





import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


_lowerCamelCase			       =      getLogger(__name__)


_lowerCamelCase			       =      'cuda' if torch.cuda.is_available() else 'cpu'




def    __UpperCAmelCase(      lowercase_ ,    lowercase_ ,    lowercase_ ,    lowercase_ = 8 ,    lowercase_ = DEFAULT_DEVICE ,    lowercase_=False ,    lowercase_="summarization" ,    lowercase_=None ,    **lowercase_ ,    ):
       _lowerCamelCase							:     Optional[Any]	=		Path(lowercase_      ).open('''w''' ,    encoding='''utf-8'''      )
       _lowerCamelCase							:     List[Any]	=		str(lowercase_      )
       _lowerCamelCase							:     Optional[int]	=		AutoModelForSeqaSeqLM.from_pretrained(lowercase_      ).to(lowercase_      )
       if fpaa:
              _lowerCamelCase							:     str	=		model.half()

       _lowerCamelCase							:     Optional[Any]	=		AutoTokenizer.from_pretrained(lowercase_      )
       logger.info(F"""Inferred tokenizer type: {tokenizer.__class__}"""      )  # if this is wrong, check config.model_type.

       _lowerCamelCase							:     Dict	=		time.time()
       # update config with task specific params
       use_task_specific_params(lowercase_ ,    lowercase_      )
       if prefix is None:
              _lowerCamelCase							:     int	=		prefix or getattr(model.config ,    '''prefix''' ,    ''''''      ) or ''''''
       for examples_chunk in tqdm(list(chunks(lowercase_ ,    lowercase_      )      )      ):
              _lowerCamelCase							:     List[Any]	=		[prefix + text for text in examples_chunk]
              _lowerCamelCase							:     Union[str, Any]	=		tokenizer(lowercase_ ,    return_tensors='''pt''' ,    truncation=lowercase_ ,    padding='''longest'''      ).to(lowercase_      )
              _lowerCamelCase							:     Tuple	=		model.generate(
                  input_ids=batch.input_ids ,    attention_mask=batch.attention_mask ,    **lowercase_ ,    )
              _lowerCamelCase							:     Optional[Any]	=		tokenizer.batch_decode(lowercase_ ,    skip_special_tokens=lowercase_ ,    clean_up_tokenization_spaces=lowercase_      )
              for hypothesis in dec:
                     fout.write(hypothesis + '''\n'''      )
                     fout.flush()
       fout.close()
       _lowerCamelCase							:     List[Any]	=		int(time.time() - start_time      )  # seconds
       _lowerCamelCase							:     List[str]	=		len(lowercase_      )
       return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs ,    4      )}




def    __UpperCAmelCase(      ):
       return datetime.datetime.now().strftime('''%Y-%m-%d %H:%M:%S'''      )




