



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


lowercase_						=       logging.get_logger(__name__)

# General docstring
lowercase_						=       "RegNetConfig"

# Base docstring
lowercase_						=       "facebook/regnet-y-040"
lowercase_						=       [1, 1088, 7, 7]

# Image classification docstring
lowercase_						=       "facebook/regnet-y-040"
lowercase_						=       "tabby, tabby cat"

lowercase_						=       [
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Union[str, Any],   __lowercase   :  Tuple,   __lowercase   :  Tuple,   __lowercase   :  Any = 3,   __lowercase   :  Union[str, Any] = 1,   __lowercase   :  Optional[int] = 1,   __lowercase   :  Optional[int] = "relu",   ):
							super().__init__()
							lowercase__   					=			nn.Convad(
							    _SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   kernel_size=_SCREAMING_SNAKE_CASE,   stride=_SCREAMING_SNAKE_CASE,   padding=kernel_size // 2,   groups=_SCREAMING_SNAKE_CASE,   bias=_SCREAMING_SNAKE_CASE,   )
							lowercase__   					=			nn.BatchNormad(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			ACTaFN[activation] if activation is not None else nn.Identity()




				def 							A__ (  self   :  Dict,   __lowercase   :  str  ):
							lowercase__   					=			self.convolution(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			self.normalization(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			self.activation(_SCREAMING_SNAKE_CASE  )
							return hidden_state
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Union[str, Any],   __lowercase   :  Optional[Any]  ):
							super().__init__()
							lowercase__   					=			RegNetConvLayer(
							    config.num_channels,   config.embedding_size,   kernel_size=3,   stride=2,   activation=config.hidden_act  )
							lowercase__   					=			config.num_channels




				def 							A__ (  self   :  Any,   __lowercase   :  Optional[Any]  ):
							lowercase__   					=			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."  )
							lowercase__   					=			self.embedder(_SCREAMING_SNAKE_CASE  )
							return hidden_state
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  List[str],   __lowercase   :  List[Any],   __lowercase   :  List[str],   __lowercase   :  List[Any] = 2  ):
							super().__init__()
							lowercase__   					=			nn.Convad(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   kernel_size=1,   stride=_SCREAMING_SNAKE_CASE,   bias=_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			nn.BatchNormad(_SCREAMING_SNAKE_CASE  )




				def 							A__ (  self   :  List[str],   __lowercase   :  Any  ):
							lowercase__   					=			self.convolution(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			self.normalization(_SCREAMING_SNAKE_CASE  )
							return hidden_state
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  str,   __lowercase   :  int,   __lowercase   :  List[str]  ):
							super().__init__()

							lowercase__   					=			nn.AdaptiveAvgPoolad((1, 1)  )
							lowercase__   					=			nn.Sequential(
							    nn.Convad(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   kernel_size=1  ),   nn.ReLU(),   nn.Convad(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   kernel_size=1  ),   nn.Sigmoid(),   )




				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  Any  ):
							lowercase__   					=			self.pooler(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			self.attention(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			hidden_state * attention
							return hidden_state
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Any,   __lowercase   :  Optional[int],   __lowercase   :  Tuple,   __lowercase   :  List[Any],   __lowercase   :  int = 1  ):
							super().__init__()
							lowercase__   					=			in_channels != out_channels or stride != 1
							lowercase__   					=			max(1,   out_channels // config.groups_width  )
							lowercase__   					=			(
							    RegNetShortCut(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   stride=_SCREAMING_SNAKE_CASE  ) if should_apply_shortcut else nn.Identity()
							)
							lowercase__   					=			nn.Sequential(
							    RegNetConvLayer(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   kernel_size=1,   activation=config.hidden_act  ),   RegNetConvLayer(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   stride=_SCREAMING_SNAKE_CASE,   groups=_SCREAMING_SNAKE_CASE,   activation=config.hidden_act  ),   RegNetConvLayer(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   kernel_size=1,   activation=_SCREAMING_SNAKE_CASE  ),   )
							lowercase__   					=			ACTaFN[config.hidden_act]




				def 							A__ (  self   :  Any,   __lowercase   :  List[str]  ):
							lowercase__   					=			hidden_state
							lowercase__   					=			self.layer(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			self.shortcut(_SCREAMING_SNAKE_CASE  )
							hidden_state += residual
							lowercase__   					=			self.activation(_SCREAMING_SNAKE_CASE  )
							return hidden_state
class 			_snake_case     (   nn.Module):




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




				def 							A__ (  self   :  str,   __lowercase   :  Optional[Any]  ):
							lowercase__   					=			hidden_state
							lowercase__   					=			self.layer(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			self.shortcut(_SCREAMING_SNAKE_CASE  )
							hidden_state += residual
							lowercase__   					=			self.activation(_SCREAMING_SNAKE_CASE  )
							return hidden_state
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Tuple,   __lowercase   :  Optional[int],   __lowercase   :  Dict,   __lowercase   :  Dict,   __lowercase   :  Dict = 2,   __lowercase   :  Optional[int] = 2,   ):
							super().__init__()

							lowercase__   					=			RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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




				def 							A__ (  self   :  Optional[int],   __lowercase   :  int  ):
							lowercase__   					=			self.layers(_SCREAMING_SNAKE_CASE  )
							return hidden_state
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Dict,   __lowercase   :  Dict  ):
							super().__init__()
							lowercase__   					=			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(
							        _SCREAMING_SNAKE_CASE,   config.embedding_size,   config.hidden_sizes[0],   stride=2 if config.downsample_in_first_stage else 1,   depth=config.depths[0],   )  )
							lowercase__   					=			zip(config.hidden_sizes,   config.hidden_sizes[1:]  )
							for (in_channels, out_channels), depth in zip(_SCREAMING_SNAKE_CASE,   config.depths[1:]  ):
										self.stages.append(RegNetStage(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE,   depth=_SCREAMING_SNAKE_CASE  )  )




				def 							A__ (  self   :  Dict,   __lowercase   :  Dict,   __lowercase   :  Optional[Any] = False,   __lowercase   :  Union[str, Any] = True  ):
							lowercase__   					=			() if output_hidden_states else None

