



"""simple docstring"""







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   UpperCamelCase	:




	"""simple docstring"""






	def __init__(     self				,UpperCAmelCase_				,UpperCAmelCase_=13				,UpperCAmelCase_=7				,UpperCAmelCase_=True				,UpperCAmelCase_=True				,UpperCAmelCase_=True				,UpperCAmelCase_=True				,UpperCAmelCase_=99				,UpperCAmelCase_=[1, 1, 2]				,UpperCAmelCase_=1				,UpperCAmelCase_=32				,UpperCAmelCase_=4				,UpperCAmelCase_=8				,UpperCAmelCase_=37				,UpperCAmelCase_="gelu_new"				,UpperCAmelCase_=0.1				,UpperCAmelCase_=0.1				,UpperCAmelCase_=0.0				,UpperCAmelCase_=5_12				,UpperCAmelCase_=3				,UpperCAmelCase_=0.02				,UpperCAmelCase_=3				,UpperCAmelCase_=4				,UpperCAmelCase_=None				,UpperCAmelCase_=False				,):
			_lowercase				: List[Any]     =   parent
			_lowercase				: Tuple     =   batch_size
			_lowercase				: Dict     =   seq_length
			_lowercase				: Tuple     =   is_training
			_lowercase				: Optional[int]     =   use_input_mask
			_lowercase				: int     =   use_token_type_ids
			_lowercase				: List[str]     =   use_labels
			_lowercase				: Optional[Any]     =   vocab_size
			_lowercase				: Optional[int]     =   block_sizes
			_lowercase				: Any     =   num_decoder_layers
			_lowercase				: List[str]     =   d_model
			_lowercase				: List[Any]     =   n_head
			_lowercase				: str     =   d_head
			_lowercase				: int     =   d_inner
			_lowercase				: Any     =   hidden_act
			_lowercase				: int     =   hidden_dropout
			_lowercase				: Optional[Any]     =   attention_dropout
			_lowercase				: Union[str, Any]     =   activation_dropout
			_lowercase				: str     =   max_position_embeddings
			_lowercase				: int     =   type_vocab_size
			_lowercase				: Optional[int]     =   2
			_lowercase				: int     =   num_labels
			_lowercase				: Dict     =   num_choices
			_lowercase				: Optional[Any]     =   scope
			_lowercase				: List[str]     =   initializer_std

			# Used in the tests to check the size of the first attention layer
			_lowercase				: Union[str, Any]     =   n_head
			# Used in the tests to check the size of the first hidden state
			_lowercase				: int     =   self.d_model
			# Used in the tests to check the number of output hidden states/attentions
			_lowercase				: Optional[int]     =   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:
					_lowercase				: List[Any]     =   self.num_hidden_layers + 2






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   ids_tensor([self.batch_size, self.seq_length]				,self.vocab_size   )

			_lowercase				: Dict     =   None
			if self.use_input_mask:
					_lowercase				: Optional[int]     =   ids_tensor([self.batch_size, self.seq_length]				,vocab_size=2   )

			_lowercase				: List[str]     =   None
			if self.use_token_type_ids:
					_lowercase				: Optional[Any]     =   ids_tensor([self.batch_size, self.seq_length]				,self.type_vocab_size   )

			_lowercase				: str     =   None
			_lowercase				: List[Any]     =   None
			_lowercase				: int     =   None
			if self.use_labels:
					_lowercase				: Optional[Any]     =   ids_tensor([self.batch_size]				,self.type_sequence_label_size   )
					_lowercase				: str     =   ids_tensor([self.batch_size, self.seq_length]				,self.num_labels   )
					_lowercase				: Union[str, Any]     =   ids_tensor([self.batch_size]				,self.num_choices   )
					_lowercase				: Union[str, Any]     =   ids_tensor([self.batch_size, self.seq_length]				,1   )

			_lowercase				: Union[str, Any]     =   self.get_config()

			return (
			    config,
			    input_ids,
			    token_type_ids,
			    input_mask,
			    sequence_labels,
			    token_labels,
			    choice_labels,
			    fake_token_labels,
			)






	def    lowerCamelCase__					(     self   ):
			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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: str     =   FunnelModel(config=UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: List[Any]     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )
			_lowercase				: int     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )
			_lowercase				: Dict     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.d_model)   )

			_lowercase				: Tuple     =   False
			_lowercase				: Optional[int]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.d_model)   )

			_lowercase				: Optional[Any]     =   False
			_lowercase				: List[str]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, self.seq_length, self.d_model)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: str     =   FunnelBaseModel(config=UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: Dict     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )
			_lowercase				: List[str]     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )
			_lowercase				: int     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, 2, self.d_model)   )

