





from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


_UpperCAmelCase										=					logging.get_logger(__name__)

# General docstring
_UpperCAmelCase										=					'ResNetConfig'

# Base docstring
_UpperCAmelCase										=					'microsoft/resnet-50'
_UpperCAmelCase										=					[1, 2_0_4_8, 7, 7]

# Image classification docstring
_UpperCAmelCase										=					'microsoft/resnet-50'
_UpperCAmelCase										=					'tiger cat'

_UpperCAmelCase										=					[
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]



class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):

       def __init__(		self:			Any       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int = 3       , _SCREAMING_SNAKE_CASE:			Dict = 1       , _SCREAMING_SNAKE_CASE:			Any = "relu"       , **_SCREAMING_SNAKE_CASE:			List[Any]		)							->			None:




              """simple docstring"""


              super().__init__(**a_		)
              UpperCamelCase_ 							=	kernel_size // 2
              UpperCamelCase_ 							=	tf.keras.layers.ConvaD(
                  a_       , kernel_size=a_       , strides=a_       , padding="valid"       , use_bias=a_       , name="convolution"		)
              # Use same default momentum and epsilon as PyTorch equivalent
              UpperCamelCase_ 							=	tf.keras.layers.BatchNormalization(epsilon=1e-5       , momentum=0.9       , name="normalization"		)
              UpperCamelCase_ 							=	ACTaFN[activation] if activation is not None else tf.keras.layers.Activation("linear"		)

       def lowercase			(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			Dict		)							->			tf.Tensor:




              """simple docstring"""


              UpperCamelCase_ 							=	UpperCamelCase_ 							=	(self.pad_value, self.pad_value)
              UpperCamelCase_ 							=	tf.pad(a_       , [(0, 0), height_pad, width_pad, (0, 0)]		)
              UpperCamelCase_ 							=	self.conv(a_		)
              return hidden_state



       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Optional[int] = False		)							->			tf.Tensor:




              """simple docstring"""


              UpperCamelCase_ 							=	self.convolution(a_		)
              UpperCamelCase_ 							=	self.normalization(a_       , training=a_		)
              UpperCamelCase_ 							=	self.activation(a_		)
              return hidden_state



class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):

       def __init__(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			List[str]       , **_SCREAMING_SNAKE_CASE:			Any		)							->			None:




              """simple docstring"""


              super().__init__(**a_		)
              UpperCamelCase_ 							=	TFResNetConvLayer(
                  config.embedding_size       , kernel_size=7       , stride=2       , activation=config.hidden_act       , name="embedder"       , )
              UpperCamelCase_ 							=	tf.keras.layers.MaxPoolaD(pool_size=3       , strides=2       , padding="valid"       , name="pooler"		)
              UpperCamelCase_ 							=	config.num_channels



       def lowercase			(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Tuple = False		)							->			tf.Tensor:




              """simple docstring"""


              UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_							,			UpperCamelCase_ 							=	shape_list(a_		)
              if tf.executing_eagerly() and num_channels != self.num_channels:
                     raise ValueError(
                         "Make sure that the channel dimension of the pixel values match with the one set in the configuration."		)
              UpperCamelCase_ 							=	pixel_values
              UpperCamelCase_ 							=	self.embedder(a_		)
              UpperCamelCase_ 							=	tf.pad(a_       , [[0, 0], [1, 1], [1, 1], [0, 0]]		)
              UpperCamelCase_ 							=	self.pooler(a_		)
              return hidden_state



class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):

       def __init__(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			int = 2       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]		)							->			None:




              """simple docstring"""


              super().__init__(**a_		)
              UpperCamelCase_ 							=	tf.keras.layers.ConvaD(
                  a_       , kernel_size=1       , strides=a_       , use_bias=a_       , name="convolution"		)
              # Use same default momentum and epsilon as PyTorch equivalent
              UpperCamelCase_ 							=	tf.keras.layers.BatchNormalization(epsilon=1e-5       , momentum=0.9       , name="normalization"		)



       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			List[Any]       , _SCREAMING_SNAKE_CASE:			Optional[int] = False		)							->			tf.Tensor:




              """simple docstring"""


              UpperCamelCase_ 							=	x
              UpperCamelCase_ 							=	self.convolution(a_		)
              UpperCamelCase_ 							=	self.normalization(a_       , training=a_		)
              return hidden_state



class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):

       def __init__(		self:			int       , _SCREAMING_SNAKE_CASE:			Tuple       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[int] = 1       , _SCREAMING_SNAKE_CASE:			List[Any] = "relu"       , **_SCREAMING_SNAKE_CASE:			int		)							->			None:




