


import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel



def        UpperCamelCase_(   lowerCamelCase_=32							,				lowerCamelCase_=10							,				lowerCamelCase_=100							,				lowerCamelCase_=1026							,				lowerCamelCase_=True							,				lowerCamelCase_="data/tokenized_stories_train_wikitext103.jbl"							,				lowerCamelCase_="igf_context_pairs.jbl"							,				)					->		int:
    set_seed(3				)
    # generate train_data and objective_set
    _lowercase	,       _lowercase :							List[Any]   	=	generate_datasets(
        _UpperCamelCase							,				_UpperCamelCase							,				number=_UpperCamelCase							,				min_len=1026							,				trim=_UpperCamelCase				)
    # keeps model same across runs
    set_seed(4				)
    # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
    # can we train on GPU?
    _lowercase :							Optional[int]   	=	torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)

    # load pretrained model
    _lowercase :							Union[str, Any]   	=	load_gpta('gpt2'				).to(_UpperCamelCase				)
    print('computing perplexity on objective set'				)
    _lowercase :							Tuple   	=	compute_perplexity(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase				).item()
    print('perplexity on objective set:'							,				_UpperCamelCase				)

    # collect igf pairs and save to file demo.jbl
    collect_objective_set(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase				)

    # clean up, delete model and data we don't need anymore
    del model, train_data, objective_set
    torch.cuda.empty_cache()



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_=15							,				lowerCamelCase_=128							,				lowerCamelCase_=100							,				lowerCamelCase_="igf_model.pt"							,				)					->		List[str]:
    set_seed(42				)

    # Load pre-trained model
    _lowercase :							Optional[int]   	=	GPTaLMHeadModel.from_pretrained('gpt2'				)

    # Initialize secondary learner to use embedding weights of model
    _lowercase :							Optional[int]   	=	SecondaryLearner(_UpperCamelCase				)

    # Train secondary learner
    _lowercase :							Any   	=	train_secondary_learner(
        _UpperCamelCase							,				_UpperCamelCase							,				max_epochs=_UpperCamelCase							,				batch_size=_UpperCamelCase							,				eval_freq=100							,				igf_model_path=_UpperCamelCase							,				)

    del model, secondary_learner_train_data
    torch.cuda.empty_cache()

    return secondary_learner



def        UpperCamelCase_(   lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_							,				lowerCamelCase_=32							,				lowerCamelCase_=1000							,				lowerCamelCase_=16							,				lowerCamelCase_=1.0							,				lowerCamelCase_=recopy_gpta							,				lowerCamelCase_=None							,				lowerCamelCase_=10							,				lowerCamelCase_="gpt2_finetuned.pt"							,				)					->		Optional[Any]:
    _lowercase :							Optional[int]   	=	torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'				)
    _lowercase :							int   	=	RandomSampler(_UpperCamelCase				)
    _lowercase :							Optional[int]   	=	DataLoader(_UpperCamelCase							,				sampler=_UpperCamelCase				)

    _lowercase :							Optional[int]   	=	max_steps // (len(_UpperCamelCase				)) + 1
    _lowercase :							Dict   	=	0
    _lowercase :							int   	=	torch.zeros((1, context_len)							,				dtype=torch.long							,				device=_UpperCamelCase				)
    _lowercase	,       _lowercase	,       _lowercase :							str   	=	recopy_model(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase				)

    model.train()
    if secondary_learner is not None:
        secondary_learner.to(_UpperCamelCase				)
        secondary_learner.eval()
    _lowercase :							Dict   	=	[]
    _lowercase :							Optional[int]   	=	0

    _lowercase :							Any   	=	[]
    _lowercase :							Optional[int]   	=	[]