def    __UpperCAmelCase(      lowercase_=True      ):
       _lowerCamelCase							:     Optional[int]	=		argparse.ArgumentParser()
       parser.add_argument('''model_name''' ,    type=lowercase_ ,    help='''like facebook/bart-large-cnn,t5-base, etc.'''      )
       parser.add_argument('''input_path''' ,    type=lowercase_ ,    help='''like cnn_dm/test.source'''      )
       parser.add_argument('''save_path''' ,    type=lowercase_ ,    help='''where to save summaries'''      )
       parser.add_argument('''--reference_path''' ,    type=lowercase_ ,    required=lowercase_ ,    help='''like cnn_dm/test.target'''      )
       parser.add_argument('''--score_path''' ,    type=lowercase_ ,    required=lowercase_ ,    default='''metrics.json''' ,    help='''where to save metrics'''      )
       parser.add_argument('''--device''' ,    type=lowercase_ ,    required=lowercase_ ,    default=lowercase_ ,    help='''cuda, cuda:1, cpu etc.'''      )
       parser.add_argument(
           '''--prefix''' ,    type=lowercase_ ,    required=lowercase_ ,    default=lowercase_ ,    help='''will be added to the begininng of src examples'''      )
       parser.add_argument('''--task''' ,    type=lowercase_ ,    default='''summarization''' ,    help='''used for task_specific_params + metrics'''      )
       parser.add_argument('''--bs''' ,    type=lowercase_ ,    default=8 ,    required=lowercase_ ,    help='''batch size'''      )
       parser.add_argument(
           '''--n_obs''' ,    type=lowercase_ ,    default=-1 ,    required=lowercase_ ,    help='''How many observations. Defaults to all.'''      )
       parser.add_argument('''--fp16''' ,    action='''store_true'''      )
       parser.add_argument('''--dump-args''' ,    action='''store_true''' ,    help='''print the custom hparams with the results'''      )
       parser.add_argument(
           '''--info''' ,    nargs='''?''' ,    type=lowercase_ ,    const=datetime_now() ,    help=(
               '''use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'''
               ''' lang=en-ru. If no value is passed, the current datetime string will be used.'''
           ) ,    )
       # Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
       _lowerCamelCase,  _lowerCamelCase							:     Dict	=		parser.parse_known_args()
       _lowerCamelCase							:     List[Any]	=		parse_numeric_n_bool_cl_kwargs(lowercase_      )
       if parsed_args and verbose:
              print(F"""parsed the following generate kwargs: {parsed_args}"""      )
       _lowerCamelCase							:     Union[str, Any]	=		[''' ''' + x.rstrip() if '''t5''' in args.model_name else x.rstrip() for x in open(args.input_path      ).readlines()]
       if args.n_obs > 0:
              _lowerCamelCase							:     Union[str, Any]	=		examples[: args.n_obs]
       Path(args.save_path      ).parent.mkdir(exist_ok=lowercase_      )

       if args.reference_path is None and Path(args.score_path      ).exists():
              warnings.warn(F"""score_path {args.score_path} will be overwritten unless you type ctrl-c."""      )

       if args.device == "cpu" and args.fpaa:
              # this mix leads to RuntimeError: "threshold_cpu" not implemented for 'Half'
              raise ValueError('''Can\'t mix --fp16 and --device cpu'''      )

       _lowerCamelCase							:     str	=		generate_summaries_or_translations(
           lowercase_ ,    args.save_path ,    args.model_name ,    batch_size=args.bs ,    device=args.device ,    fpaa=args.fpaa ,    task=args.task ,    prefix=args.prefix ,    **lowercase_ ,    )

       if args.reference_path is None:
              return {}

       # Compute scores
       _lowerCamelCase							:     Optional[Any]	=		calculate_bleu if '''translation''' in args.task else calculate_rouge
       _lowerCamelCase							:     str	=		[x.rstrip() for x in open(args.save_path      ).readlines()]
       _lowerCamelCase							:     List[str]	=		[x.rstrip() for x in open(args.reference_path      ).readlines()][: len(lowercase_      )]
       _lowerCamelCase							:     dict	=		score_fn(lowercase_ ,    lowercase_      )
       scores.update(lowercase_      )

       if args.dump_args:
              scores.update(lowercase_      )
       if args.info:
              _lowerCamelCase							:     Dict	=		args.info

       if verbose:
              print(lowercase_      )

       if args.score_path is not None:
              json.dump(lowercase_ ,    open(args.score_path ,    '''w'''      )      )

       return scores


if __name__ == "__main__":
       # Usage for MT:
       # python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
       run_generate(verbose=True)








from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


_lowerCamelCase			       =      logging.get_logger(__name__)

# General docstring
_lowerCamelCase			       =      'RegNetConfig'

# Base docstring
_lowerCamelCase			       =      'facebook/regnet-y-040'
_lowerCamelCase			       =      [1, 1088, 7, 7]

# Image classification docstring
_lowerCamelCase			       =      'facebook/regnet-y-040'
_lowerCamelCase			       =      'tabby, tabby cat'

_lowerCamelCase			       =      [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__ = 3      ,							a__ = 1      ,							a__ = 1      ,							a__ = "relu"      ,							**a__      ,							):



