

'''simple docstring'''





import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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 MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
				from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def   _UpperCamelCase	(		__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase=None    ,__UpperCamelCase=None    ,__UpperCamelCase=None    ,__UpperCamelCase=None    ,__UpperCamelCase=None    ,)       ->			List[Any]:
					if attention_mask is None:
										lowerCamelCase_  				=      input_ids.ne(config.pad_token_id	)
					if decoder_attention_mask is None:
										lowerCamelCase_  				=      decoder_input_ids.ne(config.pad_token_id	)
					if head_mask is None:
										lowerCamelCase_  				=      torch.ones(config.encoder_layers    ,config.encoder_attention_heads    ,device=__UpperCamelCase	)
					if decoder_head_mask is None:
										lowerCamelCase_  				=      torch.ones(config.decoder_layers    ,config.decoder_attention_heads    ,device=__UpperCamelCase	)
					if cross_attn_head_mask is None:
										lowerCamelCase_  				=      torch.ones(config.decoder_layers    ,config.decoder_attention_heads    ,device=__UpperCamelCase	)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}


class  UpperCAmelCase :

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=13   ,			SCREAMING_SNAKE_CASE_=7   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=99   ,			SCREAMING_SNAKE_CASE_=16   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=4   ,			SCREAMING_SNAKE_CASE_=4   ,			SCREAMING_SNAKE_CASE_="relu"   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=20   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=1   ,			SCREAMING_SNAKE_CASE_=0   ,			)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      parent
							lowerCamelCase_  				=      batch_size
							lowerCamelCase_  				=      seq_length
							lowerCamelCase_  				=      is_training
							lowerCamelCase_  				=      use_labels
							lowerCamelCase_  				=      vocab_size
							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      num_hidden_layers
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      intermediate_size
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      hidden_dropout_prob
							lowerCamelCase_  				=      attention_probs_dropout_prob
							lowerCamelCase_  				=      encoder_layerdrop
							lowerCamelCase_  				=      decoder_layerdrop
							lowerCamelCase_  				=      max_position_embeddings
							lowerCamelCase_  				=      eos_token_id
							lowerCamelCase_  				=      pad_token_id
							lowerCamelCase_  				=      bos_token_id
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size		)
							lowerCamelCase_  				=      self.eos_token_id  # Eos Token
							lowerCamelCase_  				=      ids_tensor([self.batch_size, self.seq_length]   ,			self.vocab_size		)

							# we need to clamp the input ids here to avoid having pad token in between
							# this is because for M2M100 the position_ids are prepared such that
							# all pad tokens have pos id = 2 and rest are between 2..seq_length
							# and the seq_length here is seq_length - num_pad_tokens
							# but when using past, there is no way of knowing if the past input ids had
							# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
							# position_ids being off by num_pad_tokens in past input
							lowerCamelCase_  				=      input_ids.clamp(self.pad_token_id + 1		)
							lowerCamelCase_  				=      decoder_input_ids.clamp(self.pad_token_id + 1		)

							lowerCamelCase_  				=      self.get_config()
							lowerCamelCase_  				=      prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							return config, inputs_dict
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							return MaMaaaConfig(
							    vocab_size=self.vocab_size   ,			d_model=self.hidden_size   ,			encoder_layers=self.num_hidden_layers   ,			decoder_layers=self.num_hidden_layers   ,			encoder_attention_heads=self.num_attention_heads   ,			decoder_attention_heads=self.num_attention_heads   ,			encoder_ffn_dim=self.intermediate_size   ,			decoder_ffn_dim=self.intermediate_size   ,			dropout=self.hidden_dropout_prob   ,			attention_dropout=self.attention_probs_dropout_prob   ,			encoder_layerdrop=self.encoder_layerdrop   ,			decoder_layerdrop=self.decoder_layerdrop   ,			max_position_embeddings=self.max_position_embeddings   ,			eos_token_id=self.eos_token_id   ,			bos_token_id=self.bos_token_id   ,			pad_token_id=self.pad_token_id   ,			)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.prepare_config_and_inputs()
							return config, inputs_dict
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      MaMaaaModel(config=SCREAMING_SNAKE_CASE_		).get_decoder().to(SCREAMING_SNAKE_CASE_		).eval()
							lowerCamelCase_  				=      inputs_dict['input_ids']
							lowerCamelCase_  				=      inputs_dict['attention_mask']
							lowerCamelCase_  				=      inputs_dict['head_mask']

							# first forward pass
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_   ,			head_mask=SCREAMING_SNAKE_CASE_   ,			use_cache=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_				,lowerCamelCase_  				=      outputs.to_tuple()

