



"""simple docstring"""
from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


__SCREAMING_SNAKE_CASE  :						int    							=      logging.get_logger(__name__)

# General docstring
__SCREAMING_SNAKE_CASE  :						List[str]    							=      '''RegNetConfig'''

# Base docstring
__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      '''facebook/regnet-y-040'''
__SCREAMING_SNAKE_CASE  :						int    							=      [1, 1_0_8_8, 7, 7]

# Image classification docstring
__SCREAMING_SNAKE_CASE  :						List[str]    							=      '''facebook/regnet-y-040'''
__SCREAMING_SNAKE_CASE  :						int    							=      '''tabby, tabby cat'''

__SCREAMING_SNAKE_CASE  :						str    							=      [
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 3				,       lowerCamelCase__ = 1				,       lowerCamelCase__ = 1				,       lowerCamelCase__ = "relu"				,       ):
									super().__init__()
									_lowerCamelCase        =				nn.Convad(
									    lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=lowerCamelCase__				,       stride=lowerCamelCase__				,       padding=kernel_size // 2				,       groups=lowerCamelCase__				,       bias=lowerCamelCase__				,       )
									_lowerCamelCase        =				nn.BatchNormad(lowerCamelCase__   )
									_lowerCamelCase        =				ACTaFN[activation] if activation is not None else nn.Identity()





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.convolution(lowerCamelCase__   )
									_lowerCamelCase        =				self.normalization(lowerCamelCase__   )
									_lowerCamelCase        =				self.activation(lowerCamelCase__   )
									return hidden_state


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									super().__init__()
									_lowerCamelCase        =				RegNetConvLayer(
									    config.num_channels				,       config.embedding_size				,       kernel_size=3				,       stride=2				,       activation=config.hidden_act   )
									_lowerCamelCase        =				config.num_channels





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				pixel_values.shape[1]
									if 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.'''   )
									_lowerCamelCase        =				self.embedder(lowerCamelCase__   )
									return hidden_state


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 2   ):
									super().__init__()
									_lowerCamelCase        =				nn.Convad(lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=1				,       stride=lowerCamelCase__				,       bias=lowerCamelCase__   )
									_lowerCamelCase        =				nn.BatchNormad(lowerCamelCase__   )





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.convolution(lowerCamelCase__   )
									_lowerCamelCase        =				self.normalization(lowerCamelCase__   )
									return hidden_state


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__   ):
									super().__init__()

									_lowerCamelCase        =				nn.AdaptiveAvgPoolad((1, 1)   )
									_lowerCamelCase        =				nn.Sequential(
									    nn.Convad(lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=1   )				,       nn.ReLU()				,       nn.Convad(lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=1   )				,       nn.Sigmoid()				,       )





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									# b c h w -> b c 1 1
									_lowerCamelCase        =				self.pooler(lowerCamelCase__   )
									_lowerCamelCase        =				self.attention(lowerCamelCase__   )
									_lowerCamelCase        =				hidden_state * attention
									return hidden_state


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 1   ):
									super().__init__()
									_lowerCamelCase        =				in_channels != out_channels or stride != 1
									_lowerCamelCase        =				max(1				,       out_channels // config.groups_width   )
									_lowerCamelCase        =				(
									    RegNetShortCut(lowerCamelCase__				,       lowerCamelCase__				,       stride=lowerCamelCase__   ) if should_apply_shortcut else nn.Identity()
									)
									_lowerCamelCase        =				nn.Sequential(
									    RegNetConvLayer(lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=1				,       activation=config.hidden_act   )				,       RegNetConvLayer(lowerCamelCase__				,       lowerCamelCase__				,       stride=lowerCamelCase__				,       groups=lowerCamelCase__				,       activation=config.hidden_act   )				,       RegNetConvLayer(lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=1				,       activation=lowerCamelCase__   )				,       )
									_lowerCamelCase        =				ACTaFN[config.hidden_act]





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				hidden_state
									_lowerCamelCase        =				self.layer(lowerCamelCase__   )
									_lowerCamelCase        =				self.shortcut(lowerCamelCase__   )
									hidden_state += residual
									_lowerCamelCase        =				self.activation(lowerCamelCase__   )
									return hidden_state


