

from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


lowerCAmelCase__		      =       logging.get_logger(__name__)


# General docstring
lowerCAmelCase__		      =       """MobileNetV1Config"""

# Base docstring
lowerCAmelCase__		      =       """google/mobilenet_v1_1.0_224"""
lowerCAmelCase__		      =       [1, 1_0_2_4, 7, 7]

# Image classification docstring
lowerCAmelCase__		      =       """google/mobilenet_v1_1.0_224"""
lowerCAmelCase__		      =       """tabby, tabby cat"""


lowerCAmelCase__		      =       [
    """google/mobilenet_v1_1.0_224""",
    """google/mobilenet_v1_0.75_192""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Any		,				SCREAMING_SNAKE_CASE_:							Dict		,				SCREAMING_SNAKE_CASE_:							Optional[Any]=None					)	->					Dict:




				'''simple docstring'''





				A__						=	{}

				if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					):
								A__						=	model.mobilenet_va
				else:
								A__						=	model

				A__						=	"MobilenetV1/Conv2d_0/"
				A__						=	backbone.conv_stem.convolution.weight
				A__						=	backbone.conv_stem.normalization.bias
				A__						=	backbone.conv_stem.normalization.weight
				A__						=	backbone.conv_stem.normalization.running_mean
				A__						=	backbone.conv_stem.normalization.running_var

				for i in range(1_3					):
								A__						=	i + 1
								A__						=	i * 2

								A__						=	backbone.layer[pt_index]
								A__						=	F'MobilenetV1/Conv2d_{tf_index}_depthwise/'
								A__						=	pointer.convolution.weight
								A__						=	pointer.normalization.bias
								A__						=	pointer.normalization.weight
								A__						=	pointer.normalization.running_mean
								A__						=	pointer.normalization.running_var

								A__						=	backbone.layer[pt_index + 1]
								A__						=	F'MobilenetV1/Conv2d_{tf_index}_pointwise/'
								A__						=	pointer.convolution.weight
								A__						=	pointer.normalization.bias
								A__						=	pointer.normalization.weight
								A__						=	pointer.normalization.running_mean
								A__						=	pointer.normalization.running_var

				if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					):
								A__						=	"MobilenetV1/Logits/Conv2d_1c_1x1/"
								A__						=	model.classifier.weight
								A__						=	model.classifier.bias

				return tf_to_pt_map



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							List[Any]		,				SCREAMING_SNAKE_CASE_:							int		,				SCREAMING_SNAKE_CASE_:							str					)	->					Union[str, Any]:




				'''simple docstring'''





				try:
								import numpy as np
								import tensorflow as tf
				except ImportError:
								logger.error(
								    "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
								    "https://www.tensorflow.org/install/ for installation instructions."					)
								raise

				# Load weights from TF model
				A__						=	tf.train.list_variables(SCREAMING_SNAKE_CASE_					)
				A__						=	{}
				for name, shape in init_vars:
								logger.info(F'Loading TF weight {name} with shape {shape}'					)
								A__						=	tf.train.load_variable(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
								A__						=	array

				# Build TF to PyTorch weights loading map
				A__						=	_build_tf_to_pytorch_map(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)

				for name, pointer in tf_to_pt_map.items():
								logger.info(F'Importing {name}'					)
								if name not in tf_weights:
												logger.info(F'{name} not in tf pre-trained weights, skipping'					)
												continue

								A__						=	tf_weights[name]

								if "depthwise_weights" in name:
												logger.info("Transposing depthwise"					)
												A__						=	np.transpose(SCREAMING_SNAKE_CASE_		,				(2, 3, 0, 1)					)
								elif "weights" in name:
												logger.info("Transposing"					)
												if len(pointer.shape					) == 2:  # copying into linear layer
																A__						=	array.squeeze().transpose()
												else:
																A__						=	np.transpose(SCREAMING_SNAKE_CASE_		,				(3, 2, 0, 1)					)

								if pointer.shape != array.shape:
												raise ValueError(F'Pointer shape {pointer.shape} and array shape {array.shape} mismatched'					)

								logger.info(F'Initialize PyTorch weight {name} {array.shape}'					)
								A__						=	torch.from_numpy(SCREAMING_SNAKE_CASE_					)

								tf_weights.pop(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_					)
								tf_weights.pop(name + "/RMSProp"		,				SCREAMING_SNAKE_CASE_					)
								tf_weights.pop(name + "/RMSProp_1"		,				SCREAMING_SNAKE_CASE_					)
								tf_weights.pop(name + "/ExponentialMovingAverage"		,				SCREAMING_SNAKE_CASE_					)

				logger.info(F'Weights not copied to PyTorch model: {", ".join(tf_weights.keys()					)}'					)
				return model



def     lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							torch.Tensor		,				SCREAMING_SNAKE_CASE_:							nn.Convad					)	->					torch.Tensor:




				'''simple docstring'''





				A__			, A__						=	features.shape[-2:]
				A__			, A__						=	conv_layer.stride
				A__			, A__						=	conv_layer.kernel_size

				if in_height % stride_height == 0:
								A__						=	max(kernel_height - stride_height		,				0					)
				else:
								A__						=	max(kernel_height - (in_height % stride_height)		,				0					)

				if in_width % stride_width == 0:
								A__						=	max(kernel_width - stride_width		,				0					)
				else:
								A__						=	max(kernel_width - (in_width % stride_width)		,				0					)

				A__						=	pad_along_width // 2
				A__						=	pad_along_width - pad_left
				A__						=	pad_along_height // 2
				A__						=	pad_along_height - pad_top

				A__						=	(pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				"constant"		,				0.0					)

class     a__  (    nn.Module	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase					,					lowercase = 1					,					lowercase = 1					,					lowercase = False					,					lowercase = True					,					lowercase = True					,					)     ->  None:
							'''simple docstring'''






							super().__init__()
							A__						=	config

							if in_channels % groups != 0:
											raise ValueError(F'Input channels ({in_channels}) are not divisible by {groups} groups.'				)
							if out_channels % groups != 0:
											raise ValueError(F'Output channels ({out_channels}) are not divisible by {groups} groups.'				)

							A__						=	0 if config.tf_padding else int((kernel_size - 1) / 2				)

							A__						=	nn.Convad(
							    in_channels=lowercase					,					out_channels=lowercase					,					kernel_size=lowercase					,					stride=lowercase					,					padding=lowercase					,					groups=lowercase					,					bias=lowercase					,					padding_mode="zeros"					,					)

							if use_normalization:
											A__						=	nn.BatchNormad(
											    num_features=lowercase					,					eps=config.layer_norm_eps					,					momentum=0.9997					,					affine=lowercase					,					track_running_stats=lowercase					,					)
							else:
											A__						=	None

							if use_activation:
											if isinstance(lowercase					,					lowercase				):
															A__						=	ACTaFN[use_activation]
											elif isinstance(config.hidden_act					,					lowercase				):
															A__						=	ACTaFN[config.hidden_act]
											else:
															A__						=	config.hidden_act
							else:
											A__						=	None


			def    UpperCamelCase						(			self					,					lowercase				)     ->  torch.Tensor:
							'''simple docstring'''






							if self.config.tf_padding:
											A__						=	apply_tf_padding(lowercase					,					self.convolution				)
							A__						=	self.convolution(lowercase				)
							if self.normalization is not None:
											A__						=	self.normalization(lowercase				)
							if self.activation is not None:
											A__						=	self.activation(lowercase				)
							return features

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				MobileNetVaConfig
			__lowerCamelCase       				=				load_tf_weights_in_mobilenet_va
			__lowerCamelCase       				=				'mobilenet_v1'
			__lowerCamelCase       				=				'pixel_values'
			__lowerCamelCase       				=				False


			def    UpperCamelCase						(			self					,					lowercase				)     ->  None:
							'''simple docstring'''






							if isinstance(lowercase					,					(nn.Linear, nn.Convad)				):
											module.weight.data.normal_(mean=0.0					,					std=self.config.initializer_range				)
											if module.bias is not None:
															module.bias.data.zero_()
							elif isinstance(lowercase					,					nn.BatchNormad				):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0				)

lowerCAmelCase__		      =       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 ([`MobileNetV1Config`]): 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.
"""

lowerCAmelCase__		      =       R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`MobileNetV1ImageProcessor.__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 MobileNetV1 model outputting raw hidden-states without any specific head on top.'							,		snake_case							,		)
class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase					,					lowercase = True				)     ->  Union[str, Any]:
							'''simple docstring'''






							super().__init__(lowercase				)
							A__						=	config

							A__						=	32
							A__						=	max(int(depth * config.depth_multiplier				)					,					config.min_depth				)