    # Compute the performance of the transformer model at the beginning
    _lowercase :							int   	=	compute_perplexity(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase				)
    test_perps.append(_UpperCamelCase				)
    print('Test perplexity, step'							,				_UpperCamelCase							,				':'							,				_UpperCamelCase				)
    for epoch in range(int(_UpperCamelCase				)				):
        for step, example in enumerate(_UpperCamelCase				):
            torch.cuda.empty_cache()
            _lowercase :							Optional[int]   	=	random.randint(0							,				example.size(2				) - context_len - 1				)
            _lowercase :							Any   	=	example[0, 0, start : start + context_len]
            lm_optimizer.zero_grad()
            _lowercase :							Optional[Any]   	=	model(_UpperCamelCase							,				labels=_UpperCamelCase				)
            _lowercase :							Optional[int]   	=	True

            if secondary_learner is not None:
                _lowercase :							Optional[int]   	=	secondary_learner.forward(
                    torch.tensor(_UpperCamelCase							,				dtype=torch.long							,				device=_UpperCamelCase				).unsqueeze(0				)				)[0].item()
                observed_qs.append(float(_UpperCamelCase				)				)

                # Here we implement the simple non-constant threshold for the predicted IG(X) value
                # We will decay the selectivity of our secondary learner filter from
                # 1 standard deviation above average to 1 below average after 10 batches.

                if global_step == 10:
                    _lowercase :							int   	=	-1
                if predicted_q < threshold:
                    _lowercase :							Tuple   	=	False

            # If we passed the filter, add the context to the batch!
            if do_backprop:
                contexts.append(np.array(context.cpu()				)				)
                _lowercase :							Optional[int]   	=	outputs[0]
                lm_loss.backward()
                examples += 1

            del outputs

            # Once the batch is filled with enough contexts, backprop on the batch.
            if examples == batch_size:
                torch.cuda.empty_cache()
                _lowercase :							List[Any]   	=	0
                # Do LM backprop
                torch.nn.utils.clip_grad_norm_(model.parameters()							,				3.0				)
                lm_optimizer.step()
                lm_scheduler.step()  # Update learning rate schedule
                global_step += 1
                # Compute the performance of the transformer model at this batch
                if global_step % eval_interval == 0:
                    _lowercase :							List[str]   	=	compute_perplexity(_UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase				)
                    test_perps.append(_UpperCamelCase				)

                    print('Test perplexity, step'							,				_UpperCamelCase							,				':'							,				_UpperCamelCase				)
            # Break out of the loop after 60 batches
            if max_steps > 0 and global_step > 60:
                break
        if max_steps > 0 and global_step > 60:
            break

    # save finetuned transformer model
    torch.save(model.state_dict()							,				_UpperCamelCase				)
    torch.cuda.empty_cache()
    # Do some cleaning up so we can reinitialize for the next run of this function
    del lm_optimizer
    del lm_scheduler
    return model



def        UpperCamelCase_(   )					->		Optional[Any]:
    _lowercase :							Optional[int]   	=	argparse.ArgumentParser(description='Fine-tune a transformer model with IGF on a language modeling task'				)

    # Required parameters
    parser.add_argument(
        '--data_dir'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				help='The input data dir. Should contain data files for WikiText.'							,				)
    parser.add_argument(
        '--model_name_or_path'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				help='Path to pretrained model or model identifier from huggingface.co/models'							,				)
    parser.add_argument(
        '--data_file'							,				type=_UpperCamelCase							,				default=_UpperCamelCase							,				help=(
            'A jbl file containing tokenized data which can be split as objective dataset, '
            'train_dataset and test_dataset.'
        )							,				)

    parser.add_argument(
        '--igf_data_file'							,				type=_UpperCamelCase							,				default=_UpperCamelCase							,				help='A jbl file containing the context and information gain pairs to train secondary learner.'							,				)

    parser.add_argument(
        '--output_dir'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				required=_UpperCamelCase							,				help='The output directory where the final fine-tuned model is stored.'							,				)

