
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_    :			Union[str, Any]    =	logging.get_logger(__name__)


# General docstring
lowerCamelCase_    :			str    =	'''MobileNetV1Config'''

# Base docstring
lowerCamelCase_    :			Union[str, Any]    =	'''google/mobilenet_v1_1.0_224'''
lowerCamelCase_    :			Union[str, Any]    =	[1, 10_24, 7, 7]

# Image classification docstring
lowerCamelCase_    :			str    =	'''google/mobilenet_v1_1.0_224'''
lowerCamelCase_    :			Union[str, Any]    =	'''tabby, tabby cat'''


lowerCamelCase_    :			Tuple    =	[
    '''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 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=None    )	->						int:
				UpperCamelCase_:				List[str]						=    {}

				if isinstance(__snake_case  ,     __snake_case    ):
								UpperCamelCase_:				int						=    model.mobilenet_va
				else:
								UpperCamelCase_:				Union[str, Any]						=    model

				UpperCamelCase_:				Optional[int]						=    """MobilenetV1/Conv2d_0/"""
				UpperCamelCase_:				int						=    backbone.conv_stem.convolution.weight
				UpperCamelCase_:				List[str]						=    backbone.conv_stem.normalization.bias
				UpperCamelCase_:				List[str]						=    backbone.conv_stem.normalization.weight
				UpperCamelCase_:				Dict						=    backbone.conv_stem.normalization.running_mean
				UpperCamelCase_:				str						=    backbone.conv_stem.normalization.running_var

				for i in range(13    ):
								UpperCamelCase_:				Optional[int]						=    i + 1
								UpperCamelCase_:				List[Any]						=    i * 2

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

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

				if isinstance(__snake_case  ,     __snake_case    ):
								UpperCamelCase_:				Optional[int]						=    """MobilenetV1/Logits/Conv2d_1c_1x1/"""
								UpperCamelCase_:				str						=    model.classifier.weight
								UpperCamelCase_:				List[str]						=    model.classifier.bias

				return tf_to_pt_map







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Dict:
				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
				UpperCamelCase_:				Optional[Any]						=    tf.train.list_variables(__snake_case    )
				UpperCamelCase_:				List[Any]						=    {}
				for name, shape in init_vars:
								logger.info(F'''Loading TF weight {name} with shape {shape}'''    )
								UpperCamelCase_:				Dict						=    tf.train.load_variable(__snake_case  ,     __snake_case    )
								UpperCamelCase_:				List[str]						=    array

				# Build TF to PyTorch weights loading map
				UpperCamelCase_:				List[Any]						=    _build_tf_to_pytorch_map(__snake_case  ,     __snake_case  ,     __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

								UpperCamelCase_:				Any						=    tf_weights[name]

								if "depthwise_weights" in name:
												logger.info("""Transposing depthwise"""    )
												UpperCamelCase_:				Optional[int]						=    np.transpose(__snake_case  ,     (2, 3, 0, 1)    )
								elif "weights" in name:
												logger.info("""Transposing"""    )
												if len(pointer.shape    ) == 2:  # copying into linear layer
																UpperCamelCase_:				Optional[Any]						=    array.squeeze().transpose()
												else:
																UpperCamelCase_:				List[Any]						=    np.transpose(__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}'''    )
								UpperCamelCase_:				Optional[int]						=    torch.from_numpy(__snake_case    )

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

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







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_, UpperCamelCase_:				Dict						=    features.shape[-2:]
				UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    conv_layer.stride
				UpperCamelCase_, UpperCamelCase_:				str						=    conv_layer.kernel_size

				if in_height % stride_height == 0:
								UpperCamelCase_:				Union[str, Any]						=    max(kernel_height - stride_height  ,     0    )
				else:
								UpperCamelCase_:				str						=    max(kernel_height - (in_height % stride_height)  ,     0    )

				if in_width % stride_width == 0:
								UpperCamelCase_:				Any						=    max(kernel_width - stride_width  ,     0    )
				else:
								UpperCamelCase_:				int						=    max(kernel_width - (in_width % stride_width)  ,     0    )

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

				UpperCamelCase_:				List[str]						=    (pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(__snake_case  ,     __snake_case  ,     """constant"""  ,     0.0    )

class 							_UpperCamelCase					(       nn.Module	):






			'''simple docstring'''

			def __init__(							self			:							Dict    ,						snake_case_			:							int    ,						snake_case_			:							List[Any]    ,						snake_case_			:							Dict    ,						snake_case_			:							Any    ,						snake_case_			:							List[Any] = 1    ,						snake_case_			:							List[Any] = 1    ,						snake_case_			:							List[str] = False    ,						snake_case_			:							Union[str, Any] = True    ,						snake_case_			:							List[str] = True    ,						):
							super().__init__()
							UpperCamelCase_:				Union[str, 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.'''   )