              """simple docstring"""


              super().__init__(**a_		)
              UpperCamelCase_ 							=	in_channels != out_channels or stride != 1
              UpperCamelCase_ 							=	TFResNetConvLayer(a_       , stride=a_       , name="layer.0"		)
              UpperCamelCase_ 							=	TFResNetConvLayer(a_       , activation=a_       , name="layer.1"		)
              UpperCamelCase_ 							=	(
                  TFResNetShortCut(a_       , stride=a_       , name="shortcut"		)
                  if should_apply_shortcut
                  else tf.keras.layers.Activation("linear"       , name="shortcut"		)
              )
              UpperCamelCase_ 							=	ACTaFN[activation]



       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Union[str, Any] = False		)							->			tf.Tensor:




              """simple docstring"""


              UpperCamelCase_ 							=	hidden_state
              UpperCamelCase_ 							=	self.conva(a_       , training=a_		)
              UpperCamelCase_ 							=	self.conva(a_       , training=a_		)
              UpperCamelCase_ 							=	self.shortcut(a_       , training=a_		)
              hidden_state += residual
              UpperCamelCase_ 							=	self.activation(a_		)
              return hidden_state



class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):

       def __init__(		self:			Union[str, Any]       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			Any       , _SCREAMING_SNAKE_CASE:			Dict = 1       , _SCREAMING_SNAKE_CASE:			int = "relu"       , _SCREAMING_SNAKE_CASE:			Dict = 4       , **_SCREAMING_SNAKE_CASE:			Dict       , )							->			None:




              """simple docstring"""


              super().__init__(**a_		)
              UpperCamelCase_ 							=	in_channels != out_channels or stride != 1
              UpperCamelCase_ 							=	out_channels // reduction
              UpperCamelCase_ 							=	TFResNetConvLayer(a_       , kernel_size=1       , name="layer.0"		)
              UpperCamelCase_ 							=	TFResNetConvLayer(a_       , stride=a_       , name="layer.1"		)
              UpperCamelCase_ 							=	TFResNetConvLayer(a_       , kernel_size=1       , activation=a_       , name="layer.2"		)
              UpperCamelCase_ 							=	(
                  TFResNetShortCut(a_       , stride=a_       , name="shortcut"		)
                  if should_apply_shortcut
                  else tf.keras.layers.Activation("linear"       , name="shortcut"		)
              )
              UpperCamelCase_ 							=	ACTaFN[activation]



       def lowercase			(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Dict = False		)							->			tf.Tensor:




              """simple docstring"""


              UpperCamelCase_ 							=	hidden_state
              UpperCamelCase_ 							=	self.conva(a_       , training=a_		)
              UpperCamelCase_ 							=	self.conva(a_       , training=a_		)
              UpperCamelCase_ 							=	self.conva(a_       , training=a_		)
              UpperCamelCase_ 							=	self.shortcut(a_       , training=a_		)
              hidden_state += residual
              UpperCamelCase_ 							=	self.activation(a_		)
              return hidden_state



class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):

       def __init__(		self:			Tuple       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			str       , _SCREAMING_SNAKE_CASE:			Dict       , _SCREAMING_SNAKE_CASE:			str = 2       , _SCREAMING_SNAKE_CASE:			Optional[Any] = 2       , **_SCREAMING_SNAKE_CASE:			Any		)							->			None:




              """simple docstring"""


              super().__init__(**a_		)

              UpperCamelCase_ 							=	TFResNetBottleNeckLayer if config.layer_type == "bottleneck" else TFResNetBasicLayer

              UpperCamelCase_ 							=	[layer(a_       , a_       , stride=a_       , activation=config.hidden_act       , name="layers.0"		)]
              layers += [
                  layer(a_       , a_       , activation=config.hidden_act       , name=f'''layers.{i + 1}'''		)
                  for i in range(depth - 1		)
              ]
              UpperCamelCase_ 							=	layers



       def lowercase			(		self:			int       , _SCREAMING_SNAKE_CASE:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[str, Any] = False		)							->			tf.Tensor:




              """simple docstring"""


              for layer in self.stage_layers:
                     UpperCamelCase_ 							=	layer(a_       , training=a_		)
              return hidden_state



class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):

       def __init__(		self:			int       , _SCREAMING_SNAKE_CASE:			Optional[int]       , **_SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			None:




