




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


UpperCAmelCase_		:				Optional[int]									=     logging.get_logger(__name__)


# General docstring
UpperCAmelCase_		:				List[str]									=     "MobileNetV1Config"

# Base docstring
UpperCAmelCase_		:				Optional[int]									=     "google/mobilenet_v1_1.0_224"
UpperCAmelCase_		:				List[Any]									=     [1, 1024, 7, 7]

# Image classification docstring
UpperCAmelCase_		:				Tuple									=     "google/mobilenet_v1_1.0_224"
UpperCAmelCase_		:				List[str]									=     "tabby, tabby cat"


UpperCAmelCase_		:				List[Any]									=     [
    "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   ):
				__magic_name__	:	Any								={}

				if isinstance(lowerCamelCase	,     lowerCamelCase   ):
								__magic_name__	:	List[Any]								=model.mobilenet_va
				else:
								__magic_name__	:	List[str]								=model

				__magic_name__	:	Optional[int]								="""MobilenetV1/Conv2d_0/"""
				__magic_name__	:	List[str]								=backbone.conv_stem.convolution.weight
				__magic_name__	:	List[Any]								=backbone.conv_stem.normalization.bias
				__magic_name__	:	int								=backbone.conv_stem.normalization.weight
				__magic_name__	:	Union[str, Any]								=backbone.conv_stem.normalization.running_mean
				__magic_name__	:	List[str]								=backbone.conv_stem.normalization.running_var

				for i in range(13   ):
								__magic_name__	:	Union[str, Any]								=i + 1
								__magic_name__	:	Optional[int]								=i * 2

								__magic_name__	:	Dict								=backbone.layer[pt_index]
								__magic_name__	:	Tuple								=F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
								__magic_name__	:	List[Any]								=pointer.convolution.weight
								__magic_name__	:	Union[str, Any]								=pointer.normalization.bias
								__magic_name__	:	Tuple								=pointer.normalization.weight
								__magic_name__	:	str								=pointer.normalization.running_mean
								__magic_name__	:	str								=pointer.normalization.running_var

								__magic_name__	:	Any								=backbone.layer[pt_index + 1]
								__magic_name__	:	Union[str, Any]								=F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
								__magic_name__	:	Optional[int]								=pointer.convolution.weight
								__magic_name__	:	str								=pointer.normalization.bias
								__magic_name__	:	Optional[Any]								=pointer.normalization.weight
								__magic_name__	:	Any								=pointer.normalization.running_mean
								__magic_name__	:	str								=pointer.normalization.running_var

				if isinstance(lowerCamelCase	,     lowerCamelCase   ):
								__magic_name__	:	Dict								="""MobilenetV1/Logits/Conv2d_1c_1x1/"""
								__magic_name__	:	Tuple								=model.classifier.weight
								__magic_name__	:	List[Any]								=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
				__magic_name__	:	Dict								=tf.train.list_variables(lowerCamelCase   )
				__magic_name__	:	Union[str, Any]								={}
				for name, shape in init_vars:
								logger.info(F"Loading TF weight {name} with shape {shape}"   )
								__magic_name__	:	Tuple								=tf.train.load_variable(lowerCamelCase	,     lowerCamelCase   )
								__magic_name__	:	List[str]								=array

				# Build TF to PyTorch weights loading map
				__magic_name__	:	Union[str, Any]								=_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

								__magic_name__	:	Dict								=tf_weights[name]

								if "depthwise_weights" in name:
												logger.info("""Transposing depthwise"""   )
												__magic_name__	:	int								=np.transpose(lowerCamelCase	,     (2, 3, 0, 1)   )
								elif "weights" in name:
												logger.info("""Transposing"""   )
												if len(pointer.shape   ) == 2:  # copying into linear layer
																__magic_name__	:	Any								=array.squeeze().transpose()
												else:
																__magic_name__	:	Dict								=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}"   )
								__magic_name__	:	List[Any]								=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   ):
				__magic_name__				, __magic_name__	:	int								=features.shape[-2:]
				__magic_name__				, __magic_name__	:	int								=conv_layer.stride
				__magic_name__				, __magic_name__	:	Tuple								=conv_layer.kernel_size

				if in_height % stride_height == 0:
								__magic_name__	:	Dict								=max(kernel_height - stride_height	,     0   )
				else:
								__magic_name__	:	Any								=max(kernel_height - (in_height % stride_height)	,     0   )

				if in_width % stride_width == 0:
								__magic_name__	:	Optional[Any]								=max(kernel_width - stride_width	,     0   )
				else:
								__magic_name__	:	Any								=max(kernel_width - (in_width % stride_width)	,     0   )

				__magic_name__	:	int								=pad_along_width // 2
				__magic_name__	:	List[str]								=pad_along_width - pad_left
				__magic_name__	:	List[Any]								=pad_along_height // 2
				__magic_name__	:	Optional[int]								=pad_along_height - pad_top

