


'''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


lowercase__				     =		logging.get_logger(__name__)

# General docstring
lowercase__				     =		"RegNetConfig"

# Base docstring
lowercase__				     =		"facebook/regnet-y-040"
lowercase__				     =		[1, 1_088, 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 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






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






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Tuple:
          _a						=		self.convolution(__UpperCamelCase		)
          _a						=		self.normalization(__UpperCamelCase		)
          _a						=		self.activation(__UpperCamelCase		)
          return hidden_state







class 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






     def __init__(			self     ,							__UpperCamelCase		)       ->    Dict:
          super().__init__()
          _a						=		RegNetConvLayer(
              config.num_channels     ,							config.embedding_size     ,							kernel_size=3     ,							stride=2     ,							activation=config.hidden_act		)
          _a						=		config.num_channels






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          _a						=		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."		)
          _a						=		self.embedder(__UpperCamelCase		)
          return hidden_state







class 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 2		)       ->    str:
          super().__init__()
          _a						=		nn.Convad(__UpperCamelCase     ,							__UpperCamelCase     ,							kernel_size=1     ,							stride=__UpperCamelCase     ,							bias=__UpperCamelCase		)
          _a						=		nn.BatchNormad(__UpperCamelCase		)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Tensor:
          _a						=		self.convolution(__UpperCamelCase		)
          _a						=		self.normalization(__UpperCamelCase		)
          return hidden_state







class 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Tuple:
          super().__init__()

          _a						=		nn.AdaptiveAvgPoolad((1, 1)		)
          _a						=		nn.Sequential(
              nn.Convad(__UpperCamelCase     ,							__UpperCamelCase     ,							kernel_size=1		)     ,							nn.ReLU()     ,							nn.Convad(__UpperCamelCase     ,							__UpperCamelCase     ,							kernel_size=1		)     ,							nn.Sigmoid()     ,							)






     def  a_					(			self     ,							__UpperCamelCase		)       ->    int:
          # b c h w -> b c 1 1
          _a						=		self.pooler(__UpperCamelCase		)
          _a						=		self.attention(__UpperCamelCase		)
          _a						=		hidden_state * attention
          return hidden_state







class 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






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






     def  a_					(			self     ,							__UpperCamelCase		)       ->    str:
          _a						=		hidden_state
          _a						=		self.layer(__UpperCamelCase		)
          _a						=		self.shortcut(__UpperCamelCase		)
          hidden_state += residual
          _a						=		self.activation(__UpperCamelCase		)
          return hidden_state







class 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






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






     def  a_					(			self     ,							__UpperCamelCase		)       ->    List[Any]:
          _a						=		hidden_state
          _a						=		self.layer(__UpperCamelCase		)
          _a						=		self.shortcut(__UpperCamelCase		)
          hidden_state += residual
          _a						=		self.activation(__UpperCamelCase		)
          return hidden_state







class 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase = 2     ,							__UpperCamelCase = 2     ,							)       ->    int:
          super().__init__()

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

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






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Union[str, Any]:
          _a						=		self.layers(__UpperCamelCase		)
          return hidden_state







class 						__SCREAMING_SNAKE_CASE					(      nn.Module ):






     def __init__(			self     ,							__UpperCamelCase		)       ->    int:
          super().__init__()
          _a						=		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(
                  __UpperCamelCase     ,							config.embedding_size     ,							config.hidden_sizes[0]     ,							stride=2 if config.downsample_in_first_stage else 1     ,							depth=config.depths[0]     ,							)		)
          _a						=		zip(config.hidden_sizes     ,							config.hidden_sizes[1:]		)
          for (in_channels, out_channels), depth in zip(__UpperCamelCase     ,							config.depths[1:]		):
               self.stages.append(RegNetStage(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							depth=__UpperCamelCase		)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = False     ,							__UpperCamelCase = True		)       ->    BaseModelOutputWithNoAttention:
          _a						=		() if output_hidden_states else None

