





"""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.

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


_UpperCamelCase							=      'Create a default config file for Accelerate with only a few flags set.'



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[str]="no"    ,						SCREAMING_SNAKE_CASE       :							str = default_json_config_file    ,						SCREAMING_SNAKE_CASE       :							bool = False   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =Path(SCREAMING_SNAKE_CASE   )
							path.parent.mkdir(parents=SCREAMING_SNAKE_CASE    ,						exist_ok=SCREAMING_SNAKE_CASE   )
							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
							__lowerCamelCase      :      Any         =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}'   )
							__lowerCamelCase      :      Optional[int]         ={
							    '''compute_environment''': '''LOCAL_MACHINE''',
							    '''mixed_precision''': mixed_precision,
							}
							if torch.cuda.is_available():
														__lowerCamelCase      :      List[Any]         =torch.cuda.device_count()
														__lowerCamelCase      :      Any         =num_gpus
														__lowerCamelCase      :      Any         =False
														if num_gpus > 1:
																					__lowerCamelCase      :      List[Any]         ='''MULTI_GPU'''
														else:
																					__lowerCamelCase      :      Any         ='''NO'''
							elif is_xpu_available() and use_xpu:
														__lowerCamelCase      :      Optional[Any]         =torch.xpu.device_count()
														__lowerCamelCase      :      str         =num_xpus
														__lowerCamelCase      :      List[str]         =False
														if num_xpus > 1:
																					__lowerCamelCase      :      Any         ='''MULTI_XPU'''
														else:
																					__lowerCamelCase      :      List[Any]         ='''NO'''
							elif is_npu_available():
														__lowerCamelCase      :      Tuple         =torch.npu.device_count()
														__lowerCamelCase      :      Union[str, Any]         =num_npus
														__lowerCamelCase      :      List[str]         =False
														if num_npus > 1:
																					__lowerCamelCase      :      int         ='''MULTI_NPU'''
														else:
																					__lowerCamelCase      :      Tuple         ='''NO'''
							else:
														__lowerCamelCase      :      Optional[Any]         =0
														__lowerCamelCase      :      int         =True
														__lowerCamelCase      :      Optional[int]         =1
														__lowerCamelCase      :      Any         ='''NO'''
							__lowerCamelCase      :      Optional[int]         =ClusterConfig(**SCREAMING_SNAKE_CASE   )
							config.to_json_file(SCREAMING_SNAKE_CASE   )
							return path



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							List[Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[Any]         =parser.add_parser('''default'''    ,						parents=SCREAMING_SNAKE_CASE    ,						help=SCREAMING_SNAKE_CASE    ,						formatter_class=SCREAMING_SNAKE_CASE   )
							parser.add_argument(
							    '''--config_file'''    ,						default=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE    ,						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=SCREAMING_SNAKE_CASE   )
							return parser



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =write_basic_config(args.mixed_precision    ,						args.save_location   )
							if config_file:
														print(F'accelerate configuration saved at {config_file}'   )











"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCamelCase							=      logging.get_logger(__name__)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[tf.Tensor, np.ndarray]   ):


							'''simple docstring'''







							if isinstance(SCREAMING_SNAKE_CASE    ,						np.ndarray   ):
														return list(tensor.shape   )

							__lowerCamelCase      :      Tuple         =tf.shape(SCREAMING_SNAKE_CASE   )

							if tensor.shape == tf.TensorShape(SCREAMING_SNAKE_CASE   ):
														return dynamic

							__lowerCamelCase      :      Union[str, Any]         =tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(SCREAMING_SNAKE_CASE   )]



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor    ,						SCREAMING_SNAKE_CASE       :							Optional[int] = None    ,						SCREAMING_SNAKE_CASE       :							Optional[str] = None   ):


							'''simple docstring'''







							return tf.nn.softmax(logits=logits + 1E-9    ,						axis=SCREAMING_SNAKE_CASE    ,						name=SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Tuple=1E-5    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]=-1   ):


							'''simple docstring'''







