

import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
						from transformers.models.rag.configuration_rag import RagConfig
						from transformers.models.rag.tokenization_rag import RagTokenizer



@require_faiss
@require_torch
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Any  		=       tempfile.mkdtemp()
									SCREAMING_SNAKE_CASE_: List[Any]  		=       8

									# DPR tok
									SCREAMING_SNAKE_CASE_: str  		=       [
									    "[UNK]",
									    "[CLS]",
									    "[SEP]",
									    "[PAD]",
									    "[MASK]",
									    "want",
									    "##want",
									    "##ed",
									    "wa",
									    "un",
									    "runn",
									    "##ing",
									    ",",
									    "low",
									    "lowest",
									]
									SCREAMING_SNAKE_CASE_: Tuple  		=       os.path.join(self.tmpdirname   ,						"dpr_tokenizer")
									os.makedirs(lowerCAmelCase__   ,						exist_ok=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: str  		=       os.path.join(lowerCAmelCase__   ,						DPR_VOCAB_FILES_NAMES["vocab_file"])
									with open(self.vocab_file   ,						"w"   ,						encoding="utf-8") as vocab_writer:
															vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))

									# BART tok
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [
									    "l",
									    "o",
									    "w",
									    "e",
									    "r",
									    "s",
									    "t",
									    "i",
									    "d",
									    "n",
									    "\u0120",
									    "\u0120l",
									    "\u0120n",
									    "\u0120lo",
									    "\u0120low",
									    "er",
									    "\u0120lowest",
									    "\u0120newer",
									    "\u0120wider",
									    "<unk>",
									]
									SCREAMING_SNAKE_CASE_: Tuple  		=       dict(zip(lowerCAmelCase__   ,						range(len(lowerCAmelCase__))))
									SCREAMING_SNAKE_CASE_: List[str]  		=       ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       {"unk_token": "<unk>"}

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       os.path.join(self.tmpdirname   ,						"bart_tokenizer")
									os.makedirs(lowerCAmelCase__   ,						exist_ok=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: int  		=       os.path.join(lowerCAmelCase__   ,						BART_VOCAB_FILES_NAMES["vocab_file"])
									SCREAMING_SNAKE_CASE_: Tuple  		=       os.path.join(lowerCAmelCase__   ,						BART_VOCAB_FILES_NAMES["merges_file"])
									with open(self.vocab_file   ,						"w"   ,						encoding="utf-8") as fp:
															fp.write(json.dumps(lowerCAmelCase__) + "\n")
									with open(self.merges_file   ,						"w"   ,						encoding="utf-8") as fp:
															fp.write("\n".join(lowerCAmelCase__))



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,						"dpr_tokenizer"))



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname   ,						"bart_tokenizer"))



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									shutil.rmtree(self.tmpdirname)



			@require_tokenizers
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       os.path.join(self.tmpdirname   ,						"rag_tokenizer")
									SCREAMING_SNAKE_CASE_: Tuple  		=       RagConfig(question_encoder=DPRConfig().to_dict()   ,						generator=BartConfig().to_dict())
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       RagTokenizer(question_encoder=self.get_dpr_tokenizer()   ,						generator=self.get_bart_tokenizer())
									rag_config.save_pretrained(lowerCAmelCase__)
									rag_tokenizer.save_pretrained(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       RagTokenizer.from_pretrained(lowerCAmelCase__   ,						config=lowerCAmelCase__)
									self.assertIsInstance(new_rag_tokenizer.question_encoder   ,						lowerCAmelCase__)
									self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()   ,						rag_tokenizer.question_encoder.get_vocab())
									self.assertIsInstance(new_rag_tokenizer.generator   ,						lowerCAmelCase__)
									self.assertEqual(new_rag_tokenizer.generator.get_vocab()   ,						rag_tokenizer.generator.get_vocab())



			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: int  		=       RagTokenizer.from_pretrained("facebook/rag-token-nq")
									SCREAMING_SNAKE_CASE_: str  		=       [
									    "who got the first nobel prize in physics",
									    "when is the next deadpool movie being released",
									    "which mode is used for short wave broadcast service",
									    "who is the owner of reading football club",
									    "when is the next scandal episode coming out",
									    "when is the last time the philadelphia won the superbowl",
									    "what is the most current adobe flash player version",
									    "how many episodes are there in dragon ball z",
									    "what is the first step in the evolution of the eye",
									    "where is gall bladder situated in human body",
									    "what is the main mineral in lithium batteries",
									    "who is the president of usa right now",
									    "where do the greasers live in the outsiders",
									    "panda is a national animal of which country",
									    "what is the name of manchester united stadium",
									]
									SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer(lowerCAmelCase__)
									self.assertIsNotNone(lowerCAmelCase__)




