


import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


snake_case       :					int        =  {0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
snake_case       :					List[str]        =  set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))
def        __lowerCamelCase		(				UpperCAmelCase_		:	Any				,						UpperCAmelCase_		:	Dict=0				,						UpperCAmelCase_		:	Union[str, Any]=None		):




      """simple docstring"""





      return [" ".join(x.split(''' '''		)[begin:end]		) for x in answers if len(x.split(''' '''		)		) > 1]
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[str]				,						UpperCAmelCase_		:	str=False		):




      """simple docstring"""





      if make_sub_answers:
            # if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
            # *e.g.* if the correct answer contains a prefix such as "the", or "a"
            a		:Dict											= (
                given_answers + get_sub_answers(UpperCAmelCase_				,						begin=1		) + get_sub_answers(UpperCAmelCase_				,						end=-1		)
            )
      a		:List[Any]											= []
      for answer in given_answers:
            a		:List[str]											= answer.replace('''_'''				,						''' '''		).lower()
            a		:Any											= ''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias		)
            answers.append(''' '''.join(alias.split()		).strip()		)
      return set(UpperCAmelCase_		)
def        __lowerCamelCase		(				UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	Optional[int]				,						UpperCAmelCase_		:	List[str]=1				,						UpperCAmelCase_		:	str=100		):




      """simple docstring"""





      a							,       a		:List[str]											= jax.lax.top_k(UpperCAmelCase_				,						UpperCAmelCase_		)
      a							,       a		:Tuple											= jax.lax.top_k(UpperCAmelCase_				,						UpperCAmelCase_		)

      a		:Dict											= best_end_idx[:, None] - best_start_idx[None, :]
      a		:Dict											= jnp.logical_or(widths < 0				,						widths > max_size		)
      a		:Any											= (best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
      a		:Any											= jnp.argmax(UpperCAmelCase_		).item()

      return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]
def        __lowerCamelCase		(				UpperCAmelCase_		:	List[Any]		):




      """simple docstring"""





      a		:Union[str, Any]											= sample['''question''']['''text''']
      a		:Tuple											= sample['''document''']['''tokens''']['''token''']
      a		:int											= sample['''document''']['''tokens''']['''is_html''']
      a		:List[str]											= sample['''annotations''']['''long_answer''']
      a		:Dict											= sample['''annotations''']['''short_answers''']

      a		:Dict											= ''' '''.join([context[i] for i in range(len(UpperCAmelCase_		)		) if not is_html[i]]		)

      # 0 - No ; 1 - Yes
      for answer in sample["annotations"]["yes_no_answer"]:
            if answer == 0 or answer == 1:
                  return {
                      "question": question,
                      "context": context_string,
                      "short": [],
                      "long": [],
                      "category": "no" if answer == 0 else "yes",
                  }

      a		:Dict											= []
      for s in short_answers:
            short_targets.extend(s['''text''']		)
      a		:int											= list(set(UpperCAmelCase_		)		)

      a		:Tuple											= []
      for s in long_answers:
            if s["start_token"] == -1:
                  continue
            a		:List[str]											= context[s['''start_token'''] : s['''end_token''']]
            a		:Optional[Any]											= is_html[s['''start_token'''] : s['''end_token''']]
            a		:Dict											= ''' '''.join([answer[i] for i in range(len(UpperCAmelCase_		)		) if not html[i]]		)
            if new_answer not in long_targets:
                  long_targets.append(UpperCAmelCase_		)

      a		:Any											= '''long_short''' if len(short_targets + long_targets		) > 0 else '''null'''

      return {
          "question": question,
          "context": context_string,
          "short": short_targets,
          "long": long_targets,
          "category": category,
      }
def        __lowerCamelCase		(				):




      """simple docstring"""





      a		:List[str]											= load_from_disk('''natural-questions-validation'''		)
      a		:List[Any]											= dataset.map(UpperCAmelCase_		).remove_columns(['''annotations''', '''document''', '''id''']		)
      print(UpperCAmelCase_		)

      a		:Dict											= dataset.filter(lambda UpperCAmelCase_		: (len(x['''question''']		) + len(x['''context''']		)) < 4 * 4096		)
      a		:List[Any]											= short_validation_dataset.filter(lambda UpperCAmelCase_		: x["category"] != "null"		)
      short_validation_dataset

      a		:Any											= '''vasudevgupta/flax-bigbird-natural-questions'''
      a		:Any											= FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCAmelCase_		)
      a		:List[Any]											= BigBirdTokenizerFast.from_pretrained(UpperCAmelCase_		)