             """simple docstring"""





             super().__init__(**a__)
             # The padding and conv has been verified in
             # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
             _lowerCamelCase							:     List[str]	=		tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2)
             _lowerCamelCase							:     List[str]	=		tf.keras.layers.ConvaD(
                 filters=a__      ,							kernel_size=a__      ,							strides=a__      ,							padding='''VALID'''      ,							groups=a__      ,							use_bias=a__      ,							name='''convolution'''      ,							)
             _lowerCamelCase							:     List[str]	=		tf.keras.layers.BatchNormalization(epsilon=1e-5      ,							momentum=0.9      ,							name='''normalization''')
             _lowerCamelCase							:     Tuple	=		ACTaFN[activation] if activation is not None else tf.identity



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     List[Any]	=		self.convolution(self.padding(a__))
             _lowerCamelCase							:     Any	=		self.normalization(a__)
             _lowerCamelCase							:     Any	=		self.activation(a__)
             return hidden_state







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							**a__):



             """simple docstring"""





             super().__init__(**a__)
             _lowerCamelCase							:     Optional[int]	=		config.num_channels
             _lowerCamelCase							:     Any	=		TFRegNetConvLayer(
                 out_channels=config.embedding_size      ,							kernel_size=3      ,							stride=2      ,							activation=config.hidden_act      ,							name='''embedder'''      ,							)



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     int	=		shape_list(a__)[1]
             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.''')

             # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
             # So change the input format from `NCHW` to `NHWC`.
             # shape = (batch_size, in_height, in_width, in_channels=num_channels)
             _lowerCamelCase							:     List[str]	=		tf.transpose(a__      ,							perm=(0, 2, 3, 1))
             _lowerCamelCase							:     List[Any]	=		self.embedder(a__)
             return hidden_state







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__ = 2      ,							**a__):



             """simple docstring"""





             super().__init__(**a__)
             _lowerCamelCase							:     Dict	=		tf.keras.layers.ConvaD(
                 filters=a__      ,							kernel_size=1      ,							strides=a__      ,							use_bias=a__      ,							name='''convolution''')
             _lowerCamelCase							:     List[Any]	=		tf.keras.layers.BatchNormalization(epsilon=1e-5      ,							momentum=0.9      ,							name='''normalization''')



      def 				__snake_case			(		self      ,							a__      ,							a__ = False):



             """simple docstring"""





             return self.normalization(self.convolution(a__)      ,							training=a__)







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__      ,							**a__):



             """simple docstring"""





             super().__init__(**a__)
             _lowerCamelCase							:     List[Any]	=		tf.keras.layers.GlobalAveragePoolingaD(keepdims=a__      ,							name='''pooler''')
             _lowerCamelCase							:     str	=		[
                 tf.keras.layers.ConvaD(filters=a__      ,							kernel_size=1      ,							activation='''relu'''      ,							name='''attention.0'''),
                 tf.keras.layers.ConvaD(filters=a__      ,							kernel_size=1      ,							activation='''sigmoid'''      ,							name='''attention.2'''),
             ]



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Tuple	=		self.pooler(a__)
             for layer_module in self.attention:
                    _lowerCamelCase							:     Optional[Any]	=		layer_module(a__)
             _lowerCamelCase							:     List[str]	=		hidden_state * pooled
             return hidden_state







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__      ,							a__      ,							a__ = 1      ,							**a__):



             """simple docstring"""





             super().__init__(**a__)
             _lowerCamelCase							:     Optional[int]	=		in_channels != out_channels or stride != 1
             _lowerCamelCase							:     Optional[int]	=		max(1      ,							out_channels // config.groups_width)
             _lowerCamelCase							:     int	=		(
                 TFRegNetShortCut(a__      ,							stride=a__      ,							name='''shortcut''')
                 if should_apply_shortcut
                 else tf.keras.layers.Activation('''linear'''      ,							name='''shortcut''')
             )
             # `self.layers` instead of `self.layer` because that is a reserved argument.
             _lowerCamelCase							:     List[Any]	=		[
                 TFRegNetConvLayer(a__      ,							kernel_size=1      ,							activation=config.hidden_act      ,							name='''layer.0'''),
                 TFRegNetConvLayer(
                     a__      ,							stride=a__      ,							groups=a__      ,							activation=config.hidden_act      ,							name='''layer.1'''),
                 TFRegNetConvLayer(a__      ,							kernel_size=1      ,							activation=a__      ,							name='''layer.2'''),
             ]
             _lowerCamelCase							:     Dict	=		ACTaFN[config.hidden_act]



