


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[int]		=				logging.get_logger(__name__)

# General docstring
a : List[Any]		=				'''RegNetConfig'''

# Base docstring
a : Optional[int]		=				'''facebook/regnet-y-040'''
a : List[Any]		=				[1, 1088, 7, 7]

# Image classification docstring
a : List[Any]		=				'''facebook/regnet-y-040'''
a : List[Any]		=				'''tabby, tabby cat'''

a : Union[str, Any]		=				[
    '''facebook/regnet-y-040''',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ = 3 ,       snake_case_ = 1 ,       snake_case_ = 1 ,       snake_case_ = "relu" ,       )   ->			Tuple:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   self.convolution(snake_case_						)
								__lowercase 					=   self.normalization(snake_case_						)
								__lowercase 					=   self.activation(snake_case_						)
								return hidden_state




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								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 ,       snake_case_						)   ->			List[Any]:







								'''simple docstring'''

								__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(snake_case_						)
								return hidden_state




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ = 2						)   ->			Union[str, Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   nn.Convad(snake_case_ ,       snake_case_ ,       kernel_size=1 ,       stride=snake_case_ ,       bias=snake_case_						)
								__lowercase 					=   nn.BatchNormad(snake_case_						)






					def A      (				self ,       snake_case_						)   ->			Tensor:







								'''simple docstring'''

								__lowercase 					=   self.convolution(snake_case_						)
								__lowercase 					=   self.normalization(snake_case_						)
								return hidden_state




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()

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






					def A      (				self ,       snake_case_						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   self.pooler(snake_case_						)
								__lowercase 					=   self.attention(snake_case_						)
								__lowercase 					=   hidden_state * attention
								return hidden_state




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ = 1						)   ->			Optional[int]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

								__lowercase 					=   hidden_state
								__lowercase 					=   self.layer(snake_case_						)
								__lowercase 					=   self.shortcut(snake_case_						)
								hidden_state += residual
								__lowercase 					=   self.activation(snake_case_						)
								return hidden_state




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ = 1						)   ->			List[str]:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   hidden_state
								__lowercase 					=   self.layer(snake_case_						)
								__lowercase 					=   self.shortcut(snake_case_						)
								hidden_state += residual
								__lowercase 					=   self.activation(snake_case_						)
								return hidden_state




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_ ,       snake_case_ ,       snake_case_ ,       snake_case_ = 2 ,       snake_case_ = 2 ,       )   ->			int:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_						)   ->			Tuple:







								'''simple docstring'''

								__lowercase 					=   self.layers(snake_case_						)
								return hidden_state




class 	lowerCamelCase_       (				nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_						)   ->			Dict:







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_ = False ,       snake_case_ = True						)   ->			BaseModelOutputWithNoAttention:







								'''simple docstring'''

								__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(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=snake_case_ ,       hidden_states=snake_case_						)




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				RegNetConfig
					__UpperCAmelCase            =				"regnet"
					__UpperCAmelCase            =				"pixel_values"
					__UpperCAmelCase            =				True






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







								'''simple docstring'''

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






					def A      (				self ,       snake_case_ ,       snake_case_=False						)   ->			Dict:







								'''simple docstring'''

								if isinstance(snake_case_ ,       snake_case_						):
											__lowercase 					=   value




a : Optional[int]		=				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 : Dict		=				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."     ,     lowerCAmelCase__     ,     )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_						)   ->			str:







								'''simple docstring'''

								super().__init__(snake_case_						)
								__lowercase 					=   config
								__lowercase 					=   RegNetEmbeddings(snake_case_						)
								__lowercase 					=   RegNetEncoder(snake_case_						)
								__lowercase 					=   nn.AdaptiveAvgPoolad((1, 1)						)
								# Initialize weights and apply final processing
								self.post_init()






					@add_start_docstrings_to_model_forward(snake_case_						)
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC ,       output_type=snake_case_ ,       config_class=_CONFIG_FOR_DOC ,       modality='''vision''' ,       expected_output=_EXPECTED_OUTPUT_SHAPE ,       )
					def A      (				self ,       snake_case_ ,       snake_case_ = None ,       snake_case_ = None						)   ->			BaseModelOutputWithPoolingAndNoAttention:







								'''simple docstring'''

								__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(snake_case_						)

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

								__lowercase 					=   encoder_outputs[0]

								__lowercase 					=   self.pooler(snake_case_						)

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

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



					'''simple docstring'''







					def __init__(				self ,       snake_case_						)   ->			int:







								'''simple docstring'''

								super().__init__(snake_case_						)
								__lowercase 					=   config.num_labels
								__lowercase 					=   RegNetModel(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(snake_case_						)
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT ,       output_type=snake_case_ ,       config_class=_CONFIG_FOR_DOC ,       expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,       )
					def A      (				self ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = None ,       snake_case_ = None ,       )   ->			ImageClassifierOutputWithNoAttention:







								'''simple docstring'''

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

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

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

								__lowercase 					=   self.classifier(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(snake_case_ ,       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(snake_case_ ,       snake_case_						)

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

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






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}'  )
			model.save_pretrained(_UpperCamelCase  )


if __name__ == "__main__":
	a : Optional[int]		=				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(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


