





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					UpperCAmelCase_   				=  """platform"""

					import jax
					import jax.numpy as jnp

					from transformers import MarianTokenizer
					from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right





def     lowerCamelCase__  (  UpperCamelCase__      :							Any							,     UpperCamelCase__      :							Dict							,     UpperCamelCase__      :							List[Any]=None							,     UpperCamelCase__      :							Optional[int]=None							,     UpperCamelCase__      :							Optional[Any]=None							,     UpperCamelCase__      :							Any=None							,     UpperCamelCase__      :							List[Any]=None							,     UpperCamelCase__      :							int=None							,     )      ->			List[Any]:



					'''simple docstring'''




					if attention_mask is None:
										_snake_case						=					np.where(input_ids != config.pad_token_id							,     1							,     0    )
					if decoder_attention_mask is None:
										_snake_case						=					np.where(decoder_input_ids != config.pad_token_id							,     1							,     0    )
					if head_mask is None:
										_snake_case						=					np.ones((config.encoder_layers, config.encoder_attention_heads)    )
					if decoder_head_mask is None:
										_snake_case						=					np.ones((config.decoder_layers, config.decoder_attention_heads)    )
					if cross_attn_head_mask is None:
										_snake_case						=					np.ones((config.decoder_layers, config.decoder_attention_heads)    )
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": attention_mask,
					}
class       UpperCamelCase_		:




					def __init__(		self					,    lowerCAmelCase_					,    lowerCAmelCase_=13					,    lowerCAmelCase_=7					,    lowerCAmelCase_=True					,    lowerCAmelCase_=False					,    lowerCAmelCase_=99					,    lowerCAmelCase_=16					,    lowerCAmelCase_=2					,    lowerCAmelCase_=4					,    lowerCAmelCase_=4					,    lowerCAmelCase_="gelu"					,    lowerCAmelCase_=0.1					,    lowerCAmelCase_=0.1					,    lowerCAmelCase_=32					,    lowerCAmelCase_=2					,    lowerCAmelCase_=1					,    lowerCAmelCase_=0					,    lowerCAmelCase_=0.02					,    )		->				List[str]:
										_snake_case						=					parent
										_snake_case						=					batch_size
										_snake_case						=					seq_length
										_snake_case						=					is_training
										_snake_case						=					use_labels
										_snake_case						=					vocab_size
										_snake_case						=					hidden_size
										_snake_case						=					num_hidden_layers
										_snake_case						=					num_attention_heads
										_snake_case						=					intermediate_size
										_snake_case						=					hidden_act
										_snake_case						=					hidden_dropout_prob
										_snake_case						=					attention_probs_dropout_prob
										_snake_case						=					max_position_embeddings
										_snake_case						=					eos_token_id
										_snake_case						=					pad_token_id
										_snake_case						=					bos_token_id
										_snake_case						=					initializer_range




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,    self.vocab_size	)					,    3					,    self.vocab_size	)
										_snake_case						=					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,    dtype=np.intaa	))					,    -1	)

										_snake_case						=					shift_tokens_right(lowerCAmelCase_					,    1					,    2	)

										_snake_case						=					MarianConfig(
										    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					,    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					,    initializer_range=self.initializer_range					,    use_cache=lowerCAmelCase_					,    )
										_snake_case						=					prepare_marian_inputs_dict(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										return config, inputs_dict




					def 						lowerCAmelCase      (		self	)		->				Union[str, Any]:
										_snake_case				,  _snake_case						=					self.prepare_config_and_inputs()
										return config, inputs_dict




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Optional[Any]:
										_snake_case						=					20
										_snake_case						=					model_class_name(lowerCAmelCase_	)

										_snake_case						=					model.encode(inputs_dict['input_ids']	)

										_snake_case				,  _snake_case						=					(
										    inputs_dict['decoder_input_ids'],
										    inputs_dict['decoder_attention_mask'],
										)

										_snake_case						=					model.init_cache(decoder_input_ids.shape[0]					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										_snake_case						=					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,    dtype='i4'	)

										_snake_case						=					jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]					,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,    )
										_snake_case						=					model.decode(
										    decoder_input_ids[:, :-1]					,    lowerCAmelCase_					,    decoder_attention_mask=lowerCAmelCase_					,    past_key_values=lowerCAmelCase_					,    decoder_position_ids=lowerCAmelCase_					,    )

										_snake_case						=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,    dtype='i4'	)
										_snake_case						=					model.decode(
										    decoder_input_ids[:, -1:]					,    lowerCAmelCase_					,    decoder_attention_mask=lowerCAmelCase_					,    past_key_values=outputs_cache.past_key_values					,    decoder_position_ids=lowerCAmelCase_					,    )

										_snake_case						=					model.decode(lowerCAmelCase_					,    lowerCAmelCase_	)

										_snake_case						=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
										self.parent.assertTrue(diff < 1E-3					,    msg=F'''Max diff is {diff}'''	)






					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Dict:
										_snake_case						=					20
										_snake_case						=					model_class_name(lowerCAmelCase_	)