							A__						=	MobileNetVaConvLayer(
							    lowercase					,					in_channels=config.num_channels					,					out_channels=lowercase					,					kernel_size=3					,					stride=2					,					)

							A__						=	[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

							A__						=	nn.ModuleList()
							for i in range(13				):
											A__						=	out_channels

											if strides[i] == 2 or i == 0:
															depth *= 2
															A__						=	max(int(depth * config.depth_multiplier				)					,					config.min_depth				)

											self.layer.append(
											    MobileNetVaConvLayer(
											        lowercase					,					in_channels=lowercase					,					out_channels=lowercase					,					kernel_size=3					,					stride=strides[i]					,					groups=lowercase					,					)				)

											self.layer.append(
											    MobileNetVaConvLayer(
											        lowercase					,					in_channels=lowercase					,					out_channels=lowercase					,					kernel_size=1					,					)				)

							A__						=	nn.AdaptiveAvgPoolad((1, 1)				) if add_pooling_layer else None

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


			def    UpperCamelCase						(			self					,					lowercase				)     ->  List[str]:
							'''simple docstring'''






							raise NotImplementedError


			@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    UpperCamelCase						(			self					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					)     ->  Union[tuple, 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

							if pixel_values is None:
											raise ValueError("You have to specify pixel_values"				)

							A__						=	self.conv_stem(lowercase				)

							A__						=	() if output_hidden_states else None

							for i, layer_module in enumerate(self.layer				):
											A__						=	layer_module(lowercase				)

											if output_hidden_states:
															A__						=	all_hidden_states + (hidden_states,)

							A__						=	hidden_states

							if self.pooler is not None:
											A__						=	torch.flatten(self.pooler(lowercase				)					,					start_dim=1				)
							else:
											A__						=	None

							if not return_dict:
											return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None				)

							return BaseModelOutputWithPoolingAndNoAttention(
							    last_hidden_state=lowercase					,					pooler_output=lowercase					,					hidden_states=lowercase					,					)

@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '							,		snake_case							,		)
class     a__  (    snake_case	):
			"""simple docstring"""







			def __init__(			self					,					lowercase				)     ->  None:
							'''simple docstring'''






							super().__init__(lowercase				)

							A__						=	config.num_labels
							A__						=	MobileNetVaModel(lowercase				)

							A__						=	self.mobilenet_va.layer[-1].convolution.out_channels

							# Classifier head
							A__						=	nn.Dropout(config.classifier_dropout_prob					,					inplace=lowercase				)
							A__						=	nn.Linear(lowercase					,					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    UpperCamelCase						(			self					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					)     ->  Union[tuple, ImageClassifierOutputWithNoAttention]:
							'''simple docstring'''






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

							A__						=	self.mobilenet_va(lowercase					,					output_hidden_states=lowercase					,					return_dict=lowercase				)

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

							A__						=	self.classifier(self.dropout(lowercase				)				)

							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(lowercase					,					lowercase				)
											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(lowercase					,					lowercase				)

							if not return_dict:
											A__						=	(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 typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   _UpperCamelCase :				pyspark.sql.DataFrame							,   _UpperCamelCase :				Optional[NamedSplit] = None							,   _UpperCamelCase :				Optional[Features] = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = False							,   _UpperCamelCase :				str = None							,   _UpperCamelCase :				bool = True							,   _UpperCamelCase :				str = "arrow"							,   **_UpperCamelCase :				Tuple							,   )      ->str:
									super().__init__(
									    split=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   keep_in_memory=_UpperCamelCase							,   streaming=_UpperCamelCase							,   **_UpperCamelCase							,   )
									snake_case_   =	load_from_cache_file
									snake_case_   =	file_format
									snake_case_   =	Spark(
									    df=_UpperCamelCase							,   features=_UpperCamelCase							,   cache_dir=_UpperCamelCase							,   working_dir=_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				int							)      ->Tuple:
									if self.streaming:
															return self.builder.as_streaming_dataset(split=self.split							)
									snake_case_   =	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
									self.builder.download_and_prepare(
									    download_mode=_UpperCamelCase							,   file_format=self._file_format							,   )
									return self.builder.as_dataset(split=self.split							)