
"""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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


_lowercase							      = logging.get_logger(__name__)

# General docstring
_lowercase							      = '''ResNetConfig'''

# Base docstring
_lowercase							      = '''microsoft/resnet-50'''
_lowercase							      = [1, 20_48, 7, 7]

# Image classification docstring
_lowercase							      = '''microsoft/resnet-50'''
_lowercase							      = '''tiger cat'''

_lowercase							      = [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]







class  lowerCAmelCase_ (					nn.Module			):



						'''simple docstring'''


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




						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					Tensor					)   ->      Tensor:
							A        =      self.convolution(A_					)
							A        =      self.normalization(A_					)
							A        =      self.activation(A_					)
							return hidden_state







class  lowerCAmelCase_ (					nn.Module			):



						'''simple docstring'''


						def __init__(       self   :					List[Any]     ,A_   :					ResNetConfig					)   ->      Union[str, Any]:
							super().__init__()
							A        =      ResNetConvLayer(
							    config.num_channels     ,config.embedding_size     ,kernel_size=7     ,stride=2     ,activation=config.hidden_act					)
							A        =      nn.MaxPoolad(kernel_size=3     ,stride=2     ,padding=1					)
							A        =      config.num_channels




						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					Tensor					)   ->      Tensor:
							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(A_					)
							A        =      self.pooler(A_					)
							return embedding







class  lowerCAmelCase_ (					nn.Module			):



						'''simple docstring'''


						def __init__(       self   :					List[str]     ,A_   :					int     ,A_   :					int     ,A_   :					int = 2					)   ->      Optional[int]:
							super().__init__()
							A        =      nn.Convad(A_     ,A_     ,kernel_size=1     ,stride=A_     ,bias=A_					)
							A        =      nn.BatchNormad(A_					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Tensor					)   ->      Tensor:
							A        =      self.convolution(A_					)
							A        =      self.normalization(A_					)
							return hidden_state







class  lowerCAmelCase_ (					nn.Module			):



						'''simple docstring'''


						def __init__(       self   :					str     ,A_   :					int     ,A_   :					int     ,A_   :					int = 1     ,A_   :					str = "relu"					)   ->      Dict:
							super().__init__()
							A        =      in_channels != out_channels or stride != 1
							A        =      (
							    ResNetShortCut(A_     ,A_     ,stride=A_					) if should_apply_shortcut else nn.Identity()
							)
							A        =      nn.Sequential(
							    ResNetConvLayer(A_     ,A_     ,stride=A_					)     ,ResNetConvLayer(A_     ,A_     ,activation=A_					)     ,)
							A        =      ACTaFN[activation]




						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]     ,A_   :					Union[str, Any]					)   ->      Optional[Any]:
							A        =      hidden_state
							A        =      self.layer(A_					)
							A        =      self.shortcut(A_					)
							hidden_state += residual
							A        =      self.activation(A_					)
							return hidden_state







class  lowerCAmelCase_ (					nn.Module			):



						'''simple docstring'''


						def __init__(       self   :					Optional[Any]     ,A_   :					int     ,A_   :					int     ,A_   :					int = 1     ,A_   :					str = "relu"     ,A_   :					int = 4					)   ->      Dict:
							super().__init__()
							A        =      in_channels != out_channels or stride != 1
							A        =      out_channels // reduction
							A        =      (
							    ResNetShortCut(A_     ,A_     ,stride=A_					) if should_apply_shortcut else nn.Identity()
							)
							A        =      nn.Sequential(
							    ResNetConvLayer(A_     ,A_     ,kernel_size=1					)     ,ResNetConvLayer(A_     ,A_     ,stride=A_					)     ,ResNetConvLayer(A_     ,A_     ,kernel_size=1     ,activation=A_					)     ,)
							A        =      ACTaFN[activation]




						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					str					)   ->      List[Any]:
							A        =      hidden_state
							A        =      self.layer(A_					)
							A        =      self.shortcut(A_					)
							hidden_state += residual
							A        =      self.activation(A_					)
							return hidden_state







class  lowerCAmelCase_ (					nn.Module			):



						'''simple docstring'''


						def __init__(       self   :					List[str]     ,A_   :					ResNetConfig     ,A_   :					int     ,A_   :					int     ,A_   :					int = 2     ,A_   :					int = 2     ,)   ->      Tuple:
							super().__init__()

							A        =      ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

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




						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					Tensor					)   ->      Tensor:
							A        =      input
							for layer in self.layers:
								A        =      layer(A_					)
							return hidden_state







class  lowerCAmelCase_ (					nn.Module			):



						'''simple docstring'''


						def __init__(       self   :					Optional[int]     ,A_   :					ResNetConfig					)   ->      Dict:
							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(
							    ResNetStage(
							        A_     ,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(A_     ,config.depths[1:]					):
								self.stages.append(ResNetStage(A_     ,A_     ,A_     ,depth=A_					)					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					str     ,A_   :					Tensor     ,A_   :					bool = False     ,A_   :					bool = 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(A_					)

							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=A_     ,hidden_states=A_     ,)







