
"""simple docstring"""



import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case          =						logging.get_logger(__name__)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[int]     ,  SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					Tuple						):






							"""simple docstring"""



							_lowerCAmelCase  	=			os.path.abspath(SCREAMING_SNAKE_CASE						)
							logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""						)
							# Load weights from TF model
							_lowerCAmelCase  	=			tf.train.list_variables(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			[]
							_lowerCAmelCase  	=			[]
							_lowerCAmelCase  	=			[]
							for full_name, shape in init_vars:
														# logger.info(f"Loading TF weight {name} with shape {shape}")
														_lowerCAmelCase  	=			full_name.split('/'						)
														if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																					logger.info(f"""Skipping non-model layer {full_name}"""						)
																					continue
														if "optimizer" in full_name:
																					logger.info(f"""Skipping optimization layer {full_name}"""						)
																					continue
														if name[0] == "model":
																					# ignore initial 'model'
																					_lowerCAmelCase  	=			name[1:]
														# figure out how many levels deep the name is
														_lowerCAmelCase  	=			0
														for _name in name:
																					if _name.startswith('layer_with_weights'						):
																												depth += 1
																					else:
																												break
														layer_depth.append(SCREAMING_SNAKE_CASE						)
														# read data
														_lowerCAmelCase  	=			tf.train.load_variable(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
														names.append('/'.join(SCREAMING_SNAKE_CASE						)						)
														arrays.append(SCREAMING_SNAKE_CASE						)
							logger.info(f"""Read a total of {len(SCREAMING_SNAKE_CASE						):,} layers"""						)

							# Sanity check
							if len(set(SCREAMING_SNAKE_CASE						)						) != 1:
														raise ValueError(f"""Found layer names with different depths (layer depth {list(set(SCREAMING_SNAKE_CASE						)						)})"""						)
							_lowerCAmelCase  	=			list(set(SCREAMING_SNAKE_CASE						)						)[0]
							if layer_depth != 1:
														raise ValueError(
														    'The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'
														    ' heads.'						)

							# convert layers
							logger.info('Converting weights...'						)
							for full_name, array in zip(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						):
														_lowerCAmelCase  	=			full_name.split('/'						)
														_lowerCAmelCase  	=			model
														_lowerCAmelCase  	=			[]
														for i, m_name in enumerate(SCREAMING_SNAKE_CASE						):
																					if m_name == ".ATTRIBUTES":
																												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																												break
																					if m_name.startswith('layer_with_weights'						):
																												_lowerCAmelCase  	=			int(m_name.split('-'						)[-1]						)
																												if layer_num <= 2:
																																			# embedding layers
																																			# layer_num 0: word_embeddings
																																			# layer_num 1: position_embeddings
																																			# layer_num 2: token_type_embeddings
																																			continue
																												elif layer_num == 3:
																																			# embedding LayerNorm
																																			trace.extend(['embeddings', 'LayerNorm']						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'embeddings'						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'LayerNorm'						)
																												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																																			# encoder layers
																																			trace.extend(['encoder', 'layer', str(layer_num - 4						)]						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'encoder'						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'layer'						)
																																			_lowerCAmelCase  	=			pointer[layer_num - 4]
																												elif layer_num == config.num_hidden_layers + 4:
																																			# pooler layer
																																			trace.extend(['pooler', 'dense']						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'pooler'						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'dense'						)
																					elif m_name == "embeddings":
																												trace.append('embeddings'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'embeddings'						)
																												if layer_num == 0:
																																			trace.append('word_embeddings'						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'word_embeddings'						)
																												elif layer_num == 1:
																																			trace.append('position_embeddings'						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'position_embeddings'						)
																												elif layer_num == 2:
																																			trace.append('token_type_embeddings'						)
																																			_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'token_type_embeddings'						)
																												else:
																																			raise ValueError(f"""Unknown embedding layer with name {full_name}"""						)
																												trace.append('weight'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'weight'						)
																					elif m_name == "_attention_layer":
																												# self-attention layer
																												trace.extend(['attention', 'self']						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'attention'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'self'						)
																					elif m_name == "_attention_layer_norm":
																												# output attention norm
																												trace.extend(['attention', 'output', 'LayerNorm']						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'attention'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'output'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'LayerNorm'						)
																					elif m_name == "_attention_output_dense":
																												# output attention dense
																												trace.extend(['attention', 'output', 'dense']						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'attention'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'output'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'dense'						)
																					elif m_name == "_output_dense":
																												# output dense
																												trace.extend(['output', 'dense']						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'output'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'dense'						)
																					elif m_name == "_output_layer_norm":
																												# output dense
																												trace.extend(['output', 'LayerNorm']						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'output'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'LayerNorm'						)
																					elif m_name == "_key_dense":
																												# attention key
																												trace.append('key'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'key'						)
																					elif m_name == "_query_dense":
																												# attention query
																												trace.append('query'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'query'						)
																					elif m_name == "_value_dense":
																												# attention value
																												trace.append('value'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'value'						)
																					elif m_name == "_intermediate_dense":
																												# attention intermediate dense
																												trace.extend(['intermediate', 'dense']						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'intermediate'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'dense'						)
																					elif m_name == "_output_layer_norm":
																												# output layer norm
																												trace.append('output'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'output'						)
																					# weights & biases
																					elif m_name in ["bias", "beta"]:
																												trace.append('bias'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'bias'						)
																					elif m_name in ["kernel", "gamma"]:
																												trace.append('weight'						)
																												_lowerCAmelCase  	=			getattr(SCREAMING_SNAKE_CASE     ,  'weight'						)
																					else:
																												logger.warning(f"""Ignored {m_name}"""						)
        # for certain layers reshape is necessary
														_lowerCAmelCase  	=			'.'.join(SCREAMING_SNAKE_CASE						)
														if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'     ,  SCREAMING_SNAKE_CASE						) or re.match(
														    R'(\S+)\.attention\.output\.dense\.weight'     ,  SCREAMING_SNAKE_CASE						):
																					_lowerCAmelCase  	=			array.reshape(pointer.data.shape						)
														if "kernel" in full_name:
																					_lowerCAmelCase  	=			array.transpose()
														if pointer.shape == array.shape:
																					_lowerCAmelCase  	=			torch.from_numpy(SCREAMING_SNAKE_CASE						)
														else:
																					raise ValueError(
																					    f"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
																					    f""" {array.shape}"""						)
														logger.info(f"""Successfully set variable {full_name} to PyTorch layer {trace}"""						)
							return model