    parser.add_argument(
        '--tokenizer_name'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				help='Pretrained tokenizer name or path if not the same as model_name'							,				)
    parser.add_argument('--seed'							,				type=_UpperCamelCase							,				default=_UpperCamelCase							,				help='A seed for reproducible training.'				)

    parser.add_argument(
        '--context_len'							,				default=32							,				type=_UpperCamelCase							,				help=(
            'The maximum total input sequence length after tokenization. Sequences longer '
            'than this will be truncated, sequences shorter will be padded.'
        )							,				)

    parser.add_argument(
        '--size_objective_set'							,				default=100							,				type=_UpperCamelCase							,				help='number of articles that are long enough to be used as our objective set'							,				)
    parser.add_argument(
        '--eval_freq'							,				default=100							,				type=_UpperCamelCase							,				help='secondary model evaluation is triggered at eval_freq'				)

    parser.add_argument('--max_steps'							,				default=1000							,				type=_UpperCamelCase							,				help='To calculate training epochs'				)

    parser.add_argument(
        '--secondary_learner_batch_size'							,				default=128							,				type=_UpperCamelCase							,				help='batch size of training data for secondary learner'							,				)

    parser.add_argument(
        '--batch_size'							,				default=16							,				type=_UpperCamelCase							,				help='batch size of training data of language model(gpt2) '				)

    parser.add_argument(
        '--eval_interval'							,				default=10							,				type=_UpperCamelCase							,				help=(
            'decay the selectivity of our secondary learner filter from'
            '1 standard deviation above average to 1 below average after 10 batches'
        )							,				)

    parser.add_argument(
        '--number'							,				default=100							,				type=_UpperCamelCase							,				help='The number of examples split to be used as objective_set/test_data'				)

    parser.add_argument(
        '--min_len'							,				default=1026							,				type=_UpperCamelCase							,				help='The minimum length of the article to be used as objective set'				)

    parser.add_argument(
        '--secondary_learner_max_epochs'							,				default=15							,				type=_UpperCamelCase							,				help='number of epochs to train secondary learner'				)

    parser.add_argument('--trim'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				help='truncate the example if it exceeds context length'				)

    parser.add_argument(
        '--threshold'							,				default=1.0							,				type=_UpperCamelCase							,				help=(
            'The threshold value used by secondary learner to filter the train_data and allow only'
            ' informative data as input to the model'
        )							,				)

    parser.add_argument('--finetuned_model_name'							,				default='gpt2_finetuned.pt'							,				type=_UpperCamelCase							,				help='finetuned_model_name'				)

    parser.add_argument(
        '--recopy_model'							,				default=_UpperCamelCase							,				type=_UpperCamelCase							,				help='Reset the model to the original pretrained GPT-2 weights after each iteration'							,				)

    # function calls
    # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
    generate_n_pairs(
        context_len=32							,				max_steps=10							,				size_objective_set=100							,				min_len=1026							,				trim=_UpperCamelCase							,				data_file='data/tokenized_stories_train_wikitext103.jbl'							,				igf_data_file='igf_context_pairs.jbl'							,				)

    # Load train data for secondary learner
    _lowercase :							Tuple   	=	joblib.load('data/IGF_values.jbl'				)

    # Train secondary learner
    _lowercase :							Tuple   	=	training_secondary_learner(
        _UpperCamelCase							,				secondary_learner_max_epochs=15							,				secondary_learner_batch_size=128							,				eval_freq=100							,				igf_model_path='igf_model.pt'							,				)

    # load pretrained gpt2 model
    _lowercase :							int   	=	GPTaLMHeadModel.from_pretrained('gpt2'				)
    set_seed(42				)

    # Generate train and test data to train and evaluate gpt2 model
    _lowercase	,       _lowercase :							Optional[Any]   	=	generate_datasets(
        context_len=32							,				file='data/tokenized_stories_train_wikitext103.jbl'							,				number=100							,				min_len=1026							,				trim=_UpperCamelCase				)

    # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
    finetune(
        _UpperCamelCase							,				_UpperCamelCase							,				_UpperCamelCase							,				context_len=32							,				max_steps=1000							,				batch_size=16							,				threshold=1.0							,				recopy_model=_UpperCamelCase							,				secondary_learner=_UpperCamelCase							,				eval_interval=10							,				finetuned_model_name='gpt2_finetuned.pt'							,				)


if __name__ == "__main__":
 main()






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


SCREAMING_SNAKE_CASE :   Dict							      =				logging.get_logger(__name__)

# General docstring
SCREAMING_SNAKE_CASE :   str							      =				"RegNetConfig"

# Base docstring
SCREAMING_SNAKE_CASE :   Tuple							      =				"facebook/regnet-y-040"
SCREAMING_SNAKE_CASE :   Dict							      =				[1, 1088, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE :   Optional[Any]							      =				"facebook/regnet-y-040"
SCREAMING_SNAKE_CASE :   List[str]							      =				"tabby, tabby cat"

SCREAMING_SNAKE_CASE :   Optional[int]							      =				[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase = 3,   lowerCamelCase = 1,   lowerCamelCase = 1,   lowerCamelCase = "relu",   **lowerCamelCase,   ) ->     int:




           """simple docstring"""

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




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




           """simple docstring"""

           _lowercase :							str   	=	self.convolution(self.padding(lowerCamelCase))
           _lowercase :							int   	=	self.normalization(lowerCamelCase)
           _lowercase :							Optional[Any]   	=	self.activation(lowerCamelCase)
           return hidden_state






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   **lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase :							Tuple   	=	config.num_channels
           _lowercase :							Tuple   	=	TFRegNetConvLayer(
               out_channels=config.embedding_size,   kernel_size=3,   stride=2,   activation=config.hidden_act,   name='embedder',   )




       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Union[str, Any]:




           """simple docstring"""

           _lowercase :							Dict   	=	shape_list(lowerCamelCase)[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)
           _lowercase :							Optional[Any]   	=	tf.transpose(lowerCamelCase,   perm=(0, 2, 3, 1))
           _lowercase :							int   	=	self.embedder(lowerCamelCase)
           return hidden_state






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase = 2,   **lowerCamelCase) ->     Optional[Any]:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase :							List[Any]   	=	tf.keras.layers.ConvaD(
               filters=lowerCamelCase,   kernel_size=1,   strides=lowerCamelCase,   use_bias=lowerCamelCase,   name='convolution')
           _lowercase :							Optional[int]   	=	tf.keras.layers.BatchNormalization(epsilon=1E-5,   momentum=0.9,   name='normalization')




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = False) ->     tf.Tensor:




           """simple docstring"""

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






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase,   **lowerCamelCase) ->     Any:




           """simple docstring"""

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




       def     UpperCamelCase  (      self,   lowerCamelCase) ->     str:




           """simple docstring"""

           _lowercase :							Optional[int]   	=	self.pooler(lowerCamelCase)
           for layer_module in self.attention:
               _lowercase :							Optional[int]   	=	layer_module(lowerCamelCase)
           _lowercase :							Optional[int]   	=	hidden_state * pooled
           return hidden_state






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = 1,   **lowerCamelCase) ->     Optional[int]:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase :							Optional[Any]   	=	in_channels != out_channels or stride != 1
           _lowercase :							List[Any]   	=	max(1,   out_channels // config.groups_width)
           _lowercase :							List[str]   	=	(
               TFRegNetShortCut(lowerCamelCase,   stride=lowerCamelCase,   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.
           _lowercase :							str   	=	[
               TFRegNetConvLayer(lowerCamelCase,   kernel_size=1,   activation=config.hidden_act,   name='layer.0'),
               TFRegNetConvLayer(
                   lowerCamelCase,   stride=lowerCamelCase,   groups=lowerCamelCase,   activation=config.hidden_act,   name='layer.1'),
               TFRegNetConvLayer(lowerCamelCase,   kernel_size=1,   activation=lowerCamelCase,   name='layer.2'),
           ]
           _lowercase :							Tuple   	=	ACTaFN[config.hidden_act]




