"""simple docstring"""






import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger
class 				snake_case       ( lowerCAmelCase__				):




					"""simple docstring"""

					def __init__(				self   :	Optional[int]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	int			):
									UpperCAmelCase__             =							params

									UpperCAmelCase__             =							np.array(lowercase_			)
									UpperCAmelCase__             =							np.array([len(lowercase_			) for t in data]			)

									self.check()
									self.remove_long_sequences()
									self.remove_empty_sequences()
									self.remove_unknown_sequences()
									self.check()
									self.print_statistics()







					def __getitem__(				self   :	Dict    ,lowerCamelCase__   :	Union[str, Any]			):
									return (self.token_ids[index], self.lengths[index])







					def __len__(				self   :	List[Any]			):
									return len(self.lengths			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									assert len(self.token_ids			) == len(self.lengths			)
									assert all(self.lengths[i] == len(self.token_ids[i]			) for i in range(len(self.lengths			)			)			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							self.params.max_model_input_size
									UpperCAmelCase__             =							self.lengths > max_len
									logger.info(f'''Splitting {sum(lowercase_			)} too long sequences.'''			)

									def divide_chunks(lowerCamelCase__   :	Tuple    ,lowerCamelCase__   :	Optional[Any]			):
													return [l[i : i + n] for i in range(0    ,len(lowercase_			)    ,lowercase_			)]

									UpperCAmelCase__             =							[]
									UpperCAmelCase__             =							[]
									if self.params.mlm:
													UpperCAmelCase__             =							self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
									else:
													UpperCAmelCase__             =							self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

									for seq_, len_ in zip(self.token_ids    ,self.lengths			):
													assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
													if len_ <= max_len:
																	new_tok_ids.append(seq_			)
																	new_lengths.append(len_			)
													else:
																	UpperCAmelCase__             =							[]
																	for sub_s in divide_chunks(seq_    ,max_len - 2			):
																					if sub_s[0] != cls_id:
																									UpperCAmelCase__             =							np.insert(lowercase_    ,0    ,lowercase_			)
																					if sub_s[-1] != sep_id:
																									UpperCAmelCase__             =							np.insert(lowercase_    ,len(lowercase_			)    ,lowercase_			)
																					assert len(lowercase_			) <= max_len
																					assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																					sub_seqs.append(lowercase_			)

																	new_tok_ids.extend(lowercase_			)
																	new_lengths.extend([len(lowercase_			) for l in sub_seqs]			)

									UpperCAmelCase__             =							np.array(lowercase_			)
									UpperCAmelCase__             =							np.array(lowercase_			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							len(self			)
									UpperCAmelCase__             =							self.lengths > 11
									UpperCAmelCase__             =							self.token_ids[indices]
									UpperCAmelCase__             =							self.lengths[indices]
									UpperCAmelCase__             =							len(self			)
									logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''			)







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									if "unk_token" not in self.params.special_tok_ids:
													return
									else:
													UpperCAmelCase__             =							self.params.special_tok_ids['''unk_token''']
									UpperCAmelCase__             =							len(self			)
									UpperCAmelCase__             =							np.array([np.count_nonzero(a == unk_token_id			) for a in self.token_ids]			)
									UpperCAmelCase__             =							(unk_occs / self.lengths) < 0.5
									UpperCAmelCase__             =							self.token_ids[indices]
									UpperCAmelCase__             =							self.lengths[indices]
									UpperCAmelCase__             =							len(self			)
									logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).'''			)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									if not self.params.is_master:
													return
									logger.info(f'''{len(self			)} sequences'''			)
									# data_len = sum(self.lengths)
									# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
									# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

									# unk_idx = self.params.special_tok_ids['unk_token']
									# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
									# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







					def 		__lowerCAmelCase						(				self   :	List[str]    ,lowerCamelCase__   :	Dict			):
									UpperCAmelCase__             =							[t[0] for t in batch]
									UpperCAmelCase__             =							[t[1] for t in batch]
									assert len(lowercase_			) == len(lowercase_			)

									# Max for paddings
									UpperCAmelCase__             =							max(lowercase_			)

									# Pad token ids
									if self.params.mlm:
													UpperCAmelCase__             =							self.params.special_tok_ids['''pad_token''']
									else:
													UpperCAmelCase__             =							self.params.special_tok_ids['''unk_token''']
									UpperCAmelCase__             =							[list(t.astype(lowercase_			)			) + [pad_idx] * (max_seq_len_ - len(lowercase_			)) for t in token_ids]
									assert len(tk_			) == len(lowercase_			)
									assert all(len(lowercase_			) == max_seq_len_ for t in tk_			)

									UpperCAmelCase__             =							torch.tensor(tk_			)  # (bs, max_seq_len_)
									UpperCAmelCase__             =							torch.tensor(lowercase_			)  # (bs)
									return tk_t, lg_t








