
"""simple docstring"""







from __future__ import annotations

import os
import tempfile
import unittest

from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFConvBertForMaskedLM,
           TFConvBertForMultipleChoice,
           TFConvBertForQuestionAnswering,
           TFConvBertForSequenceClassification,
           TFConvBertForTokenClassification,
           TFConvBertModel,
       )



class 		UpperCAmelCase_						:
 """simple docstring"""




 def __init__(			self							:			Union[str, Any]  , a_							:			List[str]  , a_							:			Tuple=13  , a_							:			List[Any]=7  , a_							:			Union[str, Any]=True  , a_							:			Tuple=True  , a_							:			Optional[int]=True  , a_							:			int=True  , a_							:			Optional[Any]=99  , a_							:			Dict=32  , a_							:			int=2  , a_							:			Optional[Any]=4  , a_							:			Optional[int]=37  , a_							:			List[str]="gelu"  , a_							:			Union[str, Any]=0.1  , a_							:			Tuple=0.1  , a_							:			Dict=5_12  , a_							:			Union[str, Any]=16  , a_							:			List[str]=2  , a_							:			Dict=0.02  , a_							:			str=3  , a_							:			Tuple=4  , a_							:			Any=None  , )->       int:



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	parent
      UpperCAmelCase_    :     int            =	13
      UpperCAmelCase_    :     int            =	7
      UpperCAmelCase_    :     Optional[int]            =	True
      UpperCAmelCase_    :     int            =	True
      UpperCAmelCase_    :     Union[str, Any]            =	True
      UpperCAmelCase_    :     Optional[Any]            =	True
      UpperCAmelCase_    :     Optional[Any]            =	99
      UpperCAmelCase_    :     Optional[int]            =	3_84
      UpperCAmelCase_    :     str            =	2
      UpperCAmelCase_    :     Optional[Any]            =	4
      UpperCAmelCase_    :     Any            =	37
      UpperCAmelCase_    :     Tuple            =	"""gelu"""
      UpperCAmelCase_    :     Optional[Any]            =	0.1
      UpperCAmelCase_    :     List[Any]            =	0.1
      UpperCAmelCase_    :     int            =	5_12
      UpperCAmelCase_    :     List[str]            =	16
      UpperCAmelCase_    :     List[Any]            =	2
      UpperCAmelCase_    :     Dict            =	0.02
      UpperCAmelCase_    :     str            =	3
      UpperCAmelCase_    :     Union[str, Any]            =	4
      UpperCAmelCase_    :     Any            =	1_28
      UpperCAmelCase_    :     str            =	2
      UpperCAmelCase_    :     List[Any]            =	9
      UpperCAmelCase_    :     Optional[Any]            =	1
      UpperCAmelCase_    :     int            =	None




 def 	a   (			self							:			Optional[int]			)->       Dict:



      """simple docstring"""

      UpperCAmelCase_    :     int            =	ids_tensor([self.batch_size, self.seq_length]  , self.vocab_size			)

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

      UpperCAmelCase_    :     Union[str, Any]            =	None
      if self.use_token_type_ids:
           UpperCAmelCase_    :     Optional[int]            =	ids_tensor([self.batch_size, self.seq_length]  , self.type_vocab_size			)

      UpperCAmelCase_    :     List[str]            =	None
      UpperCAmelCase_    :     Optional[int]            =	None
      UpperCAmelCase_    :     List[Any]            =	None
      if self.use_labels:
           UpperCAmelCase_    :     Any            =	ids_tensor([self.batch_size]  , self.type_sequence_label_size			)
           UpperCAmelCase_    :     List[str]            =	ids_tensor([self.batch_size, self.seq_length]  , self.num_labels			)
           UpperCAmelCase_    :     int            =	ids_tensor([self.batch_size]  , self.num_choices			)

      UpperCAmelCase_    :     Optional[int]            =	ConvBertConfig(
          vocab_size=self.vocab_size  , hidden_size=self.hidden_size  , num_hidden_layers=self.num_hidden_layers  , num_attention_heads=self.num_attention_heads  , intermediate_size=self.intermediate_size  , hidden_act=self.hidden_act  , hidden_dropout_prob=self.hidden_dropout_prob  , attention_probs_dropout_prob=self.attention_probs_dropout_prob  , max_position_embeddings=self.max_position_embeddings  , type_vocab_size=self.type_vocab_size  , initializer_range=self.initializer_range  , return_dict=__lowerCAmelCase  , )