							UpperCamelCase_:				List[str]						=    0 if config.tf_padding else int((kernel_size - 1) / 2   )

							UpperCamelCase_:				Tuple						=    nn.Convad(
							    in_channels=__UpperCamelCase    ,						out_channels=__UpperCamelCase    ,						kernel_size=__UpperCamelCase    ,						stride=__UpperCamelCase    ,						padding=__UpperCamelCase    ,						groups=__UpperCamelCase    ,						bias=__UpperCamelCase    ,						padding_mode="""zeros"""    ,						)

							if use_normalization:
											UpperCamelCase_:				Tuple						=    nn.BatchNormad(
											    num_features=__UpperCamelCase    ,						eps=config.layer_norm_eps    ,						momentum=0.9997    ,						affine=__UpperCamelCase    ,						track_running_stats=__UpperCamelCase    ,						)
							else:
											UpperCamelCase_:				int						=    None

							if use_activation:
											if isinstance(__UpperCamelCase    ,						__UpperCamelCase   ):
															UpperCamelCase_:				Optional[Any]						=    ACTaFN[use_activation]
											elif isinstance(config.hidden_act    ,						__UpperCamelCase   ):
															UpperCamelCase_:				Any						=    ACTaFN[config.hidden_act]
											else:
															UpperCamelCase_:				Dict						=    config.hidden_act
							else:
											UpperCamelCase_:				Optional[Any]						=    None




			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							List[str]   ):
							if self.config.tf_padding:
											UpperCamelCase_:				Optional[Any]						=    apply_tf_padding(__UpperCamelCase    ,						self.convolution   )
							UpperCamelCase_:				str						=    self.convolution(__UpperCamelCase   )
							if self.normalization is not None:
											UpperCamelCase_:				Union[str, Any]						=    self.normalization(__UpperCamelCase   )
							if self.activation is not None:
											UpperCamelCase_:				Optional[int]						=    self.activation(__UpperCamelCase   )
							return features




class 							_UpperCamelCase					(       a_	):






			'''simple docstring'''

			__UpperCamelCase   :    Optional[int]          =			MobileNetVaConfig
			__UpperCamelCase   :    List[Any]          =			load_tf_weights_in_mobilenet_va
			__UpperCamelCase   :    Tuple          =			"""mobilenet_v1"""
			__UpperCamelCase   :    Any          =			"""pixel_values"""
			__UpperCamelCase   :    Dict          =			False

			def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							List[Any]   ):
							if isinstance(__UpperCamelCase    ,						(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(__UpperCamelCase    ,						nn.BatchNormad   ):
											module.bias.data.zero_()
											module.weight.data.fill_(1.0   )




lowerCamelCase_    :			Union[str, Any]    =	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_    :			int    =	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."""  ,							a_  ,							)
class 							_UpperCamelCase					(       a_	):






			'''simple docstring'''

			def __init__(							self			:							Optional[int]    ,						snake_case_			:							Dict    ,						snake_case_			:							Dict = True   ):
							super().__init__(__UpperCamelCase   )
							UpperCamelCase_:				List[Any]						=    config

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

							UpperCamelCase_:				Union[str, Any]						=    MobileNetVaConvLayer(
							    __UpperCamelCase    ,						in_channels=config.num_channels    ,						out_channels=__UpperCamelCase    ,						kernel_size=3    ,						stride=2    ,						)

							UpperCamelCase_:				Union[str, Any]						=    [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

							UpperCamelCase_:				str						=    nn.ModuleList()
							for i in range(13   ):
											UpperCamelCase_:				Optional[int]						=    out_channels

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

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

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

							UpperCamelCase_:				int						=    nn.AdaptiveAvgPoolad((1, 1)   ) if add_pooling_layer else None

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

			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							Dict   ):
							raise NotImplementedError




			@add_start_docstrings_to_model_forward(__UpperCamelCase   )
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC    ,						output_type=__UpperCamelCase    ,						config_class=_CONFIG_FOR_DOC    ,						modality="""vision"""    ,						expected_output=_EXPECTED_OUTPUT_SHAPE    ,						)
			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							Dict = None    ,						snake_case_			:							Union[str, Any] = None    ,						snake_case_			:							Any = None    ,						):
							UpperCamelCase_:				Optional[Any]						=    (
							    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
							)
							UpperCamelCase_:				Optional[Any]						=    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"""   )

