




"""simple docstring"""
import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json





def 						UpperCAmelCase				(			A__:    Dict     )  ->     Optional[int]:
			__lowerCamelCase					:	Any	       =				filter(lambda A__     : p.requires_grad							,				model.parameters()     )
			__lowerCamelCase					:	List[str]	       =				sum([np.prod(p.size()     ) for p in model_parameters]     )
			return params


a_				:    Union[str, Any]										=     logging.getLogger(__name__)





def 						UpperCAmelCase				(			A__:    Optional[int]							,				A__:    Any     )  ->     Optional[int]:
			if metric == "rouge2":
						__lowerCamelCase					:	List[Any]	       =				'{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						__lowerCamelCase					:	List[Any]	       =				'{val_avg_bleu:.4f}-{step_count}'
			elif metric == "em":
						__lowerCamelCase					:	Any	       =				'{val_avg_em:.4f}-{step_count}'
			elif metric == "loss":
						__lowerCamelCase					:	Optional[int]	       =				'{val_avg_loss:.4f}-{step_count}'
			else:
						raise NotImplementedError(
						    f'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
						    ' function.'     )

			__lowerCamelCase					:	Optional[int]	       =				ModelCheckpoint(
			    dirpath=A__							,				filename=A__							,				monitor=f'''val_{metric}'''							,				mode='max'							,				save_top_k=1							,				every_n_epochs=1							,				)

			return checkpoint_callback





def 						UpperCAmelCase				(			A__:    Dict							,				A__:    int     )  ->     Union[str, Any]:
			return EarlyStopping(
			    monitor=f'''val_{metric}'''							,				mode='min' if 'loss' in metric else 'max'							,				patience=A__							,				verbose=A__							,				)






class __lowercase(      pl.Callback				):




					'''simple docstring'''


					def     snake_case_				(    self						,	__a						,	__a     ):
								__lowerCamelCase					:	List[Any]	       =				{f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups     )}
								pl_module.logger.log_metrics(__a     )
					@rank_zero_only
					def     snake_case_				(    self						,	__a						,	__a						,	__a						,	__a=True     ):
								logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''     )
								__lowerCamelCase					:	List[str]	       =				trainer.callback_metrics
								trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']}     )
								# Log results
								__lowerCamelCase					:	Any	       =				Path(pl_module.hparams.output_dir     )
								if type_path == "test":
											__lowerCamelCase					:	Dict	       =				od / 'test_results.txt'
											__lowerCamelCase					:	Optional[Any]	       =				od / 'test_generations.txt'
								else:
											# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
											# If people want this it will be easy enough to add back.
											__lowerCamelCase					:	Optional[Any]	       =				od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
											__lowerCamelCase					:	Optional[int]	       =				od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
											results_file.parent.mkdir(exist_ok=__a     )
											generations_file.parent.mkdir(exist_ok=__a     )
								with open(__a						,	'a+'     ) as writer:
											for key in sorted(__a     ):
														if key in ["log", "progress_bar", "preds"]:
																	continue
														__lowerCamelCase					:	Tuple	       =				metrics[key]
														if isinstance(__a						,	torch.Tensor     ):
																	__lowerCamelCase					:	str	       =				val.item()
														__lowerCamelCase					:	Optional[int]	       =				f'''{key}: {val:.6f}\n'''
														writer.write(__a     )

								if not save_generations:
											return

								if "preds" in metrics:
											__lowerCamelCase					:	Optional[Any]	       =				'\n'.join(metrics['preds']     )
											generations_file.open('w+'     ).write(__a     )
					@rank_zero_only
					def     snake_case_				(    self						,	__a						,	__a     ):
								try:
											__lowerCamelCase					:	Union[str, Any]	       =				pl_module.model.model.num_parameters()
								except AttributeError:
											__lowerCamelCase					:	Optional[int]	       =				pl_module.model.num_parameters()

