


'''simple docstring'''
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				:							Optional[Any]		       =		getLogger(__name__)


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







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_ = 8	,     UpperCamelCase_ = DEFAULT_DEVICE	,     UpperCamelCase_=False	,     UpperCamelCase_="summarization"	,     UpperCamelCase_=None	,     **UpperCamelCase_	,     ):
							A_ 						=		Path(UpperCamelCase_   ).open("w"	,     encoding="utf-8"   )
							A_ 						=		str(UpperCamelCase_   )
							A_ 						=		AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_   ).to(UpperCamelCase_   )
							if fpaa:
														A_ 						=		model.half()

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







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







def 			a_							(  UpperCamelCase_=True   ):
							A_ 						=		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
							A_						,				A_ 						=		parser.parse_known_args()
							A_ 						=		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:
														A_ 						=		[" " + x.rstrip() if "t5" in args.model_name else x.rstrip() for x in f.readlines()]
							if args.n_obs > 0:
														A_ 						=		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."   )
							A_ 						=		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
							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(UpperCamelCase_   )]
							A_ 						=		score_fn(UpperCamelCase_	,     UpperCamelCase_   )
							scores.update(UpperCamelCase_   )

							if args.dump_args:
														scores.update(UpperCamelCase_   )
							if args.info:
														A_ 						=		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)









'''simple docstring'''
import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling







def 			a_							(  UpperCamelCase_   ):
							A_ 						=		"https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint"

							with open(UpperCamelCase_	,     "wb"   ) as f:
														with requests.get(UpperCamelCase_	,     stream=UpperCamelCase_   ) as req:
																					for chunk in req.iter_content(chunk_size=2_0_4_8   ):
																												f.write(UpperCamelCase_   )







def 			a_							(  ):
							A_ 						=		VivitConfig()

							A_ 						=		4_0_0
							A_ 						=		"huggingface/label-files"
							A_ 						=		"kinetics400-id2label.json"

							A_ 						=		json.load(open(hf_hub_download(UpperCamelCase_	,     UpperCamelCase_	,     repo_type="dataset"   )	,     "r"   )   )
							A_ 						=		{int(UpperCamelCase_   ): v for k, v in idalabel.items()}
							A_ 						=		idalabel
							A_ 						=		{v: k for k, v in idalabel.items()}
							return config







def 			a_							(  ):
							A_ 						=		hf_hub_download(
							    repo_id="hf-internal-testing/spaghetti-video"	,     filename="eating_spaghetti_32_frames.npy"	,     repo_type="dataset"   )
							A_ 						=		np.load(UpperCamelCase_   )
							return list(UpperCamelCase_   )







def 			a_							(  UpperCamelCase_   ):
							if np.ndim(UpperCamelCase_   ) == 2:
														return transform_attention_bias(UpperCamelCase_   )

							elif np.ndim(UpperCamelCase_   ) == 3:
														return transform_attention_kernel(UpperCamelCase_   )

							else:
														raise Exception(f"Invalid number of dimesions: {np.ndim(UpperCamelCase_   )}"   )







def 			a_							(  UpperCamelCase_   ):
							return current.flatten()







def 			a_							(  UpperCamelCase_   ):
							return np.reshape(UpperCamelCase_	,     (current.shape[0], current.shape[1] * current.shape[2])   ).T







def 			a_							(  UpperCamelCase_   ):
							return np.reshape(UpperCamelCase_	,     (current.shape[0] * current.shape[1], current.shape[2])   ).T







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_   ):
							A_ 						=		state_dict["optimizer"]["target"]["Transformer"][f"encoderblock_{i}"]

							A_ 						=		f"encoder.layer.{i}."
							A_ 						=		{
							    prefix + "intermediate.dense.bias": state["MlpBlock_0"]["Dense_0"]["bias"],
							    prefix + "intermediate.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_0"]["kernel"]   ),
							    prefix + "output.dense.bias": state["MlpBlock_0"]["Dense_1"]["bias"],
							    prefix + "output.dense.weight": np.transpose(state["MlpBlock_0"]["Dense_1"]["kernel"]   ),
							    prefix + "layernorm_before.bias": state["LayerNorm_0"]["bias"],
							    prefix + "layernorm_before.weight": state["LayerNorm_0"]["scale"],
							    prefix + "layernorm_after.bias": state["LayerNorm_1"]["bias"],
							    prefix + "layernorm_after.weight": state["LayerNorm_1"]["scale"],
							    prefix
							    + "attention.attention.query.bias": transform_attention(
							        state["MultiHeadDotProductAttention_0"]["query"]["bias"]   ),
							    prefix
							    + "attention.attention.query.weight": transform_attention(
							        state["MultiHeadDotProductAttention_0"]["query"]["kernel"]   ),
							    prefix
							    + "attention.attention.key.bias": transform_attention(state["MultiHeadDotProductAttention_0"]["key"]["bias"]   ),
							    prefix
							    + "attention.attention.key.weight": transform_attention(
							        state["MultiHeadDotProductAttention_0"]["key"]["kernel"]   ),
							    prefix
							    + "attention.attention.value.bias": transform_attention(
							        state["MultiHeadDotProductAttention_0"]["value"]["bias"]   ),
							    prefix
							    + "attention.attention.value.weight": transform_attention(
							        state["MultiHeadDotProductAttention_0"]["value"]["kernel"]   ),
							    prefix + "attention.output.dense.bias": state["MultiHeadDotProductAttention_0"]["out"]["bias"],
							    prefix
							    + "attention.output.dense.weight": transform_attention_output_weight(
							        state["MultiHeadDotProductAttention_0"]["out"]["kernel"]   ),
							}