			_lowercase				: List[Any]     =   False
			_lowercase				: List[str]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, 3, self.d_model)   )

			_lowercase				: str     =   False
			_lowercase				: Optional[int]     =   model(UpperCAmelCase_   )
			self.parent.assertEqual(result.last_hidden_state.shape				,(self.batch_size, 2, self.d_model)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: str     =   self.num_labels
			_lowercase				: Optional[int]     =   FunnelForPreTraining(config=UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: Any     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,labels=UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: Tuple     =   FunnelForMaskedLM(config=UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: Dict     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,labels=UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length, self.vocab_size)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: List[str]     =   self.num_labels
			_lowercase				: Any     =   FunnelForSequenceClassification(UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: List[str]     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,labels=UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_labels)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: Union[str, Any]     =   self.num_choices
			_lowercase				: int     =   FunnelForMultipleChoice(config=UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: Optional[int]     =   input_ids.unsqueeze(1   ).expand(-1				,self.num_choices				,-1   ).contiguous()
			_lowercase				: Union[str, Any]     =   token_type_ids.unsqueeze(1   ).expand(-1				,self.num_choices				,-1   ).contiguous()
			_lowercase				: str     =   input_mask.unsqueeze(1   ).expand(-1				,self.num_choices				,-1   ).contiguous()
			_lowercase				: Dict     =   model(
			    UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,labels=UpperCAmelCase_				,)
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.num_choices)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: List[Any]     =   self.num_labels
			_lowercase				: str     =   FunnelForTokenClassification(config=UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: Any     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,labels=UpperCAmelCase_   )
			self.parent.assertEqual(result.logits.shape				,(self.batch_size, self.seq_length, self.num_labels)   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_				,):
			_lowercase				: List[str]     =   FunnelForQuestionAnswering(config=UpperCAmelCase_   )
			model.to(UpperCAmelCase_   )
			model.eval()
			_lowercase				: Union[str, Any]     =   model(
			    UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_				,start_positions=UpperCAmelCase_				,end_positions=UpperCAmelCase_				,)
			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    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   self.prepare_config_and_inputs()
			(
			    (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  (
			    _lowercase
			)     ,  
			)				: Tuple     =   config_and_inputs
			_lowercase				: List[str]     =   {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
			return config, inputs_dict







@require_torch
class   UpperCamelCase	(     snake_case					,   snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  False
	SCREAMING_SNAKE_CASE_							:       List[str]						       =  False
	SCREAMING_SNAKE_CASE_							:       str						       =  (
	    (
	        FunnelModel,
	        FunnelForMaskedLM,
	        FunnelForPreTraining,
	        FunnelForQuestionAnswering,
	        FunnelForTokenClassification,
	    )
	    if is_torch_available()
	    else ()
	)
	SCREAMING_SNAKE_CASE_							:       str						       =  (
	    {
	        "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    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_				,UpperCAmelCase_=False   ):
			_lowercase				: int     =   super()._prepare_for_class(UpperCAmelCase_				,UpperCAmelCase_				,return_labels=UpperCAmelCase_   )

			if return_labels:
					if model_class in get_values(UpperCAmelCase_   ):
							_lowercase				: Any     =   torch.zeros(
							    (self.model_tester.batch_size, self.model_tester.seq_length)				,dtype=torch.long				,device=UpperCAmelCase_   )
			return inputs_dict






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   FunnelModelTester(self   )
			_lowercase				: Dict     =   ConfigTester(self				,config_class=UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			self.config_tester.run_common_tests()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_pretraining(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if hasattr(UpperCAmelCase_				,"""weight"""   ) and module.weight is not None:
					module.weight.data.fill_(3   )
			if hasattr(UpperCAmelCase_				,"""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(UpperCAmelCase_				,UpperCAmelCase_   ) and getattr(UpperCAmelCase_				,UpperCAmelCase_   ) is not None:
							_lowercase				: Dict     =   getattr(UpperCAmelCase_				,UpperCAmelCase_   )
							weight.data.fill_(3   )







@require_torch
class   UpperCamelCase	(     snake_case					,   unittest.TestCase       ):




	"""simple docstring"""
	SCREAMING_SNAKE_CASE_							:       Union[str, Any]						       =  False
	SCREAMING_SNAKE_CASE_							:       int						       =  False
	SCREAMING_SNAKE_CASE_							:       int						       =  (
	    (FunnelBaseModel, FunnelForMultipleChoice, FunnelForSequenceClassification) if is_torch_available() else ()
	)






	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   FunnelModelTester(self				,base=UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   ConfigTester(self				,config_class=UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			self.config_tester.run_common_tests()






