



'''simple docstring'''




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_       :  Tuple					=  logging.get_logger(__name__)


# General docstring
UpperCamelCase_       :  List[Any]					=  """MobileNetV1Config"""

# Base docstring
UpperCamelCase_       :  Optional[int]					=  """google/mobilenet_v1_1.0_224"""
UpperCamelCase_       :  Union[str, Any]					=  [1, 1_024, 7, 7]

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


UpperCamelCase_       :  str					=  [
    """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				(_lowercase   ,					_lowercase   ,					_lowercase=None			):

       """simple docstring"""

       a__								= {}

       if isinstance(_snake_case   ,					_snake_case			):
              a__								= model.mobilenet_va
       else:
              a__								= model

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

       for i in range(13			):
              a__								= i + 1
              a__								= i * 2

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

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

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

       return tf_to_pt_map






def 		_lowerCAmelCase				(_lowercase   ,					_lowercase   ,					_lowercase			):

       """simple docstring"""

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

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

       # Build TF to PyTorch weights loading map
       a__								= _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

              a__								= tf_weights[name]

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

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

              logger.info(F'Initialize PyTorch weight {name} {array.shape}'			)
              a__								= torch.from_numpy(_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 		_lowerCAmelCase				(_lowercase   ,					_lowercase			):

       """simple docstring"""

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

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

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

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

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



class    lowerCamelCase__				(			nn.Module						):






      """simple docstring"""




      def __init__(       self							:     List[Any]			,a__							:     MobileNetVaConfig			,a__							:     int			,a__							:     int			,a__							:     int			,a__							:     Optional[int] = 1			,a__							:     Optional[int] = 1			,a__							:     bool = False			,a__							:     Optional[bool] = True			,a__							:     Optional[bool or str] = True			,):
             super().__init__()
             a__								= config

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

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

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

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

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




      def lowerCAmelCase_					(       self							:     Optional[Any]			,a__							:     torch.Tensor   ):

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



class    lowerCamelCase__				(			_a						):






      """simple docstring"""
      UpperCamelCase__			 =     MobileNetVaConfig
      UpperCamelCase__			 =     load_tf_weights_in_mobilenet_va
      UpperCamelCase__			 =     '''mobilenet_v1'''
      UpperCamelCase__			 =     '''pixel_values'''
      UpperCamelCase__			 =     False




      def lowerCAmelCase_					(       self							:     Dict			,a__							:     Union[nn.Linear, nn.Convad]   ):

             if isinstance(a__			,(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(a__			,nn.BatchNormad   ):
                    module.bias.data.zero_()
                    module.weight.data.fill_(1.0   )
UpperCamelCase_       :  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_       :  Dict					=  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.'''	,    _a	,    )
class    lowerCamelCase__				(			_a						):






      """simple docstring"""




      def __init__(       self							:     Tuple			,a__							:     MobileNetVaConfig			,a__							:     bool = True   ):
             super().__init__(a__   )
             a__								= config

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

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

             a__								= [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

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

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

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

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

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

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




      def lowerCAmelCase_					(       self							:     Dict			,a__							:     Tuple   ):
             raise NotImplementedError




      @add_start_docstrings_to_model_forward(a__   )
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC			,output_type=a__			,config_class=_CONFIG_FOR_DOC			,modality="vision"			,expected_output=_EXPECTED_OUTPUT_SHAPE			,)
      def lowerCAmelCase_					(       self							:     Union[str, Any]			,a__							:     Optional[torch.Tensor] = None			,a__							:     Optional[bool] = None			,a__							:     Optional[bool] = None			,):
             a__								= (
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             a__								= return_dict if return_dict is not None else self.config.use_return_dict

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

             a__								= self.conv_stem(a__   )

             a__								= () if output_hidden_states else None

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

                    if output_hidden_states:
                           a__								= all_hidden_states + (hidden_states,)

             a__								= hidden_states

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

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

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



@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    lowerCamelCase__				(			_a						):






      """simple docstring"""




      def __init__(       self							:     str			,a__							:     MobileNetVaConfig   ):
             super().__init__(a__   )

             a__								= config.num_labels
             a__								= MobileNetVaModel(a__   )

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

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

             a__								= self.mobilenet_va(a__			,output_hidden_states=a__			,return_dict=a__   )

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

             a__								= self.classifier(self.dropout(a__   )   )

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

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

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

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








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_SCREAMING_SNAKE_CASE   =  {
    "configuration_xmod": [
        "XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "XmodConfig",
        "XmodOnnxConfig",
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE   =  [
        "XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
        "XmodForCausalLM",
        "XmodForMaskedLM",
        "XmodForMultipleChoice",
        "XmodForQuestionAnswering",
        "XmodForSequenceClassification",
        "XmodForTokenClassification",
        "XmodModel",
        "XmodPreTrainedModel",
    ]

if TYPE_CHECKING:
    from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_xmod import (
            XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
            XmodForCausalLM,
            XmodForMaskedLM,
            XmodForMultipleChoice,
            XmodForQuestionAnswering,
            XmodForSequenceClassification,
            XmodForTokenClassification,
            XmodModel,
            XmodPreTrainedModel,
        )

else:
    import sys

    _SCREAMING_SNAKE_CASE   =  _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