              """simple docstring"""


              super().__init__(**a_		)
              # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
              UpperCamelCase_ 							=	[
                  TFResNetStage(
                      a_       , config.embedding_size       , config.hidden_sizes[0]       , stride=2 if config.downsample_in_first_stage else 1       , depth=config.depths[0]       , name="stages.0"       , )
              ]
              for i, (in_channels, out_channels, depth) in enumerate(
                  zip(config.hidden_sizes       , config.hidden_sizes[1:]       , config.depths[1:]		)		):
                     self.stages.append(TFResNetStage(a_       , a_       , a_       , depth=a_       , name=f'''stages.{i + 1}'''		)		)



       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any] = False       , _SCREAMING_SNAKE_CASE:			Tuple = True       , _SCREAMING_SNAKE_CASE:			Optional[Any] = False       , )							->			TFBaseModelOutputWithNoAttention:




              """simple docstring"""


              UpperCamelCase_ 							=	() if output_hidden_states else None

              for stage_module in self.stages:
                     if output_hidden_states:
                            UpperCamelCase_ 							=	hidden_states + (hidden_state,)

                     UpperCamelCase_ 							=	stage_module(a_       , training=a_		)

              if output_hidden_states:
                     UpperCamelCase_ 							=	hidden_states + (hidden_state,)

              if not return_dict:
                     return tuple(v for v in [hidden_state, hidden_states] if v is not None		)

              return TFBaseModelOutputWithNoAttention(last_hidden_state=a_       , hidden_states=a_		)



class 				_UpperCamelCase       (						_UpperCAmelCase  ):
       _UpperCamelCase					:       str       = ResNetConfig
       _UpperCamelCase					:       int       = '''resnet'''
       _UpperCamelCase					:       List[str]       = '''pixel_values'''

       @property
       def lowercase			(		self:			Optional[int]		)							->			Union[str, Any]:




              """simple docstring"""


              return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)       , dtype=tf.floataa		)}

_UpperCAmelCase										=					r'\n    This model is a TensorFlow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'


_UpperCAmelCase										=					r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'



@keras_serializable
class 				_UpperCamelCase       (						tf.keras.layers.Layer  ):
       _UpperCamelCase					:       Dict       = ResNetConfig

       def __init__(		self:			List[Any]       , _SCREAMING_SNAKE_CASE:			Dict       , **_SCREAMING_SNAKE_CASE:			Optional[Any]		)							->			None:




              """simple docstring"""


              super().__init__(**a_		)
              UpperCamelCase_ 							=	config
              UpperCamelCase_ 							=	TFResNetEmbeddings(a_       , name="embedder"		)
              UpperCamelCase_ 							=	TFResNetEncoder(a_       , name="encoder"		)
              UpperCamelCase_ 							=	tf.keras.layers.GlobalAveragePoolingaD(keepdims=a_		)



       @unpack_inputs
       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int       , _SCREAMING_SNAKE_CASE:			int = None       , _SCREAMING_SNAKE_CASE:			Any = None       , _SCREAMING_SNAKE_CASE:			Optional[int] = False       , )							->			Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:




              """simple docstring"""


              UpperCamelCase_ 							=	(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase_ 							=	return_dict if return_dict is not None else self.config.use_return_dict

              # TF 2.0 image layers can't use NCHW format when running on CPU.
              # We transpose to NHWC format and then transpose back after the full forward pass.
              # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
              UpperCamelCase_ 							=	tf.transpose(a_       , perm=[0, 2, 3, 1]		)
              UpperCamelCase_ 							=	self.embedder(a_       , training=a_		)

              UpperCamelCase_ 							=	self.encoder(
                  a_       , output_hidden_states=a_       , return_dict=a_       , training=a_		)

              UpperCamelCase_ 							=	encoder_outputs[0]

              UpperCamelCase_ 							=	self.pooler(a_		)

              # Transpose all the outputs to the NCHW format
              # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
              UpperCamelCase_ 							=	tf.transpose(a_       , (0, 3, 1, 2)		)
              UpperCamelCase_ 							=	tf.transpose(a_       , (0, 3, 1, 2)		)
              UpperCamelCase_ 							=	()
              for hidden_state in encoder_outputs[1:]:
                     UpperCamelCase_ 							=	hidden_states + tuple(tf.transpose(a_       , (0, 3, 1, 2)		) for h in hidden_state		)

              if not return_dict:
                     return (last_hidden_state, pooled_output) + hidden_states