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 1   ):
									super().__init__()
									_lowerCamelCase        =				in_channels != out_channels or stride != 1
									_lowerCamelCase        =				max(1				,       out_channels // config.groups_width   )
									_lowerCamelCase        =				(
									    RegNetShortCut(lowerCamelCase__				,       lowerCamelCase__				,       stride=lowerCamelCase__   ) if should_apply_shortcut else nn.Identity()
									)
									_lowerCamelCase        =				nn.Sequential(
									    RegNetConvLayer(lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=1				,       activation=config.hidden_act   )				,       RegNetConvLayer(lowerCamelCase__				,       lowerCamelCase__				,       stride=lowerCamelCase__				,       groups=lowerCamelCase__				,       activation=config.hidden_act   )				,       RegNetSELayer(lowerCamelCase__				,       reduced_channels=int(round(in_channels / 4   )   )   )				,       RegNetConvLayer(lowerCamelCase__				,       lowerCamelCase__				,       kernel_size=1				,       activation=lowerCamelCase__   )				,       )
									_lowerCamelCase        =				ACTaFN[config.hidden_act]





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				hidden_state
									_lowerCamelCase        =				self.layer(lowerCamelCase__   )
									_lowerCamelCase        =				self.shortcut(lowerCamelCase__   )
									hidden_state += residual
									_lowerCamelCase        =				self.activation(lowerCamelCase__   )
									return hidden_state


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__ = 2				,       lowerCamelCase__ = 2				,       ):
									super().__init__()

									_lowerCamelCase        =				RegNetXLayer if config.layer_type == '''x''' else RegNetYLayer

									_lowerCamelCase        =				nn.Sequential(
									    # downsampling is done in the first layer with stride of 2
									    layer(
									        lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       stride=lowerCamelCase__				,       )				,       *[layer(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__   ) for _ in range(depth - 1   )]				,       )





					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									_lowerCamelCase        =				self.layers(lowerCamelCase__   )
									return hidden_state


class 							lowerCamelCase_(   nn.Module    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									super().__init__()
									_lowerCamelCase        =				nn.ModuleList([]   )
									# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
									self.stages.append(
									    RegNetStage(
									        lowerCamelCase__				,       config.embedding_size				,       config.hidden_sizes[0]				,       stride=2 if config.downsample_in_first_stage else 1				,       depth=config.depths[0]				,       )   )
									_lowerCamelCase        =				zip(config.hidden_sizes				,       config.hidden_sizes[1:]   )
									for (in_channels, out_channels), depth in zip(lowerCamelCase__				,       config.depths[1:]   ):
													self.stages.append(RegNetStage(lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__				,       depth=lowerCamelCase__   )   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = False				,       lowerCamelCase__ = True   ):
									_lowerCamelCase        =				() if output_hidden_states else None

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

													_lowerCamelCase        =				stage_module(lowerCamelCase__   )

									if output_hidden_states:
													_lowerCamelCase        =				hidden_states + (hidden_state,)

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

									return BaseModelOutputWithNoAttention(last_hidden_state=lowerCamelCase__				,       hidden_states=lowerCamelCase__   )


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					lowercase__   :       Tuple										= RegNetConfig
					lowercase__   :       str										= 'regnet'
					lowercase__   :       List[str]										= 'pixel_values'
					lowercase__   :       str										= True

					def 			snake_case__      (    self				,       lowerCamelCase__   ):
									if isinstance(lowerCamelCase__				,       nn.Convad   ):
													nn.init.kaiming_normal_(module.weight				,       mode='''fan_out'''				,       nonlinearity='''relu'''   )
									elif isinstance(lowerCamelCase__				,       (nn.BatchNormad, nn.GroupNorm)   ):
													nn.init.constant_(module.weight				,       1   )
													nn.init.constant_(module.bias				,       0   )





					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__=False   ):
									if isinstance(lowerCamelCase__				,       lowerCamelCase__   ):
													_lowerCamelCase        =				value
__SCREAMING_SNAKE_CASE  :						List[str]    							=      R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__SCREAMING_SNAKE_CASE  :						Any    							=      R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
'''