										_snake_case						=					model.encode(inputs_dict['input_ids']	)

										_snake_case				,  _snake_case						=					(
										    inputs_dict['decoder_input_ids'],
										    inputs_dict['decoder_attention_mask'],
										)

										_snake_case						=					jnp.concatenate(
										    [
										        decoder_attention_mask,
										        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
										    ]					,    axis=-1					,    )

										_snake_case						=					model.init_cache(decoder_input_ids.shape[0]					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										_snake_case						=					jnp.broadcast_to(
										    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]					,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,    )

										_snake_case						=					model.decode(
										    decoder_input_ids[:, :-1]					,    lowerCAmelCase_					,    decoder_attention_mask=lowerCAmelCase_					,    past_key_values=lowerCAmelCase_					,    decoder_position_ids=lowerCAmelCase_					,    )
										_snake_case						=					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,    dtype='i4'	)
										_snake_case						=					model.decode(
										    decoder_input_ids[:, -1:]					,    lowerCAmelCase_					,    past_key_values=outputs_cache.past_key_values					,    decoder_attention_mask=lowerCAmelCase_					,    decoder_position_ids=lowerCAmelCase_					,    )

										_snake_case						=					model.decode(lowerCAmelCase_					,    lowerCAmelCase_					,    decoder_attention_mask=lowerCAmelCase_	)

										_snake_case						=					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
										self.parent.assertTrue(diff < 1E-3					,    msg=F'''Max diff is {diff}'''	)





@require_flax
class       UpperCamelCase_		(	_lowerCamelCase				,	unittest.TestCase				,	_lowerCamelCase       ):
					lowerCAmelCase_				=						True
					lowerCAmelCase_				=						(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
					lowerCAmelCase_				=						(FlaxMarianMTModel,) if is_flax_available() else ()




					def 						lowerCAmelCase      (		self	)		->				List[Any]:
										_snake_case						=					FlaxMarianModelTester(self	)




					def 						lowerCAmelCase      (		self	)		->				Any:
										_snake_case				,  _snake_case						=					self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case				,  _snake_case						=					self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
															self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case				,  _snake_case						=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__	):
																				_snake_case						=					self._prepare_for_class(lowerCAmelCase_					,    lowerCAmelCase_	)
																				_snake_case						=					model_class(lowerCAmelCase_	)

																				@jax.jit
																				def encode_jitted(lowerCAmelCase_					,    lowerCAmelCase_=None					,    **lowerCAmelCase_	):
																									return model.encode(input_ids=lowerCAmelCase_					,    attention_mask=lowerCAmelCase_	)

																				with self.subTest('JIT Enabled'	):
																									_snake_case						=					encode_jitted(**lowerCAmelCase_	).to_tuple()

																				with self.subTest('JIT Disabled'	):
																									with jax.disable_jit():
																														_snake_case						=					encode_jitted(**lowerCAmelCase_	).to_tuple()

																				self.assertEqual(len(lowerCAmelCase_	)					,    len(lowerCAmelCase_	)	)
																				for jitted_output, output in zip(lowerCAmelCase_					,    lowerCAmelCase_	):
																									self.assertEqual(jitted_output.shape					,    output.shape	)




					def 						lowerCAmelCase      (		self	)		->				str:
										_snake_case				,  _snake_case						=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__	):
																				_snake_case						=					model_class(lowerCAmelCase_	)
																				_snake_case						=					model.encode(inputs_dict['input_ids']					,    inputs_dict['attention_mask']	)

																				_snake_case						=					{
																				    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																				    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																				    'encoder_outputs': encoder_outputs,
																				}

																				@jax.jit
																				def decode_jitted(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	):
																									return model.decode(
																									    decoder_input_ids=lowerCAmelCase_					,    decoder_attention_mask=lowerCAmelCase_					,    encoder_outputs=lowerCAmelCase_					,    )

																				with self.subTest('JIT Enabled'	):
																									_snake_case						=					decode_jitted(**lowerCAmelCase_	).to_tuple()

																				with self.subTest('JIT Disabled'	):
																									with jax.disable_jit():
																														_snake_case						=					decode_jitted(**lowerCAmelCase_	).to_tuple()

																				self.assertEqual(len(lowerCAmelCase_	)					,    len(lowerCAmelCase_	)	)
																				for jitted_output, output in zip(lowerCAmelCase_					,    lowerCAmelCase_	):
																									self.assertEqual(jitted_output.shape					,    output.shape	)






					@slow
					def 						lowerCAmelCase      (		self	)		->				Any:
										for model_class_name in self.all_model_classes:
															_snake_case						=					model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de'	)
															# FlaxMarianForSequenceClassification expects eos token in input_ids
															_snake_case						=					np.ones((1, 1)	) * model.config.eos_token_id
															_snake_case						=					model(lowerCAmelCase_	)
															self.assertIsNotNone(lowerCAmelCase_	)





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	unittest.TestCase       ):
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None




