import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


UpperCAmelCase_    		=				logging.getLogger(__name__)





@dataclass
class   lowerCAmelCase    :
     _SCREAMING_SNAKE_CASE  :  str		=field(
         metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}      )
     _SCREAMING_SNAKE_CASE  :  Union[str, Any]		=field(
         default=lowercase_      ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}      )
     _SCREAMING_SNAKE_CASE  :  Tuple		=field(
         default="""NER"""      ,		metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}      )
     _SCREAMING_SNAKE_CASE  :  Tuple		=field(
         default=lowercase_      ,		metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}      )
     _SCREAMING_SNAKE_CASE  :  List[str]		=field(default=lowercase_      ,		metadata={"""help""": """Set this flag to use fast tokenization."""}      )
     # If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
     # or just modify its tokenizer_config.json.
     _SCREAMING_SNAKE_CASE  :  List[Any]		=field(
         default=lowercase_      ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}      ,		)





@dataclass
class   lowerCAmelCase    :
     _SCREAMING_SNAKE_CASE  :  Optional[Any]		=field(
         metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}      )
     _SCREAMING_SNAKE_CASE  :  Tuple		=field(
         metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}      )
     _SCREAMING_SNAKE_CASE  :  List[Any]		=field(
         default=128      ,		metadata={
             """help""": (
                 """The maximum total input sequence length after tokenization. Sequences longer """
                 """than this will be truncated, sequences shorter will be padded."""
             )
         }      ,		)
     _SCREAMING_SNAKE_CASE  :  Any		=field(
         default=lowercase_      ,		metadata={"""help""": """Overwrite the cached training and evaluation sets"""}      )


def 				UpperCamelCase					(     )		->    Optional[int]:




       '''simple docstring'''






       _A=						HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
       _A,      _A,      _A=						parser.parse_args_into_dataclasses()

       if (
           os.path.exists(training_args.output_dir )
           and os.listdir(training_args.output_dir )
           and training_args.do_train
           and not training_args.overwrite_output_dir
       ):
              raise ValueError(
                  F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
                  ' --overwrite_output_dir to overcome.' )

       _A=						import_module('tasks' )

       try:
              _A=						getattr(_lowercase							,       model_args.task_type )
              _A=						token_classification_task_clazz()
       except AttributeError:
              raise ValueError(
                  F"Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. "
                  F"Available tasks classes are: {TokenClassificationTask.__subclasses__()}" )

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'							,       datefmt='%m/%d/%Y %H:%M:%S'							,       level=logging.INFO							,       )
       logger.info(
           'n_replicas: %s, distributed training: %s, 16-bits training: %s'							,       training_args.n_replicas							,       bool(training_args.n_replicas > 1 )							,       training_args.fpaa							,       )
       logger.info('Training/evaluation parameters %s'							,       _lowercase )

       # Prepare Token Classification task
       _A=						token_classification_task.get_labels(data_args.labels )
       _A=						dict(enumerate(_lowercase ) )
       _A=						len(_lowercase )

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       _A=						AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path							,       num_labels=_lowercase							,       idalabel=_lowercase							,       labelaid={label: i for i, label in enumerate(_lowercase )}							,       cache_dir=model_args.cache_dir							,       )
       _A=						AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path							,       cache_dir=model_args.cache_dir							,       use_fast=model_args.use_fast							,       )

       with training_args.strategy.scope():
              _A=						TFAutoModelForTokenClassification.from_pretrained(
                  model_args.model_name_or_path							,       from_pt=bool('.bin' in model_args.model_name_or_path )							,       config=_lowercase							,       cache_dir=model_args.cache_dir							,       )

