


import unittest

from transformers import FunnelConfig, FunnelTokenizer, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         FunnelBaseModel,
         FunnelForMaskedLM,
         FunnelForMultipleChoice,
         FunnelForPreTraining,
         FunnelForQuestionAnswering,
         FunnelForSequenceClassification,
         FunnelForTokenClassification,
         FunnelModel,
     )

class     __snake_case							:
     def __init__(	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	Union[str, Any]  ,			_lowerCAmelCase					:	Optional[Any]=1_3  ,			_lowerCAmelCase					:	Union[str, Any]=7  ,			_lowerCAmelCase					:	List[Any]=True  ,			_lowerCAmelCase					:	Any=True  ,			_lowerCAmelCase					:	Tuple=True  ,			_lowerCAmelCase					:	int=True  ,			_lowerCAmelCase					:	Dict=9_9  ,			_lowerCAmelCase					:	Dict=[1, 1, 2]  ,			_lowerCAmelCase					:	List[Any]=1  ,			_lowerCAmelCase					:	int=3_2  ,			_lowerCAmelCase					:	str=4  ,			_lowerCAmelCase					:	str=8  ,			_lowerCAmelCase					:	List[str]=3_7  ,			_lowerCAmelCase					:	Optional[int]="gelu_new"  ,			_lowerCAmelCase					:	Union[str, Any]=0.1  ,			_lowerCAmelCase					:	Dict=0.1  ,			_lowerCAmelCase					:	List[Any]=0.0  ,			_lowerCAmelCase					:	Union[str, Any]=5_1_2  ,			_lowerCAmelCase					:	List[str]=3  ,			_lowerCAmelCase					:	List[str]=0.02  ,			_lowerCAmelCase					:	Tuple=3  ,			_lowerCAmelCase					:	Dict=4  ,			_lowerCAmelCase					:	List[str]=None  ,			_lowerCAmelCase					:	str=False  ,			)       ->				str:



           '''simple docstring'''





           __magic_name__ 		=			parent
           __magic_name__ 		=			batch_size
           __magic_name__ 		=			seq_length
           __magic_name__ 		=			is_training
           __magic_name__ 		=			use_input_mask
           __magic_name__ 		=			use_token_type_ids
           __magic_name__ 		=			use_labels
           __magic_name__ 		=			vocab_size
           __magic_name__ 		=			block_sizes
           __magic_name__ 		=			num_decoder_layers
           __magic_name__ 		=			d_model
           __magic_name__ 		=			n_head
           __magic_name__ 		=			d_head
           __magic_name__ 		=			d_inner
           __magic_name__ 		=			hidden_act
           __magic_name__ 		=			hidden_dropout
           __magic_name__ 		=			attention_dropout
           __magic_name__ 		=			activation_dropout
           __magic_name__ 		=			max_position_embeddings
           __magic_name__ 		=			type_vocab_size
           __magic_name__ 		=			2
           __magic_name__ 		=			num_labels
           __magic_name__ 		=			num_choices
           __magic_name__ 		=			scope
           __magic_name__ 		=			initializer_std

           # Used in the tests to check the size of the first attention layer
           __magic_name__ 		=			n_head
           # Used in the tests to check the size of the first hidden state
           __magic_name__ 		=			self.d_model
           # Used in the tests to check the number of output hidden states/attentions
           __magic_name__ 		=			sum(self.block_sizes     ) + (0 if base else self.num_decoder_layers)
           # FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
           # the last hidden state of the first block (which is the first hidden state of the decoder).
           if not base:
                 __magic_name__ 		=			self.num_hidden_layers + 2
     def   a (	self					:	Optional[int]     )       ->				Optional[Any]:



           '''simple docstring'''





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

           __magic_name__ 		=			None
           if self.use_input_mask:
                 __magic_name__ 		=			ids_tensor([self.batch_size, self.seq_length]  ,			vocab_size=2     )

           __magic_name__ 		=			None
           if self.use_token_type_ids:
                 __magic_name__ 		=			ids_tensor([self.batch_size, self.seq_length]  ,			self.type_vocab_size     )

           __magic_name__ 		=			None
           __magic_name__ 		=			None
           __magic_name__ 		=			None
           if self.use_labels:
                 __magic_name__ 		=			ids_tensor([self.batch_size]  ,			self.type_sequence_label_size     )
                 __magic_name__ 		=			ids_tensor([self.batch_size, self.seq_length]  ,			self.num_labels     )
                 __magic_name__ 		=			ids_tensor([self.batch_size]  ,			self.num_choices     )
                 __magic_name__ 		=			ids_tensor([self.batch_size, self.seq_length]  ,			1     )