								__lowerCamelCase					:	Optional[Any]	       =				count_trainable_parameters(__a     )
								# mp stands for million parameters
								trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1E6, 'grad_mp': n_trainable_pars / 1E6}     )
					@rank_zero_only
					def     snake_case_				(    self						,	__a						,	__a     ):
								save_json(pl_module.metrics						,	pl_module.metrics_save_path     )
								return self._write_logs(__a						,	__a						,	'test'     )





					@rank_zero_only
					def     snake_case_				(    self						,	__a						,	__a     ):
								save_json(pl_module.metrics						,	pl_module.metrics_save_path     )
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")









"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


a_				:    Dict										=     logging.get_logger(__name__)


a_				:    List[Any]										=     {
    '''vocab_file''': '''vocab.json''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
    '''merges_file''': '''merges.txt''',
}

a_				:    Tuple										=     {
    '''vocab_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'''
        ),
    },
    '''tokenizer_config_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'''
        ),
    },
    '''merges_file''': {
        '''facebook/s2t-wav2vec2-large-en-de''': (
            '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'''
        ),
    },
}

a_				:    Optional[Any]										=     '''</w>'''
a_				:    Dict										=     '''@@ '''





def 						UpperCAmelCase				(			A__:    Optional[Any]     )  ->     Optional[int]:
			__lowerCamelCase					:	Any	       =				set()
			__lowerCamelCase					:	Optional[int]	       =				word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)     )
						__lowerCamelCase					:	Optional[int]	       =				char
			return pairs


# Speech2Text2 has no max input length
a_				:    Union[str, Any]										=     {'''facebook/s2t-wav2vec2-large-en-de''': 10_24}






class __lowercase(      lowercase__				):




					'''simple docstring'''


					__a						:						List[str]     		= VOCAB_FILES_NAMES
					__a						:						Optional[int]     		= PRETRAINED_VOCAB_FILES_MAP
					__a						:						Union[str, Any]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__a						:						Dict     		= ['input_ids', 'attention_mask']
					def __init__(    self						,	__a						,	__a="<s>"						,	__a="<pad>"						,	__a="</s>"						,	__a="<unk>"						,	__a=False						,	__a=None						,	**__a						,	):
								super().__init__(
								    unk_token=__a						,	bos_token=__a						,	eos_token=__a						,	pad_token=__a						,	do_lower_case=__a						,	**__a						,	)

								__lowerCamelCase					:	List[Any]	       =				do_lower_case

								with open(__a						,	encoding='utf-8'     ) as vocab_handle:
											__lowerCamelCase					:	str	       =				json.load(__a     )
								__lowerCamelCase					:	int	       =				{v: k for k, v in self.encoder.items()}

								if merges_file is None:
											logger.info(f'''No merges files provided. {self.__class__.__name__} can only be used for decoding.'''     )

											__lowerCamelCase					:	str	       =				None
											__lowerCamelCase					:	Optional[int]	       =				None
								else:
											with open(__a						,	encoding='utf-8'     ) as merges_handle:
														__lowerCamelCase					:	Optional[int]	       =				merges_handle.read().split('\n'     )[:-1]

											__lowerCamelCase					:	List[str]	       =				[tuple(merge.split()[:2]     ) for merge in merges]
											__lowerCamelCase					:	Any	       =				dict(zip(__a						,	range(len(__a     )     )     )     )
											__lowerCamelCase					:	List[str]	       =				{}
					@property
					def     snake_case_				(    self     ):
								return len(self.decoder     )
					def     snake_case_				(    self     ):
								return dict(self.encoder						,	**self.added_tokens_encoder     )
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Optional[Any]	       =				tuple(token[:-1]     ) + (token[-1] + BPE_TOKEN_MERGES,)
								if token in self.cache:
											return self.cache[token]
								__lowerCamelCase					:	Union[str, Any]	       =				get_pairs(__a     )

