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       UpperCAmelCase       (     _snake_case						, _snake_case						, _snake_case						, _snake_case = 8						, _snake_case = DEFAULT_DEVICE						, _snake_case=False						, _snake_case="summarization"						, _snake_case=None						, **_snake_case						, ):
							lowerCAmelCase   =		Path(_snake_case							).open('''w'''						, encoding='''utf-8'''							)
							lowerCAmelCase   =		str(_snake_case							)
							lowerCAmelCase   =		AutoModelForSeqaSeqLM.from_pretrained(_snake_case							).to(_snake_case							)
							if fpaa:
														lowerCAmelCase   =		model.half()

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

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



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



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

							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'''							)

							lowerCAmelCase   =		generate_summaries_or_translations(
							    _snake_case						, args.save_path						, args.model_name						, batch_size=args.bs						, device=args.device						, fpaa=args.fpaa						, task=args.task						, prefix=args.prefix						, **_snake_case						, )

							if args.reference_path is None:
														return {}

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

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

							if verbose:
														print(_snake_case							)

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









from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						={
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										="""switch_transformers"""
				__a							: Union[str, Any]										=["""past_key_values"""]
				__a							: Dict										={"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}



				def __init__( self       , UpperCAmelCase_=3_21_28       , UpperCAmelCase_=7_68       , UpperCAmelCase_=64       , UpperCAmelCase_=20_48       , UpperCAmelCase_=64       , UpperCAmelCase_=12       , UpperCAmelCase_=3       , UpperCAmelCase_=12       , UpperCAmelCase_=3       , UpperCAmelCase_=12       , UpperCAmelCase_=8       , UpperCAmelCase_=False       , UpperCAmelCase_=0.01       , UpperCAmelCase_="float32"       , UpperCAmelCase_=False       , UpperCAmelCase_=32       , UpperCAmelCase_=1_28       , UpperCAmelCase_=0.1       , UpperCAmelCase_=1E-6       , UpperCAmelCase_=0.001       , UpperCAmelCase_=0.001       , UpperCAmelCase_=1.0       , UpperCAmelCase_="relu"       , UpperCAmelCase_=True       , UpperCAmelCase_=False       , UpperCAmelCase_=True       , UpperCAmelCase_=0       , UpperCAmelCase_=1       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		vocab_size
											lowerCAmelCase   =		d_model
											lowerCAmelCase   =		d_kv
											lowerCAmelCase   =		d_ff

											lowerCAmelCase   =		num_sparse_encoder_layers

											lowerCAmelCase   =		num_layers
											lowerCAmelCase   =		(
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											lowerCAmelCase   =		num_sparse_decoder_layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_encoder_layers > 0:
																		lowerCAmelCase   =		self.num_layers // self.num_sparse_encoder_layers
											else:
																		lowerCAmelCase   =		self.num_layers  # HACK: this will create 0 sparse layers

											# This tells us, each how many encoder layer we'll have to set a sparse layer.
											if self.num_sparse_decoder_layers > 0:
																		lowerCAmelCase   =		self.num_decoder_layers // self.num_sparse_decoder_layers
											else:
																		lowerCAmelCase   =		self.num_decoder_layers  # HACK: this will create 0 sparse layers

											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		num_experts
											lowerCAmelCase   =		expert_capacity
											lowerCAmelCase   =		router_bias
											lowerCAmelCase   =		router_jitter_noise
											if router_dtype not in ["float32", "float16", "bfloat16"]:
																		raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""						)
											lowerCAmelCase   =		router_dtype

											lowerCAmelCase   =		router_ignore_padding_tokens
											lowerCAmelCase   =		relative_attention_num_buckets
											lowerCAmelCase   =		relative_attention_max_distance

											lowerCAmelCase   =		dropout_rate
											lowerCAmelCase   =		layer_norm_epsilon
											lowerCAmelCase   =		initializer_factor
											lowerCAmelCase   =		feed_forward_proj
											lowerCAmelCase   =		use_cache
											lowerCAmelCase   =		add_router_probs

											lowerCAmelCase   =		router_z_loss_coef
											lowerCAmelCase   =		router_aux_loss_coef

											lowerCAmelCase   =		self.feed_forward_proj.split('''-'''						)
											lowerCAmelCase   =		act_info[-1]
											lowerCAmelCase   =		act_info[0] == '''gated'''

											if len(UpperCAmelCase_						) > 1 and act_info[0] != "gated" or len(UpperCAmelCase_						) > 2:
																		raise ValueError(
																		    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
																		    '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
																		    '''\'gated-gelu\' or \'relu\''''						)

											# for backwards compatibility
											if feed_forward_proj == "gated-gelu":
																		lowerCAmelCase   =		'''gelu_new'''

											super().__init__(
											    pad_token_id=UpperCAmelCase_       , eos_token_id=UpperCAmelCase_       , is_encoder_decoder=UpperCAmelCase_       , **UpperCAmelCase_       , )