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			int					=			ResNetConfig
						_lowerCamelCase:			List[Any]					=			'''resnet'''
						_lowerCamelCase:			int					=			'''pixel_values'''
						_lowerCamelCase:			int					=			True


						def   _SCREAMING_SNAKE_CASE					(       self   :					Union[str, Any]     ,A_   :					Any					)   ->      Any:
							if isinstance(A_     ,nn.Convad					):
								nn.init.kaiming_normal_(module.weight     ,mode='fan_out'     ,nonlinearity='relu'					)
							elif isinstance(A_     ,(nn.BatchNormad, nn.GroupNorm)					):
								nn.init.constant_(module.weight     ,1					)
								nn.init.constant_(module.bias     ,0					)




						def   _SCREAMING_SNAKE_CASE					(       self   :					List[Any]     ,A_   :					List[Any]     ,A_   :					Tuple=False					)   ->      str:
							if isinstance(A_     ,A_					):
								A        =      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 ([`ResNetConfig`]): 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 [`~utils.ModelOutput`] instead of a plain tuple.
'''







@add_start_docstrings(
    '''The bare ResNet model outputting raw features without any specific head on top.'''						,    _lowercase						,    )
class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						def __init__(       self   :					List[Any]     ,A_   :					Dict					)   ->      Dict:
							super().__init__(A_					)
							A        =      config
							A        =      ResNetEmbeddings(A_					)
							A        =      ResNetEncoder(A_					)
							A        =      nn.AdaptiveAvgPoolad((1, 1)					)
							# Initialize weights and apply final processing
							self.post_init()




						@add_start_docstrings_to_model_forward(A_					)
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC     ,output_type=A_     ,config_class=_CONFIG_FOR_DOC     ,modality='vision'     ,expected_output=_EXPECTED_OUTPUT_SHAPE     ,)
						def   _SCREAMING_SNAKE_CASE					(       self   :					Any     ,A_   :					Tensor     ,A_   :					Optional[bool] = None     ,A_   :					Optional[bool] = 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(A_					)

							A        =      self.encoder(
							    A_     ,output_hidden_states=A_     ,return_dict=A_					)

							A        =      encoder_outputs[0]

							A        =      self.pooler(A_					)

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

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







@add_start_docstrings(
    '''
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    '''						,    _lowercase						,    )
class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						def __init__(       self   :					List[Any]     ,A_   :					Any					)   ->      List[Any]:
							super().__init__(A_					)
							A        =      config.num_labels
							A        =      ResNetModel(A_					)
							# 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(A_					)
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT     ,output_type=A_     ,config_class=_CONFIG_FOR_DOC     ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT     ,)
						def   _SCREAMING_SNAKE_CASE					(       self   :					List[str]     ,A_   :					Optional[torch.FloatTensor] = None     ,A_   :					Optional[torch.LongTensor] = None     ,A_   :					Optional[bool] = None     ,A_   :					Optional[bool] = None     ,)   ->      ImageClassifierOutputWithNoAttention:
							A        =      return_dict if return_dict is not None else self.config.use_return_dict

							A        =      self.resnet(A_     ,output_hidden_states=A_     ,return_dict=A_					)

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

							A        =      self.classifier(A_					)

							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(A_     ,A_					)
								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(A_     ,A_					)

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

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







@add_start_docstrings(
    '''
    ResNet backbone, to be used with frameworks like DETR and MaskFormer.
    '''						,    _lowercase						,    )
class  lowerCAmelCase_ (					_lowercase						,    _lowercase			):



						'''simple docstring'''


						def __init__(       self   :					int     ,A_   :					Union[str, Any]					)   ->      Optional[int]:
							super().__init__(A_					)
							super()._init_backbone(A_					)

							A        =      [config.embedding_size] + config.hidden_sizes
							A        =      ResNetEmbeddings(A_					)
							A        =      ResNetEncoder(A_					)

							# initialize weights and apply final processing
							self.post_init()




						@add_start_docstrings_to_model_forward(A_					)
						@replace_return_docstrings(output_type=A_     ,config_class=_CONFIG_FOR_DOC					)
						def   _SCREAMING_SNAKE_CASE					(       self   :					Tuple     ,A_   :					Tensor     ,A_   :					Optional[bool] = None     ,A_   :					Optional[bool] = None					)   ->      BackboneOutput:
							A        =      return_dict if return_dict is not None else self.config.use_return_dict
							A        =      (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)

							A        =      self.embedder(A_					)

							A        =      self.encoder(A_     ,output_hidden_states=A_     ,return_dict=A_					)

							A        =      outputs.hidden_states

							A        =      ()
							for idx, stage in enumerate(self.stage_names					):
								if stage in self.out_features:
									feature_maps += (hidden_states[idx],)

							if not return_dict:
								A        =      (feature_maps,)
								if output_hidden_states:
									output += (outputs.hidden_states,)
								return output

							return BackboneOutput(
							    feature_maps=A_     ,hidden_states=outputs.hidden_states if output_hidden_states else None     ,attentions=A_     ,)
"""simple docstring"""




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							AlbertConfig.from_json_file(UpperCamelCase__ )
							print(F'''Building PyTorch model from configuration: {config}''' )
							A__		   =							AlbertForPreTraining(UpperCamelCase__ )

							# Load weights from tf checkpoint
							load_tf_weights_in_albert(UpperCamelCase__				,			UpperCamelCase__				,			UpperCamelCase__ )

							# Save pytorch-model
							print(F'''Save PyTorch model to {pytorch_dump_path}''' )
							torch.save(model.state_dict()				,			UpperCamelCase__ )


if __name__ == "__main__":
				__lowerCamelCase							 =     argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--albert_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained ALBERT model. \n"
				        "This specifies the model architecture."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__lowerCamelCase							 =     parser.parse_args()
				convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





