

# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


_a:    Union[str, Any] 							=			"""Create a default config file for Accelerate with only a few flags set."""



def       __lowerCAmelCase      (						A="no"       ,     A = default_json_config_file       ,     A = False    ):
	UpperCAmelCase_       				=			Path(A    )
	path.parent.mkdir(parents=A       ,     exist_ok=A    )
	if path.exists():
		print(
		    F"Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."    )
		return False
	UpperCAmelCase_       				=			mixed_precision.lower()
	if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
		raise ValueError(
		    F"`mixed_precision` should be one of 'no', 'fp16', 'bf16', or 'fp8'. Received {mixed_precision}"    )
	UpperCAmelCase_       				=			{
	    "compute_environment": "LOCAL_MACHINE",
	    "mixed_precision": mixed_precision,
	}
	if torch.cuda.is_available():
		UpperCAmelCase_       				=			torch.cuda.device_count()
		UpperCAmelCase_       				=			num_gpus
		UpperCAmelCase_       				=			False
		if num_gpus > 1:
			UpperCAmelCase_       				=			"MULTI_GPU"
		else:
			UpperCAmelCase_       				=			"NO"
	elif is_xpu_available() and use_xpu:
		UpperCAmelCase_       				=			torch.xpu.device_count()
		UpperCAmelCase_       				=			num_xpus
		UpperCAmelCase_       				=			False
		if num_xpus > 1:
			UpperCAmelCase_       				=			"MULTI_XPU"
		else:
			UpperCAmelCase_       				=			"NO"
	elif is_npu_available():
		UpperCAmelCase_       				=			torch.npu.device_count()
		UpperCAmelCase_       				=			num_npus
		UpperCAmelCase_       				=			False
		if num_npus > 1:
			UpperCAmelCase_       				=			"MULTI_NPU"
		else:
			UpperCAmelCase_       				=			"NO"
	else:
		UpperCAmelCase_       				=			0
		UpperCAmelCase_       				=			True
		UpperCAmelCase_       				=			1
		UpperCAmelCase_       				=			"NO"
	UpperCAmelCase_       				=			ClusterConfig(**A    )
	config.to_json_file(A    )
	return path



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			parser.add_parser("default"       ,     parents=A       ,     help=A       ,     formatter_class=A    )
	parser.add_argument(
	    "--config_file"       ,     default=A       ,     help=(
	        "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
	        "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
	        "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
	        "with 'huggingface'."
	    )       ,     dest="save_location"       ,     )

	parser.add_argument(
	    "--mixed_precision"       ,     choices=["no", "fp16", "bf16"]       ,     type=A       ,     help="Whether or not to use mixed precision training. "
	    "Choose between FP16 and BF16 (bfloat16) training. "
	    "BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."       ,     default="no"       ,     )
	parser.set_defaults(func=A    )
	return parser




def       __lowerCAmelCase      (						A    ):
	UpperCAmelCase_       				=			write_basic_config(args.mixed_precision       ,     args.save_location    )
	if config_file:
		print(F"accelerate configuration saved at {config_file}"    )


import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_a:    List[Any] 							=			logging.get_logger(__name__)

_a:    List[str] 							=			"""▁"""

_a:    Union[str, Any] 							=			{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
}

_a:    Tuple 							=			{
    """vocab_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"""
        ),
    },
    """spm_file""": {
        """facebook/s2t-small-librispeech-asr""": (
            """https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"""
        )
    },
}

_a:    Optional[int] 							=			{
    """facebook/s2t-small-librispeech-asr""": 1024,
}

_a:    List[Any] 							=			["""pt""", """fr""", """ru""", """nl""", """ro""", """it""", """es""", """de"""]

_a:    Tuple 							=			{"""mustc""": MUSTC_LANGS}

class 						__UpperCamelCase    (					lowercase						):
	SCREAMING_SNAKE_CASE__    				=     VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE__    				=     PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE__    				=     MAX_MODEL_INPUT_SIZES
	SCREAMING_SNAKE_CASE__    				=     ['input_ids', 'attention_mask']

	SCREAMING_SNAKE_CASE__    				=     []




	def __init__(		self	:						Union[str, Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Tuple		,	lowerCAmelCase	:						Tuple="<s>"		,	lowerCAmelCase	:						Optional[int]="</s>"		,	lowerCAmelCase	:						Any="<pad>"		,	lowerCAmelCase	:						Union[str, Any]="<unk>"		,	lowerCAmelCase	:						Tuple=False		,	lowerCAmelCase	:						List[Any]=False		,	lowerCAmelCase	:						Optional[int]=None		,	lowerCAmelCase	:						List[str]=None		,	lowerCAmelCase	:						Optional[Dict[str, Any]] = None		,	**lowerCAmelCase	:						Any		,	):


		'''simple docstring'''







		UpperCAmelCase_       				=			{} if sp_model_kwargs is None else sp_model_kwargs

		super().__init__(
		    bos_token=lowerCAmelCase		,	eos_token=lowerCAmelCase		,	unk_token=lowerCAmelCase		,	pad_token=lowerCAmelCase		,	do_upper_case=lowerCAmelCase		,	do_lower_case=lowerCAmelCase		,	tgt_lang=lowerCAmelCase		,	lang_codes=lowerCAmelCase		,	sp_model_kwargs=self.sp_model_kwargs		,	**lowerCAmelCase		,	)
		UpperCAmelCase_       				=			do_upper_case
		UpperCAmelCase_       				=			do_lower_case