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					Tuple     ,  SCREAMING_SNAKE_CASE:					Optional[int]						):






							"""simple docstring"""



							logger.info(f"""Loading model based on config from {config_path}..."""						)
							_lowerCAmelCase  	=			BertConfig.from_json_file(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			BertModel(SCREAMING_SNAKE_CASE						)

							# Load weights from checkpoint
							logger.info(f"""Loading weights from checkpoint {tf_checkpoint_path}..."""						)
							load_tfa_weights_in_bert(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)

							# Save pytorch-model
							logger.info(f"""Saving PyTorch model to {pytorch_dump_path}..."""						)
							torch.save(model.state_dict()     ,  SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
	_snake_case          =						argparse.ArgumentParser()
	parser.add_argument(
	    '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
	)
	parser.add_argument(
	    '''--bert_config_file''',
	    type=str,
	    required=True,
	    help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
	)
	parser.add_argument(
	    '''--pytorch_dump_path''',
	    type=str,
	    required=True,
	    help='''Path to the output PyTorch model (must include filename).''',
	)
	_snake_case          =						parser.parse_args()
	convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








"""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


_snake_case          =						getLogger(__name__)


_snake_case          =						'''cuda''' if torch.cuda.is_available() else '''cpu'''






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					List[str]     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					int = 8     ,  SCREAMING_SNAKE_CASE:					str = DEFAULT_DEVICE     ,  SCREAMING_SNAKE_CASE:					Optional[int]=False     ,  SCREAMING_SNAKE_CASE:					int="summarization"     ,  SCREAMING_SNAKE_CASE:					List[Any]=None     ,  **SCREAMING_SNAKE_CASE:					Tuple     ,  ):






							"""simple docstring"""



							_lowerCAmelCase  	=			Path(SCREAMING_SNAKE_CASE						).open('w'     ,  encoding='utf-8'						)
							_lowerCAmelCase  	=			str(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			AutoModelForSeqaSeqLM.from_pretrained(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
							if fpaa:
														_lowerCAmelCase  	=			model.half()

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






def 	__snake_case  (    ):






							"""simple docstring"""



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






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any=True						):






							"""simple docstring"""



							_lowerCAmelCase  	=			argparse.ArgumentParser()
							parser.add_argument('model_name'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like facebook/bart-large-cnn,t5-base, etc.'						)
							parser.add_argument('input_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.source'						)
							parser.add_argument('save_path'     ,  type=SCREAMING_SNAKE_CASE     ,  help='where to save summaries'						)
							parser.add_argument('--reference_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  help='like cnn_dm/test.target'						)
							parser.add_argument('--score_path'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default='metrics.json'     ,  help='where to save metrics'						)
							parser.add_argument('--device'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='cuda, cuda:1, cpu etc.'						)
							parser.add_argument(
							    '--prefix'     ,  type=SCREAMING_SNAKE_CASE     ,  required=SCREAMING_SNAKE_CASE     ,  default=SCREAMING_SNAKE_CASE     ,  help='will be added to the begininng of src examples'						)
							parser.add_argument('--task'     ,  type=SCREAMING_SNAKE_CASE     ,  default='summarization'     ,  help='used for task_specific_params + metrics'						)
							parser.add_argument('--bs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=8     ,  required=SCREAMING_SNAKE_CASE     ,  help='batch size'						)
							parser.add_argument(
							    '--n_obs'     ,  type=SCREAMING_SNAKE_CASE     ,  default=-1     ,  required=SCREAMING_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=SCREAMING_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(SCREAMING_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=SCREAMING_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(
							    SCREAMING_SNAKE_CASE     ,  args.save_path     ,  args.model_name     ,  batch_size=args.bs     ,  device=args.device     ,  fpaa=args.fpaa     ,  task=args.task     ,  prefix=args.prefix     ,  **SCREAMING_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(SCREAMING_SNAKE_CASE						)]
							_lowerCAmelCase  	=			score_fn(SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							scores.update(SCREAMING_SNAKE_CASE						)

							if args.dump_args:
														scores.update(SCREAMING_SNAKE_CASE						)
							if args.info:
														_lowerCAmelCase  	=			args.info

							if verbose:
														print(SCREAMING_SNAKE_CASE						)

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