@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.',				A__,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									super().__init__(lowerCamelCase__   )
									_lowerCamelCase        =				config
									_lowerCamelCase        =				RegNetEmbeddings(lowerCamelCase__   )
									_lowerCamelCase        =				RegNetEncoder(lowerCamelCase__   )
									_lowerCamelCase        =				nn.AdaptiveAvgPoolad((1, 1)   )
									# Initialize weights and apply final processing
									self.post_init()





					@add_start_docstrings_to_model_forward(lowerCamelCase__   )
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC				,       output_type=lowerCamelCase__				,       config_class=_CONFIG_FOR_DOC				,       modality='''vision'''				,       expected_output=_EXPECTED_OUTPUT_SHAPE				,       )
					def 			snake_case__      (    self				,       lowerCamelCase__				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None   ):
									_lowerCamelCase        =				(
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									_lowerCamelCase        =				return_dict if return_dict is not None else self.config.use_return_dict

									_lowerCamelCase        =				self.embedder(lowerCamelCase__   )

									_lowerCamelCase        =				self.encoder(
									    lowerCamelCase__				,       output_hidden_states=lowerCamelCase__				,       return_dict=lowerCamelCase__   )

									_lowerCamelCase        =				encoder_outputs[0]

									_lowerCamelCase        =				self.pooler(lowerCamelCase__   )

									if not return_dict:
													return (last_hidden_state, pooled_output) + encoder_outputs[1:]

									return BaseModelOutputWithPoolingAndNoAttention(
									    last_hidden_state=lowerCamelCase__				,       pooler_output=lowerCamelCase__				,       hidden_states=encoder_outputs.hidden_states				,       )


@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    ',				A__,				)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__   ):
									super().__init__(lowerCamelCase__   )
									_lowerCamelCase        =				config.num_labels
									_lowerCamelCase        =				RegNetModel(lowerCamelCase__   )
									# classification head
									_lowerCamelCase        =				nn.Sequential(
									    nn.Flatten()				,       nn.Linear(config.hidden_sizes[-1]				,       config.num_labels   ) if config.num_labels > 0 else nn.Identity()				,       )
									# initialize weights and apply final processing
									self.post_init()





					@add_start_docstrings_to_model_forward(lowerCamelCase__   )
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT				,       output_type=lowerCamelCase__				,       config_class=_CONFIG_FOR_DOC				,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,       )
					def 			snake_case__      (    self				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       lowerCamelCase__ = None				,       ):
									_lowerCamelCase        =				return_dict if return_dict is not None else self.config.use_return_dict

									_lowerCamelCase        =				self.regnet(lowerCamelCase__				,       output_hidden_states=lowerCamelCase__				,       return_dict=lowerCamelCase__   )

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

									_lowerCamelCase        =				self.classifier(lowerCamelCase__   )

									_lowerCamelCase        =				None

									if labels is not None:
													if self.config.problem_type is None:
																	if self.num_labels == 1:
																					_lowerCamelCase        =				'''regression'''
																	elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																					_lowerCamelCase        =				'''single_label_classification'''
																	else:
																					_lowerCamelCase        =				'''multi_label_classification'''
													if self.config.problem_type == "regression":
																	_lowerCamelCase        =				MSELoss()
																	if self.num_labels == 1:
																					_lowerCamelCase        =				loss_fct(logits.squeeze()				,       labels.squeeze()   )
																	else:
																					_lowerCamelCase        =				loss_fct(lowerCamelCase__				,       lowerCamelCase__   )
													elif self.config.problem_type == "single_label_classification":
																	_lowerCamelCase        =				CrossEntropyLoss()
																	_lowerCamelCase        =				loss_fct(logits.view(-1				,       self.num_labels   )				,       labels.view(-1   )   )
													elif self.config.problem_type == "multi_label_classification":
																	_lowerCamelCase        =				BCEWithLogitsLoss()
																	_lowerCamelCase        =				loss_fct(lowerCamelCase__				,       lowerCamelCase__   )