           __magic_name__ 		=			self.get_config()

           return (
               config,
               input_ids,
               token_type_ids,
               input_mask,
               sequence_labels,
               token_labels,
               choice_labels,
               fake_token_labels,
           )
     def   a (	self					:	Dict     )       ->				int:



           '''simple docstring'''





           return FunnelConfig(
               vocab_size=self.vocab_size  ,			block_sizes=self.block_sizes  ,			num_decoder_layers=self.num_decoder_layers  ,			d_model=self.d_model  ,			n_head=self.n_head  ,			d_head=self.d_head  ,			d_inner=self.d_inner  ,			hidden_act=self.hidden_act  ,			hidden_dropout=self.hidden_dropout  ,			attention_dropout=self.attention_dropout  ,			activation_dropout=self.activation_dropout  ,			max_position_embeddings=self.max_position_embeddings  ,			type_vocab_size=self.type_vocab_size  ,			initializer_std=self.initializer_std  ,			)
     def   a (	self					:	str  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Union[str, Any]  ,			)       ->				Dict:



           '''simple docstring'''





           __magic_name__ 		=			FunnelModel(config=__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE     )
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE     )
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.d_model)     )

           __magic_name__ 		=			False
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.d_model)     )

           __magic_name__ 		=			False
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, self.seq_length, self.d_model)     )
     def   a (	self					:	Dict  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	str  ,			)       ->				int:



           '''simple docstring'''





           __magic_name__ 		=			FunnelBaseModel(config=__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE     )
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE     )
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, 2, self.d_model)     )

           __magic_name__ 		=			False
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, 3, self.d_model)     )

           __magic_name__ 		=			False
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.last_hidden_state.shape  ,			(self.batch_size, 2, self.d_model)     )
     def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Dict  ,			)       ->				Optional[int]:



           '''simple docstring'''





           __magic_name__ 		=			self.num_labels
           __magic_name__ 		=			FunnelForPreTraining(config=__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE  ,			labels=__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length)     )
     def   a (	self					:	str  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	str  ,			)       ->				Union[str, Any]:



           '''simple docstring'''





           __magic_name__ 		=			FunnelForMaskedLM(config=__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE  ,			labels=__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.vocab_size)     )
     def   a (	self					:	str  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Union[str, Any]  ,			)       ->				str:



           '''simple docstring'''





           __magic_name__ 		=			self.num_labels
           __magic_name__ 		=			FunnelForSequenceClassification(__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE  ,			labels=__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_labels)     )
     def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Optional[Any]  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	int  ,			)       ->				List[str]:



           '''simple docstring'''





           __magic_name__ 		=			self.num_choices
           __magic_name__ 		=			FunnelForMultipleChoice(config=__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			input_ids.unsqueeze(1     ).expand(-1  ,			self.num_choices  ,			-1     ).contiguous()
           __magic_name__ 		=			token_type_ids.unsqueeze(1     ).expand(-1  ,			self.num_choices  ,			-1     ).contiguous()
           __magic_name__ 		=			input_mask.unsqueeze(1     ).expand(-1  ,			self.num_choices  ,			-1     ).contiguous()
           __magic_name__ 		=			model(
               __SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE  ,			labels=__SCREAMING_SNAKE_CASE  ,			)
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.num_choices)     )
     def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Optional[Any]  ,			)       ->				Tuple:



           '''simple docstring'''





           __magic_name__ 		=			self.num_labels
           __magic_name__ 		=			FunnelForTokenClassification(config=__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE  ,			labels=__SCREAMING_SNAKE_CASE     )
           self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.seq_length, self.num_labels)     )
     def   a (	self					:	Tuple  ,			_lowerCAmelCase					:	List[str]  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	Dict  ,			)       ->				int:



           '''simple docstring'''





           __magic_name__ 		=			FunnelForQuestionAnswering(config=__SCREAMING_SNAKE_CASE     )
           model.to(__SCREAMING_SNAKE_CASE     )
           model.eval()
           __magic_name__ 		=			model(
               __SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE  ,			start_positions=__SCREAMING_SNAKE_CASE  ,			end_positions=__SCREAMING_SNAKE_CASE  ,			)
           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					:	int     )       ->				str:



           '''simple docstring'''





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







@require_torch
class     __snake_case							(      lowerCAmelCase_  ,			lowerCAmelCase_  ,			unittest.TestCase):
     SCREAMING_SNAKE_CASE_		:					Optional[int] 				= False
     SCREAMING_SNAKE_CASE_		:					Tuple 				= False
     SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= (
         (
             FunnelModel,
             FunnelForMaskedLM,
             FunnelForPreTraining,
             FunnelForQuestionAnswering,
             FunnelForTokenClassification,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= (
         {
             """feature-extraction""": (FunnelBaseModel, FunnelModel),
             """fill-mask""": FunnelForMaskedLM,
             """question-answering""": FunnelForQuestionAnswering,
             """text-classification""": FunnelForSequenceClassification,
             """token-classification""": FunnelForTokenClassification,
             """zero-shot""": FunnelForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     def   a (	self					:	Optional[int]  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	Optional[Any]=False     )       ->				List[Any]:



           '''simple docstring'''





           __magic_name__ 		=			super()._prepare_for_class(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE  ,			return_labels=__SCREAMING_SNAKE_CASE     )

           if return_labels:
                 if model_class in get_values(__SCREAMING_SNAKE_CASE     ):
                       __magic_name__ 		=			torch.zeros(
                           (self.model_tester.batch_size, self.model_tester.seq_length)  ,			dtype=torch.long  ,			device=__SCREAMING_SNAKE_CASE     )
           return inputs_dict
     def   a (	self					:	Dict     )       ->				int:



           '''simple docstring'''





           __magic_name__ 		=			FunnelModelTester(self     )
           __magic_name__ 		=			ConfigTester(self  ,			config_class=__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	int     )       ->				str:



           '''simple docstring'''





           self.config_tester.run_common_tests()
     def   a (	self					:	str     )       ->				int:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	Optional[Any]     )       ->				List[Any]:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_pretraining(*__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	Any     )       ->				Tuple:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_lm(*__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	Optional[Any]     )       ->				Dict:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	str     )       ->				Dict:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*__SCREAMING_SNAKE_CASE     )







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



           '''simple docstring'''





           if hasattr(__SCREAMING_SNAKE_CASE  ,			'weight'     ) and module.weight is not None:
                 module.weight.data.fill_(3     )
           if hasattr(__SCREAMING_SNAKE_CASE  ,			'bias'     ) and module.bias is not None:
                 module.bias.data.fill_(3     )

           for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
                 if hasattr(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE     ) and getattr(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE     ) is not None:
                       __magic_name__ 		=			getattr(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE     )
                       weight.data.fill_(3     )







@require_torch
class     __snake_case							(      lowerCAmelCase_  ,			unittest.TestCase):
     SCREAMING_SNAKE_CASE_		:					str 				= False
     SCREAMING_SNAKE_CASE_		:					Dict 				= False
     SCREAMING_SNAKE_CASE_		:					Any 				= (
         (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
     )
     def   a (	self					:	Union[str, Any]     )       ->				List[str]:



           '''simple docstring'''





           __magic_name__ 		=			FunnelModelTester(self  ,			base=__SCREAMING_SNAKE_CASE     )
           __magic_name__ 		=			ConfigTester(self  ,			config_class=__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	Optional[Any]     )       ->				Any:



           '''simple docstring'''





           self.config_tester.run_common_tests()
     def   a (	self					:	Optional[int]     )       ->				int:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_base_model(*__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	Optional[int]     )       ->				List[str]:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	Union[str, Any]     )       ->				List[str]:



           '''simple docstring'''





           __magic_name__ 		=			self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_multiple_choice(*__SCREAMING_SNAKE_CASE     )
     def   a (	self					:	Dict     )       ->				Any:



           '''simple docstring'''





           __magic_name__		,			__magic_name__ 		=			self.model_tester.prepare_config_and_inputs_for_common()
           __magic_name__ 		=			True

           for model_class in self.all_model_classes:
                 if model_class.__name__ == "FunnelBaseModel":
                       continue
                 __magic_name__ 		=			model_class(__SCREAMING_SNAKE_CASE     )
                 model.to(__SCREAMING_SNAKE_CASE     )
                 model.train()
                 __magic_name__ 		=			self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE  ,			return_labels=__SCREAMING_SNAKE_CASE     )
                 __magic_name__ 		=			model(**__SCREAMING_SNAKE_CASE     ).loss
                 loss.backward()







