


from __future__ import annotations

import os
import tempfile
import unittest

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

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


if is_tf_available():
						import tensorflow as tf

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



class       a   :






		def __init__(	self						,  __UpperCamelCase						,  __UpperCamelCase=13						,  __UpperCamelCase=7						,  __UpperCamelCase=True						,  __UpperCamelCase=True						,  __UpperCamelCase=True						,  __UpperCamelCase=True						,  __UpperCamelCase=99						,  __UpperCamelCase=32						,  __UpperCamelCase=2						,  __UpperCamelCase=4						,  __UpperCamelCase=37						,  __UpperCamelCase="gelu"						,  __UpperCamelCase=0.1						,  __UpperCamelCase=0.1						,  __UpperCamelCase=5_12						,  __UpperCamelCase=16						,  __UpperCamelCase=2						,  __UpperCamelCase=0.02						,  __UpperCamelCase=3						,  __UpperCamelCase=4						,  __UpperCamelCase=None						,  )->      str:
						'''simple docstring'''


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






		def    lowerCAmelCase_	(	self				)->      Any:
						'''simple docstring'''


						A__  :				Dict 					=ids_tensor([self.batch_size, self.seq_length]						,  self.vocab_size				)

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

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

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

						A__  :				str 					=ConvBertConfig(
						    vocab_size=self.vocab_size						,  hidden_size=self.hidden_size						,  num_hidden_layers=self.num_hidden_layers						,  num_attention_heads=self.num_attention_heads						,  intermediate_size=self.intermediate_size						,  hidden_act=self.hidden_act						,  hidden_dropout_prob=self.hidden_dropout_prob						,  attention_probs_dropout_prob=self.attention_probs_dropout_prob						,  max_position_embeddings=self.max_position_embeddings						,  type_vocab_size=self.type_vocab_size						,  initializer_range=self.initializer_range						,  return_dict=snake_case__						,  )

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






		def    lowerCAmelCase_	(	self						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase				)->      Any:
						'''simple docstring'''


						A__  :				Dict 					=TFConvBertModel(config=snake_case__				)
						A__  :				Union[str, Any] 					={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

						A__  :				Optional[Any] 					=[input_ids, input_mask]
						A__  :				Union[str, Any] 					=model(snake_case__				)

						A__  :				Any 					=model(snake_case__				)

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






		def    lowerCAmelCase_	(	self						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase				)->      Any:
						'''simple docstring'''


						A__  :				Optional[int] 					=TFConvBertForMaskedLM(config=snake_case__				)
						A__  :				List[Any] 					={
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						A__  :				Optional[Any] 					=model(snake_case__				)
						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				)->      Optional[int]:
						'''simple docstring'''


						A__  :				str 					=self.num_labels
						A__  :				Optional[int] 					=TFConvBertForSequenceClassification(config=snake_case__				)
						A__  :				Optional[Any] 					={
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						A__  :				Optional[int] 					=model(snake_case__				)
						self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)				)






		def    lowerCAmelCase_	(	self						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase				)->      List[str]:
						'''simple docstring'''


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






		def    lowerCAmelCase_	(	self						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase						,  __UpperCamelCase				)->      Optional[Any]:
						'''simple docstring'''


						A__  :				str 					=self.num_labels
						A__  :				Any 					=TFConvBertForTokenClassification(config=snake_case__				)
						A__  :				Tuple 					={
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}
						A__  :				Dict 					=model(snake_case__				)
						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				)->      str:
						'''simple docstring'''


						A__  :				str 					=TFConvBertForQuestionAnswering(config=snake_case__				)
						A__  :				List[str] 					={
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'token_type_ids': token_type_ids,
						}

						A__  :				Optional[Any] 					=model(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    lowerCAmelCase_	(	self				)->      Dict:
						'''simple docstring'''


						A__  :				List[Any] 					=self.prepare_config_and_inputs()
						(
						    A__    
						)  :				int 					=config_and_inputs
						A__  :				Tuple 					={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
						return config, inputs_dict