					@classmethod
					def 						lowerCAmelCase      (		cls	)		->				None:
										_snake_case						=					F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
										return cls




					@cached_property
					def 						lowerCAmelCase      (		self	)		->				int:
										return MarianTokenizer.from_pretrained(self.model_name	)




					@property
					def 						lowerCAmelCase      (		self	)		->				int:
										return self.tokenizer.eos_token_id




					@cached_property
					def 						lowerCAmelCase      (		self	)		->				Union[str, Any]:
										_snake_case						=					FlaxMarianMTModel.from_pretrained(self.model_name	)
										self.assertEqual(model.config.decoder_start_token_id					,    model.config.pad_token_id	)
										return model




					def 						lowerCAmelCase      (		self					,    **lowerCAmelCase_	)		->				List[str]:
										_snake_case						=					self.translate_src_text(**lowerCAmelCase_	)
										self.assertListEqual(self.expected_text					,    lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self					,    **lowerCAmelCase_	)		->				int:
										_snake_case						=					self.tokenizer(self.src_text					,    padding=lowerCAmelCase_					,    return_tensors='np'					,    **lowerCAmelCase_	)
										_snake_case						=					self.model.generate(
										    model_inputs.input_ids					,    attention_mask=model_inputs.attention_mask					,    num_beams=2					,    max_length=128					,    ).sequences
										_snake_case						=					self.tokenizer.batch_decode(lowerCAmelCase_					,    skip_special_tokens=lowerCAmelCase_	)
										return generated_words





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''en'''
					lowerCAmelCase_				=						'''fr'''
					lowerCAmelCase_				=						[
					    '''I am a small frog.''',
					    '''Now I can forget the 100 words of german that I know.''',
					]
					lowerCAmelCase_				=						[
					    '''Je suis une petite grenouille.''',
					    '''Maintenant, je peux oublier les 100 mots d\'allemand que je connais.''',
					]




					@slow
					def 						lowerCAmelCase      (		self	)		->				List[str]:
										self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''fr'''
					lowerCAmelCase_				=						'''en'''
					lowerCAmelCase_				=						[
					    '''Donnez moi le micro.''',
					    '''Tom et Mary étaient assis à une table.''',  # Accents
					]
					lowerCAmelCase_				=						[
					    '''Give me the microphone.''',
					    '''Tom and Mary were sitting at a table.''',
					]




					@slow
					def 						lowerCAmelCase      (		self	)		->				Dict:
										self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''mt'''
					lowerCAmelCase_				=						'''en'''
					lowerCAmelCase_				=						['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
					lowerCAmelCase_				=						['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']




					@slow
					def 						lowerCAmelCase      (		self	)		->				int:
										self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''en'''
					lowerCAmelCase_				=						'''de'''
					lowerCAmelCase_				=						[
					    '''I am a small frog.''',
					    '''Now I can forget the 100 words of german that I know.''',
					    '''Tom asked his teacher for advice.''',
					    '''That\'s how I would do it.''',
					    '''Tom really admired Mary\'s courage.''',
					    '''Turn around and close your eyes.''',
					]
					lowerCAmelCase_				=						[
					    '''Ich bin ein kleiner Frosch.''',
					    '''Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.''',
					    '''Tom bat seinen Lehrer um Rat.''',
					    '''So würde ich das machen.''',
					    '''Tom bewunderte Marias Mut wirklich.''',
					    '''Drehen Sie sich um und schließen Sie die Augen.''',
					]




					@slow
					def 						lowerCAmelCase      (		self	)		->				List[str]:
										self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''en'''
					lowerCAmelCase_				=						'''zh'''
					lowerCAmelCase_				=						['''My name is Wolfgang and I live in Berlin''']
					lowerCAmelCase_				=						['''我叫沃尔夫冈 我住在柏林''']




					@slow
					def 						lowerCAmelCase      (		self	)		->				Tuple:
										self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''ru'''
					lowerCAmelCase_				=						'''fr'''
					lowerCAmelCase_				=						['''Он показал мне рукопись своей новой пьесы.''']
					lowerCAmelCase_				=						['''Il m\'a montré le manuscrit de sa nouvelle pièce.''']




					@slow
					def 						lowerCAmelCase      (		self	)		->				Dict:
										self._assert_generated_batch_equal_expected()





@require_flax
@require_sentencepiece
@require_tokenizers
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''en'''
					lowerCAmelCase_				=						'''ROMANCE'''
					lowerCAmelCase_				=						[
					    '''>>fr<< Don\'t spend so much time watching TV.''',
					    '''>>pt<< Your message has been sent.''',
					    '''>>es<< He\'s two years older than me.''',
					]
					lowerCAmelCase_				=						[
					    '''Ne passez pas autant de temps à regarder la télé.''',
					    '''A sua mensagem foi enviada.''',
					    '''Es dos años más viejo que yo.''',
					]




					@slow
					def 						lowerCAmelCase      (		self	)		->				Tuple:
										self._assert_generated_batch_equal_expected()










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)