				__magic_name__	:	Optional[Any]								=(pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(lowerCamelCase	,     lowerCamelCase	,     """constant"""	,     0.0   )




class 		__A      (			nn.Module			):

						def __init__(		self   :str    , __snake_case   :MobileNetVaConfig    , __snake_case   :int    , __snake_case   :int    , __snake_case   :int    , __snake_case   :Optional[int] = 1    , __snake_case   :Optional[int] = 1    , __snake_case   :bool = False    , __snake_case   :Optional[bool] = True    , __snake_case   :Optional[bool or str] = True    , ):
										'''simple docstring'''

										super().__init__()
										__magic_name__	:	Optional[Any]								=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." )

										__magic_name__	:	Any								=0 if config.tf_padding else int((kernel_size - 1) / 2 )

										__magic_name__	:	Any								=nn.Convad(
										    in_channels=__snake_case    , out_channels=__snake_case    , kernel_size=__snake_case    , stride=__snake_case    , padding=__snake_case    , groups=__snake_case    , bias=__snake_case    , padding_mode="""zeros"""    , )

										if use_normalization:
														__magic_name__	:	Union[str, Any]								=nn.BatchNormad(
														    num_features=__snake_case    , eps=config.layer_norm_eps    , momentum=0.9997    , affine=__snake_case    , track_running_stats=__snake_case    , )
										else:
														__magic_name__	:	int								=None

										if use_activation:
														if isinstance(__snake_case    , __snake_case ):
																		__magic_name__	:	Union[str, Any]								=ACTaFN[use_activation]
														elif isinstance(config.hidden_act    , __snake_case ):
																		__magic_name__	:	Any								=ACTaFN[config.hidden_act]
														else:
																		__magic_name__	:	List[Any]								=config.hidden_act
										else:
														__magic_name__	:	Any								=None


						def     A__						(		self   :Dict    , __snake_case   :torch.Tensor ):
										'''simple docstring'''

										if self.config.tf_padding:
														__magic_name__	:	Optional[int]								=apply_tf_padding(__snake_case    , self.convolution )
										__magic_name__	:	Any								=self.convolution(__snake_case )
										if self.normalization is not None:
														__magic_name__	:	Any								=self.normalization(__snake_case )
										if self.activation is not None:
														__magic_name__	:	Tuple								=self.activation(__snake_case )
										return features




class 		__A      (			UpperCamelCase__			):
						UpperCamelCase     						=					MobileNetVaConfig
						UpperCamelCase     						=					load_tf_weights_in_mobilenet_va
						UpperCamelCase     						=					"""mobilenet_v1"""
						UpperCamelCase     						=					"""pixel_values"""
						UpperCamelCase     						=					False

						def     A__						(		self   :Optional[Any]    , __snake_case   :Union[nn.Linear, nn.Convad] ):
										'''simple docstring'''

										if isinstance(__snake_case    , (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(__snake_case    , nn.BatchNormad ):
														module.bias.data.zero_()
														module.weight.data.fill_(1.0 )


UpperCAmelCase_		:				Optional[int]									=     R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

UpperCAmelCase_		:				Optional[Any]									=     R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"




@add_start_docstrings(
    """The bare MobileNetV1 model outputting raw hidden-states without any specific head on top."""					,						UpperCamelCase__					,						)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :str    , __snake_case   :MobileNetVaConfig    , __snake_case   :bool = True ):
										'''simple docstring'''

										super().__init__(__snake_case )
										__magic_name__	:	Optional[int]								=config

										__magic_name__	:	str								=32
										__magic_name__	:	Optional[int]								=max(int(depth * config.depth_multiplier )    , config.min_depth )

										__magic_name__	:	Optional[Any]								=MobileNetVaConvLayer(
										    __snake_case    , in_channels=config.num_channels    , out_channels=__snake_case    , kernel_size=3    , stride=2    , )

										__magic_name__	:	Optional[Any]								=[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

										__magic_name__	:	Union[str, Any]								=nn.ModuleList()
										for i in range(13 ):
														__magic_name__	:	str								=out_channels

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

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

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

										__magic_name__	:	Dict								=nn.AdaptiveAvgPoolad((1, 1) ) if add_pooling_layer else None

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

						def     A__						(		self   :str    , __snake_case   :List[str] ):
										'''simple docstring'''

										raise NotImplementedError


						@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   :Union[str, Any]    , __snake_case   :Optional[torch.Tensor] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[bool] = None    , ):
										'''simple docstring'''

										__magic_name__	:	Any								=(
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										__magic_name__	:	str								=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""" )

										__magic_name__	:	Dict								=self.conv_stem(__snake_case )

										__magic_name__	:	Any								=() if output_hidden_states else None

										for i, layer_module in enumerate(self.layer ):
														__magic_name__	:	int								=layer_module(__snake_case )

														if output_hidden_states:
																		__magic_name__	:	Optional[Any]								=all_hidden_states + (hidden_states,)

										__magic_name__	:	Optional[int]								=hidden_states

										if self.pooler is not None:
														__magic_name__	:	Union[str, Any]								=torch.flatten(self.pooler(__snake_case )    , start_dim=1 )
										else:
														__magic_name__	:	Dict								=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=__snake_case    , pooler_output=__snake_case    , hidden_states=__snake_case    , )