      @jax.jit
      def forward(*UpperCAmelCase_		:	List[Any]				,						**UpperCAmelCase_		:	List[str]		):
            a							,       a							,       a		:Dict											= model(*UpperCAmelCase_				,						**UpperCAmelCase_		)
            return start_logits, end_logits, jnp.argmax(UpperCAmelCase_				,						axis=-1		)

      def evaluate(UpperCAmelCase_		:	List[Any]		):
            # encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
            a		:Optional[Any]											= tokenizer(
                example['''question''']				,						example['''context''']				,						return_tensors='''np'''				,						max_length=4096				,						padding='''max_length'''				,						truncation=UpperCAmelCase_				,						)

            a							,       a							,       a		:List[str]											= forward(**UpperCAmelCase_		)

            a		:Optional[int]											= CATEGORY_MAPPING[category.item()]

            a		:Any											= example['''long'''] + example['''short''']
            if example["category"] in ["yes", "no", "null"]:
                  a		:Tuple											= [example['''category''']]
            a		:Optional[Any]											= example['''category'''] != '''null'''
            # Now target can be: "yes", "no", "null", "list of long & short answers"

            if predicted_category in ["yes", "no", "null"]:
                  a		:Union[str, Any]											= [predicted_category]
                  a		:Tuple											= example['''output'''] == example['''targets''']
                  a		:Dict											= predicted_category != '''null'''
                  return example

            a		:Dict											= 38 if predicted_category == '''short''' else 1024
            a							,       a		:Union[str, Any]											= get_best_valid_start_end_idx(
                start_scores[0]				,						end_scores[0]				,						top_k=8				,						max_size=UpperCAmelCase_		)

            a		:int											= inputs['''input_ids'''][0].tolist()
            a		:Optional[Any]											= [tokenizer.decode(input_ids[start_score : end_score + 1]		)]

            a		:Dict											= expand_to_aliases(example['''targets''']				,						make_sub_answers=UpperCAmelCase_		)
            a		:Any											= expand_to_aliases(example['''output''']		)

            # some preprocessing to both prediction and answer
            a		:Optional[Any]											= {''''''.join(a.split()		) for a in answers}
            a		:List[Any]											= {''''''.join(p.split()		) for p in predictions}
            a		:Tuple											= {s for s in predictions if s not in ['''``''', '''\'\'''', '''`''', '''\'''']}

            # if there is a common element, it's a exact match
            a		:Tuple											= len(list(answers & predictions		)		) > 0
            a		:Union[str, Any]											= predicted_category != '''null''' and len(UpperCAmelCase_		) > 0

            return example

      a		:str											= short_validation_dataset.map(UpperCAmelCase_		)

      a		:Dict											= len(UpperCAmelCase_		)
      a		:Any											= len(short_validation_dataset.filter(lambda UpperCAmelCase_		: x["match"] == 1		)		)
      print('''EM score:'''				,						(matched / total) * 100				,						'''%'''		)


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


snake_case       :					int        =  logging.get_logger(__name__)

# General docstring
snake_case       :					Optional[int]        =  '''ResNetConfig'''

# Base docstring
snake_case       :					Dict        =  '''microsoft/resnet-50'''
snake_case       :					List[str]        =  [1, 20_48, 7, 7]

# Image classification docstring
snake_case       :					int        =  '''microsoft/resnet-50'''
snake_case       :					Tuple        =  '''tiger cat'''

