




import argparse

import torch
from clip import load

from transformers import CLIPConfig, CLIPModel


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	pt_attn_layer.in_proj_weight.chunk(3      ,       dim=0		)
						_UpperCAmelCase				,					_UpperCAmelCase				,					_UpperCAmelCase						=	pt_attn_layer.in_proj_bias.chunk(3      ,       dim=0		)

						_UpperCAmelCase						=	pt_attn_layer.out_proj.weight
						_UpperCAmelCase						=	pt_attn_layer.out_proj.bias

						_UpperCAmelCase						=	q_proj
						_UpperCAmelCase						=	q_proj_bias

						_UpperCAmelCase						=	k_proj
						_UpperCAmelCase						=	k_proj_bias

						_UpperCAmelCase						=	v_proj
						_UpperCAmelCase						=	v_proj_bias

						_UpperCAmelCase						=	out_proj_weights
						_UpperCAmelCase						=	out_proj_bias


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						copy_linear(hf_mlp.fca      ,       pt_mlp.c_fc		)
						copy_linear(hf_mlp.fca      ,       pt_mlp.c_proj		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	pt_linear.weight
						_UpperCAmelCase						=	pt_linear.bias


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						copy_linear(hf_layer.layer_norma      ,       pt_layer.ln_a		)
						copy_linear(hf_layer.layer_norma      ,       pt_layer.ln_a		)

						# copy MLP
						copy_mlp(hf_layer.mlp      ,       pt_layer.mlp		)

						# copy attn
						copy_attn_layer(hf_layer.self_attn      ,       pt_layer.attn		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						for hf_layer, pt_layer in zip(snake_case_      ,       snake_case_		):
												copy_layer(snake_case_      ,       snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	pt_model.token_embedding.weight
						_UpperCAmelCase						=	pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm      ,       pt_model.ln_final		)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers      ,       pt_model.transformer.resblocks		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model      ,       snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layrnorm      ,       pt_model.visual.ln_pre		)
						copy_linear(hf_model.vision_model.post_layernorm      ,       pt_model.visual.ln_post		)

						# copy embeds
						_UpperCAmelCase						=	pt_model.visual.conva.weight.data
						_UpperCAmelCase						=	pt_model.visual.class_embedding
						_UpperCAmelCase						=	pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers      ,       pt_model.visual.transformer.resblocks		)


@torch.no_grad()
def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=None		):

						'''simple docstring'''


						if config_path is not None:
												_UpperCAmelCase						=	CLIPConfig.from_pretrained(snake_case_		)
						else:
												_UpperCAmelCase						=	CLIPConfig(projection_dim=512      ,       text_config={}      ,       vision_config={}		)

						_UpperCAmelCase						=	CLIPModel(snake_case_		).eval()

						_UpperCAmelCase				,					_UpperCAmelCase						=	load(snake_case_      ,       device="cpu"      ,       jit=snake_case_		)
						_UpperCAmelCase						=	pt_model.eval()

						copy_text_model_and_projection(snake_case_      ,       snake_case_		)
						copy_vison_model_and_projection(snake_case_      ,       snake_case_		)
						_UpperCAmelCase						=	pt_model.logit_scale

						_UpperCAmelCase						=	torch.arange(0      ,       77		).unsqueeze(0		)
						_UpperCAmelCase						=	torch.randn(1      ,       3      ,       224      ,       224		)

						_UpperCAmelCase						=	hf_model(input_ids=snake_case_      ,       pixel_values=snake_case_      ,       return_dict=snake_case_		)
						_UpperCAmelCase						=	hf_outputs.logits_per_image
						_UpperCAmelCase						=	hf_outputs.logits_per_text
						_UpperCAmelCase				,					_UpperCAmelCase						=	pt_model(snake_case_      ,       snake_case_		)

						assert torch.allclose(snake_case_      ,       snake_case_      ,       atol=1e-3		)
						assert torch.allclose(snake_case_      ,       snake_case_      ,       atol=1e-3		)

						hf_model.save_pretrained(snake_case_		)


if __name__ == "__main__":
				lowercase_      :							str    							=					argparse.ArgumentParser()
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
				parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
				lowercase_      :							Any    							=					parser.parse_args()

				convert_clip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










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


lowercase_      :							int    							=					getLogger(__name__)


lowercase_      :							str    							=					'cuda' if torch.cuda.is_available() else 'cpu'


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = 8      ,       snake_case_ = DEFAULT_DEVICE      ,       snake_case_=False      ,       snake_case_="summarization"      ,       snake_case_=None      ,       **snake_case_      ,       ):

						'''simple docstring'''


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

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

						_UpperCAmelCase						=	time.time()
						# update config with task specific params
						use_task_specific_params(snake_case_      ,       snake_case_		)
						if prefix is None:
												_UpperCAmelCase						=	prefix or getattr(model.config      ,       "prefix"      ,       ""		) or ""
						for examples_chunk in tqdm(list(chunks(snake_case_      ,       snake_case_		)		)		):
												_UpperCAmelCase						=	[prefix + text for text in examples_chunk]
												_UpperCAmelCase						=	tokenizer(snake_case_      ,       return_tensors="pt"      ,       truncation=snake_case_      ,       padding="longest"		).to(snake_case_		)
												_UpperCAmelCase						=	model.generate(
												    input_ids=batch.input_ids      ,       attention_mask=batch.attention_mask      ,       **snake_case_      ,       )
												_UpperCAmelCase						=	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()
						_UpperCAmelCase						=	int(time.time() - start_time		)  # seconds
						_UpperCAmelCase						=	len(snake_case_		)
						return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs      ,       4		)}


def 				__SCREAMING_SNAKE_CASE    (     ):

						'''simple docstring'''


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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_=True		):

						'''simple docstring'''


						_UpperCAmelCase						=	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
						_UpperCAmelCase				,					_UpperCAmelCase						=	parser.parse_known_args()
						_UpperCAmelCase						=	parse_numeric_n_bool_cl_kwargs(snake_case_		)
						if parsed_args and verbose:
												print(f"""parsed the following generate kwargs: {parsed_args}"""		)
						with open(args.input_path		) as f:
												_UpperCAmelCase						=	[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
						if args.n_obs > 0:
												_UpperCAmelCase						=	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."""		)
						_UpperCAmelCase						=	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
						_UpperCAmelCase						=	calculate_bleu if "translation" in args.task else calculate_rouge
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.save_path		).readlines()]
						_UpperCAmelCase						=	[x.rstrip() for x in open(args.reference_path		).readlines()][: len(snake_case_		)]
						_UpperCAmelCase						=	score_fn(snake_case_      ,       snake_case_		)
						scores.update(snake_case_		)

						if args.dump_args:
												scores.update(snake_case_		)
						if args.info:
												_UpperCAmelCase						=	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)




