


'''simple docstring'''
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


__SCREAMING_SNAKE_CASE					:				str									=		logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE					:				str									=		{
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

__SCREAMING_SNAKE_CASE					:				Dict									=		{
    '''artists_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json''',
    },
    '''genres_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json''',
    },
    '''lyrics_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json''',
    },
}

__SCREAMING_SNAKE_CASE					:				str									=		{
    '''jukebox''': 512,
}






class 					__lowerCAmelCase   (   lowercase						):

						"""simple docstring"""






						_UpperCAmelCase		:       Dict   						=VOCAB_FILES_NAMES
						_UpperCAmelCase		:       str   						=PRETRAINED_VOCAB_FILES_MAP
						_UpperCAmelCase		:       Any   						=PRETRAINED_LYRIC_TOKENS_SIZES
						_UpperCAmelCase		:       Optional[Any]   						=["input_ids", "attention_mask"]






						def __init__(  self							:      int ,  lowerCAmelCase							:      List[str] ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      Optional[int] ,  lowerCAmelCase							:      List[str]=["v3", "v2", "v2"] ,  lowerCAmelCase							:      Tuple=5_12 ,  lowerCAmelCase							:      List[str]=5 ,  lowerCAmelCase							:      Union[str, Any]="<|endoftext|>" ,  **lowerCAmelCase							:      Optional[Any] ,  ):
													A_			    =		AddedToken(lowerCAmelCase ,  lstrip=lowerCAmelCase ,  rstrip=lowerCAmelCase	) if isinstance(lowerCAmelCase ,  lowerCAmelCase	) else unk_token
													super().__init__(
													    unk_token=lowerCAmelCase ,  n_genres=lowerCAmelCase ,  version=lowerCAmelCase ,  max_n_lyric_tokens=lowerCAmelCase ,  **lowerCAmelCase ,  )
													A_			    =		version
													A_			    =		max_n_lyric_tokens
													A_			    =		n_genres

													with open(lowerCAmelCase ,  encoding="utf-8"	) as vocab_handle:
																				A_			    =		json.load(lowerCAmelCase	)

													with open(lowerCAmelCase ,  encoding="utf-8"	) as vocab_handle:
																				A_			    =		json.load(lowerCAmelCase	)

													with open(lowerCAmelCase ,  encoding="utf-8"	) as vocab_handle:
																				A_			    =		json.load(lowerCAmelCase	)

													A_			    =		r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"
													# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
													if len(self.lyrics_encoder	) == 79:
																				A_			    =		oov.replace(r"\-'" ,  r"\-+'"	)

													A_			    =		regex.compile(lowerCAmelCase	)
													A_			    =		{v: k for k, v in self.artists_encoder.items()}
													A_			    =		{v: k for k, v in self.genres_encoder.items()}
													A_			    =		{v: k for k, v in self.lyrics_encoder.items()}






						@property
						def 				_UpperCAmelCase       (  self							:      Optional[int]	):
													return len(self.artists_encoder	) + len(self.genres_encoder	) + len(self.lyrics_encoder	)






						def 				_UpperCAmelCase       (  self							:      List[str]	):
													return dict(self.artists_encoder ,  self.genres_encoder ,  self.lyrics_encoder	)






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      List[Any] ,  lowerCAmelCase							:      Tuple ,  lowerCAmelCase							:      Any	):
													A_			    =		[self.artists_encoder.get(lowerCAmelCase ,  0	) for artist in list_artists]
													for genres in range(len(lowerCAmelCase	)	):
																				A_			    =		[self.genres_encoder.get(lowerCAmelCase ,  0	) for genre in list_genres[genres]]
																				A_			    =		list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]	))

													A_			    =		[[self.lyrics_encoder.get(lowerCAmelCase ,  0	) for character in list_lyrics[0]], [], []]
													return artists_id, list_genres, lyric_ids






						def 				_UpperCAmelCase       (  self							:      Union[str, Any] ,  lowerCAmelCase							:      List[str]	):
													return list(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Union[str, Any] ,  lowerCAmelCase							:      Union[str, Any] ,  **lowerCAmelCase							:      Any	):
													A_    ,					A_    ,					A_			    =		self.prepare_for_tokenization(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
													A_			    =		self._tokenize(lowerCAmelCase	)
													return artist, genre, lyrics