							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   ):
														raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''   )

							# Get mean and variance on the axis to be normalized
							__lowerCamelCase    ,						__lowerCamelCase      :      Dict         =tf.nn.moments(SCREAMING_SNAKE_CASE    ,						axes=[axis]    ,						keepdims=SCREAMING_SNAKE_CASE   )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														__lowerCamelCase      :      Dict         =[1] * inputs.shape.rank
														__lowerCamelCase      :      Dict         =shape_list(SCREAMING_SNAKE_CASE   )[axis]
														__lowerCamelCase      :      List[str]         =tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      List[str]         =tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# Compute layer normalization using the batch_normalization
							# function.
							__lowerCamelCase      :      int         =tf.nn.batch_normalization(
							    SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						offset=SCREAMING_SNAKE_CASE    ,						scale=SCREAMING_SNAKE_CASE    ,						variance_epsilon=SCREAMING_SNAKE_CASE    ,						)
							return outputs



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							Dict=0    ,						SCREAMING_SNAKE_CASE       :							int=-1   ):


							'''simple docstring'''







							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							__lowerCamelCase      :      List[str]         =tf.shape(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      Optional[Any]         =tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]   )
							__lowerCamelCase      :      int         =tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]    ,						axis=0   )
							return tf.reshape(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor   ):


							'''simple docstring'''







							if not isinstance(SCREAMING_SNAKE_CASE    ,						tf.Tensor   ):
														__lowerCamelCase      :      Union[str, Any]         =tf.convert_to_tensor(SCREAMING_SNAKE_CASE   )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														__lowerCamelCase      :      str         =encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														__lowerCamelCase      :      Optional[int]         =encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							__lowerCamelCase      :      int         =(
							    tf.cast(1    ,						encoder_attention_mask.dtype   ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							tf.Tensor    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							str = "input_ids"   ):


							'''simple docstring'''







							tf.debugging.assert_less(
							    SCREAMING_SNAKE_CASE    ,						tf.cast(SCREAMING_SNAKE_CASE    ,						dtype=tensor.dtype   )    ,						message=(
							        F'The maximum value of {tensor_name} ({tf.math.reduce_max(SCREAMING_SNAKE_CASE   )}) must be smaller than the embedding '
							        F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
							    )    ,						)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =64512
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							__lowerCamelCase      :      Any         =[x for x in data if len(SCREAMING_SNAKE_CASE   ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    '''The following attributes cannot be saved to HDF5 file because '''
														    F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
														    F'bytes: {bad_attributes}'   )

							__lowerCamelCase      :      Tuple         =np.asarray(SCREAMING_SNAKE_CASE   )

							__lowerCamelCase      :      Any         =1
							__lowerCamelCase      :      Optional[Any]         =np.array_split(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data   ):
														num_chunks += 1
														__lowerCamelCase      :      Dict         =np.array_split(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(SCREAMING_SNAKE_CASE   ):
																					__lowerCamelCase      :      List[Any]         =chunk_data
							else:
														__lowerCamelCase      :      Optional[int]         =data



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]   ):


							'''simple docstring'''







							if name in group.attrs:
														__lowerCamelCase      :      Optional[int]         =[n.decode('''utf8'''   ) if hasattr(SCREAMING_SNAKE_CASE    ,						'''decode'''   ) else n for n in group.attrs[name]]
							else:
														__lowerCamelCase      :      Tuple         =[]
														__lowerCamelCase      :      List[str]         =0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode('''utf8'''   ) if hasattr(SCREAMING_SNAKE_CASE    ,						'''decode'''   ) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]   )
																					chunk_id += 1
							return data



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							def _expand_single_ad_tensor(SCREAMING_SNAKE_CASE       :							List[str]   ):
														if isinstance(SCREAMING_SNAKE_CASE    ,						tf.Tensor   ) and t.shape.rank == 1:
																					return tf.expand_dims(SCREAMING_SNAKE_CASE    ,						axis=-1   )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor    ,						SCREAMING_SNAKE_CASE   )




