





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase_   				=  logging.get_logger(__name__)

UpperCAmelCase_   				=  {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						'''mbart'''
					lowerCAmelCase_				=						['''past_key_values''']
					lowerCAmelCase_				=						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}




					def __init__(		self					,    lowerCAmelCase_=5_0265					,    lowerCAmelCase_=1024					,    lowerCAmelCase_=12					,    lowerCAmelCase_=4096					,    lowerCAmelCase_=16					,    lowerCAmelCase_=12					,    lowerCAmelCase_=4096					,    lowerCAmelCase_=16					,    lowerCAmelCase_=0.0					,    lowerCAmelCase_=0.0					,    lowerCAmelCase_=True					,    lowerCAmelCase_=True					,    lowerCAmelCase_="gelu"					,    lowerCAmelCase_=1024					,    lowerCAmelCase_=0.1					,    lowerCAmelCase_=0.0					,    lowerCAmelCase_=0.0					,    lowerCAmelCase_=0.02					,    lowerCAmelCase_=0.0					,    lowerCAmelCase_=False					,    lowerCAmelCase_=1					,    lowerCAmelCase_=0					,    lowerCAmelCase_=2					,    lowerCAmelCase_=2					,    **lowerCAmelCase_					,    )		->				Optional[int]:
										_snake_case						=					vocab_size
										_snake_case						=					max_position_embeddings
										_snake_case						=					d_model
										_snake_case						=					encoder_ffn_dim
										_snake_case						=					encoder_layers
										_snake_case						=					encoder_attention_heads
										_snake_case						=					decoder_ffn_dim
										_snake_case						=					decoder_layers
										_snake_case						=					decoder_attention_heads
										_snake_case						=					dropout
										_snake_case						=					attention_dropout
										_snake_case						=					activation_dropout
										_snake_case						=					activation_function
										_snake_case						=					init_std
										_snake_case						=					encoder_layerdrop
										_snake_case						=					decoder_layerdrop
										_snake_case						=					classifier_dropout
										_snake_case						=					use_cache
										_snake_case						=					encoder_layers
										_snake_case						=					scale_embedding  # scale factor will be sqrt(d_model) if True
										super().__init__(
										    pad_token_id=lowerCAmelCase_					,    bos_token_id=lowerCAmelCase_					,    eos_token_id=lowerCAmelCase_					,    is_encoder_decoder=lowerCAmelCase_					,    forced_eos_token_id=lowerCAmelCase_					,    **lowerCAmelCase_					,    )





class       UpperCamelCase_		(	_lowerCamelCase       ):




					@property
					def 						lowerCAmelCase      (		self	)		->				Mapping[str, Mapping[int, str]]:
										if self.task in ["default", "seq2seq-lm"]:
															_snake_case						=					OrderedDict(
															    [
															        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
															        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
															    ]	)

															if self.use_past:
																				_snake_case						=					{0: 'batch'}
																				_snake_case						=					{0: 'batch', 1: 'past_decoder_sequence + sequence'}
															else:
																				_snake_case						=					{0: 'batch', 1: 'decoder_sequence'}
																				_snake_case						=					{0: 'batch', 1: 'decoder_sequence'}

															if self.use_past:
																				self.fill_with_past_key_values_(lowerCAmelCase_					,    direction='inputs'	)
										elif self.task == "causal-lm":
															# TODO: figure this case out.
															_snake_case						=					OrderedDict(
															    [
															        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
															        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
															    ]	)
															if self.use_past:
																				_snake_case				,  _snake_case						=					self.num_layers
																				for i in range(lowerCAmelCase_	):
																									_snake_case						=					{0: 'batch', 2: 'past_sequence + sequence'}
																									_snake_case						=					{0: 'batch', 2: 'past_sequence + sequence'}
										else:
															_snake_case						=					OrderedDict(
															    [
															        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
															        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
															        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
															        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
															    ]	)

										return common_inputs




					@property
					def 						lowerCAmelCase      (		self	)		->				Mapping[str, Mapping[int, str]]:
										if self.task in ["default", "seq2seq-lm"]:
															_snake_case						=					super().outputs
										else:
															_snake_case						=					super(lowerCAmelCase_					,    self	).outputs
															if self.use_past:
																				_snake_case				,  _snake_case						=					self.num_layers
																				for i in range(lowerCAmelCase_	):
																									_snake_case						=					{0: 'batch', 2: 'past_sequence + sequence'}
																									_snake_case						=					{0: 'batch', 2: 'past_sequence + sequence'}
										return common_outputs




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = False					,    lowerCAmelCase_ = None					,    )		->				Mapping[str, Any]:
										_snake_case						=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)

										# Generate decoder inputs
										_snake_case						=					seq_length if not self.use_past else 1
										_snake_case						=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										_snake_case						=					{F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
										_snake_case						=					dict(**lowerCAmelCase_					,    **lowerCAmelCase_	)

										if self.use_past:
															if not is_torch_available():
																				raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
															else:
																				import torch
															_snake_case				,  _snake_case						=					common_inputs['input_ids'].shape
															_snake_case						=					common_inputs['decoder_input_ids'].shape[1]
															_snake_case				,  _snake_case						=					self.num_attention_heads
															_snake_case						=					(
															    batch,
															    num_encoder_attention_heads,
															    encoder_seq_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)
															_snake_case						=					decoder_seq_length + 3
															_snake_case						=					(
															    batch,
															    num_decoder_attention_heads,
															    decoder_past_length,
															    self._config.hidden_size // num_decoder_attention_heads,
															)