						def 				_UpperCAmelCase       (  self							:      Dict ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      bool = False	):
													for idx in range(len(self.version	)	):
																				if self.version[idx] == "v3":
																											A_			    =		artists[idx].lower()
																											A_			    =		[genres[idx].lower()]
																				else:
																											A_			    =		self._normalize(artists[idx]	) + ".v2"
																											A_			    =		[
																											    self._normalize(lowerCAmelCase	) + ".v2" for genre in genres[idx].split("_"	)
																											]  # split is for the full dictionary with combined genres

													if self.version[0] == "v2":
																				A_			    =		regex.compile(r"[^A-Za-z0-9.,:;!?\-'\"()\[\] \t\n]+"	)
																				A_			    =		"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+'\"()[] \t\n"
																				A_			    =		{vocab[index]: index + 1 for index in range(len(lowerCAmelCase	)	)}
																				A_			    =		0
																				A_			    =		len(lowerCAmelCase	) + 1
																				A_			    =		self.vocab
																				A_			    =		{v: k for k, v in self.vocab.items()}
																				A_			    =		""
													else:
																				A_			    =		regex.compile(r"[^A-Za-z0-9.,:;!?\-+'\"()\[\] \t\n]+"	)

													A_			    =		self._run_strip_accents(lowerCAmelCase	)
													A_			    =		lyrics.replace("\\" ,  "\n"	)
													A_			    =		self.out_of_vocab.sub("" ,  lowerCAmelCase	), [], []
													return artists, genres, lyrics






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      List[str]	):
													A_			    =		unicodedata.normalize("NFD" ,  lowerCAmelCase	)
													A_			    =		[]
													for char in text:
																				A_			    =		unicodedata.category(lowerCAmelCase	)
																				if cat == "Mn":
																											continue
																				output.append(lowerCAmelCase	)
													return "".join(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      int ,  lowerCAmelCase							:      str	):
													A_			    =		(
													    [chr(lowerCAmelCase	) for i in range(ord("a"	) ,  ord("z"	) + 1	)]
													    + [chr(lowerCAmelCase	) for i in range(ord("A"	) ,  ord("Z"	) + 1	)]
													    + [chr(lowerCAmelCase	) for i in range(ord("0"	) ,  ord("9"	) + 1	)]
													    + ["."]
													)
													A_			    =		frozenset(lowerCAmelCase	)
													A_			    =		re.compile(r"_+"	)
													A_			    =		"".join([c if c in accepted else "_" for c in text.lower()]	)
													A_			    =		pattern.sub("_" ,  lowerCAmelCase	).strip("_"	)
													return text






						def 				_UpperCAmelCase       (  self							:      List[str] ,  lowerCAmelCase							:      List[str]	):
													return " ".join(lowerCAmelCase	)






						def 				_UpperCAmelCase       (  self							:      Optional[Any] ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      Optional[Union[str, TensorType]] = None ,  lowerCAmelCase							:      bool = False	):
													# Convert to TensorType
													if not isinstance(lowerCAmelCase ,  lowerCAmelCase	):
																				A_			    =		TensorType(lowerCAmelCase	)

													# Get a function reference for the correct framework
													if tensor_type == TensorType.TENSORFLOW:
																				if not is_tf_available():
																											raise ImportError(
																											    "Unable to convert output to TensorFlow tensors format, TensorFlow is not installed."	)
																				import tensorflow as tf

																				A_			    =		tf.constant
																				A_			    =		tf.is_tensor
													elif tensor_type == TensorType.PYTORCH:
																				if not is_torch_available():
																											raise ImportError("Unable to convert output to PyTorch tensors format, PyTorch is not installed."	)
																				import torch

																				A_			    =		torch.tensor
																				A_			    =		torch.is_tensor
													elif tensor_type == TensorType.JAX:
																				if not is_flax_available():
																											raise ImportError("Unable to convert output to JAX tensors format, JAX is not installed."	)
																				import jax.numpy as jnp  # noqa: F811

																				A_			    =		jnp.array
																				A_			    =		_is_jax
													else:
																				A_			    =		np.asarray
																				A_			    =		_is_numpy

													# Do the tensor conversion in batch

													try:
																				if prepend_batch_axis:
																											A_			    =		[inputs]

																				if not is_tensor(lowerCAmelCase	):
																											A_			    =		as_tensor(lowerCAmelCase	)
													except:  # noqa E722
																				raise ValueError(
																				    "Unable to create tensor, you should probably activate truncation and/or padding "
																				    "with 'padding=True' 'truncation=True' to have batched tensors with the same length."	)

													return inputs






						def __call__(  self							:      List[str] ,  lowerCAmelCase							:      Dict ,  lowerCAmelCase							:      int ,  lowerCAmelCase							:      str="" ,  lowerCAmelCase							:      Tuple="pt"	):
													A_			    =		[0, 0, 0]
													A_			    =		[artist] * len(self.version	)
													A_			    =		[genres] * len(self.version	)

