



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




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




				def __init__(  self   :  List[str],   __lowercase   :  RegNetConfig  ):
							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   :  Union[str, Any],   __lowercase   :  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(__lowercase  )
							return hidden_state
class 			_snake_case     (   nn.Module):




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




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




				def __init__(  self   :  Tuple,   __lowercase   :  int,   __lowercase   :  int  ):
							super().__init__()

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




				def 							A__ (  self   :  Dict,   __lowercase   :  Optional[int]  ):
							# b c h w -> b c 1 1
							lowercase__   					=			self.pooler(__lowercase  )
							lowercase__   					=			self.attention(__lowercase  )
							lowercase__   					=			hidden_state * attention
							return hidden_state
class 			_snake_case     (   nn.Module):




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




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




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




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




				def __init__(  self   :  Any,   __lowercase   :  RegNetConfig,   __lowercase   :  int,   __lowercase   :  int,   __lowercase   :  int = 2,   __lowercase   :  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(
							        __lowercase,   __lowercase,   __lowercase,   stride=__lowercase,   ),   *[layer(__lowercase,   __lowercase,   __lowercase  ) for _ in range(depth - 1  )],   )




				def 							A__ (  self   :  List[Any],   __lowercase   :  Tuple  ):
							lowercase__   					=			self.layers(__lowercase  )
							return hidden_state
class 			_snake_case     (   nn.Module):




				def __init__(  self   :  Any,   __lowercase   :  RegNetConfig  ):
							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(
							        __lowercase,   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(__lowercase,   config.depths[1:]  ):
										self.stages.append(RegNetStage(__lowercase,   __lowercase,   __lowercase,   depth=__lowercase  )  )




				def 							A__ (  self   :  List[str],   __lowercase   :  Tensor,   __lowercase   :  bool = False,   __lowercase   :  bool = 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(__lowercase  )

							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=__lowercase,   hidden_states=__lowercase  )
class 			_snake_case     (   lowercase__):
				UpperCamelCase__			:   Any       						=RegNetConfig
				UpperCamelCase__			:   List[str]       						="""regnet"""
				UpperCamelCase__			:   Dict       						="""pixel_values"""
				UpperCamelCase__			:   List[Any]       						=True




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




				def 							A__ (  self   :  Optional[Any],   __lowercase   :  List[Any],   __lowercase   :  Dict=False  ):
							if isinstance(__lowercase,   __lowercase  ):
										lowercase__   					=			value
lowercase_						=       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.
"""

lowercase_						=       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."""      ,		lowercase__      ,		)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 			_snake_case     (   lowercase__):




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




				@add_start_docstrings_to_model_forward(__lowercase  )
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC,   output_type=__lowercase,   config_class=_CONFIG_FOR_DOC,   modality="vision",   expected_output=_EXPECTED_OUTPUT_SHAPE,   )
				def 							A__ (  self   :  Union[str, Any],   __lowercase   :  Tensor,   __lowercase   :  Optional[bool] = None,   __lowercase   :  Optional[bool] = 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(__lowercase  )

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

							lowercase__   					=			encoder_outputs[0]

							lowercase__   					=			self.pooler(__lowercase  )

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

							return BaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=__lowercase,   pooler_output=__lowercase,   hidden_states=encoder_outputs.hidden_states,   )
@add_start_docstrings(
    """
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """      ,		lowercase__      ,		)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 			_snake_case     (   lowercase__):




				def __init__(  self   :  Optional[int],   __lowercase   :  Dict  ):
							super().__init__(__lowercase  )
							lowercase__   					=			config.num_labels
							lowercase__   					=			RegNetModel(__lowercase  )
							# 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(__lowercase  )
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT,   output_type=__lowercase,   config_class=_CONFIG_FOR_DOC,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,   )
				def 							A__ (  self   :  Optional[Any],   __lowercase   :  Optional[torch.FloatTensor] = None,   __lowercase   :  Optional[torch.LongTensor] = None,   __lowercase   :  Optional[bool] = None,   __lowercase   :  Optional[bool] = None,   ):
							lowercase__   					=			return_dict if return_dict is not None else self.config.use_return_dict

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

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

							lowercase__   					=			self.classifier(__lowercase  )

							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(__lowercase,   __lowercase  )
										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(__lowercase,   __lowercase  )

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

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









from pathlib import Path

import fire



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			lowercase__   					=			Path(SCREAMING_SNAKE_CASE_					)
			dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)
			for path in src_dir.iterdir():
						lowercase__   					=			[x.rstrip() for x in list(path.open().readlines()					)][:n]
						lowercase__   					=			dest_dir.joinpath(path.name					)
						print(SCREAMING_SNAKE_CASE_					)
						dest_path.open("w"					).write("\n".join(SCREAMING_SNAKE_CASE_					)					)


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