"""simple docstring"""

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
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 (
         OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
         OpenAIGPTConfig,
         OpenAIGPTDoubleHeadsModel,
         OpenAIGPTForSequenceClassification,
         OpenAIGPTLMHeadModel,
         OpenAIGPTModel,
     )




class        _UpperCAmelCase       :

 def __init__(     self		:       Union[str, Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       int=13  ,     lowercase_		:       Optional[int]=7  ,     lowercase_		:       Any=True  ,     lowercase_		:       Dict=True  ,     lowercase_		:       Dict=True  ,     lowercase_		:       Optional[Any]=99  ,     lowercase_		:       Union[str, Any]=32  ,     lowercase_		:       str=5  ,     lowercase_		:       Union[str, Any]=4  ,     lowercase_		:       Any=37  ,     lowercase_		:       Tuple="gelu"  ,     lowercase_		:       Dict=0.1  ,     lowercase_		:       Tuple=0.1  ,     lowercase_		:       Optional[int]=512  ,     lowercase_		:       Optional[Any]=16  ,     lowercase_		:       Optional[Any]=2  ,     lowercase_		:       Optional[Any]=0.02  ,     lowercase_		:       List[Any]=3  ,     lowercase_		:       Union[str, Any]=4  ,     lowercase_		:       List[Any]=None  ,     ):
     snake_case_ :     Any				=	parent
     snake_case_ :     List[str]				=	batch_size
     snake_case_ :     List[Any]				=	seq_length
     snake_case_ :     Optional[int]				=	is_training
     snake_case_ :     Union[str, Any]				=	use_token_type_ids
     snake_case_ :     Optional[Any]				=	use_labels
     snake_case_ :     Union[str, Any]				=	vocab_size
     snake_case_ :     Any				=	hidden_size
     snake_case_ :     List[Any]				=	num_hidden_layers
     snake_case_ :     Any				=	num_attention_heads
     snake_case_ :     Dict				=	intermediate_size
     snake_case_ :     Union[str, Any]				=	hidden_act
     snake_case_ :     Optional[int]				=	hidden_dropout_prob
     snake_case_ :     Optional[Any]				=	attention_probs_dropout_prob
     snake_case_ :     Tuple				=	max_position_embeddings
     snake_case_ :     int				=	type_vocab_size
     snake_case_ :     Tuple				=	type_sequence_label_size
     snake_case_ :     str				=	initializer_range
     snake_case_ :     Tuple				=	num_labels
     snake_case_ :     str				=	num_choices
     snake_case_ :     Any				=	scope
     snake_case_ :     Dict				=	self.vocab_size - 1

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Optional[Any]				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size				)

     snake_case_ :     Optional[Any]				=	None
     if self.use_token_type_ids:
         snake_case_ :     Tuple				=	ids_tensor([self.batch_size, self.seq_length]  ,     self.type_vocab_size				)

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

     snake_case_ :     int				=	OpenAIGPTConfig(
         vocab_size=self.vocab_size  ,     n_embd=self.hidden_size  ,     n_layer=self.num_hidden_layers  ,     n_head=self.num_attention_heads  ,     n_positions=self.max_position_embeddings  ,     pad_token_id=self.pad_token_id  ,     )

     snake_case_ :     Any				=	ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,     2				)

     return (
         config,
         input_ids,
         head_mask,
         token_type_ids,
         sequence_labels,
         token_labels,
         choice_labels,
     )

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Any  ,     lowercase_		:       Union[str, Any]  ,     lowercase_		:       str  ,     lowercase_		:       Dict  ,     *lowercase_		:       Dict				):
     snake_case_ :     List[Any]				=	OpenAIGPTModel(config=lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Any				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     head_mask=lowercase_				)
     snake_case_ :     Optional[Any]				=	model(lowercase_  ,     token_type_ids=lowercase_				)
     snake_case_ :     Optional[Any]				=	model(lowercase_				)

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

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Dict  ,     lowercase_		:       str  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       List[Any]  ,     *lowercase_		:       Optional[Any]				):
     snake_case_ :     Union[str, Any]				=	OpenAIGPTLMHeadModel(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Union[str, Any]				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     labels=lowercase_				)
     self.parent.assertEqual(result.loss.shape  ,     ()				)
     self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)				)

 def 						_snake_case					(     self		:       List[str]  ,     lowercase_		:       Dict  ,     lowercase_		:       List[str]  ,     lowercase_		:       Any  ,     lowercase_		:       Dict  ,     *lowercase_		:       Union[str, Any]				):
     snake_case_ :     Tuple				=	OpenAIGPTDoubleHeadsModel(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Dict				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     labels=lowercase_				)
     self.parent.assertEqual(result.loss.shape  ,     ()				)
     self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)				)

 def 						_snake_case					(     self		:       Any  ,     lowercase_		:       str  ,     lowercase_		:       List[str]  ,     lowercase_		:       Optional[Any]  ,     lowercase_		:       Optional[Any]  ,     *lowercase_		:       Any				):
     snake_case_ :     int				=	self.num_labels
     snake_case_ :     Any				=	OpenAIGPTForSequenceClassification(lowercase_				)
     model.to(lowercase_				)
     model.eval()

     snake_case_ :     Optional[int]				=	ids_tensor([self.batch_size]  ,     self.type_sequence_label_size				)
     snake_case_ :     Optional[Any]				=	model(lowercase_  ,     token_type_ids=lowercase_  ,     labels=lowercase_				)
     self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)				)