													A_    ,					A_    ,					A_			    =		self.tokenize(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)
													A_    ,					A_    ,					A_			    =		self._convert_token_to_id(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase	)

													A_			    =		[-INFINITY] * len(full_tokens[-1]	)
													A_			    =		[
													    self.convert_to_tensors(
													        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]] ,  tensor_type=lowerCAmelCase	)
													    for i in range(len(self.version	)	)
													]
													return BatchEncoding({"input_ids": input_ids, "attention_masks": attention_masks}	)






						def 				_UpperCAmelCase       (  self							:      str ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Optional[str] = None	):
													if not os.path.isdir(lowerCAmelCase	):
																				logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
																				return

													A_			    =		os.path.join(
													    lowerCAmelCase ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["artists_file"]	)
													with open(lowerCAmelCase ,  "w" ,  encoding="utf-8"	) as f:
																				f.write(json.dumps(self.artists_encoder ,  ensure_ascii=lowerCAmelCase	)	)

													A_			    =		os.path.join(
													    lowerCAmelCase ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["genres_file"]	)
													with open(lowerCAmelCase ,  "w" ,  encoding="utf-8"	) as f:
																				f.write(json.dumps(self.genres_encoder ,  ensure_ascii=lowerCAmelCase	)	)

													A_			    =		os.path.join(
													    lowerCAmelCase ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["lyrics_file"]	)
													with open(lowerCAmelCase ,  "w" ,  encoding="utf-8"	) as f:
																				f.write(json.dumps(self.lyrics_encoder ,  ensure_ascii=lowerCAmelCase	)	)

													return (artists_file, genres_file, lyrics_file)






						def 				_UpperCAmelCase       (  self							:      Tuple ,  lowerCAmelCase							:      Any ,  lowerCAmelCase							:      str ,  lowerCAmelCase							:      Optional[int]	):
													A_			    =		self.artists_decoder.get(lowerCAmelCase	)
													A_			    =		[self.genres_decoder.get(lowerCAmelCase	) for genre in genres_index]
													A_			    =		[self.lyrics_decoder.get(lowerCAmelCase	) for character in lyric_index]
													return artist, genres, lyrics









'''simple docstring'''
# 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.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
							import botoa  # noqa: F401







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )

							A_			    =		{
							    "Version": "2012-10-17",
							    "Statement": [
							        {"Effect": "Allow", "Principal": {"Service": "sagemaker.amazonaws.com"}, "Action": "sts:AssumeRole"}
							    ],
							}
							try:
														# create the role, associated with the chosen trust policy
														iam_client.create_role(
														    RoleName=UpperCamelCase_						,  AssumeRolePolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 ) )
														A_			    =		{
														    "Version": "2012-10-17",
														    "Statement": [
														        {
														            "Effect": "Allow",
														            "Action": [
														                "sagemaker:*",
														                "ecr:GetDownloadUrlForLayer",
														                "ecr:BatchGetImage",
														                "ecr:BatchCheckLayerAvailability",
														                "ecr:GetAuthorizationToken",
														                "cloudwatch:PutMetricData",
														                "cloudwatch:GetMetricData",
														                "cloudwatch:GetMetricStatistics",
														                "cloudwatch:ListMetrics",
														                "logs:CreateLogGroup",
														                "logs:CreateLogStream",
														                "logs:DescribeLogStreams",
														                "logs:PutLogEvents",
														                "logs:GetLogEvents",
														                "s3:CreateBucket",
														                "s3:ListBucket",
														                "s3:GetBucketLocation",
														                "s3:GetObject",
														                "s3:PutObject",
														            ],
														            "Resource": "*",
														        }
														    ],
														}
														# attach policy to role
														iam_client.put_role_policy(
														    RoleName=UpperCamelCase_						,  PolicyName=f"{role_name}_policy_permission"						,  PolicyDocument=json.dumps(UpperCamelCase_						,  indent=2 )						,  )
							except iam_client.exceptions.EntityAlreadyExistsException:
														print(f"role {role_name} already exists. Using existing one" )







def       a_				(	UpperCamelCase_ ):
							A_			    =		botoa.client("iam" )
							return iam_client.get_role(RoleName=UpperCamelCase_ )["Role"]["Arn"]







def       a_				(	):
							A_			    =		_ask_options(
							    "How do you want to authorize?"						,  ["AWS Profile", "Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) "]						,  UpperCamelCase_						,  )
							A_			    =		None
							if credentials_configuration == 0:
														A_			    =		_ask_field("Enter your AWS Profile name: [default] "						,  default="default" )
														A_			    =		aws_profile
							else:
														print(
														    "Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,"
														    "`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`" )
														A_			    =		_ask_field("AWS Access Key ID: " )
														A_			    =		aws_access_key_id

