"""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       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	      =  parent
        __a	      =  batch_size
        __a	      =  seq_length
        __a	      =  is_training
        __a	      =  use_attention_mask
        __a	      =  use_token_type_ids
        __a	      =  use_labels
        __a	      =  vocab_size
        __a	      =  hidden_size
        __a	      =  num_hidden_layers
        __a	      =  num_attention_heads
        __a	      =  intermediate_size
        __a	      =  hidden_act
        __a	      =  hidden_dropout_prob
        __a	      =  attention_probs_dropout_prob
        __a	      =  max_position_embeddings
        __a	      =  type_vocab_size
        __a	      =  type_sequence_label_size
        __a	      =  initializer_range
        __a	      =  num_choices


 def 		a__					(      self		):
        __a	      =  ids_tensor([self.batch_size, self.seq_length]  ,						self.vocab_size		)

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

        __a	      =  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_=_SCREAMING_SNAKE_CASE  ,						)

        return config, input_ids, attention_mask






 def 		a__					(      self		):
        __a	      =  self.prepare_config_and_inputs()
        __a	      =  config_and_inputs
        __a	      =  {"input_ids": input_ids, "attention_mask": attention_mask}
        return config, inputs_dict






@require_flax
class       snake_case__	(    __SCREAMING_SNAKE_CASE,		unittest.TestCase    ):
 _snake_case    : Optional[int]				   =		(
     (
         FlaxDistilBertModel,
         FlaxDistilBertForMaskedLM,
         FlaxDistilBertForMultipleChoice,
         FlaxDistilBertForQuestionAnswering,
         FlaxDistilBertForSequenceClassification,
         FlaxDistilBertForTokenClassification,
         FlaxDistilBertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )


 def 		a__					(      self		):
        __a	      =  FlaxDistilBertModelTester(self		)






 @slow
 def 		a__					(      self		):

        for model_class_name in self.all_model_classes:
               __a	      =  model_class_name.from_pretrained("distilbert-base-uncased"		)
               __a	      =  model(np.ones((1, 1)		)		)
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)






@require_flax
class       snake_case__	(    unittest.TestCase    ):


 @slow
 def 		a__					(      self		):
        __a	      =  FlaxDistilBertModel.from_pretrained("distilbert-base-uncased"		)
        __a	      =  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]		)
        __a	      =  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]		)
        __a	      =  model(_SCREAMING_SNAKE_CASE  ,						attention_mask=_SCREAMING_SNAKE_CASE		)[0]
        __a	      =  (1, 11, 768)
        self.assertEqual(output.shape  ,						_SCREAMING_SNAKE_CASE		)
        __a	      =  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]  ,						_SCREAMING_SNAKE_CASE  ,						atol=1E-4		)		)





"""simple docstring"""






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case							:   Optional[int]      =    50_000
__snake_case							:   Dict      =    5_000

__snake_case      ,		__snake_case							:   Union[str, Any]      =    os.path.split(__file__)
__snake_case							:   Any      =    os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case	)						->   Dict:
			for i in range(__snake_case	):
						__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			for i in range(0					,len(__snake_case	)					,__snake_case	):
						__lowerCAmelCase      :  List[str]												=       dataset[i : i + batch_size]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case	)						->   Dict:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(__snake_case	):
									__lowerCAmelCase      :  Union[str, Any]												=       dataset[i]


@get_duration
def 			_lowercase				(  __snake_case					,__snake_case					,__snake_case					,__snake_case	)						->   str:
			with dataset.formatted_as(type=__snake_case	):
						for i in range(0					,__snake_case					,__snake_case	):
									__lowerCAmelCase      :  Optional[int]												=       dataset[i : i + batch_size]


def 			_lowercase				(  )						->   Union[str, Any]:
			__lowerCAmelCase      :  Optional[int]												=       {"num examples": SPEED_TEST_N_EXAMPLES}
			__lowerCAmelCase      :  Optional[int]												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
			    (read_formatted, {"type": "torch", "length": SMALL_TEST}),
			    (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]

			__lowerCAmelCase      :  Any												=       [
			    (read, {"length": SMALL_TEST}),
			    (read, {"length": SPEED_TEST_N_EXAMPLES}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 100}),
			    (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_000}),
			    (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
			    (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 1_000}),
			]
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"	)
						__lowerCAmelCase      :  int												=       datasets.Features(
						    {"list": datasets.Sequence(datasets.Value("float32"	)	), "numbers": datasets.Value("float32"	)}	)
						__lowerCAmelCase      :  str												=       generate_example_dataset(
						    os.path.join(__snake_case					,"dataset.arrow"	)					,__snake_case					,num_examples=__snake_case					,seq_shapes={"list": (100,)}					,)
						print("first set of iterations"	)
						for func, kwargs in functions:
									print(func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  str												=       func(__snake_case					,**__snake_case	)

						print("shuffling dataset"	)
						__lowerCAmelCase      :  Optional[int]												=       dataset.shuffle()
						print("Second set of iterations (after shuffling"	)
						for func, kwargs in functions_shuffled:
									print("shuffled "					,func.__name__					,str(__snake_case	)	)
									__lowerCAmelCase      :  List[str]												=       func(
									    __snake_case					,**__snake_case	)

			with open(__snake_case					,"wb"	) as f:
						f.write(json.dumps(__snake_case	).encode("utf-8"	)	)


if __name__ == "__main__":  # useful to run the profiler
	benchmark_iterating()