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




           """simple docstring"""

           _lowercase :							str   	=	hidden_state
           for layer_module in self.layers:
               _lowercase :							List[Any]   	=	layer_module(lowerCamelCase)
           _lowercase :							Dict   	=	self.shortcut(lowerCamelCase)
           hidden_state += residual
           _lowercase :							Tuple   	=	self.activation(lowerCamelCase)
           return hidden_state






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = 1,   **lowerCamelCase) ->     List[str]:




           """simple docstring"""

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




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




           """simple docstring"""

           _lowercase :							Dict   	=	hidden_state
           for layer_module in self.layers:
               _lowercase :							Any   	=	layer_module(lowerCamelCase)
           _lowercase :							Optional[int]   	=	self.shortcut(lowerCamelCase)
           hidden_state += residual
           _lowercase :							Optional[int]   	=	self.activation(lowerCamelCase)
           return hidden_state






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   lowerCamelCase = 2,   lowerCamelCase = 2,   **lowerCamelCase) ->     Tuple:




           """simple docstring"""

           super().__init__(**lowerCamelCase)

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




       def     UpperCamelCase  (      self,   lowerCamelCase) ->     Tuple:




           """simple docstring"""

           for layer_module in self.layers:
               _lowercase :							Tuple   	=	layer_module(lowerCamelCase)
           return hidden_state






class _lowerCamelCase(		tf.keras.layers.Layer     ):
       def __init__(      self,   lowerCamelCase,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase :							List[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(
                   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',   ))
           _lowercase :							str   	=	zip(config.hidden_sizes,   config.hidden_sizes[1:])
           for i, ((in_channels, out_channels), depth) in enumerate(zip(lowerCamelCase,   config.depths[1:])):
               self.stages.append(TFRegNetStage(lowerCamelCase,   lowerCamelCase,   lowerCamelCase,   depth=lowerCamelCase,   name=F'''stages.{i+1}'''))




       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = False,   lowerCamelCase = True) ->     TFBaseModelOutputWithNoAttention:




           """simple docstring"""

           _lowercase :							int   	=	() if output_hidden_states else None

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

               _lowercase :							Tuple   	=	stage_module(lowerCamelCase)

           if output_hidden_states:
               _lowercase :							Tuple   	=	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)






@keras_serializable
class _lowerCamelCase(		tf.keras.layers.Layer     ):
       lowercase_				:			Dict									=    RegNetConfig
       def __init__(      self,   lowerCamelCase,   **lowerCamelCase) ->     str:




           """simple docstring"""

           super().__init__(**lowerCamelCase)
           _lowercase :							int   	=	config
           _lowercase :							List[Any]   	=	TFRegNetEmbeddings(lowerCamelCase,   name='embedder')
           _lowercase :							List[Any]   	=	TFRegNetEncoder(lowerCamelCase,   name='encoder')
           _lowercase :							Union[str, Any]   	=	tf.keras.layers.GlobalAveragePoolingaD(keepdims=lowerCamelCase,   name='pooler')




       @unpack_inputs
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = False,   ) ->     TFBaseModelOutputWithPoolingAndNoAttention:




           """simple docstring"""

           _lowercase :							Union[str, Any]   	=	(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _lowercase :							Tuple   	=	return_dict if return_dict is not None else self.config.use_return_dict

           _lowercase :							Dict   	=	self.embedder(lowerCamelCase,   training=lowerCamelCase)

           _lowercase :							int   	=	self.encoder(
               lowerCamelCase,   output_hidden_states=lowerCamelCase,   return_dict=lowerCamelCase,   training=lowerCamelCase)

           _lowercase :							Optional[int]   	=	encoder_outputs[0]
           _lowercase :							List[Any]   	=	self.pooler(lowerCamelCase)