															_snake_case						=					torch.cat(
															    [common_inputs['decoder_attention_mask'], torch.ones(lowerCAmelCase_					,    lowerCAmelCase_	)]					,    dim=1	)

															_snake_case						=					[]
															# If the number of encoder and decoder layers are present in the model configuration, both are considered
															_snake_case				,  _snake_case						=					self.num_layers
															_snake_case						=					min(lowerCAmelCase_					,    lowerCAmelCase_	)
															_snake_case						=					max(lowerCAmelCase_					,    lowerCAmelCase_	) - min_num_layers
															_snake_case						=					'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

															for _ in range(lowerCAmelCase_	):
																				common_inputs["past_key_values"].append(
																				    (
																				        torch.zeros(lowerCAmelCase_	),
																				        torch.zeros(lowerCAmelCase_	),
																				        torch.zeros(lowerCAmelCase_	),
																				        torch.zeros(lowerCAmelCase_	),
																				    )	)
															# TODO: test this.
															_snake_case						=					encoder_shape if remaining_side_name == 'encoder' else decoder_shape
															for _ in range(lowerCAmelCase_					,    lowerCAmelCase_	):
																				common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase_	), torch.zeros(lowerCAmelCase_	))	)
										return common_inputs




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = False					,    lowerCAmelCase_ = None					,    )		->				Mapping[str, Any]:
										_snake_case						=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)

										if self.use_past:
															if not is_torch_available():
																				raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'	)
															else:
																				import torch
															_snake_case				,  _snake_case						=					common_inputs['input_ids'].shape
															# Not using the same length for past_key_values
															_snake_case						=					seqlen + 2
															_snake_case				,  _snake_case						=					self.num_layers
															_snake_case				,  _snake_case						=					self.num_attention_heads
															_snake_case						=					(
															    batch,
															    num_encoder_attention_heads,
															    past_key_values_length,
															    self._config.hidden_size // num_encoder_attention_heads,
															)

															_snake_case						=					common_inputs['attention_mask'].dtype
															_snake_case						=					torch.cat(
															    [common_inputs['attention_mask'], torch.ones(lowerCAmelCase_					,    lowerCAmelCase_					,    dtype=lowerCAmelCase_	)]					,    dim=1	)
															_snake_case						=					[
															    (torch.zeros(lowerCAmelCase_	), torch.zeros(lowerCAmelCase_	)) for _ in range(lowerCAmelCase_	)
															]
										return common_inputs




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = False					,    lowerCAmelCase_ = None					,    )		->				Mapping[str, Any]:
										# Copied from OnnxConfig.generate_dummy_inputs
										# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
										# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
										_snake_case						=					compute_effective_axis_dimension(
										    lowerCAmelCase_					,    fixed_dimension=OnnxConfig.default_fixed_batch					,    num_token_to_add=0	)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										_snake_case						=					tokenizer.num_special_tokens_to_add(lowerCAmelCase_	)
										_snake_case						=					compute_effective_axis_dimension(
										    lowerCAmelCase_					,    fixed_dimension=OnnxConfig.default_fixed_sequence					,    num_token_to_add=lowerCAmelCase_	)

										# Generate dummy inputs according to compute batch and sequence
										_snake_case						=					[' '.join([tokenizer.unk_token]	) * seq_length] * batch_size
										_snake_case						=					dict(tokenizer(lowerCAmelCase_					,    return_tensors=lowerCAmelCase_	)	)
										return common_inputs




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = -1					,    lowerCAmelCase_ = False					,    lowerCAmelCase_ = None					,    )		->				Mapping[str, Any]:
										if self.task in ["default", "seq2seq-lm"]:
															_snake_case						=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
															    lowerCAmelCase_					,    batch_size=lowerCAmelCase_					,    seq_length=lowerCAmelCase_					,    is_pair=lowerCAmelCase_					,    framework=lowerCAmelCase_	)

										elif self.task == "causal-lm":
															_snake_case						=					self._generate_dummy_inputs_for_causal_lm(
															    lowerCAmelCase_					,    batch_size=lowerCAmelCase_					,    seq_length=lowerCAmelCase_					,    is_pair=lowerCAmelCase_					,    framework=lowerCAmelCase_	)
										else:
															_snake_case						=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
															    lowerCAmelCase_					,    batch_size=lowerCAmelCase_					,    seq_length=lowerCAmelCase_					,    is_pair=lowerCAmelCase_					,    framework=lowerCAmelCase_	)

										return common_inputs






					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				Dict:
										if self.task in ["default", "seq2seq-lm"]:
															_snake_case						=					super()._flatten_past_key_values_(lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										else:
															_snake_case						=					super(lowerCAmelCase_					,    self	)._flatten_past_key_values_(
															    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)










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)