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



           '''simple docstring'''





           if hasattr(__SCREAMING_SNAKE_CASE  ,			'weight'     ) and module.weight is not None:
                 module.weight.data.fill_(3     )
           if hasattr(__SCREAMING_SNAKE_CASE  ,			'bias'     ) and module.bias is not None:
                 module.bias.data.fill_(3     )

           for param in ["r_w_bias", "r_r_bias", "r_kernel", "r_s_bias", "seg_embed"]:
                 if hasattr(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE     ) and getattr(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE     ) is not None:
                       __magic_name__ 		=			getattr(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE     )
                       weight.data.fill_(3     )







@require_torch
@require_sentencepiece
@require_tokenizers
class     __snake_case							(      unittest.TestCase):
     def   a (	self					:	Optional[Any]     )       ->				Optional[Any]:



           '''simple docstring'''





           __magic_name__ 		=			1_3
           __magic_name__ 		=			7
           __magic_name__ 		=			torch.arange(0  ,			batch_size * sequence_length     ).long().reshape(__SCREAMING_SNAKE_CASE  ,			__SCREAMING_SNAKE_CASE     )
           __magic_name__ 		=			[0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
           __magic_name__ 		=			torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]     )

           __magic_name__ 		=			FunnelModel.from_pretrained('sgugger/funnel-random-tiny'     )
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE     )[0].abs()

           __magic_name__ 		=			torch.tensor(2344.8352     )
           __magic_name__ 		=			torch.tensor(0.8052     )
           self.assertTrue(torch.allclose(output.sum()  ,			__SCREAMING_SNAKE_CASE  ,			atol=1E-4     )     )
           self.assertTrue(torch.allclose(output.mean()  ,			__SCREAMING_SNAKE_CASE  ,			atol=1E-4     )     )

           __magic_name__ 		=			torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]     )
           __magic_name__ 		=			model(__SCREAMING_SNAKE_CASE  ,			attention_mask=__SCREAMING_SNAKE_CASE  ,			token_type_ids=__SCREAMING_SNAKE_CASE     )[0].abs()

           __magic_name__ 		=			torch.tensor(2343.8425     )
           __magic_name__ 		=			torch.tensor(0.8049     )
           self.assertTrue(torch.allclose(output.sum()  ,			__SCREAMING_SNAKE_CASE  ,			atol=1E-4     )     )
           self.assertTrue(torch.allclose(output.mean()  ,			__SCREAMING_SNAKE_CASE  ,			atol=1E-4     )     )







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



           '''simple docstring'''





           __magic_name__ 		=			FunnelTokenizer.from_pretrained('huggingface/funnel-small'     )
           __magic_name__ 		=			FunnelModel.from_pretrained('huggingface/funnel-small'     )
           __magic_name__ 		=			tokenizer('Hello! I am the Funnel Transformer model.'  ,			return_tensors='pt'     )
           __magic_name__ 		=			model(**__SCREAMING_SNAKE_CASE     )[0]

           __magic_name__ 		=			torch.tensor(235.7246     )
           __magic_name__ 		=			torch.tensor(0.0256     )
           self.assertTrue(torch.allclose(output.sum()  ,			__SCREAMING_SNAKE_CASE  ,			atol=1E-4     )     )
           self.assertTrue(torch.allclose(output.mean()  ,			__SCREAMING_SNAKE_CASE  ,			atol=1E-4     )     )





import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase :				List[Any]						    =       16
UpperCamelCase :				Dict						    =       32
def 							UpperCamelCase_  (				__snake_case			:						Any	,			__snake_case			:						Tuple     )->      List[Any]:




						'''simple docstring'''
						# Initialize accelerator
						if args.with_tracking:
												__magic_name__ 		=			Accelerator(
												    cpu=args.cpu	,			mixed_precision=args.mixed_precision	,			log_with='all'	,			project_dir=args.project_dir     )
						else:
												__magic_name__ 		=			Accelerator(cpu=args.cpu	,			mixed_precision=args.mixed_precision     )

						if hasattr(args.checkpointing_steps	,			'isdigit'     ):
												if args.checkpointing_steps == "epoch":
																		__magic_name__ 		=			args.checkpointing_steps
												elif args.checkpointing_steps.isdigit():
																		__magic_name__ 		=			int(args.checkpointing_steps     )
												else:
																		raise ValueError(
																		    F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''     )
						else:
												__magic_name__ 		=			None
						# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
						__magic_name__ 		=			config['lr']
						__magic_name__ 		=			int(config['num_epochs']     )
						__magic_name__ 		=			int(config['seed']     )
						__magic_name__ 		=			int(config['batch_size']     )