							return new_state







def 			a_							(  UpperCamelCase_   ):
							return sum([1 if "encoderblock_" in k else 0 for k in state_dict["optimizer"]["target"]["Transformer"].keys()]   )







def 			a_							(  UpperCamelCase_	,     UpperCamelCase_=False   ):
							A_ 						=		get_n_layers(UpperCamelCase_   )

							A_ 						=		OrderedDict()

							A_ 						=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["bias"]
							A_ 						=		state_dict["optimizer"]["target"]["Transformer"]["encoder_norm"]["scale"]

							A_ 						=		np.transpose(
							    state_dict["optimizer"]["target"]["embedding"]["kernel"]	,     (4, 3, 0, 1, 2)   )
							A_ 						=		state_dict["optimizer"]["target"]["embedding"]["bias"]

							A_ 						=		state_dict["optimizer"]["target"]["cls"]
							A_ 						=		state_dict["optimizer"]["target"]["Transformer"]["posembed_input"][
							    "pos_embedding"
							]

							for i in range(UpperCamelCase_   ):
														new_state.update(transform_state_encoder_block(UpperCamelCase_	,     UpperCamelCase_   )   )

							if classification_head:
														A_ 						=		{"vivit." + k: v for k, v in new_state.items()}
														A_ 						=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["kernel"]   )
														A_ 						=		np.transpose(state_dict["optimizer"]["target"]["output_projection"]["bias"]   )

							return {k: torch.tensor(UpperCamelCase_   ) for k, v in new_state.items()}







def 			a_							(  ):
							A_ 						=		VivitImageProcessor()

							assert extractor.do_resize is True
							assert extractor.size == {"shortest_edge": 2_5_6}
							assert extractor.do_center_crop is True
							assert extractor.crop_size == {"width": 2_2_4, "height": 2_2_4}
							assert extractor.resample == PILImageResampling.BILINEAR

							# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
							# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
							# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
							assert extractor.do_normalize is False
							assert extractor.do_rescale is True
							assert extractor.rescale_factor == 1 / 2_5_5

							# zero-centering = True in original implementation
							assert extractor.do_zero_centering is True

							return extractor







def 			a_							(  UpperCamelCase_   ):
							A_ 						=		"checkpoint"

							if not os.path.exists(UpperCamelCase_   ):
														download_checkpoint(UpperCamelCase_   )

							A_ 						=		restore_checkpoint(UpperCamelCase_	,     UpperCamelCase_   )
							A_ 						=		transform_state(UpperCamelCase_	,     classification_head=UpperCamelCase_   )

							A_ 						=		get_vivit_config()

							assert config.image_size == 2_2_4
							assert config.num_frames == 3_2

							A_ 						=		VivitForVideoClassification(UpperCamelCase_   )
							model.load_state_dict(UpperCamelCase_   )
							model.eval()

							A_ 						=		get_processor()

							A_ 						=		prepare_video()
							A_ 						=		extractor(UpperCamelCase_	,     return_tensors="pt"   )

							A_ 						=		model(**UpperCamelCase_   )

							A_ 						=		torch.Size([1, 4_0_0]   )
							A_ 						=		torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]   )

							assert outputs.logits.shape == expected_shape
							assert torch.allclose(outputs.logits[0, :5]	,     UpperCamelCase_	,     atol=1e-4   ), outputs.logits[0, :5]

							model.save_pretrained(UpperCamelCase_   )
							extractor.save_pretrained(UpperCamelCase_   )


if __name__ == "__main__":
							__SCREAMING_SNAKE_CASE				:							Optional[Any]		       =		argparse.ArgumentParser()

							parser.add_argument('''--output_model_name''', '''-o''', type=str, help='''Output path for the converted HuggingFace model''')

							__SCREAMING_SNAKE_CASE				:							Optional[int]		       =		parser.parse_args()
							convert(args.output_model_name)