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     Optional[Any]	=		hidden_state
             for layer_module in self.layers:
                    _lowerCamelCase							:     Optional[Any]	=		layer_module(a__)
             _lowerCamelCase							:     int	=		self.shortcut(a__)
             hidden_state += residual
             _lowerCamelCase							:     Union[str, Any]	=		self.activation(a__)
             return hidden_state







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__      ,							a__      ,							a__ = 1      ,							**a__):



             """simple docstring"""





             super().__init__(**a__)
             _lowerCamelCase							:     Dict	=		in_channels != out_channels or stride != 1
             _lowerCamelCase							:     str	=		max(1      ,							out_channels // config.groups_width)
             _lowerCamelCase							:     List[str]	=		(
                 TFRegNetShortCut(a__      ,							stride=a__      ,							name='''shortcut''')
                 if should_apply_shortcut
                 else tf.keras.layers.Activation('''linear'''      ,							name='''shortcut''')
             )
             _lowerCamelCase							:     List[Any]	=		[
                 TFRegNetConvLayer(a__      ,							kernel_size=1      ,							activation=config.hidden_act      ,							name='''layer.0'''),
                 TFRegNetConvLayer(
                     a__      ,							stride=a__      ,							groups=a__      ,							activation=config.hidden_act      ,							name='''layer.1'''),
                 TFRegNetSELayer(a__      ,							reduced_channels=int(round(in_channels / 4))      ,							name='''layer.2'''),
                 TFRegNetConvLayer(a__      ,							kernel_size=1      ,							activation=a__      ,							name='''layer.3'''),
             ]
             _lowerCamelCase							:     int	=		ACTaFN[config.hidden_act]



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             _lowerCamelCase							:     List[str]	=		hidden_state
             for layer_module in self.layers:
                    _lowerCamelCase							:     str	=		layer_module(a__)
             _lowerCamelCase							:     str	=		self.shortcut(a__)
             hidden_state += residual
             _lowerCamelCase							:     Optional[Any]	=		self.activation(a__)
             return hidden_state







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							a__      ,							a__      ,							a__ = 2      ,							a__ = 2      ,							**a__):



             """simple docstring"""





             super().__init__(**a__)

             _lowerCamelCase							:     int	=		TFRegNetXLayer if config.layer_type == '''x''' else TFRegNetYLayer
             _lowerCamelCase							:     List[str]	=		[
                 # downsampling is done in the first layer with stride of 2
                 layer(a__      ,							a__      ,							a__      ,							stride=a__      ,							name='''layers.0'''),
                 *[layer(a__      ,							a__      ,							a__      ,							name=F"""layers.{i+1}""") for i in range(depth - 1)],
             ]



      def 				__snake_case			(		self      ,							a__):



             """simple docstring"""





             for layer_module in self.layers:
                    _lowerCamelCase							:     Optional[Any]	=		layer_module(a__)
             return hidden_state







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							**a__):



             """simple docstring"""





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



      def 				__snake_case			(		self      ,							a__      ,							a__ = False      ,							a__ = True):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		() if output_hidden_states else None

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

                    _lowerCamelCase							:     str	=		stage_module(a__)

             if output_hidden_states:
                    _lowerCamelCase							:     int	=		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=a__      ,							hidden_states=a__)







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




      """simple docstring"""







      UpperCAmelCase__						=      RegNetConfig


      def __init__(		self      ,							a__      ,							**a__):



             """simple docstring"""





             super().__init__(**a__)
             _lowerCamelCase							:     int	=		config
             _lowerCamelCase							:     Tuple	=		TFRegNetEmbeddings(a__      ,							name='''embedder''')
             _lowerCamelCase							:     str	=		TFRegNetEncoder(a__      ,							name='''encoder''')
             _lowerCamelCase							:     int	=		tf.keras.layers.GlobalAveragePoolingaD(keepdims=a__      ,							name='''pooler''')



      @unpack_inputs
      def 				__snake_case			(		self      ,							a__      ,							a__ = None      ,							a__ = None      ,							a__ = False      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Dict	=		(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _lowerCamelCase							:     str	=		return_dict if return_dict is not None else self.config.use_return_dict