						# We need to initialize the trackers we use, and also store our configuration
						if args.with_tracking:
												__magic_name__ 		=			os.path.split(__snake_case     )[-1].split('.'     )[0]
												accelerator.init_trackers(__snake_case	,			__snake_case     )

						__magic_name__ 		=			AutoTokenizer.from_pretrained('bert-base-cased'     )
						__magic_name__ 		=			load_dataset('glue'	,			'mrpc'     )
						__magic_name__ 		=			evaluate.load('glue'	,			'mrpc'     )

						def tokenize_function(__snake_case			:						Dict     ):
												# max_length=None => use the model max length (it's actually the default)
												__magic_name__ 		=			tokenizer(examples['sentence1']	,			examples['sentence2']	,			truncation=__snake_case	,			max_length=__snake_case     )
												return outputs

						# Apply the method we just defined to all the examples in all the splits of the dataset
						# starting with the main process first:
						with accelerator.main_process_first():
												__magic_name__ 		=			datasets.map(
												    __snake_case	,			batched=__snake_case	,			remove_columns=['idx', 'sentence1', 'sentence2']	,			)

						# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
						# transformers library
						__magic_name__ 		=			tokenized_datasets.rename_column('label'	,			'labels'     )

						# If the batch size is too big we use gradient accumulation
						__magic_name__ 		=			1
						if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
												__magic_name__ 		=			batch_size // MAX_GPU_BATCH_SIZE
												__magic_name__ 		=			MAX_GPU_BATCH_SIZE

						def collate_fn(__snake_case			:						Tuple     ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												__magic_name__ 		=			1_28 if accelerator.distributed_type == DistributedType.TPU else None
												# When using mixed precision we want round multiples of 8/16
												if accelerator.mixed_precision == "fp8":
																		__magic_name__ 		=			16
												elif accelerator.mixed_precision != "no":
																		__magic_name__ 		=			8
												else:
																		__magic_name__ 		=			None

												return tokenizer.pad(
												    __snake_case	,			padding='longest'	,			max_length=__snake_case	,			pad_to_multiple_of=__snake_case	,			return_tensors='pt'	,			)

						# Instantiate dataloaders.
						__magic_name__ 		=			DataLoader(
						    tokenized_datasets['train']	,			shuffle=__snake_case	,			collate_fn=__snake_case	,			batch_size=__snake_case     )
						__magic_name__ 		=			DataLoader(
						    tokenized_datasets['validation']	,			shuffle=__snake_case	,			collate_fn=__snake_case	,			batch_size=__snake_case     )

						set_seed(__snake_case     )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__magic_name__ 		=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'	,			return_dict=__snake_case     )

						# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
						# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
						# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
						__magic_name__ 		=			model.to(accelerator.device     )

						# Instantiate optimizer
						__magic_name__ 		=			AdamW(params=model.parameters()	,			lr=__snake_case     )

						# Instantiate scheduler
						__magic_name__ 		=			get_linear_schedule_with_warmup(
						    optimizer=__snake_case	,			num_warmup_steps=1_00	,			num_training_steps=(len(__snake_case     ) * num_epochs) // gradient_accumulation_steps	,			)

						# Prepare everything
						# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
						# prepare method.
						__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__		,			__magic_name__ 		=			accelerator.prepare(
						    __snake_case	,			__snake_case	,			__snake_case	,			__snake_case	,			__snake_case     )

						# We need to keep track of how many total steps we have iterated over
						__magic_name__ 		=			0
						# We also need to keep track of the stating epoch so files are named properly
						__magic_name__ 		=			0

						# Potentially load in the weights and states from a previous save
						if args.resume_from_checkpoint:
												if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
																		accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''     )
																		accelerator.load_state(args.resume_from_checkpoint     )
																		__magic_name__ 		=			os.path.basename(args.resume_from_checkpoint     )
												else:
																		# Get the most recent checkpoint
																		__magic_name__ 		=			[f.name for f in os.scandir(os.getcwd()     ) if f.is_dir()]
																		dirs.sort(key=os.path.getctime     )
																		__magic_name__ 		=			dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
												# Extract `epoch_{i}` or `step_{i}`
												__magic_name__ 		=			os.path.splitext(__snake_case     )[0]

												if "epoch" in training_difference:
																		__magic_name__ 		=			int(training_difference.replace('epoch_'	,			''     )     ) + 1
																		__magic_name__ 		=			None
												else:
																		__magic_name__ 		=			int(training_difference.replace('step_'	,			''     )     )
																		__magic_name__ 		=			resume_step // len(__snake_case     )
																		resume_step -= starting_epoch * len(__snake_case     )

