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
     import jax.numpy as jnp

     from transformers.models.distilbert.modeling_flax_distilbert import (
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertModel,
     )







class 						_snake_case    ( unittest.TestCase				):




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=13   ,    _lowerCamelCase=7   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=True   ,    _lowerCamelCase=99   ,    _lowerCamelCase=32   ,    _lowerCamelCase=5   ,    _lowerCamelCase=4   ,    _lowerCamelCase=37   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=16   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=4   ,    ):
       a		:Optional[Any]									=			parent
       a		:str									=			batch_size
       a		:Tuple									=			seq_length
       a		:List[Any]									=			is_training
       a		:Optional[int]									=			use_attention_mask
       a		:List[str]									=			use_token_type_ids
       a		:str									=			use_labels
       a		:Optional[Any]									=			vocab_size
       a		:Optional[int]									=			hidden_size
       a		:Tuple									=			num_hidden_layers
       a		:Union[str, Any]									=			num_attention_heads
       a		:int									=			intermediate_size
       a		:int									=			hidden_act
       a		:int									=			hidden_dropout_prob
       a		:Union[str, Any]									=			attention_probs_dropout_prob
       a		:str									=			max_position_embeddings
       a		:Dict									=			type_vocab_size
       a		:str									=			type_sequence_label_size
       a		:List[str]									=			initializer_range
       a		:Optional[Any]									=			num_choices




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Tuple									=			ids_tensor([self.batch_size, self.seq_length]   ,    self.vocab_size       )

       a		:Any									=			None
       if self.use_attention_mask:
             a		:Any									=			random_attention_mask([self.batch_size, self.seq_length]       )

       a		:Any									=			DistilBertConfig(
           vocab_size=self.vocab_size   ,    dim=self.hidden_size   ,    n_layers=self.num_hidden_layers   ,    n_heads=self.num_attention_heads   ,    hidden_dim=self.intermediate_size   ,    hidden_act=self.hidden_act   ,    dropout=self.hidden_dropout_prob   ,    attention_dropout=self.attention_probs_dropout_prob   ,    max_position_embeddings=self.max_position_embeddings   ,    initializer_range=self.initializer_range   ,    tie_weights_=_lowerCamelCase   ,    )

       return config, input_ids, attention_mask




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			self.prepare_config_and_inputs()
       a				,							a				,							a		:str									=			config_and_inputs
       a		:List[Any]									=			{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict







@require_flax
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			FlaxDistilBertModelTester(self       )




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       for model_class_name in self.all_model_classes:
             a		:int									=			model_class_name.from_pretrained('''distilbert-base-uncased'''       )
             a		:List[str]									=			model(np.ones((1, 1)       )       )
             self.assertIsNotNone(_lowerCamelCase       )







@require_flax
class 						_snake_case    ( unittest.TestCase				):




 @slow
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[str]									=			FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''       )
       a		:Optional[Any]									=			np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]       )
       a		:List[Any]									=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
       a		:List[str]									=			model(_lowerCamelCase   ,    attention_mask=_lowerCamelCase       )[0]
       a		:Union[str, Any]									=			(1, 11, 768)
       self.assertEqual(output.shape   ,    _lowerCamelCase       )
       a		:int									=			np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]]       )

       self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]   ,    _lowerCamelCase   ,    atol=1e-4       )       )