       # Get datasets
       _A=						(
           TFTokenClassificationDataset(
               token_classification_task=_lowercase							,       data_dir=data_args.data_dir							,       tokenizer=_lowercase							,       labels=_lowercase							,       model_type=config.model_type							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.train							,       )
           if training_args.do_train
           else None
       )
       _A=						(
           TFTokenClassificationDataset(
               token_classification_task=_lowercase							,       data_dir=data_args.data_dir							,       tokenizer=_lowercase							,       labels=_lowercase							,       model_type=config.model_type							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.dev							,       )
           if training_args.do_eval
           else None
       )

       def align_predictions(lowerCAmelCase_							,       lowerCAmelCase_ ) -> Tuple[List[int], List[int]]:
              _A=						np.argmax(_lowercase							,       axis=2 )
              _A,      _A=						preds.shape
              _A=						[[] for _ in range(_lowercase )]
              _A=						[[] for _ in range(_lowercase )]

              for i in range(_lowercase ):
                     for j in range(_lowercase ):
                            if label_ids[i, j] != -1_00:
                                   out_label_list[i].append(label_map[label_ids[i][j]] )
                                   preds_list[i].append(label_map[preds[i][j]] )

              return preds_list, out_label_list

       def compute_metrics(lowerCAmelCase_ ) -> Dict:
              _A,      _A=						align_predictions(p.predictions							,       p.label_ids )

              return {
                  "precision": precision_score(_lowercase							,       _lowercase ),
                  "recall": recall_score(_lowercase							,       _lowercase ),
                  "f1": fa_score(_lowercase							,       _lowercase ),
              }

       # Initialize our Trainer
       _A=						TFTrainer(
           model=_lowercase							,       args=_lowercase							,       train_dataset=train_dataset.get_dataset() if train_dataset else None							,       eval_dataset=eval_dataset.get_dataset() if eval_dataset else None							,       compute_metrics=_lowercase							,       )

       # Training
       if training_args.do_train:
              trainer.train()
              trainer.save_model()
              tokenizer.save_pretrained(training_args.output_dir )

       # Evaluation
       _A=						{}
       if training_args.do_eval:
              logger.info('*** Evaluate ***' )

              _A=						trainer.evaluate()
              _A=						os.path.join(training_args.output_dir							,       'eval_results.txt' )

              with open(_lowercase							,       'w' ) as writer:
                     logger.info('***** Eval results *****' )

                     for key, value in result.items():
                            logger.info('  %s = %s'							,       _lowercase							,       _lowercase )
                            writer.write('%s = %s\n' % (key, value) )

                     results.update(_lowercase )

    # Predict
       if training_args.do_predict:
              _A=						TFTokenClassificationDataset(
                  token_classification_task=_lowercase							,       data_dir=data_args.data_dir							,       tokenizer=_lowercase							,       labels=_lowercase							,       model_type=config.model_type							,       max_seq_length=data_args.max_seq_length							,       overwrite_cache=data_args.overwrite_cache							,       mode=Split.test							,       )

              _A,      _A,      _A=						trainer.predict(test_dataset.get_dataset() )
              _A,      _A=						align_predictions(_lowercase							,       _lowercase )
              _A=						classification_report(_lowercase							,       _lowercase )

              logger.info('\n%s'							,       _lowercase )

              _A=						os.path.join(training_args.output_dir							,       'test_results.txt' )

              with open(_lowercase							,       'w' ) as writer:
                     writer.write('%s\n' % report )

              # Save predictions
              _A=						os.path.join(training_args.output_dir							,       'test_predictions.txt' )

              with open(_lowercase							,       'w' ) as writer:
                     with open(os.path.join(data_args.data_dir							,       'test.txt' )							,       'r' ) as f:
                            _A=						0

                            for line in f:
                                   if line.startswith('-DOCSTART-' ) or line == "" or line == "\n":
                                          writer.write(_lowercase )

                                          if not preds_list[example_id]:
                                                 example_id += 1
                                   elif preds_list[example_id]:
                                          _A=						line.split()[0] + ' ' + preds_list[example_id].pop(0 ) + '\n'

                                          writer.write(_lowercase )
                                   else:
                                          logger.warning('Maximum sequence length exceeded: No prediction for \'%s\'.'							,       line.split()[0] )

       return results


if __name__ == "__main__":
      main()







from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFImageClassifierOutputWithNoAttention,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_resnet import ResNetConfig


lowerCamelCase					    =	logging.get_logger(__name__)