			@slow
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
									SCREAMING_SNAKE_CASE_: Any  		=       [
									    "who got the first nobel prize in physics",
									    "when is the next deadpool movie being released",
									    "which mode is used for short wave broadcast service",
									    "who is the owner of reading football club",
									    "when is the next scandal episode coming out",
									    "when is the last time the philadelphia won the superbowl",
									    "what is the most current adobe flash player version",
									    "how many episodes are there in dragon ball z",
									    "what is the first step in the evolution of the eye",
									    "where is gall bladder situated in human body",
									    "what is the main mineral in lithium batteries",
									    "who is the president of usa right now",
									    "where do the greasers live in the outsiders",
									    "panda is a national animal of which country",
									    "what is the name of manchester united stadium",
									]
									SCREAMING_SNAKE_CASE_: Tuple  		=       tokenizer(lowerCAmelCase__)
									self.assertIsNotNone(lowerCAmelCase__)









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							:  int   	=						logging.get_logger(__name__)


# General docstring
lowerCAmelCase							:  int   	=						"""MobileNetV1Config"""

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

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


lowerCAmelCase							:  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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase=None    ):
						SCREAMING_SNAKE_CASE_: List[str]  		=       {}

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Any  		=       model.mobilenet_va
						else:
												SCREAMING_SNAKE_CASE_: int  		=       model

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

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

												SCREAMING_SNAKE_CASE_: Any  		=       backbone.layer[pt_index]
												SCREAMING_SNAKE_CASE_: Any  		=       f"MobilenetV1/Conv2d_{tf_index}_depthwise/"
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: str  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_var

												SCREAMING_SNAKE_CASE_: Tuple  		=       backbone.layer[pt_index + 1]
												SCREAMING_SNAKE_CASE_: List[str]  		=       f"MobilenetV1/Conv2d_{tf_index}_pointwise/"
												SCREAMING_SNAKE_CASE_: int  		=       pointer.convolution.weight
												SCREAMING_SNAKE_CASE_: Any  		=       pointer.normalization.bias
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       pointer.normalization.weight
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       pointer.normalization.running_mean
												SCREAMING_SNAKE_CASE_: Dict  		=       pointer.normalization.running_var

						if isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: List[str]  		=       "MobilenetV1/Logits/Conv2d_1c_1x1/"
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       model.classifier.weight
												SCREAMING_SNAKE_CASE_: Tuple  		=       model.classifier.bias

						return tf_to_pt_map







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						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
						SCREAMING_SNAKE_CASE_: int  		=       tf.train.list_variables(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       {}
						for name, shape in init_vars:
												logger.info(f"Loading TF weight {name} with shape {shape}"    )
												SCREAMING_SNAKE_CASE_: Any  		=       tf.train.load_variable(_UpperCAmelCase				,	_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       array

						# Build TF to PyTorch weights loading map
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       _build_tf_to_pytorch_map(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						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

												SCREAMING_SNAKE_CASE_: int  		=       tf_weights[name]

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

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

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       features.shape[-2:]
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       conv_layer.stride
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       conv_layer.kernel_size

						if in_height % stride_height == 0:
												SCREAMING_SNAKE_CASE_: int  		=       max(kernel_height - stride_height				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Tuple  		=       max(kernel_height - (in_height % stride_height)				,	0    )

						if in_width % stride_width == 0:
												SCREAMING_SNAKE_CASE_: str  		=       max(kernel_width - stride_width				,	0    )
						else:
												SCREAMING_SNAKE_CASE_: Dict  		=       max(kernel_width - (in_width % stride_width)				,	0    )

						SCREAMING_SNAKE_CASE_: str  		=       pad_along_width // 2
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pad_along_width - pad_left
						SCREAMING_SNAKE_CASE_: int  		=       pad_along_height // 2
						SCREAMING_SNAKE_CASE_: Tuple  		=       pad_along_height - pad_top

						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       (pad_left, pad_right, pad_top, pad_bottom)
						return nn.functional.pad(_UpperCAmelCase				,	_UpperCAmelCase				,	"constant"				,	0.0    )



class   __lowercase				( nn.Module	):




			"""simple docstring"""







			def __init__( self  :     Optional[int]   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     int   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     Optional[int] = 1   ,						lowerCAmelCase__  :     bool = False   ,						lowerCAmelCase__  :     Optional[bool] = True   ,						lowerCAmelCase__  :     Optional[bool or str] = True   ,						):
									super().__init__()
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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.")

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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       nn.Convad(
									    in_channels=lowerCAmelCase__   ,						out_channels=lowerCAmelCase__   ,						kernel_size=lowerCAmelCase__   ,						stride=lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						groups=lowerCAmelCase__   ,						bias=lowerCAmelCase__   ,						padding_mode="zeros"   ,						)

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

									if use_activation:
															if isinstance(lowerCAmelCase__   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[use_activation]
															elif isinstance(config.hidden_act   ,						lowerCAmelCase__):
																					SCREAMING_SNAKE_CASE_: Dict  		=       ACTaFN[config.hidden_act]
															else:
																					SCREAMING_SNAKE_CASE_: Any  		=       config.hidden_act
									else:
															SCREAMING_SNAKE_CASE_: int  		=       None




