



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


A_		:     Optional[Any]        =     logging.get_logger(__name__)

# General docstring
A_		:     Optional[int]        =     '''RegNetConfig'''

# Base docstring
A_		:     Union[str, Any]        =     '''facebook/regnet-y-040'''
A_		:     int        =     [1, 10_88, 7, 7]

# Image classification docstring
A_		:     Any        =     '''facebook/regnet-y-040'''
A_		:     Dict        =     '''tabby, tabby cat'''

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



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 3							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       int = 1							,   __lowerCAmelCase			:       Optional[str] = "relu"							,   )       ->				str:


				"""simple docstring"""






				super().__init__()
				a						=		nn.Convad(
				    __lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=__lowerCAmelCase							,   stride=__lowerCAmelCase							,   padding=kernel_size // 2							,   groups=__lowerCAmelCase							,   bias=__lowerCAmelCase							,   )
				a						=		nn.BatchNormad(__lowerCAmelCase							)
				a						=		ACTaFN[activation] if activation is not None else nn.Identity()




			def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							)       ->				Dict:


				"""simple docstring"""






				a						=		self.convolution(__lowerCAmelCase							)
				a						=		self.normalization(__lowerCAmelCase							)
				a						=		self.activation(__lowerCAmelCase							)
				return hidden_state



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       RegNetConfig							)       ->				Dict:


				"""simple docstring"""






				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			:       Optional[Any]							,   __lowerCAmelCase			:       Dict							)       ->				Tuple:


				"""simple docstring"""






				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(__lowerCAmelCase							)
				return hidden_state



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 2							)       ->				Optional[int]:


				"""simple docstring"""






				super().__init__()
				a						=		nn.Convad(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							,   stride=__lowerCAmelCase							,   bias=__lowerCAmelCase							)
				a						=		nn.BatchNormad(__lowerCAmelCase							)




			def 		A							(      self			:       Any							,   __lowerCAmelCase			:       Tensor							)       ->				Tensor:


				"""simple docstring"""






				a						=		self.convolution(__lowerCAmelCase							)
				a						=		self.normalization(__lowerCAmelCase							)
				return hidden_state



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       Any							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							)       ->				List[Any]:


				"""simple docstring"""






				super().__init__()

				a						=		nn.AdaptiveAvgPoolad((1, 1)							)
				a						=		nn.Sequential(
				    nn.Convad(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							)							,   nn.ReLU()							,   nn.Convad(__lowerCAmelCase							,   __lowerCAmelCase							,   kernel_size=1							)							,   nn.Sigmoid()							,   )




			def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any							)       ->				str:


				"""simple docstring"""






				a						=		self.pooler(__lowerCAmelCase							)
				a						=		self.attention(__lowerCAmelCase							)
				a						=		hidden_state * attention
				return hidden_state



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       Optional[int]							,   __lowerCAmelCase			:       RegNetConfig							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 1							)       ->				Any:


				"""simple docstring"""






				super().__init__()
				a						=		in_channels != out_channels or stride != 1
				a						=		max(1							,   out_channels // config.groups_width							)
				a						=		(
				    RegNetShortCut(__lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							) if should_apply_shortcut else nn.Identity()
				)
				a						=		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							)							,   )
				a						=		ACTaFN[config.hidden_act]




			def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       List[Any]							)       ->				int:


				"""simple docstring"""






				a						=		hidden_state
				a						=		self.layer(__lowerCAmelCase							)
				a						=		self.shortcut(__lowerCAmelCase							)
				hidden_state += residual
				a						=		self.activation(__lowerCAmelCase							)
				return hidden_state



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       Optional[Any]							,   __lowerCAmelCase			:       RegNetConfig							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 1							)       ->				Optional[Any]:


				"""simple docstring"""






				super().__init__()
				a						=		in_channels != out_channels or stride != 1
				a						=		max(1							,   out_channels // config.groups_width							)
				a						=		(
				    RegNetShortCut(__lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							) if should_apply_shortcut else nn.Identity()
				)
				a						=		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							)							,   )
				a						=		ACTaFN[config.hidden_act]




			def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							)       ->				Optional[int]:


				"""simple docstring"""






				a						=		hidden_state
				a						=		self.layer(__lowerCAmelCase							)
				a						=		self.shortcut(__lowerCAmelCase							)
				hidden_state += residual
				a						=		self.activation(__lowerCAmelCase							)
				return hidden_state



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       Any							,   __lowerCAmelCase			:       RegNetConfig							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       int = 2							,   __lowerCAmelCase			:       int = 2							,   )       ->				int:


				"""simple docstring"""






				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(
				        __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   stride=__lowerCAmelCase							,   )							,   *[layer(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							) for _ in range(depth - 1							)]							,   )




			def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Tuple							)       ->				List[Any]:


				"""simple docstring"""






				a						=		self.layers(__lowerCAmelCase							)
				return hidden_state



class 		_lowercase		(       nn.Module	):

			def __init__(      self			:       Tuple							,   __lowerCAmelCase			:       RegNetConfig							)       ->				str:


				"""simple docstring"""