							# create hypothetical multiple next token and extent to next_input_ids
							lowerCamelCase_  				=      ids_tensor((self.batch_size, 3)   ,			config.vocab_size		)
							lowerCamelCase_  				=      ids_tensor((self.batch_size, 3)   ,			2		)

							# append to next input_ids and
							lowerCamelCase_  				=      torch.cat([input_ids, next_tokens]   ,			dim=-1		)
							lowerCamelCase_  				=      torch.cat([attention_mask, next_attn_mask]   ,			dim=-1		)

							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_		)['last_hidden_state']
							lowerCamelCase_  				=      model(SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_   ,			past_key_values=SCREAMING_SNAKE_CASE_		)[
							    'last_hidden_state'
							]

							# select random slice
							lowerCamelCase_  				=      ids_tensor((1,)   ,			output_from_past.shape[-1]		).item()
							lowerCamelCase_  				=      output_from_no_past[:, -3:, random_slice_idx].detach()
							lowerCamelCase_  				=      output_from_past[:, :, random_slice_idx].detach()

							self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

							# test that outputs are equal for slice
							self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			atol=1E-2		)		)






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      MaMaaaModel(config=SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		).eval()
							lowerCamelCase_  				=      model(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      outputs.encoder_last_hidden_state
							lowerCamelCase_  				=      outputs.last_hidden_state

							with tempfile.TemporaryDirectory() as tmpdirname:
												lowerCamelCase_  				=      model.get_encoder()
												encoder.save_pretrained(SCREAMING_SNAKE_CASE_		)
												lowerCamelCase_  				=      MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      encoder(inputs_dict['input_ids']   ,			attention_mask=inputs_dict['attention_mask']		)[
							    0
							]

							self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

							with tempfile.TemporaryDirectory() as tmpdirname:
												lowerCamelCase_  				=      model.get_decoder()
												decoder.save_pretrained(SCREAMING_SNAKE_CASE_		)
												lowerCamelCase_  				=      MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE_		).to(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      decoder(
							    input_ids=inputs_dict['decoder_input_ids']   ,			attention_mask=inputs_dict['decoder_attention_mask']   ,			encoder_hidden_states=SCREAMING_SNAKE_CASE_   ,			encoder_attention_mask=inputs_dict['attention_mask']   ,			)[0]

							self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)


@require_torch
class  UpperCAmelCase (							UpperCAmelCase__  , UpperCAmelCase__  , UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					(
		    (
		        MaMaaaModel,
		        MaMaaaForConditionalGeneration,
		    )
		    if is_torch_available()
		    else ()
		)
		SCREAMING_SNAKE_CASE_           =					(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
		SCREAMING_SNAKE_CASE_           =					(
		    {
		        'conversational': MaMaaaForConditionalGeneration,
		        'feature-extraction': MaMaaaModel,
		        'summarization': MaMaaaForConditionalGeneration,
		        'text2text-generation': MaMaaaForConditionalGeneration,
		        'translation': MaMaaaForConditionalGeneration,
		    }
		    if is_torch_available()
		    else {}
		)
		SCREAMING_SNAKE_CASE_           =					True
		SCREAMING_SNAKE_CASE_           =					True
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							if pipeline_test_casse_name == "TranslationPipelineTests":
												# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
												# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
												return True

							return False
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      MaMaaaModelTester(self		)
							lowerCamelCase_  				=      ConfigTester(self   ,			config_class=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							self.config_tester.run_common_tests()
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							for model_class in self.all_model_classes:
												lowerCamelCase_  				=      model_class(SCREAMING_SNAKE_CASE_		)

												with tempfile.TemporaryDirectory() as tmpdirname:
																	model.save_pretrained(SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_				,lowerCamelCase_  				=      model_class.from_pretrained(SCREAMING_SNAKE_CASE_   ,			output_loading_info=SCREAMING_SNAKE_CASE_		)
												self.assertEqual(info['missing_keys']   ,			[]		)
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
												lowerCamelCase_  				=      model_class(SCREAMING_SNAKE_CASE_		)
												model.to(SCREAMING_SNAKE_CASE_		)
												model.eval()

												lowerCamelCase_  				=      copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)		)

												if not self.is_encoder_decoder:
																	lowerCamelCase_  				=      inputs['input_ids']
																	del inputs["input_ids"]
												else:
																	lowerCamelCase_  				=      inputs['input_ids']
																	lowerCamelCase_  				=      inputs.get('decoder_input_ids'   ,			SCREAMING_SNAKE_CASE_		)
																	del inputs["input_ids"]
																	inputs.pop('decoder_input_ids'   ,			SCREAMING_SNAKE_CASE_		)

												lowerCamelCase_  				=      model.get_input_embeddings()
												if not self.is_encoder_decoder:
																	lowerCamelCase_  				=      wte(SCREAMING_SNAKE_CASE_		)
												else:
																	lowerCamelCase_  				=      wte(SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      wte(SCREAMING_SNAKE_CASE_		)

												with torch.no_grad():
																	model(**SCREAMING_SNAKE_CASE_		)[0]