@add_start_docstrings(
    """
    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """					,						UpperCamelCase__					,						)
class 		__A      (			UpperCamelCase__			):

						def __init__(		self   :str    , __snake_case   :MobileNetVaConfig ):
										'''simple docstring'''

										super().__init__(__snake_case )

										__magic_name__	:	Union[str, Any]								=config.num_labels
										__magic_name__	:	str								=MobileNetVaModel(__snake_case )

										__magic_name__	:	Optional[int]								=self.mobilenet_va.layer[-1].convolution.out_channels

										# Classifier head
										__magic_name__	:	List[str]								=nn.Dropout(config.classifier_dropout_prob    , inplace=__snake_case )
										__magic_name__	:	Any								=nn.Linear(__snake_case    , 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   :Optional[Any]    , __snake_case   :Optional[torch.Tensor] = None    , __snake_case   :Optional[bool] = None    , __snake_case   :Optional[torch.Tensor] = None    , __snake_case   :Optional[bool] = None    , ):
										'''simple docstring'''

										__magic_name__	:	Dict								=return_dict if return_dict is not None else self.config.use_return_dict

										__magic_name__	:	Union[str, Any]								=self.mobilenet_va(__snake_case    , output_hidden_states=__snake_case    , return_dict=__snake_case )

										__magic_name__	:	Union[str, Any]								=outputs.pooler_output if return_dict else outputs[1]

										__magic_name__	:	int								=self.classifier(self.dropout(__snake_case ) )

										__magic_name__	:	Union[str, Any]								=None
										if labels is not None:
														if self.config.problem_type is None:
																		if self.num_labels == 1:
																						__magic_name__	:	Tuple								="""regression"""
																		elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																						__magic_name__	:	Optional[Any]								="""single_label_classification"""
																		else:
																						__magic_name__	:	Tuple								="""multi_label_classification"""

														if self.config.problem_type == "regression":
																		__magic_name__	:	Dict								=MSELoss()
																		if self.num_labels == 1:
																						__magic_name__	:	List[Any]								=loss_fct(logits.squeeze()    , labels.squeeze() )
																		else:
																						__magic_name__	:	List[Any]								=loss_fct(__snake_case    , __snake_case )
														elif self.config.problem_type == "single_label_classification":
																		__magic_name__	:	Tuple								=CrossEntropyLoss()
																		__magic_name__	:	List[str]								=loss_fct(logits.view(-1    , self.num_labels )    , labels.view(-1 ) )
														elif self.config.problem_type == "multi_label_classification":
																		__magic_name__	:	Tuple								=BCEWithLogitsLoss()
																		__magic_name__	:	str								=loss_fct(__snake_case    , __snake_case )

										if not return_dict:
														__magic_name__	:	Dict								=(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    , )










from json import JSONDecodeError  # Workaround for requests.exceptions.JSONDecodeError

import requests




def 		lowerCAmelCase_   (	lowerCamelCase = "isbn/0140328726"   ):
				__magic_name__	:	Optional[Any]								=olid.strip().strip("""/"""   )  # Remove leading/trailing whitespace & slashes
				if new_olid.count("""/"""   ) != 1:
								__magic_name__	:	Union[str, Any]								=F"{olid} is not a valid Open Library olid"
								raise ValueError(lowerCamelCase   )
				return requests.get(F"https://openlibrary.org/{new_olid}.json"   ).json()




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Dict								={
				    """title""": """Title""",
				    """publish_date""": """Publish date""",
				    """authors""": """Authors""",
				    """number_of_pages""": """Number of pages:""",
				    """first_sentence""": """First sentence""",
				    """isbn_10""": """ISBN (10)""",
				    """isbn_13""": """ISBN (13)""",
				}
				__magic_name__	:	List[Any]								={better_key: ol_book_data[key] for key, better_key in desired_keys.items()}
				__magic_name__	:	Dict								=[
				    get_openlibrary_data(author["""key"""]   )["""name"""] for author in data["""Authors"""]
				]
				__magic_name__	:	List[str]								=data["""First sentence"""]["""value"""]
				for key, value in data.items():
								if isinstance(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Tuple								=""", """.join(lowerCamelCase   )
				return data


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							while True:
														UpperCAmelCase_		:				List[Any]									=     input("\nEnter the ISBN code to search (or 'quit' to stop): ").strip()
														if isbn.lower() in ("", "q", "quit", "exit", "stop"):
																					break

														if len(isbn) not in (10, 13) or not isbn.isdigit():
																					print(F"""Sorry, {isbn} is not a valid ISBN.  Please, input a valid ISBN.""")
																					continue

														print(F"""\nSearching Open Library for ISBN: {isbn}...\n""")

														try:
																					UpperCAmelCase_		:				str									=     summarize_book(get_openlibrary_data(F"""isbn/{isbn}"""))
																					print("\n".join(F"""{key}: {value}""" for key, value in book_summary.items()))
														except JSONDecodeError:  # Workaround for requests.exceptions.RequestException:
																					print(F"""Sorry, there are no results for ISBN: {isbn}.""")