# General docstring
lowerCamelCase					    =	'''ResNetConfig'''

# Base docstring
lowerCamelCase					    =	'''microsoft/resnet-50'''
lowerCamelCase					    =	[1, 2048, 7, 7]

# Image classification docstring
lowerCamelCase					    =	'''microsoft/resnet-50'''
lowerCamelCase					    =	'''tiger cat'''

lowerCamelCase					    =	[
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]

class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int = 3 ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						int						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						kernel_size // 2
       _lowercase     			=						tf.keras.layers.ConvaD(
           snake_case__ ,							kernel_size=snake_case__ ,							strides=snake_case__ ,							padding='valid' ,							use_bias=snake_case__ ,							name='convolution'						)
       # Use same default momentum and epsilon as PyTorch equivalent
       _lowercase     			=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,							momentum=0.9 ,							name='normalization'						)
       _lowercase     			=						ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('linear'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						_lowercase     			=						(self.pad_value, self.pad_value)
       _lowercase     			=						tf.pad(snake_case__ ,							[(0, 0), height_pad, width_pad, (0, 0)]						)
       _lowercase     			=						self.conv(snake_case__						)
       return hidden_state

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						TFResNetConvLayer(
           config.embedding_size ,							kernel_size=7 ,							stride=2 ,							activation=config.hidden_act ,							name='embedder' ,							)
       _lowercase     			=						tf.keras.layers.MaxPoolaD(pool_size=3 ,							strides=2 ,							padding='valid' ,							name='pooler'						)
       _lowercase     			=						config.num_channels

   def       SCREAMING_SNAKE_CASE__					(     self:						Any ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase			,     _lowercase			,     _lowercase			,     _lowercase     			=						shape_list(snake_case__						)
       if tf.executing_eagerly() and num_channels != self.num_channels:
           raise ValueError(
               'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'						)
       _lowercase     			=						pixel_values
       _lowercase     			=						self.embedder(snake_case__						)
       _lowercase     			=						tf.pad(snake_case__ ,							[[0, 0], [1, 1], [1, 1], [0, 0]]						)
       _lowercase     			=						self.pooler(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						List[str] ,							snake_case__:						int ,							snake_case__:						int = 2 ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						tf.keras.layers.ConvaD(
           snake_case__ ,							kernel_size=1 ,							strides=snake_case__ ,							use_bias=snake_case__ ,							name='convolution'						)
       # Use same default momentum and epsilon as PyTorch equivalent
       _lowercase     			=						tf.keras.layers.BatchNormalization(epsilon=1E-5 ,							momentum=0.9 ,							name='normalization'						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						x
       _lowercase     			=						self.convolution(snake_case__						)
       _lowercase     			=						self.normalization(snake_case__ ,							training=snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						int ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							activation=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 1 ,							snake_case__:						str = "relu" ,							snake_case__:						int = 4 ,							**snake_case__:						Optional[Any] ,							)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						in_channels != out_channels or stride != 1
       _lowercase     			=						out_channels // reduction
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							name='layer.0'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							stride=snake_case__ ,							name='layer.1'						)
       _lowercase     			=						TFResNetConvLayer(snake_case__ ,							kernel_size=1 ,							activation=snake_case__ ,							name='layer.2'						)
       _lowercase     			=						(
           TFResNetShortCut(snake_case__ ,							stride=snake_case__ ,							name='shortcut'						)
           if should_apply_shortcut
           else tf.keras.layers.Activation('linear' ,							name='shortcut'						)
       )
       _lowercase     			=						ACTaFN[activation]

   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						hidden_state
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.conva(snake_case__ ,							training=snake_case__						)
       _lowercase     			=						self.shortcut(snake_case__ ,							training=snake_case__						)
       hidden_state += residual
       _lowercase     			=						self.activation(snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Union[str, Any] ,							snake_case__:						ResNetConfig ,							snake_case__:						int ,							snake_case__:						int ,							snake_case__:						int = 2 ,							snake_case__:						int = 2 ,							**snake_case__:						Optional[Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)