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

										lowercase__   					=			stage_module(_SCREAMING_SNAKE_CASE  )

							if output_hidden_states:
										lowercase__   					=			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=_SCREAMING_SNAKE_CASE,   hidden_states=_SCREAMING_SNAKE_CASE  )
class 			_snake_case     (   UpperCAmelCase__):
				UpperCamelCase__			:   int       						=RegNetConfig
				UpperCamelCase__			:   Any       						='regnet'
				UpperCamelCase__			:   Optional[Any]       						='pixel_values'
				UpperCamelCase__			:   Tuple       						=True




				def 							A__ (  self   :  str,   __lowercase   :  Any  ):




							if isinstance(_SCREAMING_SNAKE_CASE,   nn.Convad  ):
										nn.init.kaiming_normal_(module.weight,   mode="fan_out",   nonlinearity="relu"  )
							elif isinstance(_SCREAMING_SNAKE_CASE,   (nn.BatchNormad, nn.GroupNorm)  ):
										nn.init.constant_(module.weight,   1  )
										nn.init.constant_(module.bias,   0  )




				def 							A__ (  self   :  Optional[int],   __lowercase   :  Tuple,   __lowercase   :  int=False  ):




							if isinstance(_SCREAMING_SNAKE_CASE,   _SCREAMING_SNAKE_CASE  ):
										lowercase__   					=			value
lowercase_						=       R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`RegNetConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

lowercase_						=       R"\n    Args:\n        pixel_values (`torch.FloatTensor` 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 [`~file_utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
    """The bare RegNet model outputting raw features without any specific head on top."""      ,		UpperCAmelCase__      ,		)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 			_snake_case     (   UpperCAmelCase__):




				def __init__(  self   :  int,   __lowercase   :  Dict  ):
							super().__init__(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			config
							lowercase__   					=			RegNetEmbeddings(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			RegNetEncoder(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			nn.AdaptiveAvgPoolad((1, 1)  )
							# Initialize weights and apply final processing
							self.post_init()




				@add_start_docstrings_to_model_forward(_SCREAMING_SNAKE_CASE  )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC,   output_type=_SCREAMING_SNAKE_CASE,   config_class=_CONFIG_FOR_DOC,   modality="vision",   expected_output=_EXPECTED_OUTPUT_SHAPE,   )
				def 							A__ (  self   :  int,   __lowercase   :  Union[str, Any],   __lowercase   :  Optional[int] = None,   __lowercase   :  Tuple = None  ):
							lowercase__   					=			(
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							lowercase__   					=			return_dict if return_dict is not None else self.config.use_return_dict

							lowercase__   					=			self.embedder(_SCREAMING_SNAKE_CASE  )

							lowercase__   					=			self.encoder(
							    _SCREAMING_SNAKE_CASE,   output_hidden_states=_SCREAMING_SNAKE_CASE,   return_dict=_SCREAMING_SNAKE_CASE  )

							lowercase__   					=			encoder_outputs[0]

							lowercase__   					=			self.pooler(_SCREAMING_SNAKE_CASE  )

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

							return BaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=_SCREAMING_SNAKE_CASE,   pooler_output=_SCREAMING_SNAKE_CASE,   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    """      ,		UpperCAmelCase__      ,		)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 			_snake_case     (   UpperCAmelCase__):




				def __init__(  self   :  Union[str, Any],   __lowercase   :  Tuple  ):
							super().__init__(_SCREAMING_SNAKE_CASE  )
							lowercase__   					=			config.num_labels
							lowercase__   					=			RegNetModel(_SCREAMING_SNAKE_CASE  )
							# classification head
							lowercase__   					=			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(_SCREAMING_SNAKE_CASE  )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT,   output_type=_SCREAMING_SNAKE_CASE,   config_class=_CONFIG_FOR_DOC,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,   )
				def 							A__ (  self   :  str,   __lowercase   :  str = None,   __lowercase   :  Union[str, Any] = None,   __lowercase   :  Optional[Any] = None,   __lowercase   :  Any = None,   ):
							lowercase__   					=			return_dict if return_dict is not None else self.config.use_return_dict

							lowercase__   					=			self.regnet(_SCREAMING_SNAKE_CASE,   output_hidden_states=_SCREAMING_SNAKE_CASE,   return_dict=_SCREAMING_SNAKE_CASE  )

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

							lowercase__   					=			self.classifier(_SCREAMING_SNAKE_CASE  )

							lowercase__   					=			None

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

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

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




import fire

from utils import calculate_rouge, save_json



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=None  ,					**SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()]
			lowercase__   					=			[x.strip() for x in open(SCREAMING_SNAKE_CASE_					).readlines()][: len(SCREAMING_SNAKE_CASE_					)]
			lowercase__   					=			calculate_rouge(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					**SCREAMING_SNAKE_CASE_					)
			if save_path is not None:
						save_json(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					indent=SCREAMING_SNAKE_CASE_					)
			return metrics  # these print nicely


if __name__ == "__main__":
							fire.Fire(calculate_rouge_path)