														A_			    =		_ask_field("AWS Secret Access Key: " )
														A_			    =		aws_secret_access_key

							A_			    =		_ask_field("Enter your AWS Region: [us-east-1]"						,  default="us-east-1" )
							A_			    =		aws_region

							A_			    =		_ask_options(
							    "Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?"						,  ["Provide IAM Role name", "Create new IAM role using credentials"]						,  UpperCamelCase_						,  )
							if role_management == 0:
														A_			    =		_ask_field("Enter your IAM role name: " )
							else:
														A_			    =		"accelerate_sagemaker_execution_role"
														print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
														_create_iam_role_for_sagemaker(UpperCamelCase_ )

							A_			    =		_ask_field(
							    "Do you want to use custom Docker image? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_custom_docker_image:
														A_			    =		_ask_field("Enter your Docker image: "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower() )

							A_			    =		_ask_field(
							    "Do you want to provide SageMaker input channels with data locations? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_inputs_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_field(
							    "Do you want to enable SageMaker metrics? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		None
							if is_sagemaker_metrics_enabled:
														A_			    =		_ask_field(
														    "Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): "						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  )

							A_			    =		_ask_options(
							    "What is the distributed mode?"						,  ["No distributed training", "Data parallelism"]						,  _convert_sagemaker_distributed_mode						,  )
							A_			    =		{}
							A_			    =		_ask_field(
							    "Do you wish to optimize your script with torch dynamo?[yes/NO]:"						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							if use_dynamo:
														A_			    =		"dynamo_"
														A_			    =		_ask_options(
														    "Which dynamo backend would you like to use?"						,  [x.lower() for x in DYNAMO_BACKENDS]						,  _convert_dynamo_backend						,  default=2						,  )
														A_			    =		_ask_field(
														    "Do you want to customize the defaults sent to torch.compile? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )

														if use_custom_options:
																					A_			    =		_ask_options(
																					    "Which mode do you want to use?"						,  UpperCamelCase_						,  lambda UpperCamelCase_ : TORCH_DYNAMO_MODES[int(UpperCamelCase_ )]						,  default="default"						,  )
																					A_			    =		_ask_field(
																					    "Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
																					A_			    =		_ask_field(
																					    "Do you want to enable dynamic shape tracing? [yes/NO]: "						,  _convert_yes_no_to_bool						,  default=UpperCamelCase_						,  error_message="Please enter yes or no."						,  )
							A_			    =		"Which EC2 instance type you want to use for your training?"
							if distributed_type != SageMakerDistributedType.NO:
														A_			    =		_ask_options(
														    UpperCamelCase_						,  UpperCamelCase_						,  lambda UpperCamelCase_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(UpperCamelCase_ )] )
							else:
														eca_instance_query += "? [ml.p3.2xlarge]:"
														A_			    =		_ask_field(UpperCamelCase_						,  lambda UpperCamelCase_ : str(UpperCamelCase_ ).lower()						,  default="ml.p3.2xlarge" )

							A_			    =		1
							if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
														A_			    =		_ask_field(
														    "How many machines do you want use? [1]: "						,  UpperCamelCase_						,  default=1						,  )

							A_			    =		_ask_options(
							    "Do you wish to use FP16 or BF16 (mixed precision)?"						,  ["no", "fp16", "bf16", "fp8"]						,  _convert_mixed_precision						,  )

							if use_dynamo and mixed_precision == "no":
														print(
														    "Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts." )

							return SageMakerConfig(
							    image_uri=UpperCamelCase_						,  compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER						,  distributed_type=UpperCamelCase_						,  use_cpu=UpperCamelCase_						,  dynamo_config=UpperCamelCase_						,  eca_instance_type=UpperCamelCase_						,  profile=UpperCamelCase_						,  region=UpperCamelCase_						,  iam_role_name=UpperCamelCase_						,  mixed_precision=UpperCamelCase_						,  num_machines=UpperCamelCase_						,  sagemaker_inputs_file=UpperCamelCase_						,  sagemaker_metrics_file=UpperCamelCase_						,  )