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




 def 	a   (			self							:			Dict  , a_							:			Optional[int]  , a_							:			Any  , a_							:			int  , a_							:			int  , a_							:			Optional[Any]  , a_							:			Any  , a_							:			List[str]			)->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	TFConvBertModel(config=__lowerCAmelCase			)
      UpperCAmelCase_    :     Optional[int]            =	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

      UpperCAmelCase_    :     int            =	[input_ids, input_mask]
      UpperCAmelCase_    :     List[str]            =	model(__lowerCAmelCase			)

      UpperCAmelCase_    :     Optional[Any]            =	model(__lowerCAmelCase			)

      self.parent.assertEqual(result.last_hidden_state.shape  , (self.batch_size, self.seq_length, self.hidden_size)			)




 def 	a   (			self							:			List[Any]  , a_							:			Optional[Any]  , a_							:			Tuple  , a_							:			Union[str, Any]  , a_							:			str  , a_							:			Dict  , a_							:			List[Any]  , a_							:			Union[str, Any]			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     List[Any]            =	TFConvBertForMaskedLM(config=__lowerCAmelCase			)
      UpperCAmelCase_    :     int            =	{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      UpperCAmelCase_    :     str            =	model(__lowerCAmelCase			)
      self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length, self.vocab_size)			)




 def 	a   (			self							:			Union[str, Any]  , a_							:			List[Any]  , a_							:			List[str]  , a_							:			Optional[Any]  , a_							:			Optional[Any]  , a_							:			Union[str, Any]  , a_							:			Optional[Any]  , a_							:			List[Any]			)->       List[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	self.num_labels
      UpperCAmelCase_    :     int            =	TFConvBertForSequenceClassification(config=__lowerCAmelCase			)
      UpperCAmelCase_    :     Tuple            =	{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      UpperCAmelCase_    :     Union[str, Any]            =	model(__lowerCAmelCase			)
      self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.num_labels)			)




 def 	a   (			self							:			List[str]  , a_							:			List[Any]  , a_							:			str  , a_							:			Optional[Any]  , a_							:			List[str]  , a_							:			List[str]  , a_							:			Optional[Any]  , a_							:			Dict			)->       Any:



      """simple docstring"""

      UpperCAmelCase_    :     List[str]            =	self.num_choices
      UpperCAmelCase_    :     Union[str, Any]            =	TFConvBertForMultipleChoice(config=__lowerCAmelCase			)
      UpperCAmelCase_    :     int            =	tf.tile(tf.expand_dims(__lowerCAmelCase  , 1			)  , (1, self.num_choices, 1)			)
      UpperCAmelCase_    :     Optional[int]            =	tf.tile(tf.expand_dims(__lowerCAmelCase  , 1			)  , (1, self.num_choices, 1)			)
      UpperCAmelCase_    :     Tuple            =	tf.tile(tf.expand_dims(__lowerCAmelCase  , 1			)  , (1, self.num_choices, 1)			)
      UpperCAmelCase_    :     List[Any]            =	{
          """input_ids""": multiple_choice_inputs_ids,
          """attention_mask""": multiple_choice_input_mask,
          """token_type_ids""": multiple_choice_token_type_ids,
      }
      UpperCAmelCase_    :     str            =	model(__lowerCAmelCase			)
      self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.num_choices)			)




 def 	a   (			self							:			str  , a_							:			Union[str, Any]  , a_							:			Any  , a_							:			str  , a_							:			List[str]  , a_							:			Optional[int]  , a_							:			Dict  , a_							:			List[Any]			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	self.num_labels
      UpperCAmelCase_    :     Optional[int]            =	TFConvBertForTokenClassification(config=__lowerCAmelCase			)
      UpperCAmelCase_    :     Tuple            =	{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      UpperCAmelCase_    :     Tuple            =	model(__lowerCAmelCase			)
      self.parent.assertEqual(result.logits.shape  , (self.batch_size, self.seq_length, self.num_labels)			)




 def 	a   (			self							:			Optional[int]  , a_							:			List[str]  , a_							:			List[str]  , a_							:			str  , a_							:			List[Any]  , a_							:			Dict  , a_							:			List[str]  , a_							:			List[Any]			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     Dict            =	TFConvBertForQuestionAnswering(config=__lowerCAmelCase			)
      UpperCAmelCase_    :     List[str]            =	{
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      UpperCAmelCase_    :     Any            =	model(__lowerCAmelCase			)
      self.parent.assertEqual(result.start_logits.shape  , (self.batch_size, self.seq_length)			)
      self.parent.assertEqual(result.end_logits.shape  , (self.batch_size, self.seq_length)			)






 def 	a   (			self							:			Tuple			)->       str:



      """simple docstring"""

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



@require_tf
class 		UpperCAmelCase_						(lowerCAmelCase_	,			lowerCAmelCase_	,			unittest.TestCase    ):
 """simple docstring"""




 UpperCamelCase_    :  str					 =     (
     (
         TFConvBertModel,
         TFConvBertForMaskedLM,
         TFConvBertForQuestionAnswering,
         TFConvBertForSequenceClassification,
         TFConvBertForTokenClassification,
         TFConvBertForMultipleChoice,
     )
     if is_tf_available()
     else ()
 )
 UpperCamelCase_    :  List[Any]					 =     (
     {
         'feature-extraction': TFConvBertModel,
         'fill-mask': TFConvBertForMaskedLM,
         'question-answering': TFConvBertForQuestionAnswering,
         'text-classification': TFConvBertForSequenceClassification,
         'token-classification': TFConvBertForTokenClassification,
         'zero-shot': TFConvBertForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 UpperCamelCase_    :  Any					 =     False
 UpperCamelCase_    :  Optional[Any]					 =     False
 UpperCamelCase_    :  int					 =     False




 def 	a   (			self							:			List[str]			)->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	TFConvBertModelTester(self			)
      UpperCAmelCase_    :     Dict            =	ConfigTester(self  , config_class=__lowerCAmelCase  , hidden_size=37			)




 def 	a   (			self							:			List[Any]			)->       Dict:



      """simple docstring"""

      self.config_tester.run_common_tests()




 def 	a   (			self							:			List[Any]			)->       Tuple:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__lowerCAmelCase			)




 def 	a   (			self							:			Any			)->       Any:



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase			)




 def 	a   (			self							:			Optional[Any]			)->       Tuple:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*__lowerCAmelCase			)




 def 	a   (			self							:			Union[str, Any]			)->       Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase			)




 def 	a   (			self							:			int			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase			)




 def 	a   (			self							:			Any			)->       int:



      """simple docstring"""

      UpperCAmelCase_    :     Tuple            =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase			)




 @slow
 def 	a   (			self							:			Optional[Any]			)->       int:



      """simple docstring"""

      UpperCAmelCase_       ,UpperCAmelCase_    :     Any            =	self.model_tester.prepare_config_and_inputs_for_common()
      UpperCAmelCase_    :     int            =	True
      UpperCAmelCase_    :     str            =	True

      if hasattr(__lowerCAmelCase  , """use_cache"""			):
           UpperCAmelCase_    :     int            =	True

      UpperCAmelCase_    :     int            =	getattr(self.model_tester  , """encoder_seq_length"""  , self.model_tester.seq_length			)
      UpperCAmelCase_    :     List[str]            =	getattr(self.model_tester  , """key_length"""  , __lowerCAmelCase			)

      for model_class in self.all_model_classes:
           UpperCAmelCase_    :     str            =	self._prepare_for_class(__lowerCAmelCase  , __lowerCAmelCase			)
           UpperCAmelCase_    :     Dict            =	model_class(__lowerCAmelCase			)
           UpperCAmelCase_    :     List[str]            =	len(model(__lowerCAmelCase			)			)

           with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(__lowerCAmelCase  , saved_model=__lowerCAmelCase			)
                UpperCAmelCase_    :     List[Any]            =	os.path.join(__lowerCAmelCase  , """saved_model"""  , """1"""			)
                UpperCAmelCase_    :     Optional[int]            =	tf.keras.models.load_model(__lowerCAmelCase			)
                UpperCAmelCase_    :     Any            =	model(__lowerCAmelCase			)

                if self.is_encoder_decoder:
                     UpperCAmelCase_    :     int            =	outputs["""encoder_hidden_states"""]
                     UpperCAmelCase_    :     int            =	outputs["""encoder_attentions"""]
                else:
                     UpperCAmelCase_    :     Any            =	outputs["""hidden_states"""]
                     UpperCAmelCase_    :     Optional[Any]            =	outputs["""attentions"""]

                self.assertEqual(len(__lowerCAmelCase			)  , __lowerCAmelCase			)