								if not pairs:
											return token

								while True:
											__lowerCamelCase					:	Dict	       =				min(__a						,	key=lambda __a     : self.bpe_ranks.get(__a						,	float('inf'     )     )     )
											if bigram not in self.bpe_ranks:
														break
											__lowerCamelCase       ,      __lowerCamelCase					:	Optional[Any]	       =				bigram
											__lowerCamelCase					:	Tuple	       =				[]
											__lowerCamelCase					:	Union[str, Any]	       =				0
											while i < len(__a     ):
														try:
																	__lowerCamelCase					:	Union[str, Any]	       =				word.index(__a						,	__a     )
														except ValueError:
																	new_word.extend(word[i:]     )
																	break
														else:
																	new_word.extend(word[i:j]     )
																	__lowerCamelCase					:	Any	       =				j

														if word[i] == first and i < len(__a     ) - 1 and word[i + 1] == second:
																	new_word.append(first + second     )
																	i += 2
														else:
																	new_word.append(word[i]     )
																	i += 1
											__lowerCamelCase					:	List[str]	       =				tuple(__a     )
											__lowerCamelCase					:	Any	       =				new_word
											if len(__a     ) == 1:
														break
											else:
														__lowerCamelCase					:	Tuple	       =				get_pairs(__a     )
								__lowerCamelCase					:	Optional[int]	       =				' '.join(__a     )
								if word == "\n  " + BPE_TOKEN_MERGES:
											__lowerCamelCase					:	str	       =				'\n' + BPE_TOKEN_MERGES

								if word.endswith(__a     ):
											__lowerCamelCase					:	Tuple	       =				word.replace(__a						,	''     )

								__lowerCamelCase					:	Optional[Any]	       =				word.replace(' '						,	__a     )
								__lowerCamelCase					:	Union[str, Any]	       =				word
								return word
					def     snake_case_				(    self						,	__a     ):

								if self.bpe_ranks is None:
											raise ValueError(
											    'This tokenizer was instantiated without a `merges.txt` file, so'
											    ' that it can only be used for decoding, not for encoding.'
											    'Make sure to provide `merges.txt` file at instantiation to enable '
											    'encoding.'     )

								if self.do_lower_case:
											__lowerCamelCase					:	Any	       =				text.lower()

								__lowerCamelCase					:	Union[str, Any]	       =				text.split()

								__lowerCamelCase					:	str	       =				[]
								for token in text:
											if token:
														split_tokens.extend(list(self.bpe(__a     ).split(' '     )     )     )

								return split_tokens
					def     snake_case_				(    self						,	__a     ):
								return self.encoder.get(__a						,	self.encoder.get(self.unk_token     )     )
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	int	       =				self.decoder.get(__a						,	self.unk_token     )
								return result
					def     snake_case_				(    self						,	__a     ):
								__lowerCamelCase					:	Any	       =				' '.join(__a     )

								# make sure @@ tokens are concatenated
								__lowerCamelCase					:	Union[str, Any]	       =				''.join(string.split(__a     )     )

								return string





					def     snake_case_				(    self						,	__a						,	__a = None     ):
								if not os.path.isdir(__a     ):
											logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
											return
								__lowerCamelCase					:	int	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
								__lowerCamelCase					:	Any	       =				os.path.join(
								    __a						,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']     )

								with open(__a						,	'w'						,	encoding='utf-8'     ) as f:
											f.write(json.dumps(self.encoder						,	indent=2						,	sort_keys=__a						,	ensure_ascii=__a     ) + '\n'     )

								__lowerCamelCase					:	Optional[int]	       =				0
								if self.bpe_ranks is None:
											return (vocab_file,)

								with open(__a						,	'w'						,	encoding='utf-8'     ) as writer:
											for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,	key=lambda __a     : kv[1]     ):
														if index != token_index:
																	logger.warning(
																	    f'''Saving vocabulary to {merges_file}: BPE merge indices are not consecutive.'''
																	    ' Please check that the tokenizer is not corrupted!'     )
																	__lowerCamelCase					:	Optional[int]	       =				token_index
														writer.write(' '.join(__a     ) + '\n'     )
														index += 1

								return (vocab_file, merges_file)