@require_tf
class       a   (   lowerCamelCase_   ,lowerCamelCase_   ,unittest.TestCase ):
		__lowercase				    =		(
		    (
		        TFConvBertModel,
		        TFConvBertForMaskedLM,
		        TFConvBertForQuestionAnswering,
		        TFConvBertForSequenceClassification,
		        TFConvBertForTokenClassification,
		        TFConvBertForMultipleChoice,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase				    =		(
		    {
		        "feature-extraction": TFConvBertModel,
		        "fill-mask": TFConvBertForMaskedLM,
		        "question-answering": TFConvBertForQuestionAnswering,
		        "text-classification": TFConvBertForSequenceClassification,
		        "token-classification": TFConvBertForTokenClassification,
		        "zero-shot": TFConvBertForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowercase				    =		False
		__lowercase				    =		False
		__lowercase				    =		False






		def    lowerCAmelCase_	(	self				)->      Tuple:
						'''simple docstring'''


						A__  :				List[str] 					=TFConvBertModelTester(self				)
						A__  :				List[Any] 					=ConfigTester(self						,  config_class=snake_case__						,  hidden_size=37				)






		def    lowerCAmelCase_	(	self				)->      Any:
						'''simple docstring'''


						self.config_tester.run_common_tests()






		def    lowerCAmelCase_	(	self				)->      Dict:
						'''simple docstring'''


						A__  :				Tuple 					=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*snake_case__				)






		def    lowerCAmelCase_	(	self				)->      int:
						'''simple docstring'''


						A__  :				List[Any] 					=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*snake_case__				)






		def    lowerCAmelCase_	(	self				)->      str:
						'''simple docstring'''


						A__  :				Dict 					=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_multiple_choice(*snake_case__				)






		def    lowerCAmelCase_	(	self				)->      List[str]:
						'''simple docstring'''


						A__  :				int 					=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*snake_case__				)






		def    lowerCAmelCase_	(	self				)->      Tuple:
						'''simple docstring'''


						A__  :				Tuple 					=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*snake_case__				)






		def    lowerCAmelCase_	(	self				)->      Any:
						'''simple docstring'''


						A__  :				Optional[Any] 					=self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*snake_case__				)






		@slow
		def    lowerCAmelCase_	(	self				)->      List[str]:
						'''simple docstring'''


						A__  :				Any 					=self.model_tester.prepare_config_and_inputs_for_common()
						A__  :				int 					=True
						A__  :				Union[str, Any] 					=True

						if hasattr(snake_case__						,  '''use_cache'''				):
										A__  :				Dict 					=True

						A__  :				List[Any] 					=getattr(self.model_tester						,  '''encoder_seq_length'''						,  self.model_tester.seq_length				)
						A__  :				Tuple 					=getattr(self.model_tester						,  '''key_length'''						,  snake_case__				)

						for model_class in self.all_model_classes:
										A__  :				Tuple 					=self._prepare_for_class(snake_case__						,  snake_case__				)
										A__  :				List[str] 					=model_class(snake_case__				)
										A__  :				Optional[int] 					=len(model(snake_case__				)				)

										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(snake_case__						,  saved_model=snake_case__				)
														A__  :				List[Any] 					=os.path.join(snake_case__						,  '''saved_model'''						,  '''1'''				)
														A__  :				Tuple 					=tf.keras.models.load_model(snake_case__				)
														A__  :				Any 					=model(snake_case__				)

														if self.is_encoder_decoder:
																		A__  :				int 					=outputs['encoder_hidden_states']
																		A__  :				Any 					=outputs['encoder_attentions']
														else:
																		A__  :				Optional[int] 					=outputs['hidden_states']
																		A__  :				List[Any] 					=outputs['attentions']

														self.assertEqual(len(snake_case__				)						,  snake_case__				)

														A__  :				List[str] 					=getattr(
														    self.model_tester						,  '''expected_num_hidden_layers'''						,  self.model_tester.num_hidden_layers + 1				)

