


"""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


__snake_case             =   logging.get_logger(__name__)


# General docstring
__snake_case             =   """MobileNetV1Config"""

# Base docstring
__snake_case             =   """google/mobilenet_v1_1.0_224"""
__snake_case             =   [1, 1024, 7, 7]

# Image classification docstring
__snake_case             =   """google/mobilenet_v1_1.0_224"""
__snake_case             =   """tabby, tabby cat"""


__snake_case             =   [
    """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  :  List[str]						,							lowercase  :  Dict						,							lowercase  :  Tuple=None			)     ->						Optional[Any]:


    """simple docstring"""





    snake_case   :				int				 =   {}

    if isinstance(lowercase						,							lowercase			):
        snake_case   :				List[str]				 =   model.mobilenet_va
    else:
        snake_case   :				int				 =   model

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

    for i in range(13			):
        snake_case   :				int				 =   i + 1
        snake_case   :				Dict				 =   i * 2

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

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

    if isinstance(lowercase						,							lowercase			):
        snake_case   :				Dict				 =   "MobilenetV1/Logits/Conv2d_1c_1x1/"
        snake_case   :				Tuple				 =   model.classifier.weight
        snake_case   :				List[Any]				 =   model.classifier.bias

    return tf_to_pt_map







def   __lowerCAmelCase						(						lowercase  :  Dict						,							lowercase  :  Tuple						,							lowercase  :  Optional[Any]			)     ->						List[Any]:


    """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
    snake_case   :				List[str]				 =   tf.train.list_variables(lowercase			)
    snake_case   :				Dict				 =   {}
    for name, shape in init_vars:
        logger.info(F'Loading TF weight {name} with shape {shape}'			)
        snake_case   :				int				 =   tf.train.load_variable(lowercase						,							lowercase			)
        snake_case   :				List[str]				 =   array

    # Build TF to PyTorch weights loading map
    snake_case   :				Dict				 =   _build_tf_to_pytorch_map(lowercase						,							lowercase						,							lowercase			)

    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

        snake_case   :				Union[str, Any]				 =   tf_weights[name]

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

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

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







def   __lowerCAmelCase						(						lowercase  :  torch.Tensor						,							lowercase  :  nn.Convad			)     ->						torch.Tensor:


    """simple docstring"""





    snake_case ,snake_case   :				Optional[Any]				 =   features.shape[-2:]
    snake_case ,snake_case   :				Any				 =   conv_layer.stride
    snake_case ,snake_case   :				List[str]				 =   conv_layer.kernel_size

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

    if in_width % stride_width == 0:
        snake_case   :				List[str]				 =   max(kernel_width - stride_width						,							0			)
    else:
        snake_case   :				Tuple				 =   max(kernel_width - (in_width % stride_width)						,							0			)

    snake_case   :				Tuple				 =   pad_along_width // 2
    snake_case   :				Optional[Any]				 =   pad_along_width - pad_left
    snake_case   :				Any				 =   pad_along_height // 2
    snake_case   :				Any				 =   pad_along_height - pad_top

    snake_case   :				int				 =   (pad_left, pad_right, pad_top, pad_bottom)
    return nn.functional.pad(lowercase						,							lowercase						,							"constant"						,							0.0			)







class _lowerCAmelCase    (				nn.Module     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__ = 1						,     UpperCamelCase__ = 1						,     UpperCamelCase__ = False						,     UpperCamelCase__ = True						,     UpperCamelCase__ = True						,     )     ->		None:

       '''simple docstring'''




       super().__init__()
       snake_case   :				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.'						)

       snake_case   :				Optional[Any]				 =   0 if config.tf_padding else int((kernel_size - 1) / 2						)

       snake_case   :				Dict				 =   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:
           snake_case   :				Any				 =   nn.BatchNormad(
               num_features=UpperCamelCase__						,     eps=config.layer_norm_eps						,     momentum=0.9997						,     affine=UpperCamelCase__						,     track_running_stats=UpperCamelCase__						,     )
       else:
           snake_case   :				Any				 =   None

       if use_activation:
           if isinstance(UpperCamelCase__						,     UpperCamelCase__						):
               snake_case   :				Optional[Any]				 =   ACTaFN[use_activation]
           elif isinstance(config.hidden_act						,     UpperCamelCase__						):
               snake_case   :				Union[str, Any]				 =   ACTaFN[config.hidden_act]
           else:
               snake_case   :				int				 =   config.hidden_act
       else:
           snake_case   :				List[str]				 =   None







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		torch.Tensor:

       '''simple docstring'''




       if self.config.tf_padding:
           snake_case   :				Tuple				 =   apply_tf_padding(UpperCamelCase__						,     self.convolution						)
       snake_case   :				List[str]				 =   self.convolution(UpperCamelCase__						)
       if self.normalization is not None:
           snake_case   :				int				 =   self.normalization(UpperCamelCase__						)
       if self.activation is not None:
           snake_case   :				Optional[int]				 =   self.activation(UpperCamelCase__						)
       return features







class _lowerCAmelCase    (				snake_case_     ):
   __UpperCAmelCase			:	Optional[Any]							  =  MobileNetVaConfig
   __UpperCAmelCase			:	Dict							  =  load_tf_weights_in_mobilenet_va
   __UpperCAmelCase			:	Dict							  =  '''mobilenet_v1'''
   __UpperCAmelCase			:	str							  =  '''pixel_values'''
   __UpperCAmelCase			:	Optional[Any]							  =  False







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		None:

       '''simple docstring'''




       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						)