							UpperCamelCase_:				int						=    self.conv_stem(__UpperCamelCase   )

							UpperCamelCase_:				Union[str, Any]						=    () if output_hidden_states else None

							for i, layer_module in enumerate(self.layer   ):
											UpperCamelCase_:				int						=    layer_module(__UpperCamelCase   )

											if output_hidden_states:
															UpperCamelCase_:				Dict						=    all_hidden_states + (hidden_states,)

							UpperCamelCase_:				Optional[int]						=    hidden_states

							if self.pooler is not None:
											UpperCamelCase_:				Optional[Any]						=    torch.flatten(self.pooler(__UpperCamelCase   )    ,						start_dim=1   )
							else:
											UpperCamelCase_:				Optional[int]						=    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=__UpperCamelCase    ,						pooler_output=__UpperCamelCase    ,						hidden_states=__UpperCamelCase    ,						)




@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.
    """  ,							a_  ,							)
class 							_UpperCamelCase					(       a_	):






			'''simple docstring'''

			def __init__(							self			:							str    ,						snake_case_			:							Any   ):
							super().__init__(__UpperCamelCase   )

							UpperCamelCase_:				str						=    config.num_labels
							UpperCamelCase_:				Union[str, Any]						=    MobileNetVaModel(__UpperCamelCase   )

							UpperCamelCase_:				Optional[Any]						=    self.mobilenet_va.layer[-1].convolution.out_channels

							# Classifier head
							UpperCamelCase_:				Optional[Any]						=    nn.Dropout(config.classifier_dropout_prob    ,						inplace=__UpperCamelCase   )
							UpperCamelCase_:				Union[str, Any]						=    nn.Linear(__UpperCamelCase    ,						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(__UpperCamelCase   )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,						output_type=__UpperCamelCase    ,						config_class=_CONFIG_FOR_DOC    ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,						)
			def    lowerCAmelCase__						(							self			:							str    ,						snake_case_			:							Optional[Any] = None    ,						snake_case_			:							List[Any] = None    ,						snake_case_			:							Union[str, Any] = None    ,						snake_case_			:							Dict = None    ,						):
							UpperCamelCase_:				Tuple						=    return_dict if return_dict is not None else self.config.use_return_dict

							UpperCamelCase_:				List[str]						=    self.mobilenet_va(__UpperCamelCase    ,						output_hidden_states=__UpperCamelCase    ,						return_dict=__UpperCamelCase   )

							UpperCamelCase_:				Optional[Any]						=    outputs.pooler_output if return_dict else outputs[1]

							UpperCamelCase_:				List[str]						=    self.classifier(self.dropout(__UpperCamelCase   )   )

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

											if self.config.problem_type == "regression":
															UpperCamelCase_:				str						=    MSELoss()
															if self.num_labels == 1:
																			UpperCamelCase_:				int						=    loss_fct(logits.squeeze()    ,						labels.squeeze()   )
															else:
																			UpperCamelCase_:				Dict						=    loss_fct(__UpperCamelCase    ,						__UpperCamelCase   )
											elif self.config.problem_type == "single_label_classification":
															UpperCamelCase_:				Union[str, Any]						=    CrossEntropyLoss()
															UpperCamelCase_:				Optional[int]						=    loss_fct(logits.view(-1    ,						self.num_labels   )    ,						labels.view(-1   )   )
											elif self.config.problem_type == "multi_label_classification":
															UpperCamelCase_:				int						=    BCEWithLogitsLoss()
															UpperCamelCase_:				Optional[Any]						=    loss_fct(__UpperCamelCase    ,						__UpperCamelCase   )

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

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














from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup





def    __lowerCAmelCase   (    __snake_case = ""							):
     __lowerCAmelCase             =       url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
     __lowerCAmelCase             =       BeautifulSoup(requests.get(__snake_case							).text       ,			"html.parser"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			attrs="titleColumn"							)
     __lowerCAmelCase             =       soup.find_all("td"       ,			class_="ratingColumn imdbRating"							)
     return {
         title.a.text: float(rating.strong.text							)
         for title, rating in zip(__snake_case       ,			__snake_case							)
     }





def    __lowerCAmelCase   (    __snake_case = "IMDb_Top_250_Movies.csv"							):
     __lowerCAmelCase             =       get_imdb_top_aaa_movies()
     with open(__snake_case       ,			"w"       ,			newline=""							) as out_file:
          __lowerCAmelCase             =       csv.writer(__snake_case							)
          writer.writerow(["Movie title", "IMDb rating"]							)
          for title, rating in movies.items():
               writer.writerow([title, rating]							)


if __name__ == "__main__":
 write_movies()






