
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


SCREAMING_SNAKE_CASE							:Union[str, Any]										=    getLogger(__name__)


SCREAMING_SNAKE_CASE							:List[Any]										=    'cuda' if torch.cuda.is_available() else 'cpu'


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_ = 8		,       a_ = DEFAULT_DEVICE		,       a_=False		,       a_="summarization"		,       a_=None		,       **a_		,       )		->     Dict:







				"""simple docstring"""



				__A             =			Path(a_						).open("w"		,       encoding="utf-8"						)
				__A             =			str(a_						)
				__A             =			AutoModelForSeqaSeqLM.from_pretrained(a_						).to(a_						)
				if fpaa:
								__A             =			model.half()

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

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


def 						UpperCAmelCase      (     )		->     Union[str, Any]:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_=True						)		->     str:







				"""simple docstring"""



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

				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"						)

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

				if args.reference_path is None:
								return {}

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

				if args.dump_args:
								scores.update(a_						)
				if args.info:
								__A             =			args.info

				if verbose:
								print(a_						)

				if args.score_path is not None:
								json.dump(a_		,       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 typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					[R"h\.\d+\.attn\.bias", R"h\.\d+\.attn\.masked_bias"]






	@register_to_config
	def __init__(	self :			Tuple							,A :			int							,A :			int							,A :			Optional[int] = None							,A :			int = 5_02_57							,A :			int = 10_24							,A :			int = 7_68							,A :			int = 12							,A :			int = 12							,A :			Optional[int] = None							,A :			str = "gelu_new"							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 0.1							,A :			float = 1E-5							,A :			float = 0.02							,A :			bool = True							,A :			bool = True							,A :			bool = False							,A :			bool = False							,):
					super().__init__()

					__A             =			prefix_length

					if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
									raise ValueError(
									    f'''`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and'''
									    f''' `n_embd`: {n_embd} are not equal.'''			)

					__A             =			prefix_inner_dim
					__A             =			prefix_hidden_dim

					__A             =			(
					    nn.Linear(self.prefix_inner_dim							,self.prefix_hidden_dim			)
					    if self.prefix_hidden_dim is not None
					    else nn.Identity()
					)
					__A             =			(
					    nn.Linear(self.prefix_hidden_dim							,A			) if self.prefix_hidden_dim is not None else nn.Identity()
					)

					__A             =			GPTaConfig(
					    vocab_size=A							,n_positions=A							,n_embd=A							,n_layer=A							,n_head=A							,n_inner=A							,activation_function=A							,resid_pdrop=A							,embd_pdrop=A							,attn_pdrop=A							,layer_norm_epsilon=A							,initializer_range=A							,scale_attn_weights=A							,use_cache=A							,scale_attn_by_inverse_layer_idx=A							,reorder_and_upcast_attn=A							,)
					__A             =			GPTaLMHeadModel(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			torch.Tensor							,A :			torch.Tensor							,A :			Optional[torch.Tensor] = None							,A :			Optional[torch.Tensor] = None							,):
					__A             =			self.transformer.transformer.wte(A			)
					__A             =			self.encode_prefix(A			)
					__A             =			self.decode_prefix(A			)
					__A             =			torch.cat((prefix_embeds, embedding_text)							,dim=1			)

					if labels is not None:
									__A             =			self.get_dummy_token(input_ids.shape[0]							,input_ids.device			)
									__A             =			torch.cat((dummy_token, input_ids)							,dim=1			)
					__A             =			self.transformer(inputs_embeds=A							,labels=A							,attention_mask=A			)
					if self.prefix_hidden_dim is not None:
									return out, hidden
					else:
									return out






	def     UpperCamelCase_  (	self :			int							,A :			int							,A :			torch.device			):
					return torch.zeros(A							,self.prefix_length							,dtype=torch.intaa							,device=A			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			List[Any]			):
					return self.encode_prefix(A			)






	@torch.no_grad()
	def     UpperCamelCase_  (	self :			str							,A :			List[str]							,A :			List[Any]							,A :			List[Any]			):
					__A             =			torch.split(A							,1							,dim=0			)
					__A             =			[]
					__A             =			[]
					for feature in features:
									__A             =			self.decode_prefix(feature.to(A			)			)  # back to the clip feature
									# Only support beam search for now
									__A      ,							__A             =			self.generate_beam(
									    input_embeds=A							,device=A							,eos_token_id=A			)
									generated_tokens.append(output_tokens[0]			)
									generated_seq_lengths.append(seq_lengths[0]			)
					__A             =			torch.stack(A			)
					__A             =			torch.stack(A			)
					return generated_tokens, generated_seq_lengths







	@torch.no_grad()
	def     UpperCamelCase_  (	self :			Tuple							,A :			List[str]=None							,A :			Dict=None							,A :			Optional[int]=None							,A :			int = 5							,A :			int = 67							,A :			float = 1.0							,A :			Optional[int] = None							,):
					__A             =			eos_token_id
					__A             =			None
					__A             =			None
					__A             =			torch.ones(A							,device=A							,dtype=torch.int			)
					__A             =			torch.zeros(A							,device=A							,dtype=torch.bool			)

					if input_embeds is not None:
									__A             =			input_embeds
					else:
									__A             =			self.transformer.transformer.wte(A			)

					for i in range(A			):
									__A             =			self.transformer(inputs_embeds=A			)
									__A             =			outputs.logits
									__A             =			logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
									__A             =			logits.softmax(-1			).log()

									if scores is None:
													__A      ,							__A             =			logits.topk(A							,-1			)
													__A             =			generated.expand(A							,*generated.shape[1:]			)
													__A      ,							__A             =			next_tokens.permute(1							,0			), scores.squeeze(0			)
													if tokens is None:
																	__A             =			next_tokens
													else:
																	__A             =			tokens.expand(A							,*tokens.shape[1:]			)
																	__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
									else:
													__A             =			-float(np.inf			)
													__A             =			0
													__A             =			scores[:, None] + logits
													seq_lengths[~is_stopped] += 1
													__A             =			scores_sum / seq_lengths[:, None]
													__A      ,							__A             =			scores_sum_average.view(-1			).topk(A							,-1			)
													__A             =			next_tokens // scores_sum.shape[1]
													__A             =			seq_lengths[next_tokens_source]
													__A             =			next_tokens % scores_sum.shape[1]
													__A             =			next_tokens.unsqueeze(1			)
													__A             =			tokens[next_tokens_source]
													__A             =			torch.cat((tokens, next_tokens)							,dim=1			)
													__A             =			generated[next_tokens_source]
													__A             =			scores_sum_average * seq_lengths
													__A             =			is_stopped[next_tokens_source]

									__A             =			self.transformer.transformer.wte(next_tokens.squeeze()			).view(generated.shape[0]							,1							,-1			)
									__A             =			torch.cat((generated, next_token_embed)							,dim=1			)
									__A             =			is_stopped + next_tokens.eq(A			).squeeze()
									if is_stopped.all():
													break

					__A             =			scores / seq_lengths
					__A             =			scores.argsort(descending=A			)
					# tokens tensors are already padded to max_seq_length
					__A             =			[tokens[i] for i in order]
					__A             =			torch.stack(A							,dim=0			)
					__A             =			torch.tensor([seq_lengths[i] for i in order]							,dtype=seq_lengths.dtype			)
					return output_texts, seq_lengths