__snake_case             =   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.
"""

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







@add_start_docstrings(
    '''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'''   ,							snake_case_   ,							)
class _lowerCAmelCase    (				snake_case_     ):







   def __init__(     self						,     UpperCamelCase__						,     UpperCamelCase__ = True						)     ->		Dict:

       '''simple docstring'''




       super().__init__(UpperCamelCase__						)
       snake_case   :				Union[str, Any]				 =   config

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

       snake_case   :				str				 =   MobileNetVaConvLayer(
           UpperCamelCase__						,     in_channels=config.num_channels						,     out_channels=UpperCamelCase__						,     kernel_size=3						,     stride=2						,     )

       snake_case   :				List[Any]				 =   [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

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

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

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

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







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




       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						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     )     ->		Union[tuple, BaseModelOutputWithPoolingAndNoAttention]:

       '''simple docstring'''




       snake_case   :				Any				 =   (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       snake_case   :				List[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"						)

       snake_case   :				int				 =   self.conv_stem(UpperCamelCase__						)

       snake_case   :				Dict				 =   () if output_hidden_states else None

       for i, layer_module in enumerate(self.layer						):
           snake_case   :				List[str]				 =   layer_module(UpperCamelCase__						)

           if output_hidden_states:
               snake_case   :				Tuple				 =   all_hidden_states + (hidden_states,)

       snake_case   :				Optional[int]				 =   hidden_states

       if self.pooler is not None:
           snake_case   :				Dict				 =   torch.flatten(self.pooler(UpperCamelCase__						)						,     start_dim=1						)
       else:
           snake_case   :				Any				 =   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.
    '''   ,							snake_case_   ,							)
class _lowerCAmelCase    (				snake_case_     ):







   def __init__(     self						,     UpperCamelCase__						)     ->		None:

       '''simple docstring'''




       super().__init__(UpperCamelCase__						)

       snake_case   :				Union[str, Any]				 =   config.num_labels
       snake_case   :				Optional[int]				 =   MobileNetVaModel(UpperCamelCase__						)

       snake_case   :				List[Any]				 =   self.mobilenet_va.layer[-1].convolution.out_channels

       # Classifier head
       snake_case   :				Union[str, Any]				 =   nn.Dropout(config.classifier_dropout_prob						,     inplace=UpperCamelCase__						)
       snake_case   :				int				 =   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						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     UpperCamelCase__ = None						,     )     ->		Union[tuple, ImageClassifierOutputWithNoAttention]:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   return_dict if return_dict is not None else self.config.use_return_dict

       snake_case   :				Optional[Any]				 =   self.mobilenet_va(UpperCamelCase__						,     output_hidden_states=UpperCamelCase__						,     return_dict=UpperCamelCase__						)

       snake_case   :				Tuple				 =   outputs.pooler_output if return_dict else outputs[1]

       snake_case   :				Optional[Any]				 =   self.classifier(self.dropout(UpperCamelCase__						)						)

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

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

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

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









"""simple docstring"""



import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MgpstrTokenizer
   __UpperCAmelCase			:	Optional[int]							  =  False
   __UpperCAmelCase			:	Optional[int]							  =  {}
   __UpperCAmelCase			:	List[str]							  =  False







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       super().setUp()

       # fmt: off
       snake_case   :				List[str]				 =   ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
       # fmt: on
       snake_case   :				Union[str, Any]				 =   dict(zip(UpperCamelCase__						,     range(len(UpperCamelCase__						)						)						)						)

       snake_case   :				int				 =   os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["vocab_file"]						)
       with open(self.vocab_file						,     "w"						,     encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)







   def 			lowerCamelCase		(     self						,     **UpperCamelCase__						)     ->		List[str]:

       '''simple docstring'''




       return MgpstrTokenizer.from_pretrained(self.tmpdirname						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				int				 =   "tester"
       snake_case   :				List[str]				 =   "tester"
       return input_text, output_text







   @unittest.skip("MGP-STR always lower cases letters."						)
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       pass







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				int				 =   self.get_tokenizers(do_lower_case=UpperCamelCase__						)
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'						):
               snake_case   :				List[Any]				 =   "[SPECIAL_TOKEN]"

               tokenizer.add_special_tokens({"cls_token": special_token}						)
               snake_case   :				str				 =   tokenizer.encode([special_token]						,     add_special_tokens=UpperCamelCase__						)
               self.assertEqual(len(UpperCamelCase__						)						,     1						)

               snake_case   :				List[Any]				 =   tokenizer.decode(UpperCamelCase__						,     skip_special_tokens=UpperCamelCase__						)
               self.assertTrue(special_token not in decoded						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   self.get_tokenizers()
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'						):
               snake_case ,snake_case   :				Union[str, Any]				 =   self.get_input_output_texts(UpperCamelCase__						)

               snake_case   :				Dict				 =   tokenizer.tokenize(UpperCamelCase__						)
               snake_case   :				Tuple				 =   tokenizer.convert_tokens_to_ids(UpperCamelCase__						)
               snake_case   :				List[Any]				 =   tokenizer.encode(UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						)
               self.assertListEqual(UpperCamelCase__						,     UpperCamelCase__						)

               snake_case   :				Dict				 =   tokenizer.convert_ids_to_tokens(UpperCamelCase__						)
               self.assertNotEqual(len(UpperCamelCase__						)						,     0						)
               snake_case   :				Tuple				 =   tokenizer.decode(UpperCamelCase__						)
               self.assertIsInstance(UpperCamelCase__						,     UpperCamelCase__						)

               self.assertEqual(text_a.replace(" "						,     ""						)						,     UpperCamelCase__						)







   @unittest.skip("MGP-STR tokenizer only handles one sequence."						)
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       pass







   @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"						)
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       pass





