
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


__A				=logging.get_logger(__name__)


# General docstring
__A				='''MobileNetV1Config'''

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

# Image classification docstring
__A				='''google/mobilenet_v1_1.0_224'''
__A				='''tabby, tabby cat'''


__A				=[
    '''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_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None    ):
				lowerCamelCase_						=			{}

				if isinstance(lowerCamelCase__	, lowerCamelCase__    ):
								lowerCamelCase_						=			model.mobilenet_va
				else:
								lowerCamelCase_						=			model

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

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

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

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

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

				return tf_to_pt_map





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				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
				lowerCamelCase_						=			tf.train.list_variables(lowerCamelCase__    )
				lowerCamelCase_						=			{}
				for name, shape in init_vars:
								logger.info(F'Loading TF weight {name} with shape {shape}'    )
								lowerCamelCase_						=			tf.train.load_variable(lowerCamelCase__	, lowerCamelCase__    )
								lowerCamelCase_						=			array

				# Build TF to PyTorch weights loading map
				lowerCamelCase_						=			_build_tf_to_pytorch_map(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )

				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

								lowerCamelCase_						=			tf_weights[name]

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

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

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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_	,    lowerCamelCase_						=			features.shape[-2:]
				lowerCamelCase_	,    lowerCamelCase_						=			conv_layer.stride
				lowerCamelCase_	,    lowerCamelCase_						=			conv_layer.kernel_size

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

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

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

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




class     _SCREAMING_SNAKE_CASE     (      nn.Module						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase = 1						,  lowercase = 1						,  lowercase = False						,  lowercase = True						,  lowercase = True						,  )				->       None:
					super().__init__()
					lowerCamelCase_						=			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.' )

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

					lowerCamelCase_						=			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:
									lowerCamelCase_						=			nn.BatchNormad(
									    num_features=lowercase						,  eps=config.layer_norm_eps						,  momentum=0.9_9_9_7						,  affine=lowercase						,  track_running_stats=lowercase						,  )
					else:
									lowerCamelCase_						=			None

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       torch.Tensor:
					if self.config.tf_padding:
									lowerCamelCase_						=			apply_tf_padding(lowercase						,  self.convolution )
					lowerCamelCase_						=			self.convolution(lowercase )
					if self.normalization is not None:
									lowerCamelCase_						=			self.normalization(lowercase )
					if self.activation is not None:
									lowerCamelCase_						=			self.activation(lowercase )
					return features




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = MobileNetVaConfig
	lowerCAmelCase__         = load_tf_weights_in_mobilenet_va
	lowerCAmelCase__         = 'mobilenet_v1'
	lowerCAmelCase__         = 'pixel_values'
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       None:
					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 )



__A				=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.
'''

__A				=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     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase = True )				->       Optional[Any]:
					super().__init__(lowercase )
					lowerCamelCase_						=			config

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

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

					lowerCamelCase_						=			[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

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

									if strides[i] == 2 or i == 0:
													depth *= 2
													lowerCamelCase_						=			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						,  ) )

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

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



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase )				->       Optional[int]:
					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     SCREAMING_SNAKE_CASE_(    self						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  )				->       Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:
					lowerCamelCase_						=			(
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					lowerCamelCase_						=			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" )

					lowerCamelCase_						=			self.conv_stem(lowercase )

					lowerCamelCase_						=			() if output_hidden_states else None

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

									if output_hidden_states:
													lowerCamelCase_						=			all_hidden_states + (hidden_states,)

					lowerCamelCase_						=			hidden_states

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



	def __init__(    self						,  lowercase )				->       None:
					super().__init__(lowercase )

					lowerCamelCase_						=			config.num_labels
					lowerCamelCase_						=			MobileNetVaModel(lowercase )

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

					# Classifier head
					lowerCamelCase_						=			nn.Dropout(config.classifier_dropout_prob						,  inplace=lowercase )
					lowerCamelCase_						=			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     SCREAMING_SNAKE_CASE_(    self						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  lowercase = None						,  )				->       Union[tuple, ImageClassifierOutputWithNoAttention]:
					lowerCamelCase_						=			return_dict if return_dict is not None else self.config.use_return_dict

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

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

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

					lowerCamelCase_						=			None
					if labels is not None:
									if self.config.problem_type is None:
													if self.num_labels == 1:
																	lowerCamelCase_						=			"regression"
													elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																	lowerCamelCase_						=			"single_label_classification"
													else:
																	lowerCamelCase_						=			"multi_label_classification"

									if self.config.problem_type == "regression":
													lowerCamelCase_						=			MSELoss()
													if self.num_labels == 1:
																	lowerCamelCase_						=			loss_fct(logits.squeeze()						,  labels.squeeze() )
													else:
																	lowerCamelCase_						=			loss_fct(lowercase						,  lowercase )
									elif self.config.problem_type == "single_label_classification":
													lowerCamelCase_						=			CrossEntropyLoss()
													lowerCamelCase_						=			loss_fct(logits.view(-1						,  self.num_labels )						,  labels.view(-1 ) )
									elif self.config.problem_type == "multi_label_classification":
													lowerCamelCase_						=			BCEWithLogitsLoss()
													lowerCamelCase_						=			loss_fct(lowercase						,  lowercase )

					if not return_dict:
									lowerCamelCase_						=			(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 TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A				={'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A				=[
			    '''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''WavLMForAudioFrameClassification''',
			    '''WavLMForCTC''',
			    '''WavLMForSequenceClassification''',
			    '''WavLMForXVector''',
			    '''WavLMModel''',
			    '''WavLMPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavlm import (
						    WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavLMForAudioFrameClassification,
						    WavLMForCTC,
						    WavLMForSequenceClassification,
						    WavLMForXVector,
						    WavLMModel,
						    WavLMPreTrainedModel,
						)

else:
			import sys

			__A				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