		UpperCAmelCase_       				=			load_json(lowerCAmelCase							)
		UpperCAmelCase_       				=			{v: k for k, v in self.encoder.items()}
		UpperCAmelCase_       				=			spm_file
		UpperCAmelCase_       				=			load_spm(lowerCAmelCase		,	self.sp_model_kwargs							)

		if lang_codes is not None:
			UpperCAmelCase_       				=			lang_codes
			UpperCAmelCase_       				=			LANGUAGES[lang_codes]
			UpperCAmelCase_       				=			[F"<lang:{lang}>" for lang in self.langs]
			UpperCAmelCase_       				=			{lang: self.sp_model.PieceToId(F"<lang:{lang}>"							) for lang in self.langs}

			UpperCAmelCase_       				=			self.lang_tokens
			UpperCAmelCase_       				=			tgt_lang if tgt_lang is not None else self.langs[0]

			self.set_tgt_lang_special_tokens(self._tgt_lang							)
		else:
			UpperCAmelCase_       				=			{}




	@property
	def       __A					(		self	:						List[Any]							):


		'''simple docstring'''







		return len(self.encoder							)




	@property
	def       __A					(		self	:						Any							):


		'''simple docstring'''







		return self._tgt_lang




	@tgt_lang.setter
	def       __A					(		self	:						int		,	lowerCAmelCase	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			new_tgt_lang
		self.set_tgt_lang_special_tokens(lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.lang_code_to_id[tgt_lang]
		UpperCAmelCase_       				=			[lang_code_id]




	def       __A					(		self	:						List[str]		,	lowerCAmelCase	:						str							):


		'''simple docstring'''







		return self.sp_model.encode(lowerCAmelCase		,	out_type=lowerCAmelCase							)




	def       __A					(		self	:						str		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		return self.encoder.get(lowerCAmelCase		,	self.encoder[self.unk_token]							)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						int							):


		'''simple docstring'''







		return self.decoder.get(lowerCAmelCase		,	self.unk_token							)




	def       __A					(		self	:						Dict		,	lowerCAmelCase	:						List[str]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		UpperCAmelCase_       				=			""
		for token in tokens:
			# make sure that special tokens are not decoded using sentencepiece model
			if token in self.all_special_tokens:
				UpperCAmelCase_       				=			self.sp_model.decode(lowerCAmelCase							)
				out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
				UpperCAmelCase_       				=			[]
			else:
				current_sub_tokens.append(lowerCAmelCase							)
		UpperCAmelCase_       				=			self.sp_model.decode(lowerCAmelCase							)
		out_string += decoded.upper() if self.do_upper_case else decoded
		return out_string.strip()




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						List[Any]		,	lowerCAmelCase	:						Optional[int]=None							):


		'''simple docstring'''







		if token_ids_a is None:
			return self.prefix_tokens + token_ids_a + [self.eos_token_id]
		# We don't expect to process pairs, but leave the pair logic for API consistency
		return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						List[int]		,	lowerCAmelCase	:						Optional[List[int]] = None		,	lowerCAmelCase	:						bool = False							):


		'''simple docstring'''







		if already_has_special_tokens:
			return super().get_special_tokens_mask(
			    token_ids_a=lowerCAmelCase		,	token_ids_a=lowerCAmelCase		,	already_has_special_tokens=lowerCAmelCase							)

		UpperCAmelCase_       				=			[1] * len(self.prefix_tokens							)
		UpperCAmelCase_       				=			[1]
		if token_ids_a is None:
			return prefix_ones + ([0] * len(lowerCAmelCase							)) + suffix_ones
		return prefix_ones + ([0] * len(lowerCAmelCase							)) + ([0] * len(lowerCAmelCase							)) + suffix_ones




	def       __A					(		self	:						Optional[Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.encoder.copy()
		vocab.update(self.added_tokens_encoder							)
		return vocab




	def __getstate__(		self	:						Union[str, Any]							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self.__dict__.copy()
		UpperCAmelCase_       				=			None
		return state




	def __setstate__(		self	:						Tuple		,	lowerCAmelCase	:						Dict							):


		'''simple docstring'''







		UpperCAmelCase_       				=			d

		# for backward compatibility
		if not hasattr(self		,	"sp_model_kwargs"							):
			UpperCAmelCase_       				=			{}

		UpperCAmelCase_       				=			load_spm(self.spm_file		,	self.sp_model_kwargs							)




	def       __A					(		self	:						Optional[Any]		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			Path(lowerCAmelCase							)
		assert save_dir.is_dir(), F"{save_directory} should be a directory"
		UpperCAmelCase_       				=			save_dir / (
		    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
		)
		UpperCAmelCase_       				=			save_dir / (
		    (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
		)

		save_json(self.encoder		,	lowerCAmelCase							)

		if os.path.abspath(self.spm_file							) != os.path.abspath(lowerCAmelCase							) and os.path.isfile(self.spm_file							):
			copyfile(self.spm_file		,	lowerCAmelCase							)
		elif not os.path.isfile(self.spm_file							):
			with open(lowerCAmelCase		,	"wb"							) as fi:
				UpperCAmelCase_       				=			self.sp_model.serialized_model_proto()
				fi.write(lowerCAmelCase							)

		return (str(lowerCAmelCase							), str(lowerCAmelCase							))



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_       				=			sentencepiece.SentencePieceProcessor(**A    )
	spm.Load(str(A    )    )
	return spm



def       __lowerCAmelCase      (						A    ):
	with open(A       ,     "r"    ) as f:
		return json.load(A    )




def       __lowerCAmelCase      (						A       ,     A    ):
	with open(A       ,     "w"    ) as f:
		json.dump(A       ,     A       ,     indent=2    )