    # Now we train the model
						for epoch in range(__snake_case	,			__snake_case     ):
												model.train()
												if args.with_tracking:
																		__magic_name__ 		=			0
												if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
																		# We need to skip steps until we reach the resumed step
																		__magic_name__ 		=			accelerator.skip_first_batches(__snake_case	,			__snake_case     )
																		overall_step += resume_step
												else:
																		# After the first iteration though, we need to go back to the original dataloader
																		__magic_name__ 		=			train_dataloader
												for step, batch in enumerate(__snake_case     ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device     )
																		__magic_name__ 		=			model(**__snake_case     )
																		__magic_name__ 		=			outputs.loss
																		__magic_name__ 		=			loss / gradient_accumulation_steps
																		# We keep track of the loss at each epoch
																		if args.with_tracking:
																								total_loss += loss.detach().float()
																		accelerator.backward(__snake_case     )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

																		if isinstance(__snake_case	,			__snake_case     ):
																								__magic_name__ 		=			F'''step_{overall_step}'''
																								if overall_step % checkpointing_steps == 0:
																														if args.output_dir is not None:
																																				__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																														accelerator.save_state(__snake_case     )

												model.eval()
												for step, batch in enumerate(__snake_case     ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device     )
																		with torch.no_grad():
																								__magic_name__ 		=			model(**__snake_case     )
																		__magic_name__ 		=			outputs.logits.argmax(dim=-1     )
																		__magic_name__		,			__magic_name__ 		=			accelerator.gather_for_metrics((predictions, batch['labels'])     )
																		metric.add_batch(
																		    predictions=__snake_case	,			references=__snake_case	,			)

												__magic_name__ 		=			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(F'''epoch {epoch}:'''	,			__snake_case     )
												if args.with_tracking:
																		accelerator.log(
																		    {
																		        'accuracy': eval_metric['accuracy'],
																		        'f1': eval_metric['f1'],
																		        'train_loss': total_loss.item() / len(__snake_case     ),
																		        'epoch': epoch,
																		    }	,			step=__snake_case	,			)

												if checkpointing_steps == "epoch":
																		__magic_name__ 		=			F'''epoch_{epoch}'''
																		if args.output_dir is not None:
																								__magic_name__ 		=			os.path.join(args.output_dir	,			__snake_case     )
																		accelerator.save_state(__snake_case     )

						if args.with_tracking:
												accelerator.end_training()
def 							UpperCamelCase_  (				)->      Dict:




						'''simple docstring'''
						__magic_name__ 		=			argparse.ArgumentParser(description='Simple example of training script.'     )
						parser.add_argument(
						    '--mixed_precision'	,			type=__snake_case	,			default=__snake_case	,			choices=['no', 'fp16', 'bf16', 'fp8']	,			help='Whether to use mixed precision. Choose'
						    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
						    'and an Nvidia Ampere GPU.'	,			)
						parser.add_argument('--cpu'	,			action='store_true'	,			help='If passed, will train on the CPU.'     )
						parser.add_argument(
						    '--checkpointing_steps'	,			type=__snake_case	,			default=__snake_case	,			help='Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'	,			)
						parser.add_argument(
						    '--resume_from_checkpoint'	,			type=__snake_case	,			default=__snake_case	,			help='If the training should continue from a checkpoint folder.'	,			)
						parser.add_argument(
						    '--with_tracking'	,			action='store_true'	,			help='Whether to load in all available experiment trackers from the environment and use them for logging.'	,			)
						parser.add_argument(
						    '--output_dir'	,			type=__snake_case	,			default='.'	,			help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'	,			)
						parser.add_argument(
						    '--project_dir'	,			type=__snake_case	,			default='logs'	,			help='Location on where to store experiment tracking logs` and relevent project information'	,			)
						__magic_name__ 		=			parser.parse_args()
						__magic_name__ 		=			{'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
						training_function(__snake_case	,			__snake_case     )


if __name__ == "__main__":
					main()

