"""simple docstring"""




import os
import tempfile
import unittest
from pathlib import Path

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


if is_torch_available():
					from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class     UpperCamelCase__(			unittest.TestCase ):





						def 				snake_case__	(       self		,__UpperCAmelCase     )					->				Optional[Any]:
													for model_result in results.values():
																				for batch_size, sequence_length in zip(model_result['bs']		,model_result['ss']     ):
																											A__	     =							model_result['result'][batch_size][sequence_length]
																											self.assertIsNotNone(__UpperCAmelCase     )





						def 				snake_case__	(       self     )					->				Dict:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							'sgugger/tiny-distilbert-classification'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,only_pretrain_model=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,torchscript=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						@unittest.skipIf(torch_device == 'cpu'		,'Cant do half precision'     )
						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,fpaa=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				Any:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													# set architectures equal to `None`
													A__	     =							None
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				Tuple:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						@unittest.skipIf(torch_device == 'cpu'		,'Can\'t do half precision'     )
						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,fpaa=__UpperCAmelCase		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				int:
													A__	     =							'sshleifer/tinier_bart'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_inference_result     )
													self.check_results_dict_not_empty(results.memory_inference_result     )





						def 				snake_case__	(       self     )					->				List[str]:
													A__	     =							'sshleifer/tiny-gpt2'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						def 				snake_case__	(       self     )					->				Optional[int]:
													A__	     =							'sshleifer/tinier_bart'
													A__	     =							AutoConfig.from_pretrained(__UpperCAmelCase     )
													A__	     =							PyTorchBenchmarkArguments(
													    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,multi_process=__UpperCAmelCase		,)
													A__	     =							PyTorchBenchmark(__UpperCAmelCase		,configs=[config]     )
													A__	     =							benchmark.run()
													self.check_results_dict_not_empty(results.time_train_result     )
													self.check_results_dict_not_empty(results.memory_train_result     )





						def 				snake_case__	(       self     )					->				Optional[Any]:
													A__	     =							'sshleifer/tiny-gpt2'
													with tempfile.TemporaryDirectory() as tmp_dir:
																				A__	     =							PyTorchBenchmarkArguments(
																				    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,save_to_csv=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,inference_time_csv_file=os.path.join(__UpperCAmelCase		,'inf_time.csv'     )		,train_memory_csv_file=os.path.join(__UpperCAmelCase		,'train_mem.csv'     )		,inference_memory_csv_file=os.path.join(__UpperCAmelCase		,'inf_mem.csv'     )		,train_time_csv_file=os.path.join(__UpperCAmelCase		,'train_time.csv'     )		,env_info_csv_file=os.path.join(__UpperCAmelCase		,'env.csv'     )		,multi_process=__UpperCAmelCase		,)
																				A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
																				benchmark.run()
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'inf_time.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'train_time.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'inf_mem.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'train_mem.csv'     )     ).exists()     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'env.csv'     )     ).exists()     )





						def 				snake_case__	(       self     )					->				str:
													A__	     =							'sshleifer/tiny-gpt2'

													def _check_summary_is_not_empty(__UpperCAmelCase     ):
																				self.assertTrue(hasattr(__UpperCAmelCase		,'sequential'     )     )
																				self.assertTrue(hasattr(__UpperCAmelCase		,'cumulative'     )     )
																				self.assertTrue(hasattr(__UpperCAmelCase		,'current'     )     )
																				self.assertTrue(hasattr(__UpperCAmelCase		,'total'     )     )

													with tempfile.TemporaryDirectory() as tmp_dir:
																				A__	     =							PyTorchBenchmarkArguments(
																				    models=[MODEL_ID]		,training=__UpperCAmelCase		,inference=__UpperCAmelCase		,sequence_lengths=[8]		,batch_sizes=[1]		,log_filename=os.path.join(__UpperCAmelCase		,'log.txt'     )		,log_print=__UpperCAmelCase		,trace_memory_line_by_line=__UpperCAmelCase		,multi_process=__UpperCAmelCase		,)
																				A__	     =							PyTorchBenchmark(__UpperCAmelCase     )
																				A__	     =							benchmark.run()
																				_check_summary_is_not_empty(result.inference_summary     )
																				_check_summary_is_not_empty(result.train_summary     )
																				self.assertTrue(Path(os.path.join(__UpperCAmelCase		,'log.txt'     )     ).exists()     )