           # Change to NCHW output format have uniformity in the modules
           _lowercase :							Tuple   	=	tf.transpose(lowerCamelCase,   perm=(0, 3, 1, 2))
           _lowercase :							Tuple   	=	tf.transpose(lowerCamelCase,   perm=(0, 3, 1, 2))

           # Change the other hidden state outputs to NCHW as well
           if output_hidden_states:
               _lowercase :							Union[str, Any]   	=	tuple([tf.transpose(lowerCamelCase,   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=lowerCamelCase,   pooler_output=lowerCamelCase,   hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,   )






class _lowerCamelCase(		_a     ):
       lowercase_				:			List[str]									=    RegNetConfig
       lowercase_				:			Optional[int]									=    """regnet"""
       lowercase_				:			int									=    """pixel_values"""
       @property
       def     UpperCamelCase  (      self) ->     int:




           """simple docstring"""

           return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_24, 2_24),   dtype=tf.floataa)}


SCREAMING_SNAKE_CASE :   Union[str, Any]							      =				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"

SCREAMING_SNAKE_CASE :   Tuple							      =				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.""",				_a,				)
class _lowerCamelCase(		_a     ):
       def __init__(      self,   lowerCamelCase,   *lowerCamelCase,   **lowerCamelCase) ->     Tuple:




           """simple docstring"""

           super().__init__(lowerCamelCase,   *lowerCamelCase,   **lowerCamelCase)
           _lowercase :							List[Any]   	=	TFRegNetMainLayer(lowerCamelCase,   name='regnet')




       @unpack_inputs
       @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,   )
       def     UpperCamelCase  (      self,   lowerCamelCase,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase=False,   ) ->     Union[TFBaseModelOutputWithPoolingAndNoAttention, Tuple[tf.Tensor]]:




           """simple docstring"""

           _lowercase :							Any   	=	(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _lowercase :							List[Any]   	=	return_dict if return_dict is not None else self.config.use_return_dict

           _lowercase :							str   	=	self.regnet(
               pixel_values=lowerCamelCase,   output_hidden_states=lowerCamelCase,   return_dict=lowerCamelCase,   training=lowerCamelCase,   )
           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.
    """,				_a,				)
class _lowerCamelCase(		_a,				_a     ):
       def __init__(      self,   lowerCamelCase,   *lowerCamelCase,   **lowerCamelCase) ->     Any:




           """simple docstring"""

           super().__init__(lowerCamelCase,   *lowerCamelCase,   **lowerCamelCase)
           _lowercase :							Dict   	=	config.num_labels
           _lowercase :							Optional[int]   	=	TFRegNetMainLayer(lowerCamelCase,   name='regnet')
           # classification head
           _lowercase :							Tuple   	=	[
               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(lowerCamelCase)
       @add_code_sample_docstrings(
           checkpoint=_IMAGE_CLASS_CHECKPOINT,   output_type=lowerCamelCase,   config_class=_CONFIG_FOR_DOC,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,   )
       def     UpperCamelCase  (      self,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase = None,   lowerCamelCase=False,   ) ->     Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:




           """simple docstring"""

           _lowercase :							Optional[Any]   	=	(
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _lowercase :							Optional[Any]   	=	return_dict if return_dict is not None else self.config.use_return_dict

           _lowercase :							Dict   	=	self.regnet(
               lowerCamelCase,   output_hidden_states=lowerCamelCase,   return_dict=lowerCamelCase,   training=lowerCamelCase)

           _lowercase :							List[str]   	=	outputs.pooler_output if return_dict else outputs[1]

           _lowercase :							Union[str, Any]   	=	self.classifier[0](lowerCamelCase)
           _lowercase :							List[Any]   	=	self.classifier[1](lowerCamelCase)

           _lowercase :							Optional[Any]   	=	None if labels is None else self.hf_compute_loss(labels=lowerCamelCase,   logits=lowerCamelCase)

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

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