				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(
				        __lowerCAmelCase							,   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(__lowerCAmelCase							,   config.depths[1:]							):
					self.stages.append(RegNetStage(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   depth=__lowerCAmelCase							)							)




			def 		A							(      self			:       int							,   __lowerCAmelCase			:       Tensor							,   __lowerCAmelCase			:       bool = False							,   __lowerCAmelCase			:       bool = True							)       ->				BaseModelOutputWithNoAttention:


				"""simple docstring"""






				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(__lowerCAmelCase							)

				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=__lowerCAmelCase							,   hidden_states=__lowerCAmelCase							)



class 		_lowercase		(       UpperCAmelCase__	):
			_UpperCAmelCase     	=			RegNetConfig
			_UpperCAmelCase     	=			'''regnet'''
			_UpperCAmelCase     	=			'''pixel_values'''
			_UpperCAmelCase     	=			True

			def 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Optional[int]							)       ->				Tuple:


				"""simple docstring"""






				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 		A							(      self			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       str=False							)       ->				Tuple:


				"""simple docstring"""






				if isinstance(__lowerCAmelCase							,   __lowerCAmelCase							):
					a						=		value

A_		:     Dict        =     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.
'''

A_		:     str        =     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.''',   UpperCAmelCase__,   )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 		_lowercase		(       UpperCAmelCase__	):

			def __init__(      self			:       List[Any]							,   __lowerCAmelCase			:       int							)       ->				Optional[int]:


				"""simple docstring"""






				super().__init__(__lowerCAmelCase							)
				a						=		config
				a						=		RegNetEmbeddings(__lowerCAmelCase							)
				a						=		RegNetEncoder(__lowerCAmelCase							)
				a						=		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 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Tensor							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Optional[bool] = None							)       ->				BaseModelOutputWithPoolingAndNoAttention:


				"""simple docstring"""






				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(__lowerCAmelCase							)

				a						=		self.encoder(
				    __lowerCAmelCase							,   output_hidden_states=__lowerCAmelCase							,   return_dict=__lowerCAmelCase							)

				a						=		encoder_outputs[0]

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

			def __init__(      self			:       Tuple							,   __lowerCAmelCase			:       Dict							)       ->				Union[str, Any]:


				"""simple docstring"""






				super().__init__(__lowerCAmelCase							)
				a						=		config.num_labels
				a						=		RegNetModel(__lowerCAmelCase							)
				# 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(__lowerCAmelCase							)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT							,   output_type=__lowerCAmelCase							,   config_class=_CONFIG_FOR_DOC							,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,   )
			def 		A							(      self			:       List[str]							,   __lowerCAmelCase			:       Optional[torch.FloatTensor] = None							,   __lowerCAmelCase			:       Optional[torch.LongTensor] = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   __lowerCAmelCase			:       Optional[bool] = None							,   )       ->				ImageClassifierOutputWithNoAttention:


				"""simple docstring"""






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

				a						=		self.regnet(__lowerCAmelCase							,   output_hidden_states=__lowerCAmelCase							,   return_dict=__lowerCAmelCase							)

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

				a						=		self.classifier(__lowerCAmelCase							)

				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(__lowerCAmelCase							,   __lowerCAmelCase							)
					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(__lowerCAmelCase							,   __lowerCAmelCase							)

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

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






import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch

     from transformers.generation import DisjunctiveConstraint



@require_torch
class 		_lowercase		(       unittest.TestCase	):

   def 		A							(      self			:       Union[str, Any]							)       ->				int:


    """simple docstring"""






    a						=		[[1, 2, 4], [1, 2, 3, 4]]
    a						=		DisjunctiveConstraint(__lowerCAmelCase							)
    self.assertTrue(isinstance(dc.token_ids							,   __lowerCAmelCase							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]							)							)

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint([torch.LongTensor([1, 2, 4]							), torch.LongTensor([1, 2, 3, 4, 5]							)]							)

   def 		A							(      self			:       Tuple							)       ->				Dict:


    """simple docstring"""






    a						=		[[1, 2], [1, 2, 3, 4]]

    with self.assertRaises(__lowerCAmelCase							):
     DisjunctiveConstraint(__lowerCAmelCase							)  # fails here

   def 		A							(      self			:       int							)       ->				Any:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    a						=		stepped is True and completed is False and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(3							)
    a						=		stepped is True and completed is True and reset is False
    self.assertTrue(__lowerCAmelCase							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 3]							)




   def 		A							(      self			:       List[Any]							)       ->				List[Any]:


    """simple docstring"""






    a						=		[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

    a						=		DisjunctiveConstraint(__lowerCAmelCase							)

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(4							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.current_seq == [1, 2, 4]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.current_seq == [1, 2, 4, 5]							)

    dc.reset()

    a		,  a		,  a						=		dc.update(1							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 3							)
    self.assertTrue(dc.current_seq == [1]							)

    a		,  a		,  a						=		dc.update(2							)
    self.assertTrue(not dc.completed							)
    self.assertTrue(dc.remaining() == 2							)
    self.assertTrue(dc.current_seq == [1, 2]							)

    a		,  a		,  a						=		dc.update(5							)
    self.assertTrue(dc.completed							)  # Completed!
    self.assertTrue(dc.remaining() == 0							)
    self.assertTrue(dc.current_seq == [1, 2, 5]							)