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







class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     MobileNetVaConfig
			_UpperCAmelCase   :							List[Any]										=     load_tf_weights_in_mobilenet_va
			_UpperCAmelCase   :							List[Any]										=     '''mobilenet_v1'''
			_UpperCAmelCase   :							int										=     '''pixel_values'''
			_UpperCAmelCase   :							List[Any]										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Union[nn.Linear, nn.Convad]):
									if isinstance(lowerCAmelCase__   ,						(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(lowerCAmelCase__   ,						nn.BatchNormad):
															module.bias.data.zero_()
															module.weight.data.fill_(1.0)







lowerCAmelCase							:  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							:  List[str]   	=						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.'''		,       UpperCAmelCase_		,       )
class   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Dict   ,						lowerCAmelCase__  :     MobileNetVaConfig   ,						lowerCAmelCase__  :     bool = True):
									super().__init__(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Dict  		=       config

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       32
									SCREAMING_SNAKE_CASE_: Dict  		=       max(int(depth * config.depth_multiplier)   ,						config.min_depth)

									SCREAMING_SNAKE_CASE_: Tuple  		=       MobileNetVaConvLayer(
									    lowerCAmelCase__   ,						in_channels=config.num_channels   ,						out_channels=lowerCAmelCase__   ,						kernel_size=3   ,						stride=2   ,						)

									SCREAMING_SNAKE_CASE_: Optional[int]  		=       [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

									SCREAMING_SNAKE_CASE_: str  		=       nn.ModuleList()
									for i in range(13):
															SCREAMING_SNAKE_CASE_: List[Any]  		=       out_channels

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

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

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

									SCREAMING_SNAKE_CASE_: List[str]  		=       nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None

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



			def        _SCREAMING_SNAKE_CASE			( self  :     int   ,						lowerCAmelCase__  :     str):
									raise NotImplementedError




			@add_start_docstrings_to_model_forward(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						modality="vision"   ,						expected_output=_EXPECTED_OUTPUT_SHAPE   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       (
									    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
									)
									SCREAMING_SNAKE_CASE_: 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")

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.conv_stem(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       () if output_hidden_states else None

									for i, layer_module in enumerate(self.layer):
															SCREAMING_SNAKE_CASE_: Tuple  		=       layer_module(lowerCAmelCase__)

															if output_hidden_states:
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       all_hidden_states + (hidden_states,)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       hidden_states

									if self.pooler is not None:
															SCREAMING_SNAKE_CASE_: int  		=       torch.flatten(self.pooler(lowerCAmelCase__)   ,						start_dim=1)
									else:
															SCREAMING_SNAKE_CASE_: List[str]  		=       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=lowerCAmelCase__   ,						pooler_output=lowerCAmelCase__   ,						hidden_states=lowerCAmelCase__   ,						)







@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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







			def __init__( self  :     Any   ,						lowerCAmelCase__  :     MobileNetVaConfig):
									super().__init__(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       config.num_labels
									SCREAMING_SNAKE_CASE_: Dict  		=       MobileNetVaModel(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.mobilenet_va.layer[-1].convolution.out_channels

									# Classifier head
									SCREAMING_SNAKE_CASE_: str  		=       nn.Dropout(config.classifier_dropout_prob   ,						inplace=lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       nn.Linear(lowerCAmelCase__   ,						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(lowerCAmelCase__)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,						output_type=lowerCAmelCase__   ,						config_class=_CONFIG_FOR_DOC   ,						expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,						)
			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						lowerCAmelCase__  :     Optional[torch.Tensor] = None   ,						lowerCAmelCase__  :     Optional[bool] = None   ,						):
									SCREAMING_SNAKE_CASE_: List[str]  		=       return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.mobilenet_va(lowerCAmelCase__   ,						output_hidden_states=lowerCAmelCase__   ,						return_dict=lowerCAmelCase__)

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

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.classifier(self.dropout(lowerCAmelCase__))

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

															if self.config.problem_type == "regression":
																					SCREAMING_SNAKE_CASE_: Dict  		=       MSELoss()
																					if self.num_labels == 1:
																											SCREAMING_SNAKE_CASE_: Any  		=       loss_fct(logits.squeeze()   ,						labels.squeeze())
																					else:
																											SCREAMING_SNAKE_CASE_: int  		=       loss_fct(lowerCAmelCase__   ,						lowerCAmelCase__)
															elif self.config.problem_type == "single_label_classification":
																					SCREAMING_SNAKE_CASE_: Any  		=       CrossEntropyLoss()
																					SCREAMING_SNAKE_CASE_: Dict  		=       loss_fct(logits.view(-1   ,						self.num_labels)   ,						labels.view(-1))
															elif self.config.problem_type == "multi_label_classification":
																					SCREAMING_SNAKE_CASE_: Dict  		=       BCEWithLogitsLoss()
																					SCREAMING_SNAKE_CASE_: Dict  		=       loss_fct(lowerCAmelCase__   ,						lowerCAmelCase__)

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

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