       _lowercase     			=						TFResNetBottleNeckLayer if config.layer_type == 'bottleneck' else TFResNetBasicLayer

       _lowercase     			=						[layer(snake_case__ ,							snake_case__ ,							stride=snake_case__ ,							activation=config.hidden_act ,							name='layers.0'						)]
       layers += [
           layer(snake_case__ ,							snake_case__ ,							activation=config.hidden_act ,							name=f'''layers.{i + 1}'''						)
           for i in range(depth - 1						)
       ]
       _lowercase     			=						layers

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False						)	->		tf.Tensor:


       """simple docstring"""



       for layer in self.stage_layers:
           _lowercase     			=						layer(snake_case__ ,							training=snake_case__						)
       return hidden_state






class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   def __init__(     self:						Optional[Any] ,							snake_case__:						ResNetConfig ,							**snake_case__:						Union[str, Any]						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       _lowercase     			=						[
           TFResNetStage(
               snake_case__ ,							config.embedding_size ,							config.hidden_sizes[0] ,							stride=2 if config.downsample_in_first_stage else 1 ,							depth=config.depths[0] ,							name='stages.0' ,							)
       ]
       for i, (in_channels, out_channels, depth) in enumerate(
           zip(config.hidden_sizes ,							config.hidden_sizes[1:] ,							config.depths[1:]						)						):
           self.stages.append(TFResNetStage(snake_case__ ,							snake_case__ ,							snake_case__ ,							depth=snake_case__ ,							name=f'''stages.{i + 1}'''						)						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int ,							snake_case__:						tf.Tensor ,							snake_case__:						bool = False ,							snake_case__:						bool = True ,							snake_case__:						bool = False ,							)	->		TFBaseModelOutputWithNoAttention:


       """simple docstring"""



       _lowercase     			=						() if output_hidden_states else None

       for stage_module in self.stages:
           if output_hidden_states:
               _lowercase     			=						hidden_states + (hidden_state,)

           _lowercase     			=						stage_module(snake_case__ ,							training=snake_case__						)

       if output_hidden_states:
           _lowercase     			=						hidden_states + (hidden_state,)

       if not return_dict:
           return tuple(v for v in [hidden_state, hidden_states] if v is not None						)

       return TFBaseModelOutputWithNoAttention(last_hidden_state=snake_case__ ,							hidden_states=snake_case__						)






class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig
   __lowerCAmelCase  =    """resnet"""
   __lowerCAmelCase  =    """pixel_values"""

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		Dict:


       """simple docstring"""



       return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224) ,							dtype=tf.floataa						)}






lowerCamelCase					    =	r'''
    This model is a TensorFlow
    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a
    regular TensorFlow Module and refer to the TensorFlow documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
'''


lowerCamelCase					    =	r'''
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__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.
'''

@keras_serializable
class        snake_case_				(							tf.keras.layers.Layer   ):







   """simple docstring"""



   __lowerCAmelCase  =    ResNetConfig

   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Dict						)	->		None:


       """simple docstring"""



       super().__init__(**snake_case__						)
       _lowercase     			=						config
       _lowercase     			=						TFResNetEmbeddings(snake_case__ ,							name='embedder'						)
       _lowercase     			=						TFResNetEncoder(snake_case__ ,							name='encoder'						)
       _lowercase     			=						tf.keras.layers.GlobalAveragePoolingaD(keepdims=snake_case__						)

   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						str ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



       _lowercase     			=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       # TF 2.0 image layers can't use NCHW format when running on CPU.
       # We transpose to NHWC format and then transpose back after the full forward pass.
       # (batch_size, num_channels, height, width) -> (batch_size, height, width, num_channels)
       _lowercase     			=						tf.transpose(snake_case__ ,							perm=[0, 2, 3, 1]						)
       _lowercase     			=						self.embedder(snake_case__ ,							training=snake_case__						)

       _lowercase     			=						self.encoder(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

       _lowercase     			=						encoder_outputs[0]

       _lowercase     			=						self.pooler(snake_case__						)