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_base_model(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_multiple_choice(*UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase     ,  _lowercase				: List[str]     =   self.model_tester.prepare_config_and_inputs_for_common()
			_lowercase				: List[str]     =   True

			for model_class in self.all_model_classes:
					if model_class.__name__ == "FunnelBaseModel":
							continue
					_lowercase				: Any     =   model_class(UpperCAmelCase_   )
					model.to(UpperCAmelCase_   )
					model.train()
					_lowercase				: Tuple     =   self._prepare_for_class(UpperCAmelCase_				,UpperCAmelCase_				,return_labels=UpperCAmelCase_   )
					_lowercase				: Optional[int]     =   model(**UpperCAmelCase_   ).loss
					loss.backward()






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			if hasattr(UpperCAmelCase_				,"""weight"""   ) and module.weight is not None:
					module.weight.data.fill_(3   )
			if hasattr(UpperCAmelCase_				,"""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(UpperCAmelCase_				,UpperCAmelCase_   ) and getattr(UpperCAmelCase_				,UpperCAmelCase_   ) is not None:
							_lowercase				: Optional[Any]     =   getattr(UpperCAmelCase_				,UpperCAmelCase_   )
							weight.data.fill_(3   )







@require_torch
@require_sentencepiece
@require_tokenizers
class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   13
			_lowercase				: List[str]     =   7
			_lowercase				: Union[str, Any]     =   torch.arange(0				,batch_size * sequence_length   ).long().reshape(UpperCAmelCase_				,UpperCAmelCase_   )
			_lowercase				: Optional[Any]     =   [0, 1, 2, 3, 4, 5, 6, 4, 1, 3, 5, 0, 1]
			_lowercase				: str     =   torch.tensor([[2] + [0] * a + [1] * (sequence_length - a - 1) for a in lengths]   )

			_lowercase				: Dict     =   FunnelModel.from_pretrained("""sgugger/funnel-random-tiny"""   )
			_lowercase				: int     =   model(UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )[0].abs()

			_lowercase				: str     =   torch.tensor(2344.8352   )
			_lowercase				: Optional[int]     =   torch.tensor(0.8052   )
			self.assertTrue(torch.allclose(output.sum()				,UpperCAmelCase_				,atol=1E-4   )   )
			self.assertTrue(torch.allclose(output.mean()				,UpperCAmelCase_				,atol=1E-4   )   )

			_lowercase				: Dict     =   torch.tensor([[1] * 7, [1] * 4 + [0] * 3] * 6 + [[0, 1, 1, 0, 0, 1, 1]]   )
			_lowercase				: Dict     =   model(UpperCAmelCase_				,attention_mask=UpperCAmelCase_				,token_type_ids=UpperCAmelCase_   )[0].abs()

			_lowercase				: List[Any]     =   torch.tensor(2343.8425   )
			_lowercase				: str     =   torch.tensor(0.8049   )
			self.assertTrue(torch.allclose(output.sum()				,UpperCAmelCase_				,atol=1E-4   )   )
			self.assertTrue(torch.allclose(output.mean()				,UpperCAmelCase_				,atol=1E-4   )   )






	@slow
	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   FunnelTokenizer.from_pretrained("""huggingface/funnel-small"""   )
			_lowercase				: Tuple     =   FunnelModel.from_pretrained("""huggingface/funnel-small"""   )
			_lowercase				: Any     =   tokenizer("""Hello! I am the Funnel Transformer model."""				,return_tensors="""pt"""   )
			_lowercase				: List[Any]     =   model(**UpperCAmelCase_   )[0]

			_lowercase				: str     =   torch.tensor(235.7246   )
			_lowercase				: str     =   torch.tensor(0.0256   )
			self.assertTrue(torch.allclose(output.sum()				,UpperCAmelCase_				,atol=1E-4   )   )
			self.assertTrue(torch.allclose(output.mean()				,UpperCAmelCase_				,atol=1E-4   )   )










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCAmelCase_   				=  getLogger(__name__)


UpperCAmelCase_   				=  """cuda""" if torch.cuda.is_available() else """cpu"""





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							int = 8							,     UpperCamelCase__      :							str = DEFAULT_DEVICE							,     UpperCamelCase__      :							str=False							,     UpperCamelCase__      :							str="summarization"							,     UpperCamelCase__      :							Optional[int]=None							,     **UpperCamelCase__      :							int							,     )      ->			Dict:



					'''simple docstring'''




					_snake_case						=					Path(UpperCamelCase__    ).open('w'							,     encoding='utf-8'    )
					_snake_case						=					str(UpperCamelCase__    )
					_snake_case						=					AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__    ).to(UpperCamelCase__    )
					if fpaa:
										_snake_case						=					model.half()

					_snake_case						=					AutoTokenizer.from_pretrained(UpperCamelCase__    )
					logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''    )  # if this is wrong, check config.model_type.