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

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






		@slow
		def    lowerCAmelCase_	(	self				)->      Optional[int]:
						'''simple docstring'''


						A__  :				str 					=TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''				)
						self.assertIsNotNone(snake_case__				)






		def    lowerCAmelCase_	(	self				)->      List[str]:
						'''simple docstring'''


						A__  :				Optional[int] 					=self.model_tester.prepare_config_and_inputs_for_common()
						A__  :				Optional[int] 					=True
						A__  :				Any 					=getattr(self.model_tester						,  '''decoder_seq_length'''						,  self.model_tester.seq_length				)
						A__  :				int 					=getattr(self.model_tester						,  '''encoder_seq_length'''						,  self.model_tester.seq_length				)
						A__  :				str 					=getattr(self.model_tester						,  '''key_length'''						,  snake_case__				)
						A__  :				List[str] 					=getattr(self.model_tester						,  '''key_length'''						,  snake_case__				)

						def check_decoder_attentions_output(__UpperCamelCase				):
										A__  :				Tuple 					=len(snake_case__				)
										self.assertEqual(out_len % 2						,  0				)
										A__  :				Tuple 					=outputs.decoder_attentions
										self.assertEqual(len(snake_case__				)						,  self.model_tester.num_hidden_layers				)
										self.assertListEqual(
										    list(decoder_attentions[0].shape[-3:]				)						,  [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length]						,  )

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

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

										if self.is_encoder_decoder:
														A__  :				Any 					=model_class(snake_case__				)
														A__  :				Optional[int] 					=model(self._prepare_for_class(snake_case__						,  snake_case__				)				)
														self.assertEqual(config.output_hidden_states						,  snake_case__				)
														check_decoder_attentions_output(snake_case__				)

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

										# Check attention is always last and order is fine
										A__  :				List[Any] 					=True
										A__  :				Tuple 					=True
										A__  :				Dict 					=model_class(snake_case__				)
										A__  :				int 					=model(self._prepare_for_class(snake_case__						,  snake_case__				)				)

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







@require_tf
class       a   (   unittest.TestCase ):






		@slow
		def    lowerCAmelCase_	(	self				)->      int:
						'''simple docstring'''


						A__  :				str 					=TFConvBertModel.from_pretrained('''YituTech/conv-bert-base'''				)
						A__  :				Tuple 					=tf.constant([[0, 1, 2, 3, 4, 5]]				)
						A__  :				List[Any] 					=model(snake_case__				)[0]

						A__  :				List[Any] 					=[1, 6, 7_68]
						self.assertEqual(output.shape						,  snake_case__				)

						A__  :				Optional[Any] 					=tf.constant(
						    [
						        [
						            [-0.0347_5493, -0.468_6034, -0.3063_8832],
						            [0.2263_7248, -0.2698_8646, -0.742_3424],
						            [0.1032_4868, -0.4501_3508, -0.5828_0784],
						        ]
						    ]				)
						tf.debugging.assert_near(output[:, :3, :3]						,  snake_case__						,  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__ :   Tuple						=							getLogger(__name__)


UpperCamelCase__ :   Dict						=							'''cuda''' if torch.cuda.is_available() else '''cpu'''





def        __UpperCAmelCase						(							lowerCamelCase_ :     List[str]  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     str  ,							lowerCamelCase_ :     int = 8  ,							lowerCamelCase_ :     str = DEFAULT_DEVICE  ,							lowerCamelCase_ :     Union[str, Any]=False  ,							lowerCamelCase_ :     Any="summarization"  ,							lowerCamelCase_ :     Tuple=None  ,							**lowerCamelCase_ :     str  ,							)       ->  Dict:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : Any					  =      Path(lowerCamelCase_  ).open('w'  ,							encoding='utf-8'  )
			SCREAMING_SNAKE_CASE_   : Dict					  =      str(lowerCamelCase_  )
			SCREAMING_SNAKE_CASE_   : Optional[int]					  =      AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_  ).to(lowerCamelCase_  )
			if fpaa:
						SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      model.half()