 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Dict				=	self.prepare_config_and_inputs()
     (
         (
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	(
         snake_case_
     ),	
     ) :     str				=	config_and_inputs
     snake_case_ :     str				=	{
         '''input_ids''': input_ids,
         '''token_type_ids''': token_type_ids,
         '''head_mask''': head_mask,
     }

     return config, inputs_dict





@require_torch
class        _UpperCAmelCase       (			lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     unittest.TestCase):
 _lowerCAmelCase   :			Dict							=		(
     (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
     if is_torch_available()
     else ()
 )
 _lowerCAmelCase   :			int							=		(
     (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
 )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
 _lowerCAmelCase   :			Union[str, Any]							=		(
     {
         """feature-extraction""": OpenAIGPTModel,
         """text-classification""": OpenAIGPTForSequenceClassification,
         """text-generation""": OpenAIGPTLMHeadModel,
         """zero-shot""": OpenAIGPTForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )

 def 						_snake_case					(     self		:       Tuple  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       int  ,     lowercase_		:       List[Any]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Union[str, Any]				):
     if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
         # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
         # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
         # tiny config could not be created.
         return True

     return False

 def 						_snake_case					(     self		:       Optional[int]  ,     lowercase_		:       List[Any]  ,     lowercase_		:       Optional[int]  ,     lowercase_		:       List[str]=False				):
     snake_case_ :     Dict				=	super()._prepare_for_class(lowercase_  ,     lowercase_  ,     return_labels=lowercase_				)

     if return_labels:
         if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
             snake_case_ :     List[str]				=	torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)  ,     dtype=torch.long  ,     device=lowercase_  ,     )
             snake_case_ :     int				=	inputs_dict['''labels''']
             snake_case_ :     Optional[Any]				=	inputs_dict['''labels''']
             snake_case_ :     int				=	torch.zeros(
                 (self.model_tester.batch_size, self.model_tester.num_choices)  ,     dtype=torch.long  ,     device=lowercase_  ,     )
             snake_case_ :     Tuple				=	torch.zeros(
                 self.model_tester.batch_size  ,     dtype=torch.long  ,     device=lowercase_				)
     return inputs_dict

 def 						_snake_case					(     self		:       Any				):
     snake_case_ :     List[str]				=	OpenAIGPTModelTester(self				)
     snake_case_ :     Dict				=	ConfigTester(self  ,     config_class=lowercase_  ,     n_embd=37				)

 def 						_snake_case					(     self		:       List[str]				):
     self.config_tester.run_common_tests()

 def 						_snake_case					(     self		:       Optional[Any]				):
     snake_case_ :     Tuple				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_model(*lowercase_				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     List[str]				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_lm_head_model(*lowercase_				)

 def 						_snake_case					(     self		:       int				):
     snake_case_ :     Any				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_double_lm_head_model(*lowercase_				)

 def 						_snake_case					(     self		:       List[str]				):
     snake_case_ :     Optional[int]				=	self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_				)






 @slow
 def 						_snake_case					(     self		:       Dict				):
     for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         snake_case_ :     Optional[Any]				=	OpenAIGPTModel.from_pretrained(lowercase_				)
         self.assertIsNotNone(lowercase_				)





@require_torch
class        _UpperCAmelCase       (			unittest.TestCase):

 @slow
 def 						_snake_case					(     self		:       Optional[int]				):
     snake_case_ :     Optional[Any]				=	OpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''				)
     model.to(lowercase_				)
     snake_case_ :     List[str]				=	torch.tensor([[481, 4735, 544]]  ,     dtype=torch.long  ,     device=lowercase_				)  # the president is
     snake_case_ :     List[Any]				=	[
         481,
         4735,
         544,
         246,
         963,
         870,
         762,
         239,
         244,
         40477,
         244,
         249,
         719,
         881,
         487,
         544,
         240,
         244,
         603,
         481,
     ]  # the president is a very good man. " \n " i\'m sure he is, " said the

     snake_case_ :     Optional[Any]				=	model.generate(lowercase_  ,     do_sample=lowercase_				)
     self.assertListEqual(output_ids[0].tolist()  ,     lowercase_				)