"""simple docstring"""




import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
				import tensorflow as tf
class     UpperCamelCase__(			enum.Enum ):
						lowerCAmelCase__  :						Optional[Any]													=		0
						lowerCAmelCase__  :						Optional[int]													=		1
						lowerCAmelCase__  :						List[Any]													=		2






@add_end_docstrings(__A )
class     UpperCamelCase__(			__A ):
						lowerCAmelCase__  :						Optional[Any]													=		'\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    '





						def __init__(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Any:
													super().__init__(*__UpperCAmelCase		,**__UpperCAmelCase     )
													self.check_model_type(
													    TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == 'tf' else MODEL_FOR_CAUSAL_LM_MAPPING     )
													if "prefix" not in self._preprocess_params:
																				# This is very specific. The logic is quite complex and needs to be done
																				# as a "default".
																				# It also defines both some preprocess_kwargs and generate_kwargs
																				# which is why we cannot put them in their respective methods.
																				A__	     =							None
																				if self.model.config.prefix is not None:
																											A__	     =							self.model.config.prefix
																				if prefix is None and self.model.__class__.__name__ in [
																				    "XLNetLMHeadModel",
																				    "TransfoXLLMHeadModel",
																				    "TFXLNetLMHeadModel",
																				    "TFTransfoXLLMHeadModel",
																				]:
																											# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
																											A__	     =							self.XL_PREFIX
																				if prefix is not None:
																											# Recalculate some generate_kwargs linked to prefix.
																											A__     ,  A__     ,  A__	     =							self._sanitize_parameters(prefix=__UpperCAmelCase		,**self._forward_params     )
																											A__	     =							{**self._preprocess_params, **preprocess_params}
																											A__	     =							{**self._forward_params, **forward_params}





						def 				snake_case__	(       self		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,__UpperCAmelCase=None		,**__UpperCAmelCase		,)					->				Dict:
													A__	     =							{}
													if prefix is not None:
																				A__	     =							prefix
													if prefix:
																				A__	     =							self.tokenizer(
																				    __UpperCAmelCase		,padding=__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase		,return_tensors=self.framework     )
																				A__	     =							prefix_inputs['input_ids'].shape[-1]

													if handle_long_generation is not None:
																				if handle_long_generation not in {"hole"}:
																											raise ValueError(
																											    f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
																											    ' [None, \'hole\']'     )
																				A__	     =							handle_long_generation

													preprocess_params.update(__UpperCAmelCase     )
													A__	     =							generate_kwargs

													A__	     =							{}
													if return_full_text is not None and return_type is None:
																				if return_text is not None:
																											raise ValueError('`return_text` is mutually exclusive with `return_full_text`'     )
																				if return_tensors is not None:
																											raise ValueError('`return_full_text` is mutually exclusive with `return_tensors`'     )
																				A__	     =							ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
													if return_tensors is not None and return_type is None:
																				if return_text is not None:
																											raise ValueError('`return_text` is mutually exclusive with `return_tensors`'     )
																				A__	     =							ReturnType.TENSORS
													if return_type is not None:
																				A__	     =							return_type
													if clean_up_tokenization_spaces is not None:
																				A__	     =							clean_up_tokenization_spaces

													if stop_sequence is not None:
																				A__	     =							self.tokenizer.encode(__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase     )
																				if len(__UpperCAmelCase     ) > 1:
																											warnings.warn(
																											    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
																											    ' the stop sequence will be used as the stop sequence string in the interim.'     )
																				A__	     =							stop_sequence_ids[0]

													return preprocess_params, forward_params, postprocess_params





						def 				snake_case__	(       self		,*__UpperCAmelCase		,**__UpperCAmelCase     )					->				Dict:
													# Parse arguments
													if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
																				kwargs.update({'add_space_before_punct_symbol': True}     )

													return super()._parse_and_tokenize(*__UpperCAmelCase		,**__UpperCAmelCase     )





						def __call__(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Dict:
													return super().__call__(__UpperCAmelCase		,**__UpperCAmelCase     )