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

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





def        __UpperCAmelCase						(							)       ->  int:







			"""simple docstring"""





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





def        __UpperCAmelCase						(							lowerCamelCase_ :     Dict=True  )       ->  List[Any]:







			"""simple docstring"""





			SCREAMING_SNAKE_CASE_   : int					  =      argparse.ArgumentParser()
			parser.add_argument('model_name'  ,							type=lowerCamelCase_  ,							help='like facebook/bart-large-cnn,t5-base, etc.'  )
			parser.add_argument('input_path'  ,							type=lowerCamelCase_  ,							help='like cnn_dm/test.source'  )
			parser.add_argument('save_path'  ,							type=lowerCamelCase_  ,							help='where to save summaries'  )
			parser.add_argument('--reference_path'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							help='like cnn_dm/test.target'  )
			parser.add_argument('--score_path'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default='metrics.json'  ,							help='where to save metrics'  )
			parser.add_argument('--device'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help='cuda, cuda:1, cpu etc.'  )
			parser.add_argument(
			    '--prefix'  ,							type=lowerCamelCase_  ,							required=lowerCamelCase_  ,							default=lowerCamelCase_  ,							help='will be added to the begininng of src examples'  )
			parser.add_argument('--task'  ,							type=lowerCamelCase_  ,							default='summarization'  ,							help='used for task_specific_params + metrics'  )
			parser.add_argument('--bs'  ,							type=lowerCamelCase_  ,							default=8  ,							required=lowerCamelCase_  ,							help='batch size'  )
			parser.add_argument(
			    '--n_obs'  ,							type=lowerCamelCase_  ,							default=-1  ,							required=lowerCamelCase_  ,							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=lowerCamelCase_  ,							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
			SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      parser.parse_known_args()
			SCREAMING_SNAKE_CASE_   : str					  =      parse_numeric_n_bool_cl_kwargs(lowerCamelCase_  )
			if parsed_args and verbose:
						print(F'parsed the following generate kwargs: {parsed_args}'  )
			SCREAMING_SNAKE_CASE_   : int					  =      [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in open(args.input_path  ).readlines()]
			if args.n_obs > 0:
						SCREAMING_SNAKE_CASE_   : Tuple					  =      examples[: args.n_obs]
			Path(args.save_path  ).parent.mkdir(exist_ok=lowerCamelCase_  )

			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.'  )

			if args.device == "cpu" and args.fpaa:
						# this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
						raise ValueError('Can\'t mix --fp16 and --device cpu'  )

			SCREAMING_SNAKE_CASE_   : List[str]					  =      generate_summaries_or_translations(
			    lowerCamelCase_  ,							args.save_path  ,							args.model_name  ,							batch_size=args.bs  ,							device=args.device  ,							fpaa=args.fpaa  ,							task=args.task  ,							prefix=args.prefix  ,							**lowerCamelCase_  ,							)

			if args.reference_path is None:
						return {}

			# Compute scores
			SCREAMING_SNAKE_CASE_   : str					  =      calculate_bleu if 'translation' in args.task else calculate_rouge
			SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      [x.rstrip() for x in open(args.save_path  ).readlines()]
			SCREAMING_SNAKE_CASE_   : int					  =      [x.rstrip() for x in open(args.reference_path  ).readlines()][: len(lowerCamelCase_  )]
			SCREAMING_SNAKE_CASE_   : dict					  =      score_fn(lowerCamelCase_  ,							lowerCamelCase_  )
			scores.update(lowerCamelCase_  )

			if args.dump_args:
						scores.update(lowerCamelCase_  )
			if args.info:
						SCREAMING_SNAKE_CASE_   : List[Any]					  =      args.info

			if verbose:
						print(lowerCamelCase_  )

			if args.score_path is not None:
						json.dump(lowerCamelCase_  ,							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)