                UpperCAmelCase_    :     Optional[int]            =	getattr(
                    self.model_tester  , """expected_num_hidden_layers"""  , self.model_tester.num_hidden_layers + 1			)

                self.assertEqual(len(__lowerCAmelCase			)  , __lowerCAmelCase			)
                self.assertListEqual(
                    list(output_hidden_states[0].shape[-2:]			)  , [self.model_tester.seq_length, self.model_tester.hidden_size]  , )

                self.assertEqual(len(__lowerCAmelCase			)  , self.model_tester.num_hidden_layers			)
                self.assertListEqual(
                    list(output_attentions[0].shape[-3:]			)  , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]  , )




 @slow
 def 	a   (			self							:			str			)->       Optional[int]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""			)
      self.assertIsNotNone(__lowerCAmelCase			)






 def 	a   (			self							:			Any			)->       Tuple:



      """simple docstring"""

      UpperCAmelCase_       ,UpperCAmelCase_    :     Tuple            =	self.model_tester.prepare_config_and_inputs_for_common()
      UpperCAmelCase_    :     List[Any]            =	True
      UpperCAmelCase_    :     Dict            =	getattr(self.model_tester  , """decoder_seq_length"""  , self.model_tester.seq_length			)
      UpperCAmelCase_    :     Any            =	getattr(self.model_tester  , """encoder_seq_length"""  , self.model_tester.seq_length			)
      UpperCAmelCase_    :     Optional[int]            =	getattr(self.model_tester  , """key_length"""  , __lowerCAmelCase			)
      UpperCAmelCase_    :     Any            =	getattr(self.model_tester  , """key_length"""  , __lowerCAmelCase			)

      def check_decoder_attentions_output(a_							:			Union[str, Any]			):
           UpperCAmelCase_    :     Optional[Any]            =	len(__lowerCAmelCase			)
           self.assertEqual(out_len % 2  , 0			)
           UpperCAmelCase_    :     List[Any]            =	outputs.decoder_attentions
           self.assertEqual(len(__lowerCAmelCase			)  , self.model_tester.num_hidden_layers			)
           self.assertListEqual(
               list(decoder_attentions[0].shape[-3:]			)  , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]  , )

      def check_encoder_attentions_output(a_							:			str			):
           UpperCAmelCase_    :     Union[str, Any]            =	[
               t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
           ]
           self.assertEqual(len(__lowerCAmelCase			)  , self.model_tester.num_hidden_layers			)
           self.assertListEqual(
               list(attentions[0].shape[-3:]			)  , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length]  , )

      for model_class in self.all_model_classes:
           UpperCAmelCase_    :     Any            =	True
           UpperCAmelCase_    :     Any            =	False
           UpperCAmelCase_    :     Union[str, Any]            =	model_class(__lowerCAmelCase			)
           UpperCAmelCase_    :     int            =	model(self._prepare_for_class(__lowerCAmelCase  , __lowerCAmelCase			)			)
           UpperCAmelCase_    :     int            =	len(__lowerCAmelCase			)
           self.assertEqual(config.output_hidden_states  , __lowerCAmelCase			)
           check_encoder_attentions_output(__lowerCAmelCase			)

           if self.is_encoder_decoder:
                UpperCAmelCase_    :     Any            =	model_class(__lowerCAmelCase			)
                UpperCAmelCase_    :     int            =	model(self._prepare_for_class(__lowerCAmelCase  , __lowerCAmelCase			)			)
                self.assertEqual(config.output_hidden_states  , __lowerCAmelCase			)
                check_decoder_attentions_output(__lowerCAmelCase			)

           # Check that output attentions can also be changed via the config
           del inputs_dict["output_attentions"]
           UpperCAmelCase_    :     Union[str, Any]            =	True
           UpperCAmelCase_    :     Any            =	model_class(__lowerCAmelCase			)
           UpperCAmelCase_    :     Any            =	model(self._prepare_for_class(__lowerCAmelCase  , __lowerCAmelCase			)			)
           self.assertEqual(config.output_hidden_states  , __lowerCAmelCase			)
           check_encoder_attentions_output(__lowerCAmelCase			)