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







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase = 3      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:List[Any]											= kernel_size // 2
       a		:Optional[Any]											= tf.keras.layers.ConvaD(
           _lowerCamelCase      ,						kernel_size=_lowerCamelCase      ,						strides=_lowerCamelCase      ,						padding='''valid'''      ,						use_bias=_lowerCamelCase      ,						name='''convolution'''					)
       # Use same default momentum and epsilon as PyTorch equivalent
       a		:Tuple											= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,						momentum=0.9      ,						name='''normalization'''					)
       a		:int											= ACTaFN[activation] if activation is not None else tf.keras.layers.Activation('''linear'''					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase					):
       # Pad to match that done in the PyTorch Conv2D model
       a		:Optional[Any]											= (self.pad_value, self.pad_value)
       a		:List[str]											= tf.pad(_lowerCamelCase      ,						[(0, 0), height_pad, width_pad, (0, 0)]					)
       a		:Optional[Any]											= self.conv(_lowerCamelCase					)
       return hidden_state




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Tuple											= self.convolution(_lowerCamelCase					)
       a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:Optional[int]											= TFResNetConvLayer(
           config.embedding_size      ,						kernel_size=7      ,						stride=2      ,						activation=config.hidden_act      ,						name='''embedder'''      ,						)
       a		:List[Any]											= tf.keras.layers.MaxPoolaD(pool_size=3      ,						strides=2      ,						padding='''valid'''      ,						name='''pooler'''					)
       a		:Any											= config.num_channels




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a							,       a							,       a							,       a		:int											= shape_list(_lowerCamelCase					)
       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.'''					)
       a		:Dict											= pixel_values
       a		:int											= self.embedder(_lowerCamelCase					)
       a		:str											= tf.pad(_lowerCamelCase      ,						[[0, 0], [1, 1], [1, 1], [0, 0]]					)
       a		:int											= self.pooler(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:int											= tf.keras.layers.ConvaD(
           _lowerCamelCase      ,						kernel_size=1      ,						strides=_lowerCamelCase      ,						use_bias=_lowerCamelCase      ,						name='''convolution'''					)
       # Use same default momentum and epsilon as PyTorch equivalent
       a		:Optional[Any]											= tf.keras.layers.BatchNormalization(epsilon=1e-5      ,						momentum=0.9      ,						name='''normalization'''					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Optional[Any]											= x
       a		:Union[str, Any]											= self.convolution(_lowerCamelCase					)
       a		:str											= self.normalization(_lowerCamelCase      ,						training=_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:Union[str, Any]											= in_channels != out_channels or stride != 1
       a		:str											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.0'''					)
       a		:Any											= TFResNetConvLayer(_lowerCamelCase      ,						activation=_lowerCamelCase      ,						name='''layer.1'''					)
       a		:Any											= (
           TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
       )
       a		:Union[str, Any]											= ACTaFN[activation]




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:List[str]											= hidden_state
       a		:Optional[int]											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Tuple											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
       hidden_state += residual
       a		:Dict											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 1      ,						_lowerCamelCase = "relu"      ,						_lowerCamelCase = 4      ,						**_lowerCamelCase      ,						):
       super().__init__(**_lowerCamelCase					)
       a		:str											= in_channels != out_channels or stride != 1
       a		:Optional[int]											= out_channels // reduction
       a		:List[Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						name='''layer.0'''					)
       a		:Tuple											= TFResNetConvLayer(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''layer.1'''					)
       a		:Union[str, Any]											= TFResNetConvLayer(_lowerCamelCase      ,						kernel_size=1      ,						activation=_lowerCamelCase      ,						name='''layer.2'''					)
       a		:Optional[int]											= (
           TFResNetShortCut(_lowerCamelCase      ,						stride=_lowerCamelCase      ,						name='''shortcut'''					)
           if should_apply_shortcut
           else tf.keras.layers.Activation('''linear'''      ,						name='''shortcut'''					)
       )
       a		:str											= ACTaFN[activation]




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       a		:Union[str, Any]											= hidden_state
       a		:Tuple											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:Any											= self.conva(_lowerCamelCase      ,						training=_lowerCamelCase					)
       a		:int											= self.shortcut(_lowerCamelCase      ,						training=_lowerCamelCase					)
       hidden_state += residual
       a		:List[Any]											= self.activation(_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase = 2      ,						_lowerCamelCase = 2      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)

       a		:Dict											= TFResNetBottleNeckLayer if config.layer_type == '''bottleneck''' else TFResNetBasicLayer

       a		:Dict											= [layer(_lowerCamelCase      ,						_lowerCamelCase      ,						stride=_lowerCamelCase      ,						activation=config.hidden_act      ,						name='''layers.0'''					)]
       layers += [
           layer(_lowerCamelCase      ,						_lowerCamelCase      ,						activation=config.hidden_act      ,						name=F'''layers.{i + 1}'''					)
           for i in range(depth - 1					)
       ]
       a		:str											= layers