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

									return ImageClassifierOutputWithNoAttention(loss=lowerCamelCase__				,       logits=lowerCamelCase__				,       hidden_states=outputs.hidden_states   )





"""simple docstring"""
import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed


class 							lowerCamelCase_(   A__    ):





					'''simple docstring'''



					def __init__(    self				,       lowerCamelCase__				,       lowerCamelCase__				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=1_0_2_4				,       lowerCamelCase__=3.6   ):
									_lowerCamelCase        =				tokenizer
									_lowerCamelCase        =				tokenizer.bos_token_id
									_lowerCamelCase        =				dataset
									_lowerCamelCase        =				seq_length
									_lowerCamelCase        =				seq_length * chars_per_token * num_of_sequences





					def __iter__(    self   ):
									_lowerCamelCase        =				iter(self.dataset   )
									_lowerCamelCase        =				True
									while more_examples:
													_lowerCamelCase  ,     _lowerCamelCase        =				[], 0
													while True:
																	if buffer_len >= self.input_characters:
																					break
																	try:
																					buffer.append(next(lowerCamelCase__   )['''content''']   )
																					buffer_len += len(buffer[-1]   )
																	except StopIteration:
																					_lowerCamelCase        =				False
																					break
													_lowerCamelCase        =				tokenizer(lowerCamelCase__				,       truncation=lowerCamelCase__   )['''input_ids''']
													_lowerCamelCase        =				[]
													for tokenized_input in tokenized_inputs:
																	all_token_ids.extend(tokenized_input + [self.concat_token_id]   )
													for i in range(0				,       len(lowerCamelCase__   )				,       self.seq_length   ):
																	_lowerCamelCase        =				all_token_ids[i : i + self.seq_length]
																	if len(lowerCamelCase__   ) == self.seq_length:
																					yield torch.tensor(lowerCamelCase__   )



def        lowerCAmelCase_(   lowercase_     :							Any							)	->       Optional[Any]:
				_lowerCamelCase        =				{'''streaming''': True}
				_lowerCamelCase        =				load_dataset(args.dataset_name						,   split='''train'''						,   **lowercase_							)
				_lowerCamelCase        =				ConstantLengthDataset(lowercase_						,   lowercase_						,   seq_length=args.seq_length							)
				_lowerCamelCase        =				DataLoader(lowercase_						,   batch_size=args.batch_size							)
				return eval_dataloader



def        lowerCAmelCase_(   lowercase_     :							Tuple							)	->       str:
				model.eval()
				_lowerCamelCase        =				[]
				for step, batch in enumerate(lowercase_							):
								with torch.no_grad():
												_lowerCamelCase        =				model(lowercase_						,   labels=lowercase_							)
								_lowerCamelCase        =				outputs.loss.repeat(args.batch_size							)
								losses.append(accelerator.gather(lowercase_							)							)

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				_lowerCamelCase        =				torch.mean(torch.cat(lowercase_							)							)
				try:
								_lowerCamelCase        =				torch.exp(lowercase_							)
				except OverflowError:
								_lowerCamelCase        =				float('''inf'''							)
				return loss.item(), perplexity.item()


# Setup Accelerator
__SCREAMING_SNAKE_CASE  :						Dict    							=      Accelerator()

# Parse configuration
__SCREAMING_SNAKE_CASE  :						Tuple    							=      HfArgumentParser(EvaluationArguments)
__SCREAMING_SNAKE_CASE  :						Optional[int]    							=      parser.parse_args()
set_seed(args.seed)

# Logging
__SCREAMING_SNAKE_CASE  :						Optional[Any]    							=      logging.getLogger(__name__)
logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)

# Load model and tokenizer
__SCREAMING_SNAKE_CASE  :						str    							=      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
__SCREAMING_SNAKE_CASE  :						Union[str, Any]    							=      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
__SCREAMING_SNAKE_CASE  :						str    							=      create_dataloader(args)

# Prepare everything with our `accelerator`.
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						Any    							=      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('''Evaluating and saving model after training''')
__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE  :						List[str]    							=      evaluate(args)




logger.info(F"""loss/eval: {eval_loss}, perplexity: {perplexity}""")
