


'''simple docstring'''
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__SCREAMING_SNAKE_CASE					:				List[str]									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_=None						,  UpperCamelCase_=None ):
							return field(default_factory=lambda: default						,  metadata=UpperCamelCase_ )







@dataclass
class 					__lowerCAmelCase   :

						"""simple docstring"""






						_UpperCAmelCase		:       List[str]   						=list_field(
						    default=[]				,			metadata={
						        "help": (
						            "Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
						            " of all available models"
						        )
						    }				,			)

						_UpperCAmelCase		:       List[int]   						=list_field(
						    default=[8]				,			metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"}						)

						_UpperCAmelCase		:       List[int]   						=list_field(
						    default=[8, 32, 128, 512]				,			metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"}				,			)

						_UpperCAmelCase		:       bool   						=field(
						    default=lowercase				,			metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."}				,			)
						_UpperCAmelCase		:       bool   						=field(
						    default=lowercase				,			metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."}				,			)
						_UpperCAmelCase		:       bool   						=field(
						    default=lowercase				,			metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."}						)
						_UpperCAmelCase		:       bool   						=field(default=lowercase				,			metadata={"help": "Use FP16 to accelerate inference."}						)
						_UpperCAmelCase		:       bool   						=field(default=lowercase				,			metadata={"help": "Benchmark training of model"}						)
						_UpperCAmelCase		:       bool   						=field(default=lowercase				,			metadata={"help": "Verbose memory tracing"}						)
						_UpperCAmelCase		:       bool   						=field(
						    default=lowercase				,			metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."}				,			)
						_UpperCAmelCase		:       bool   						=field(
						    default=lowercase				,			metadata={
						        "help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
						    }				,			)
						_UpperCAmelCase		:       bool   						=field(default=lowercase				,			metadata={"help": "Trace memory line by line"}						)
						_UpperCAmelCase		:       bool   						=field(default=lowercase				,			metadata={"help": "Save result to a CSV file"}						)
						_UpperCAmelCase		:       bool   						=field(default=lowercase				,			metadata={"help": "Save all print statements in a log file"}						)
						_UpperCAmelCase		:       bool   						=field(default=lowercase				,			metadata={"help": "Whether to print environment information"}						)
						_UpperCAmelCase		:       bool   						=field(
						    default=lowercase				,			metadata={
						        "help": (
						            "Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
						            " multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
						            " for debugging / testing and on TPU."
						        )
						    }				,			)
						_UpperCAmelCase		:       str   						=field(
						    default=F'''inference_time_{round(time()						)}.csv'''				,			metadata={"help": "CSV filename used if saving time results to csv."}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default=F'''inference_memory_{round(time()						)}.csv'''				,			metadata={"help": "CSV filename used if saving memory results to csv."}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default=F'''train_time_{round(time()						)}.csv'''				,			metadata={"help": "CSV filename used if saving time results to csv for training."}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default=F'''train_memory_{round(time()						)}.csv'''				,			metadata={"help": "CSV filename used if saving memory results to csv for training."}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default=F'''env_info_{round(time()						)}.csv'''				,			metadata={"help": "CSV filename used if saving environment information."}				,			)
						_UpperCAmelCase		:       str   						=field(
						    default=F'''log_{round(time()						)}.csv'''				,			metadata={"help": "Log filename used if print statements are saved in log."}				,			)
						_UpperCAmelCase		:       int   						=field(default=3				,			metadata={"help": "Times an experiment will be run."}						)
						_UpperCAmelCase		:       bool   						=field(
						    default=lowercase				,			metadata={
						        "help": (
						            "Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
						            " model weights."
						        )
						    }				,			)






						def 				_UpperCAmelCase       (  self							:      Optional[Any]	):
													warnings.warn(
													    F"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
													    " are deprecated in general and it is advised to use external Benchmarking libraries "
													    " to benchmark Transformer models." ,  lowerCAmelCase ,  )






						def 				_UpperCAmelCase       (  self							:      int	):
													return json.dumps(dataclasses.asdict(self	) ,  indent=2	)






						@property
						def 				_UpperCAmelCase       (  self							:      Union[str, Any]	):
													if len(self.models	) <= 0:
																				raise ValueError(
																				    "Please make sure you provide at least one model name / model identifier, *e.g.* `--models"
																				    " bert-base-cased` or `args.models = ['bert-base-cased']."	)
													return self.models






						@property
						def 				_UpperCAmelCase       (  self							:      Any	):
													if not self.multi_process:
																				return False
													elif self.is_tpu:
																				logger.info("Multiprocessing is currently not possible on TPU."	)
																				return False
													else:
																				return True









'''simple docstring'''
from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


__SCREAMING_SNAKE_CASE					:				Tuple									=		logging.get_logger(__name__)







def       a_				(	UpperCamelCase_ ):
							if isinstance(UpperCamelCase_						,  np.ndarray ):
														return list(tensor.shape )

							A_			    =		tf.shape(UpperCamelCase_ )

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

							A_			    =		tensor.shape.as_list()

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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ = None						,  UpperCamelCase_ = None ):
							return tf.nn.softmax(logits=logits + 1e-9						,  axis=UpperCamelCase_						,  name=UpperCamelCase_ )







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_=1e-5						,  UpperCamelCase_=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCamelCase_						,  UpperCamelCase_ ):
														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
							A_    ,					A_			    =		tf.nn.moments(UpperCamelCase_						,  axes=[axis]						,  keepdims=UpperCamelCase_ )

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

							# Compute layer normalization using the batch_normalization
							# function.
							A_			    =		tf.nn.batch_normalization(
							    UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_						,  offset=UpperCamelCase_						,  scale=UpperCamelCase_						,  variance_epsilon=UpperCamelCase_						,  )
							return outputs







def       a_				(	UpperCamelCase_						,  UpperCamelCase_=0						,  UpperCamelCase_=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							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

							A_			    =		tf.shape(UpperCamelCase_ )
							A_			    =		tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							A_			    =		tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]						,  axis=0 )
							return tf.reshape(UpperCamelCase_						,  UpperCamelCase_ )







def       a_				(	UpperCamelCase_ ):
							if not isinstance(UpperCamelCase_						,  tf.Tensor ):
														A_			    =		tf.convert_to_tensor(UpperCamelCase_ )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														A_			    =		encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														A_			    =		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))
							A_			    =		(
							    tf.cast(1						,  encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask







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







def       a_				(	UpperCamelCase_						,  UpperCamelCase_						,  UpperCamelCase_ ):
							A_			    =		6_4_5_1_2
							# 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.
							A_			    =		[x for x in data if len(UpperCamelCase_ ) > 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}" )

							A_			    =		np.asarray(UpperCamelCase_ )

							A_			    =		1
							A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							# 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
														A_			    =		np.array_split(UpperCamelCase_						,  UpperCamelCase_ )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(UpperCamelCase_ ):
																					A_			    =		chunk_data
							else:
														A_			    =		data







def       a_				(	UpperCamelCase_						,  UpperCamelCase_ ):
							if name in group.attrs:
														A_			    =		[n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs[name]]
							else:
														A_			    =		[]
														A_			    =		0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("utf8" ) if hasattr(UpperCamelCase_						,  "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
																					chunk_id += 1
							return data







def       a_				(	UpperCamelCase_ ):

							def _expand_single_ad_tensor(UpperCamelCase_ ):
														if isinstance(UpperCamelCase_						,  tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(UpperCamelCase_						,  axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor						,  UpperCamelCase_ )