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False					):
       for layer in self.stage_layers:
             a		:List[Any]											= layer(_lowerCamelCase      ,						training=_lowerCamelCase					)
       return hidden_state







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       a		:List[Any]											= [
           TFResNetStage(
               _lowerCamelCase      ,						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(_lowerCamelCase      ,						_lowerCamelCase      ,						_lowerCamelCase      ,						depth=_lowerCamelCase      ,						name=F'''stages.{i + 1}'''					)					)




 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = False      ,						_lowerCamelCase = True      ,						_lowerCamelCase = False      ,						):
       a		:str											= () if output_hidden_states else None

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

             a		:Optional[int]											= stage_module(_lowerCamelCase      ,						training=_lowerCamelCase					)

       if output_hidden_states:
             a		:Union[str, Any]											= 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=_lowerCamelCase      ,						hidden_states=_lowerCamelCase					)







class 	_snake_case						(   _snake_case							):
 SCREAMING_SNAKE_CASE__   = ResNetConfig
 SCREAMING_SNAKE_CASE__   = 'resnet'
 SCREAMING_SNAKE_CASE__   = 'pixel_values'




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







snake_case       :					List[str]        =  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.
'''


snake_case       :					Optional[Any]        =  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							):
 SCREAMING_SNAKE_CASE__   = ResNetConfig




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(**_lowerCamelCase					)
       a		:str											= config
       a		:Union[str, Any]											= TFResNetEmbeddings(_lowerCamelCase      ,						name='''embedder'''					)
       a		:Tuple											= TFResNetEncoder(_lowerCamelCase      ,						name='''encoder'''					)
       a		:List[str]											= tf.keras.layers.GlobalAveragePoolingaD(keepdims=_lowerCamelCase					)




 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Union[str, Any]											= (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       a		:List[Any]											= 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)
       a		:Dict											= tf.transpose(_lowerCamelCase      ,						perm=[0, 2, 3, 1]					)
       a		:List[Any]											= self.embedder(_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:Union[str, Any]											= self.encoder(
           _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

       a		:List[str]											= encoder_outputs[0]

       a		:int											= self.pooler(_lowerCamelCase					)

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

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

       a		:str											= hidden_states if output_hidden_states else None

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







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
       a		:str											= TFResNetMainLayer(config=_lowerCamelCase      ,						name='''resnet'''					)




 @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      ,						)
 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Tuple											= (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       a		:List[str]											= return_dict if return_dict is not None else self.config.use_return_dict

       a		:Optional[Any]											= self.resnet(
           pixel_values=_lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase      ,						)
       return resnet_outputs







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




 def __init__(	self      ,						_lowerCamelCase      ,						**_lowerCamelCase					):
       super().__init__(_lowerCamelCase      ,						**_lowerCamelCase					)
       a		:Optional[int]											= config.num_labels
       a		:List[str]											= TFResNetMainLayer(_lowerCamelCase      ,						name='''resnet'''					)
       # classification head
       a		:Optional[Any]											= (
           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      ,						_lowerCamelCase					):
       a		:Optional[int]											= tf.keras.layers.Flatten()(_lowerCamelCase					)
       a		:Tuple											= self.classifier_layer(_lowerCamelCase					)
       return logits




 @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      ,						)
 @unpack_inputs
 def  SCREAMING_SNAKE_CASE__       (	self      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = None      ,						_lowerCamelCase = False      ,						):
       a		:Optional[Any]											= return_dict if return_dict is not None else self.config.use_return_dict

       a		:Any											= self.resnet(
           _lowerCamelCase      ,						output_hidden_states=_lowerCamelCase      ,						return_dict=_lowerCamelCase      ,						training=_lowerCamelCase					)

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

       a		:Optional[int]											= self.classifier(_lowerCamelCase					)

       a		:List[Any]											= None if labels is None else self.hf_compute_loss(_lowerCamelCase      ,						_lowerCamelCase					)

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

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