              UpperCamelCase_ 							=	hidden_states if output_hidden_states else None

              return TFBaseModelOutputWithPoolingAndNoAttention(
                  last_hidden_state=a_       , pooler_output=a_       , hidden_states=a_       , )



@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''	,    _UpperCAmelCase	,    )
class 				_UpperCamelCase       (						_UpperCAmelCase  ):

       def __init__(		self:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , **_SCREAMING_SNAKE_CASE:			Any		)							->			None:




              """simple docstring"""


              super().__init__(a_       , **a_		)
              UpperCamelCase_ 							=	TFResNetMainLayer(config=a_       , name="resnet"		)



       @add_start_docstrings_to_model_forward(a_		)
       @add_code_sample_docstrings(
           checkpoint=_CHECKPOINT_FOR_DOC       , output_type=a_       , config_class=_CONFIG_FOR_DOC       , modality="vision"       , expected_output=_EXPECTED_OUTPUT_SHAPE       , )
       @unpack_inputs
       def lowercase			(		self:			List[str]       , _SCREAMING_SNAKE_CASE:			Optional[Any]       , _SCREAMING_SNAKE_CASE:			str = None       , _SCREAMING_SNAKE_CASE:			Any = None       , _SCREAMING_SNAKE_CASE:			Optional[Any] = False       , )							->			Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:




              """simple docstring"""


              UpperCamelCase_ 							=	(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase_ 							=	return_dict if return_dict is not None else self.config.use_return_dict

              UpperCamelCase_ 							=	self.resnet(
                  pixel_values=a_       , output_hidden_states=a_       , return_dict=a_       , training=a_       , )
              return resnet_outputs



@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''	,    _UpperCAmelCase	,    )
class 				_UpperCamelCase       (						_UpperCAmelCase	,    _UpperCAmelCase  ):

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			str       , **_SCREAMING_SNAKE_CASE:			Dict		)							->			None:




              """simple docstring"""


              super().__init__(a_       , **a_		)
              UpperCamelCase_ 							=	config.num_labels
              UpperCamelCase_ 							=	TFResNetMainLayer(a_       , name="resnet"		)
              # classification head
              UpperCamelCase_ 							=	(
                  tf.keras.layers.Dense(config.num_labels       , name="classifier.1"		)
                  if config.num_labels > 0
                  else tf.keras.layers.Activation("linear"       , name="classifier.1"		)
              )

       def lowercase			(		self:			Dict       , _SCREAMING_SNAKE_CASE:			int		)							->			tf.Tensor:




              """simple docstring"""


              UpperCamelCase_ 							=	tf.keras.layers.Flatten()(a_		)
              UpperCamelCase_ 							=	self.classifier_layer(a_		)
              return logits



       @add_start_docstrings_to_model_forward(a_		)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT       , output_type=a_       , config_class=_CONFIG_FOR_DOC       , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       , )
       @unpack_inputs
       def lowercase			(		self:			str       , _SCREAMING_SNAKE_CASE:			int = None       , _SCREAMING_SNAKE_CASE:			Dict = None       , _SCREAMING_SNAKE_CASE:			List[str] = None       , _SCREAMING_SNAKE_CASE:			Dict = None       , _SCREAMING_SNAKE_CASE:			str = False       , )							->			Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:




              """simple docstring"""


              UpperCamelCase_ 							=	return_dict if return_dict is not None else self.config.use_return_dict

              UpperCamelCase_ 							=	self.resnet(
                  a_       , output_hidden_states=a_       , return_dict=a_       , training=a_		)

              UpperCamelCase_ 							=	outputs.pooler_output if return_dict else outputs[1]

              UpperCamelCase_ 							=	self.classifier(a_		)

              UpperCamelCase_ 							=	None if labels is None else self.hf_compute_loss(a_       , a_		)

              if not return_dict:
                     UpperCamelCase_ 							=	(logits,) + outputs[2:]
                     return (loss,) + output if loss is not None else output

              return TFImageClassifierOutputWithNoAttention(loss=a_       , logits=a_       , hidden_states=outputs.hidden_states		)









"""simple docstring"""




import argparse
import time

import torch
import transformers
from measures_util import end_measure, log_measures, start_measure
from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer

from accelerate.utils import compute_module_sizes


UpperCamelCase											=     {
    """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
    """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
    """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
    """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
}

UpperCamelCase											=     [
    """Hello, my name is""",
    """Are unicorns real? Unicorns are""",
    """For the first time in several years,""",
    """My name is Julien and I am""",
    """The goal of life is""",
    """Whenever I'm sad, I like to""",
]






def 							lowerCAmelCase			(   )							->				Optional[int]:







  '''simple docstring'''







  _a					      =						argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."       )
  parser.add_argument("model_name"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The name of the model to try."       )
  parser.add_argument(
      "--tokenizer_name"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The name of the tokenizer (if different from the model."       )
  parser.add_argument("--is_causal"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="Whether or not the model is causal."       )
  parser.add_argument(
      "--model_revision"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The revision to use for the model checkpoint."       )
  parser.add_argument("--torch_dtype"  ,			type=UpperCamelCase_  ,			default=UpperCamelCase_  ,			help="The dtype for the model."       )
  parser.add_argument("--disk_offload"  ,			action="store_true"       )

  _a					      =						parser.parse_args()

  # Sanitize args
  if args.model_name in DEFAULT_MODELS:
    _a					      =						DEFAULT_MODELS[args.model_name]
    _a					      =						defaults["model"]
    if args.tokenizer_name is None:
      _a					      =						defaults.get("tokenizer"  ,			args.model_name       )
    if args.is_causal is None:
      _a					      =						defaults["is_causal"]
    if args.model_revision is None:
      _a					      =						defaults.get("model_revision"  ,			"main"       )

  if args.is_causal is None:
    raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."       )
  if args.tokenizer_name is None:
    _a					      =						args.model_name
  if args.model_revision is None:
    _a					      =						"main"

  return args






def 							lowerCAmelCase			(   )							->				str:







  '''simple docstring'''







  transformers.utils.logging.set_verbosity_error()
  _a					      =						parse_args()

  if args.torch_dtype is None:
    _a					      =						AutoConfig.from_pretrained(args.model_name       )
    _a					      =						getattr(UpperCamelCase_  ,			"torch_dtype"  ,			torch.floataa       )
  else:
    _a					      =						getattr(UpperCamelCase_  ,			args.torch_dtype       )
  _a					      =						AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
  _a					      =						{
      "torch_dtype": torch_dtype,
      "revision": args.model_revision,
  }
  if args.disk_offload:
    _a					      =						"tmp_offload"
    _a					      =						True

  _a					      =						start_measure()
  _a					      =						model_cls.from_pretrained(args.model_name  ,			device_map="auto"  ,			**UpperCamelCase_       )
  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model loading"       )

  _a					      =						compute_module_sizes(UpperCamelCase_       )
  _a					      =						{v: 0 for v in model.hf_device_map.values()}
  for module, device in model.hf_device_map.items():
    device_size[device] += module_sizes[module]
  _a					      =						"\n".join([f'''- {device}: {size // 2**20}MiB''' for device, size in device_size.items()]       )
  print(f'''\nTheoretical use:\n{message}'''       )

  _a					      =						AutoTokenizer.from_pretrained(args.tokenizer_name       )

  _a					      =						start_measure()
  _a					      =						[]
  _a					      =						[]
  _a					      =						[]
  for prompt in PROMPTS:
    _a					      =						tokenizer(UpperCamelCase_  ,			return_tensors="pt"       ).to(0       )
    _a					      =						inputs["input_ids"][0].tolist()
    _a					      =						time.time()
    _a					      =						model.generate(inputs["input_ids"]       )
    _a					      =						time.time()
    _a					      =						outputs[0].tolist()
    _a					      =						len(UpperCamelCase_       ) if outputs[: len(UpperCamelCase_       )] != tokens else len(UpperCamelCase_       ) - len(UpperCamelCase_       )
    _a					      =						after_generate - before_generate

    _a					      =						tokenizer.decode(UpperCamelCase_  ,			skip_special_tokens=UpperCamelCase_       )
    texts_outs.append(UpperCamelCase_       )
    generation_times.append(UpperCamelCase_       )
    gen_tokens.append(UpperCamelCase_       )
    print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''       )

  _a					      =						end_measure(UpperCamelCase_       )
  log_measures(UpperCamelCase_  ,			"Model generation"       )

  _a					      =						[gen / tok for gen, tok in zip(UpperCamelCase_  ,			UpperCamelCase_       )]
  _a					      =						sum(UpperCamelCase_       ) / len(UpperCamelCase_       )
  print(f'''Average time of generation per token: {avg_gen:.2f}s'''       )
  print(f'''First generation (avg time per token): {generation_times_per_token[0]:.2f}s'''       )
  _a					      =						sum(generation_times_per_token[1:]       ) / (len(UpperCamelCase_       ) - 1)
  print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''       )


if __name__ == "__main__":
     main()


