




'''simple docstring'''







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     _a						(			unittest.TestCase				):
       def __init__(			self		:			Tuple     ,       lowercase		:			Optional[int]     ,       lowercase		:			Dict=13     ,       lowercase		:			List[Any]=7     ,       lowercase		:			Tuple=True     ,       lowercase		:			Optional[Any]=True     ,       lowercase		:			List[str]=True     ,       lowercase		:			str=True     ,       lowercase		:			Dict=99     ,       lowercase		:			Dict=32     ,       lowercase		:			Tuple=5     ,       lowercase		:			Optional[Any]=4     ,       lowercase		:			List[Any]=37     ,       lowercase		:			int="gelu"     ,       lowercase		:			List[str]=0.1     ,       lowercase		:			Optional[int]=0.1     ,       lowercase		:			Any=512     ,       lowercase		:			Optional[int]=16     ,       lowercase		:			Any=2     ,       lowercase		:			List[Any]=0.02     ,       lowercase		:			Optional[Any]=4     ,       ):



           '''simple docstring'''

           UpperCAmelCase     =	parent
           UpperCAmelCase     =	batch_size
           UpperCAmelCase     =	seq_length
           UpperCAmelCase     =	is_training
           UpperCAmelCase     =	use_attention_mask
           UpperCAmelCase     =	use_token_type_ids
           UpperCAmelCase     =	use_labels
           UpperCAmelCase     =	vocab_size
           UpperCAmelCase     =	hidden_size
           UpperCAmelCase     =	num_hidden_layers
           UpperCAmelCase     =	num_attention_heads
           UpperCAmelCase     =	intermediate_size
           UpperCAmelCase     =	hidden_act
           UpperCAmelCase     =	hidden_dropout_prob
           UpperCAmelCase     =	attention_probs_dropout_prob
           UpperCAmelCase     =	max_position_embeddings
           UpperCAmelCase     =	type_vocab_size
           UpperCAmelCase     =	type_sequence_label_size
           UpperCAmelCase     =	initializer_range
           UpperCAmelCase     =	num_choices
       def 					A   (			self		:			Tuple       ):



           '''simple docstring'''

           UpperCAmelCase     =	ids_tensor([self.batch_size, self.seq_length]     ,       self.vocab_size       )

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

           UpperCAmelCase     =	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_=lowercase     ,       )

           return config, input_ids, attention_mask







       def 					A   (			self		:			str       ):



           '''simple docstring'''

           UpperCAmelCase     =	self.prepare_config_and_inputs()
           UpperCAmelCase ,  UpperCAmelCase ,  UpperCAmelCase     =	config_and_inputs
           UpperCAmelCase     =	{'''input_ids''': input_ids, '''attention_mask''': attention_mask}
           return config, inputs_dict






@require_flax
class     _a						(			__a							,							unittest.TestCase				):
       __a       :  Dict            =      (
           (
               FlaxDistilBertModel,
               FlaxDistilBertForMaskedLM,
               FlaxDistilBertForMultipleChoice,
               FlaxDistilBertForQuestionAnswering,
               FlaxDistilBertForSequenceClassification,
               FlaxDistilBertForTokenClassification,
               FlaxDistilBertForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxDistilBertModelTester(self       )







       @slow
       def 					A   (			self		:			List[Any]       ):



           '''simple docstring'''

           for model_class_name in self.all_model_classes:
               UpperCAmelCase     =	model_class_name.from_pretrained('''distilbert-base-uncased'''       )
               UpperCAmelCase     =	model(np.ones((1, 1)       )       )
               self.assertIsNotNone(lowercase       )






@require_flax
class     _a						(			unittest.TestCase				):
       @slow
       def 					A   (			self		:			Union[str, Any]       ):



           '''simple docstring'''

           UpperCAmelCase     =	FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased'''       )
           UpperCAmelCase     =	np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]       )
           UpperCAmelCase     =	np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
           UpperCAmelCase     =	model(lowercase     ,       attention_mask=lowercase       )[0]
           UpperCAmelCase     =	(1, 11, 768)
           self.assertEqual(output.shape     ,       lowercase       )
           UpperCAmelCase     =	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]     ,       lowercase     ,       atol=1E-4       )       )










'''simple docstring'''







from datetime import datetime

import matplotlib.pyplot as plt
import torch





def 		snake_case_							(_a			:							List[str]  ):
    for param in module.parameters():
        UpperCAmelCase     =	False





def 		snake_case_							():
    UpperCAmelCase     =	'''cuda''' if torch.cuda.is_available() else '''cpu'''
    if torch.backends.mps.is_available() and torch.backends.mps.is_built():
        UpperCAmelCase     =	'''mps'''
    if device == "mps":
        print(
            '''WARNING: MPS currently doesn\'t seem to work, and messes up backpropagation without any visible torch'''
            ''' errors. I recommend using CUDA on a colab notebook or CPU instead if you\'re facing inexplicable issues'''
            ''' with generations.'''  )
    return device





def 		snake_case_							(_a			:							Optional[Any]  ):
    UpperCAmelCase     =	plt.imshow(_a  )
    fig.axes.get_xaxis().set_visible(_a  )
    fig.axes.get_yaxis().set_visible(_a  )
    plt.show()







def 		snake_case_							():
    UpperCAmelCase     =	datetime.now()
    UpperCAmelCase     =	current_time.strftime('''%H:%M:%S'''  )
    return timestamp