		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_				,lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							lowerCamelCase_  				=      input_dict['input_ids']
							lowerCamelCase_  				=      input_ids.ne(1		).to(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE_		).eval().to(SCREAMING_SNAKE_CASE_		)
							if torch_device == "cuda":
												model.half()
							model.generate(SCREAMING_SNAKE_CASE_   ,			attention_mask=SCREAMING_SNAKE_CASE_		)
							model.generate(num_beams=4   ,			do_sample=SCREAMING_SNAKE_CASE_   ,			early_stopping=SCREAMING_SNAKE_CASE_   ,			num_return_sequences=3		)







def   _UpperCamelCase	(		__UpperCamelCase	)       ->			List[str]:
					return torch.tensor(__UpperCamelCase    ,dtype=torch.long    ,device=__UpperCamelCase	)


A_	= 1E-4







@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		@cached_property
		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'		)
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      MaMaaaModel.from_pretrained('facebook/m2m100_418M'		).to(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
							lowerCamelCase_  				=      _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
							lowerCamelCase_  				=      prepare_mam_aaa_inputs_dict(model.config   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							with torch.no_grad():
												lowerCamelCase_  				=      model(**SCREAMING_SNAKE_CASE_		)[0]
							lowerCamelCase_  				=      torch.Size((1, 11, 1024)		)
							self.assertEqual(output.shape   ,			SCREAMING_SNAKE_CASE_		)
							# change to expected output here
							lowerCamelCase_  				=      torch.tensor(
							    [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]   ,			device=SCREAMING_SNAKE_CASE_		)
							self.assertTrue(torch.allclose(output[:, :3, :3]   ,			SCREAMING_SNAKE_CASE_   ,			atol=SCREAMING_SNAKE_CASE_		)		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'		).to(SCREAMING_SNAKE_CASE_		)

							# change to intended input
							lowerCamelCase_  				=      _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]		)
							lowerCamelCase_  				=      _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]		)
							lowerCamelCase_  				=      prepare_mam_aaa_inputs_dict(model.config   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
							with torch.no_grad():
												lowerCamelCase_  				=      model(**SCREAMING_SNAKE_CASE_		)[0]
							lowerCamelCase_  				=      torch.Size((1, 11, model.config.vocab_size)		)
							self.assertEqual(output.shape   ,			SCREAMING_SNAKE_CASE_		)
							# change to expected output here
							lowerCamelCase_  				=      torch.tensor(
							    [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]   ,			device=SCREAMING_SNAKE_CASE_		)
							self.assertTrue(torch.allclose(output[:, :3, :3]   ,			SCREAMING_SNAKE_CASE_   ,			atol=SCREAMING_SNAKE_CASE_		)		)






		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'		).to(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'   ,			src_lang='fr'   ,			tgt_lang='en'		)

							lowerCamelCase_  				=      [
							    'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
							    'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
							    'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
							    ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
							    ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
							]

							# The below article tests that we don't add any hypotheses outside of the top n_beams
							lowerCamelCase_  				=      tokenizer(SCREAMING_SNAKE_CASE_   ,			padding=SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		)

							lowerCamelCase_  				=      model.generate(
							    input_ids=dct['input_ids'].to(SCREAMING_SNAKE_CASE_		)   ,			attention_mask=dct['attention_mask'].to(SCREAMING_SNAKE_CASE_		)   ,			num_beams=5   ,			forced_bos_token_id=tokenizer.get_lang_id('en'		)   ,			)

							lowerCamelCase_  				=      [
							    'The NSA case highlights the total absence of intelligence debate',
							    'I think there are two levels of response from the French government.',
							    'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
							    ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
							    ' communications in France.',
							]

							lowerCamelCase_  				=      tokenizer.batch_decode(
							    hypotheses_batch.tolist()   ,			clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_   ,			skip_special_tokens=SCREAMING_SNAKE_CASE_		)
							assert generated == expected_en









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)