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=""		,__UpperCAmelCase=None		,**__UpperCAmelCase     )					->				Dict:
													A__	     =							self.tokenizer(
													    prefix + prompt_text		,padding=__UpperCAmelCase		,add_special_tokens=__UpperCAmelCase		,return_tensors=self.framework     )
													A__	     =							prompt_text

													if handle_long_generation == "hole":
																				A__	     =							inputs['input_ids'].shape[-1]
																				if "max_new_tokens" in generate_kwargs:
																											A__	     =							generate_kwargs['max_new_tokens']
																				else:
																											A__	     =							generate_kwargs.get('max_length'		,self.model.config.max_length     ) - cur_len
																											if new_tokens < 0:
																																		raise ValueError('We cannot infer how many new tokens are expected'     )
																				if cur_len + new_tokens > self.tokenizer.model_max_length:
																											A__	     =							self.tokenizer.model_max_length - new_tokens
																											if keep_length <= 0:
																																		raise ValueError(
																																		    'We cannot use `hole` to handle this generation the number of desired tokens exceeds the'
																																		    ' models max length'     )

																											A__	     =							inputs['input_ids'][:, -keep_length:]
																											if "attention_mask" in inputs:
																																		A__	     =							inputs['attention_mask'][:, -keep_length:]

													return inputs





						def 				snake_case__	(       self		,__UpperCAmelCase		,**__UpperCAmelCase     )					->				Optional[Any]:
													A__	     =							model_inputs['input_ids']
													A__	     =							model_inputs.get('attention_mask'		,__UpperCAmelCase     )
													# Allow empty prompts
													if input_ids.shape[1] == 0:
																				A__	     =							None
																				A__	     =							None
																				A__	     =							1
													else:
																				A__	     =							input_ids.shape[0]
													A__	     =							model_inputs.pop('prompt_text'     )

													# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
													# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
													A__	     =							generate_kwargs.pop('prefix_length'		,0     )
													if prefix_length > 0:
																				A__	     =							'max_new_tokens' in generate_kwargs or (
																				    'generation_config' in generate_kwargs
																				    and generate_kwargs['generation_config'].max_new_tokens is not None
																				)
																				if not has_max_new_tokens:
																											A__	     =							generate_kwargs.get('max_length'     ) or self.model.config.max_length
																											generate_kwargs["max_length"] += prefix_length
																				A__	     =							'min_new_tokens' in generate_kwargs or (
																				    'generation_config' in generate_kwargs
																				    and generate_kwargs['generation_config'].min_new_tokens is not None
																				)
																				if not has_min_new_tokens and "min_length" in generate_kwargs:
																											generate_kwargs["min_length"] += prefix_length

        # BS x SL
													A__	     =							self.model.generate(input_ids=__UpperCAmelCase		,attention_mask=__UpperCAmelCase		,**__UpperCAmelCase     )
													A__	     =							generated_sequence.shape[0]
													if self.framework == "pt":
																				A__	     =							generated_sequence.reshape(__UpperCAmelCase		,out_b // in_b		,*generated_sequence.shape[1:]     )
													elif self.framework == "tf":
																				A__	     =							tf.reshape(__UpperCAmelCase		,(in_b, out_b // in_b, *generated_sequence.shape[1:])     )
													return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}





						def 				snake_case__	(       self		,__UpperCAmelCase		,__UpperCAmelCase=ReturnType.FULL_TEXT		,__UpperCAmelCase=True     )					->				str:
													A__	     =							model_outputs['generated_sequence'][0]
													A__	     =							model_outputs['input_ids']
													A__	     =							model_outputs['prompt_text']
													A__	     =							generated_sequence.numpy().tolist()
													A__	     =							[]
													for sequence in generated_sequence:
																				if return_type == ReturnType.TENSORS:
																											A__	     =							{'generated_token_ids': sequence}
																				elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
																											# Decode text
																											A__	     =							self.tokenizer.decode(
																											    __UpperCAmelCase		,skip_special_tokens=__UpperCAmelCase		,clean_up_tokenization_spaces=__UpperCAmelCase		,)

																											# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
																											if input_ids is None:
																																		A__	     =							0
																											else:
																																		A__	     =							len(
																																		    self.tokenizer.decode(
																																		        input_ids[0]		,skip_special_tokens=__UpperCAmelCase		,clean_up_tokenization_spaces=__UpperCAmelCase		,)     )

																											if return_type == ReturnType.FULL_TEXT:
																																		A__	     =							prompt_text + text[prompt_length:]
																											else:
																																		A__	     =							text[prompt_length:]

																											A__	     =							{'generated_text': all_text}
																				records.append(__UpperCAmelCase     )

													return records