					_snake_case						=					time.time()
					# update config with task specific params
					use_task_specific_params(UpperCamelCase__							,     UpperCamelCase__    )
					if prefix is None:
										_snake_case						=					prefix or getattr(model.config							,     'prefix'							,     ''    ) or ''
					for examples_chunk in tqdm(list(chunks(UpperCamelCase__							,     UpperCamelCase__    )    )    ):
										_snake_case						=					[prefix + text for text in examples_chunk]
										_snake_case						=					tokenizer(UpperCamelCase__							,     return_tensors='pt'							,     truncation=UpperCamelCase__							,     padding='longest'    ).to(UpperCamelCase__    )
										_snake_case						=					model.generate(
										    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **UpperCamelCase__							,     )
										_snake_case						=					tokenizer.batch_decode(UpperCamelCase__							,     skip_special_tokens=UpperCamelCase__							,     clean_up_tokenization_spaces=UpperCamelCase__    )
										for hypothesis in dec:
															fout.write(hypothesis + '\n'    )
															fout.flush()
					fout.close()
					_snake_case						=					int(time.time() - start_time    )  # seconds
					_snake_case						=					len(UpperCamelCase__    )
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4    )}





def     lowerCamelCase__  (  )      ->			Dict:



					'''simple docstring'''




					return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]=True    )      ->			List[str]:



					'''simple docstring'''




					_snake_case						=					argparse.ArgumentParser()
					parser.add_argument('model_name'							,     type=UpperCamelCase__							,     help='like facebook/bart-large-cnn,t5-base, etc.'    )
					parser.add_argument('input_path'							,     type=UpperCamelCase__							,     help='like cnn_dm/test.source'    )
					parser.add_argument('save_path'							,     type=UpperCamelCase__							,     help='where to save summaries'    )
					parser.add_argument('--reference_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     help='like cnn_dm/test.target'    )
					parser.add_argument('--score_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default='metrics.json'							,     help='where to save metrics'    )
					parser.add_argument('--device'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='cuda, cuda:1, cpu etc.'    )
					parser.add_argument(
					    '--prefix'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='will be added to the begininng of src examples'    )
					parser.add_argument('--task'							,     type=UpperCamelCase__							,     default='summarization'							,     help='used for task_specific_params + metrics'    )
					parser.add_argument('--bs'							,     type=UpperCamelCase__							,     default=8							,     required=UpperCamelCase__							,     help='batch size'    )
					parser.add_argument(
					    '--n_obs'							,     type=UpperCamelCase__							,     default=-1							,     required=UpperCamelCase__							,     help='How many observations. Defaults to all.'    )
					parser.add_argument('--fp16'							,     action='store_true'    )
					parser.add_argument('--dump-args'							,     action='store_true'							,     help='print the custom hparams with the results'    )
					parser.add_argument(
					    '--info'							,     nargs='?'							,     type=UpperCamelCase__							,     const=datetime_now()							,     help=(
					        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
					        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
					    )							,     )
					# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
					_snake_case				,  _snake_case						=					parser.parse_known_args()
					_snake_case						=					parse_numeric_n_bool_cl_kwargs(UpperCamelCase__    )
					if parsed_args and verbose:
										print(F'''parsed the following generate kwargs: {parsed_args}'''    )
					with open(args.input_path    ) as f:
										_snake_case						=					[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										_snake_case						=					examples[: args.n_obs]
					Path(args.save_path    ).parent.mkdir(exist_ok=UpperCamelCase__    )
					if args.reference_path is None and Path(args.score_path    ).exists():
										warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''    )
					_snake_case						=					generate_summaries_or_translations(
					    UpperCamelCase__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **UpperCamelCase__							,     )

					if args.reference_path is None:
										return {}

					# Compute scores
					_snake_case						=					calculate_bleu if 'translation' in args.task else calculate_rouge
					_snake_case						=					[x.rstrip() for x in open(args.save_path    ).readlines()]
					_snake_case						=					[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(UpperCamelCase__    )]
					_snake_case						=					score_fn(UpperCamelCase__							,     UpperCamelCase__    )
					scores.update(UpperCamelCase__    )

					if args.dump_args:
										scores.update(UpperCamelCase__    )
					if args.info:
										_snake_case						=					args.info

					if verbose:
										print(UpperCamelCase__    )

					if args.score_path is not None:
										json.dump(UpperCamelCase__							,     open(args.score_path							,     'w'    )    )

					return scores


if __name__ == "__main__":
					# Usage for MT:
					# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
					run_generate(verbose=True)