             _lowerCamelCase							:     Optional[int]	=		self.embedder(a__      ,							training=a__)

             _lowerCamelCase							:     Optional[int]	=		self.encoder(
                 a__      ,							output_hidden_states=a__      ,							return_dict=a__      ,							training=a__)

             _lowerCamelCase							:     Dict	=		encoder_outputs[0]
             _lowerCamelCase							:     Union[str, Any]	=		self.pooler(a__)

             # Change to NCHW output format have uniformity in the modules
             _lowerCamelCase							:     List[str]	=		tf.transpose(a__      ,							perm=(0, 3, 1, 2))
             _lowerCamelCase							:     Optional[int]	=		tf.transpose(a__      ,							perm=(0, 3, 1, 2))

             # Change the other hidden state outputs to NCHW as well
             if output_hidden_states:
                    _lowerCamelCase							:     Tuple	=		tuple([tf.transpose(a__      ,							perm=(0, 3, 1, 2)) for h in encoder_outputs[1]])

             if not return_dict:
                    return (last_hidden_state, pooled_output) + encoder_outputs[1:]

             return TFBaseModelOutputWithPoolingAndNoAttention(
                 last_hidden_state=a__      ,							pooler_output=a__      ,							hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states      ,							)







class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      UpperCAmelCase__						=      RegNetConfig
      UpperCAmelCase__						=      """regnet"""
      UpperCAmelCase__						=      """pixel_values"""


      @property
      def 				__snake_case			(		self):



             """simple docstring"""





             return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,							dtype=tf.floataa)}
_lowerCamelCase			       =      r'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): 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 [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

_lowerCamelCase			       =      r'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__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 RegNet model outputting raw features without any specific head on top.""" ,lowerCamelCase__ ,)
class 			__A       (      lowerCamelCase__  ):




      """simple docstring"""







      def __init__(		self      ,							a__      ,							*a__      ,							**a__):



             """simple docstring"""





             super().__init__(a__      ,							*a__      ,							**a__)
             _lowerCamelCase							:     str	=		TFRegNetMainLayer(a__      ,							name='''regnet''')



      @unpack_inputs
      @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 				__snake_case			(		self      ,							a__      ,							a__ = None      ,							a__ = None      ,							a__=False      ,							):



             """simple docstring"""





             _lowerCamelCase							:     str	=		(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _lowerCamelCase							:     Any	=		return_dict if return_dict is not None else self.config.use_return_dict

             _lowerCamelCase							:     Tuple	=		self.regnet(
                 pixel_values=a__      ,							output_hidden_states=a__      ,							return_dict=a__      ,							training=a__      ,							)
             if not return_dict:
                    return (outputs[0],) + outputs[1:]

             return TFBaseModelOutputWithPoolingAndNoAttention(
                 last_hidden_state=outputs.last_hidden_state      ,							pooler_output=outputs.pooler_output      ,							hidden_states=outputs.hidden_states      ,							)







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




      """simple docstring"""







      def __init__(		self      ,							a__      ,							*a__      ,							**a__):



             """simple docstring"""





             super().__init__(a__      ,							*a__      ,							**a__)
             _lowerCamelCase							:     Dict	=		config.num_labels
             _lowerCamelCase							:     Union[str, Any]	=		TFRegNetMainLayer(a__      ,							name='''regnet''')
             # classification head
             _lowerCamelCase							:     int	=		[
                 tf.keras.layers.Flatten(),
                 tf.keras.layers.Dense(config.num_labels      ,							name='''classifier.1''') if config.num_labels > 0 else tf.identity,
             ]



      @unpack_inputs
      @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 				__snake_case			(		self      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__ = None      ,							a__=False      ,							):



             """simple docstring"""





             _lowerCamelCase							:     Any	=		(
                 output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
             )
             _lowerCamelCase							:     str	=		return_dict if return_dict is not None else self.config.use_return_dict

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

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

             _lowerCamelCase							:     Union[str, Any]	=		self.classifier[0](a__)
             _lowerCamelCase							:     str	=		self.classifier[1](a__)

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

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

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