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

               _a						=		stage_module(__UpperCamelCase		)

          if output_hidden_states:
               _a						=		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=__UpperCamelCase     ,							hidden_states=__UpperCamelCase		)







class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):
     UpperCAmelCase 					=				RegNetConfig
     UpperCAmelCase 					=				'''regnet'''
     UpperCAmelCase 					=				'''pixel_values'''
     UpperCAmelCase 					=				True






     def  a_					(			self     ,							__UpperCamelCase		)       ->    Optional[int]:
          if isinstance(__UpperCamelCase     ,							nn.Convad		):
               nn.init.kaiming_normal_(module.weight     ,							mode="fan_out"     ,							nonlinearity="relu"		)
          elif isinstance(__UpperCamelCase     ,							(nn.BatchNormad, nn.GroupNorm)		):
               nn.init.constant_(module.weight     ,							1		)
               nn.init.constant_(module.bias     ,							0		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=False		)       ->    List[Any]:
          if isinstance(__UpperCamelCase     ,							__UpperCamelCase		):
               _a						=		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.''' ,			lowerCamelCase__ ,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def __init__(			self     ,							__UpperCamelCase		)       ->    List[str]:
          super().__init__(__UpperCamelCase		)
          _a						=		config
          _a						=		RegNetEmbeddings(__UpperCamelCase		)
          _a						=		RegNetEncoder(__UpperCamelCase		)
          _a						=		nn.AdaptiveAvgPoolad((1, 1)		)
          # Initialize weights and apply final processing
          self.post_init()






     @add_start_docstrings_to_model_forward(__UpperCamelCase		)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC     ,							output_type=__UpperCamelCase     ,							config_class=_CONFIG_FOR_DOC     ,							modality="vision"     ,							expected_output=_EXPECTED_OUTPUT_SHAPE     ,							)
     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None		)       ->    BaseModelOutputWithPoolingAndNoAttention:
          _a						=		(
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _a						=		return_dict if return_dict is not None else self.config.use_return_dict

          _a						=		self.embedder(__UpperCamelCase		)

          _a						=		self.encoder(
              __UpperCamelCase     ,							output_hidden_states=__UpperCamelCase     ,							return_dict=__UpperCamelCase		)

          _a						=		encoder_outputs[0]

          _a						=		self.pooler(__UpperCamelCase		)

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

          return BaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=__UpperCamelCase     ,							pooler_output=__UpperCamelCase     ,							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.
    ''' ,			lowerCamelCase__ ,			)
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ):






     def __init__(			self     ,							__UpperCamelCase		)       ->    Optional[Any]:
          super().__init__(__UpperCamelCase		)
          _a						=		config.num_labels
          _a						=		RegNetModel(__UpperCamelCase		)
          # classification head
          _a						=		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(__UpperCamelCase		)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT     ,							output_type=__UpperCamelCase     ,							config_class=_CONFIG_FOR_DOC     ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,							)
     def  a_					(			self     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							__UpperCamelCase = None     ,							)       ->    ImageClassifierOutputWithNoAttention:
          _a						=		return_dict if return_dict is not None else self.config.use_return_dict

          _a						=		self.regnet(__UpperCamelCase     ,							output_hidden_states=__UpperCamelCase     ,							return_dict=__UpperCamelCase		)

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

          _a						=		self.classifier(__UpperCamelCase		)

          _a						=		None

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

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

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





'''simple docstring'''

import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin





class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ,			lowerCamelCase__ ):






     def  a_					(			self		)       ->    Tuple:
          _a						=		load_tool("text-classification"		)
          self.tool.setup()
          _a						=		load_tool("text-classification"     ,							remote=__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Union[str, Any]:
          _a						=		self.remote_tool("That's quite cool"     ,							["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		self.tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)






     def  a_					(			self		)       ->    str:
          _a						=		self.remote_tool(text="That's quite cool"     ,							labels=["positive", "negative"]		)
          self.assertEqual(__UpperCamelCase     ,							"positive"		)