           # Check attention is always last and order is fine
           UpperCAmelCase_    :     List[str]            =	True
           UpperCAmelCase_    :     Dict            =	True
           UpperCAmelCase_    :     Tuple            =	model_class(__lowerCAmelCase			)
           UpperCAmelCase_    :     List[str]            =	model(self._prepare_for_class(__lowerCAmelCase  , __lowerCAmelCase			)			)

           self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)  , len(__lowerCAmelCase			)			)
           self.assertEqual(model.config.output_hidden_states  , __lowerCAmelCase			)
           check_encoder_attentions_output(__lowerCAmelCase			)



@require_tf
class 		UpperCAmelCase_						(unittest.TestCase    ):
 """simple docstring"""




 @slow
 def 	a   (			self							:			Any			)->       List[str]:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	TFConvBertModel.from_pretrained("""YituTech/conv-bert-base"""			)
      UpperCAmelCase_    :     Dict            =	tf.constant([[0, 1, 2, 3, 4, 5]]			)
      UpperCAmelCase_    :     Union[str, Any]            =	model(__lowerCAmelCase			)[0]

      UpperCAmelCase_    :     Optional[int]            =	[1, 6, 7_68]
      self.assertEqual(output.shape  , __lowerCAmelCase			)

      UpperCAmelCase_    :     Tuple            =	tf.constant(
          [
              [
                  [-0.03475493, -0.4686034, -0.30638832],
                  [0.22637248, -0.26988646, -0.7423424],
                  [0.10324868, -0.45013508, -0.58280784],
              ]
          ]			)
      tf.debugging.assert_near(output[:, :3, :3]  , __lowerCAmelCase  , atol=1E-4			)


"""simple docstring"""


import pytest

from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs






@pytest.mark.parametrize(
    """kwargs, expected""" ,[
        ({"""num_shards""": 0, """max_num_jobs""": 1}, []),
        ({"""num_shards""": 10, """max_num_jobs""": 1}, [range(10		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 10}, [range(lowercase ,i + 1		) for i in range(10		)]),
        ({"""num_shards""": 1, """max_num_jobs""": 10}, [range(1		)]),
        ({"""num_shards""": 10, """max_num_jobs""": 3}, [range(0 ,4		), range(4 ,7		), range(7 ,10		)]),
        ({"""num_shards""": 3, """max_num_jobs""": 10}, [range(0 ,1		), range(1 ,2		), range(2 ,3		)]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _distribute_shards(**lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, max_num_jobs, expected""" ,[
        ({"""foo""": 0}, 10, [{"""foo""": 0}]),
        ({"""shards""": [0, 1, 2, 3]}, 1, [{"""shards""": [0, 1, 2, 3]}]),
        ({"""shards""": [0, 1, 2, 3]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}, {"""shards""": [2]}, {"""shards""": [3]}]),
        ({"""shards""": [0, 1]}, 4, [{"""shards""": [0]}, {"""shards""": [1]}]),
        ({"""shards""": [0, 1, 2, 3]}, 2, [{"""shards""": [0, 1]}, {"""shards""": [2, 3]}]),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase    			=   _split_gen_kwargs(lowercase ,lowercase		)
  assert out == expected






@pytest.mark.parametrize(
    """gen_kwargs, expected""" ,[
        ({"""foo""": 0}, 1),
        ({"""shards""": [0]}, 1),
        ({"""shards""": [0, 1, 2, 3]}, 4),
        ({"""shards""": [0, 1, 2, 3], """foo""": 0}, 4),
        ({"""shards""": [0, 1, 2, 3], """other""": (0, 1)}, 4),
        ({"""shards""": [0, 1, 2, 3], """shards2""": [0, 1]}, RuntimeError),
    ] ,)
def      __UpperCAmelCase       (      lowercase ,lowercase		):







  """simple docstring"""
  if expected is RuntimeError:
    with pytest.raises(lowercase		):
      _number_of_shards_in_gen_kwargs(lowercase		)
  else:
    _UpperCAmelCase    			=   _number_of_shards_in_gen_kwargs(lowercase		)
    assert out == expected