       # Transpose all the outputs to the NCHW format
       # (batch_size, height, width, num_channels) -> (batch_size, num_channels, height, width)
       _lowercase     			=						tf.transpose(snake_case__ ,							(0, 3, 1, 2)						)
       _lowercase     			=						tf.transpose(snake_case__ ,							(0, 3, 1, 2)						)
       _lowercase     			=						()
       for hidden_state in encoder_outputs[1:]:
           _lowercase     			=						hidden_states + tuple(tf.transpose(snake_case__ ,							(0, 3, 1, 2)						) for h in hidden_state						)

       if not return_dict:
           return (last_hidden_state, pooled_output) + hidden_states

       _lowercase     			=						hidden_states if output_hidden_states else None

       return TFBaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=snake_case__ ,							pooler_output=snake_case__ ,							hidden_states=snake_case__ ,							)






@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Tuple						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						TFResNetMainLayer(config=snake_case__ ,							name='resnet'						)

   @add_start_docstrings_to_model_forward(snake_case__						)
   @add_code_sample_docstrings(
       checkpoint=_CHECKPOINT_FOR_DOC ,							output_type=snake_case__ ,							config_class=_CONFIG_FOR_DOC ,							modality='vision' ,							expected_output=_EXPECTED_OUTPUT_SHAPE ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int] ,							snake_case__:						tf.Tensor ,							snake_case__:						Optional[bool] = None ,							snake_case__:						Optional[bool] = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFBaseModelOutputWithPoolingAndNoAttention]:


       """simple docstring"""



       _lowercase     			=						(
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       _lowercase     			=						self.resnet(
           pixel_values=snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__ ,							)
       return resnet_outputs






@add_start_docstrings(
    """
    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    """   ,      lowercase_   ,      )
class        snake_case_				(							lowercase_   ,      lowercase_   ):







   """simple docstring"""



   def __init__(     self:						Tuple ,							snake_case__:						ResNetConfig ,							**snake_case__:						Any						)	->		None:


       """simple docstring"""



       super().__init__(snake_case__ ,							**snake_case__						)
       _lowercase     			=						config.num_labels
       _lowercase     			=						TFResNetMainLayer(snake_case__ ,							name='resnet'						)
       # classification head
       _lowercase     			=						(
           tf.keras.layers.Dense(config.num_labels ,							name='classifier.1'						)
           if config.num_labels > 0
           else tf.keras.layers.Activation('linear' ,							name='classifier.1'						)
       )

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str] ,							snake_case__:						tf.Tensor						)	->		tf.Tensor:


       """simple docstring"""



       _lowercase     			=						tf.keras.layers.Flatten()(snake_case__						)
       _lowercase     			=						self.classifier_layer(snake_case__						)
       return logits

   @add_start_docstrings_to_model_forward(snake_case__						)
   @add_code_sample_docstrings(
       checkpoint=_IMAGE_CLASS_CHECKPOINT ,							output_type=snake_case__ ,							config_class=_CONFIG_FOR_DOC ,							expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,							)
   @unpack_inputs
   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[Any] ,							snake_case__:						tf.Tensor = None ,							snake_case__:						tf.Tensor = None ,							snake_case__:						bool = None ,							snake_case__:						bool = None ,							snake_case__:						bool = False ,							)	->		Union[Tuple[tf.Tensor], TFImageClassifierOutputWithNoAttention]:


       """simple docstring"""



       _lowercase     			=						return_dict if return_dict is not None else self.config.use_return_dict

       _lowercase     			=						self.resnet(
           snake_case__ ,							output_hidden_states=snake_case__ ,							return_dict=snake_case__ ,							training=snake_case__						)

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

       _lowercase     			=						self.classifier(snake_case__						)

       _lowercase     			=						None if labels is None else self.hf_compute_loss(snake_case__ ,							snake_case__						)

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

       return TFImageClassifierOutputWithNoAttention(loss=snake_case__ ,							logits=snake_case__ ,							hidden_states=outputs.hidden_states						)
