


import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


lowerCAmelCase    :     Any					=						{
    """distilbert""": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    """roberta""": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    """bert""": (BertConfig, BertForMaskedLM, BertTokenizer),
    """gpt2""": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def 			lowercase				(_A					):







       """simple docstring"""







       assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
       assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
       if args.mlm:
              assert os.path.isfile(args.token_counts					)
              assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
       else:
              assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

       assert args.teacher_type == args.student_type or (
           args.student_type == "distilbert" and args.teacher_type == "bert"
       )
       assert os.path.isfile(args.student_config					)
       if args.student_pretrained_weights is not None:
              assert os.path.isfile(args.student_pretrained_weights					)

       if args.freeze_token_type_embds:
              assert args.student_type in ["roberta"]

       assert args.alpha_ce >= 0.0
       assert args.alpha_mlm >= 0.0
       assert args.alpha_clm >= 0.0
       assert args.alpha_mse >= 0.0
       assert args.alpha_cos >= 0.0
       assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       if args.student_type == "roberta":
              _lowerCAmelCase    :							Tuple   			=       False
       elif args.student_type == "gpt2":
              _lowerCAmelCase    :							Tuple   			=       False






def 			lowercase				(_A   ,						_A					):







       """simple docstring"""







       if args.student_type == "roberta":
              _lowerCAmelCase    :							Tuple   			=       False






def 			lowercase				():







       """simple docstring"""







       _lowerCAmelCase    :							Optional[int]   			=       argparse.ArgumentParser(description='Training'					)
       parser.add_argument('--force'   ,						action='store_true'   ,						help='Overwrite dump_path if it already exists.'					)

       parser.add_argument(
           '--dump_path'   ,						type=_A   ,						required=_A   ,						help='The output directory (log, checkpoints, parameters, etc.)'					)
       parser.add_argument(
           '--data_file'   ,						type=_A   ,						required=_A   ,						help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'   ,						)

       parser.add_argument(
           '--student_type'   ,						type=_A   ,						choices=['distilbert', 'roberta', 'gpt2']   ,						required=_A   ,						help='The student type (DistilBERT, RoBERTa).'   ,						)
       parser.add_argument('--student_config'   ,						type=_A   ,						required=_A   ,						help='Path to the student configuration.'					)
       parser.add_argument(
           '--student_pretrained_weights'   ,						default=_A   ,						type=_A   ,						help='Load student initialization checkpoint.'					)

       parser.add_argument(
           '--teacher_type'   ,						choices=['bert', 'roberta', 'gpt2']   ,						required=_A   ,						help='Teacher type (BERT, RoBERTa).'					)
       parser.add_argument('--teacher_name'   ,						type=_A   ,						required=_A   ,						help='The teacher model.'					)

       parser.add_argument('--temperature'   ,						default=2.0   ,						type=_A   ,						help='Temperature for the softmax temperature.'					)
       parser.add_argument(
           '--alpha_ce'   ,						default=0.5   ,						type=_A   ,						help='Linear weight for the distillation loss. Must be >=0.'					)
       parser.add_argument(
           '--alpha_mlm'   ,						default=0.0   ,						type=_A   ,						help='Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag.'   ,						)
       parser.add_argument('--alpha_clm'   ,						default=0.5   ,						type=_A   ,						help='Linear weight for the CLM loss. Must be >=0.'					)
       parser.add_argument('--alpha_mse'   ,						default=0.0   ,						type=_A   ,						help='Linear weight of the MSE loss. Must be >=0.'					)
       parser.add_argument(
           '--alpha_cos'   ,						default=0.0   ,						type=_A   ,						help='Linear weight of the cosine embedding loss. Must be >=0.'					)

       parser.add_argument(
           '--mlm'   ,						action='store_true'   ,						help='The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM.'					)
       parser.add_argument(
           '--mlm_mask_prop'   ,						default=0.15   ,						type=_A   ,						help='Proportion of tokens for which we need to make a prediction.'   ,						)
       parser.add_argument('--word_mask'   ,						default=0.8   ,						type=_A   ,						help='Proportion of tokens to mask out.'					)
       parser.add_argument('--word_keep'   ,						default=0.1   ,						type=_A   ,						help='Proportion of tokens to keep.'					)
       parser.add_argument('--word_rand'   ,						default=0.1   ,						type=_A   ,						help='Proportion of tokens to randomly replace.'					)
       parser.add_argument(
           '--mlm_smoothing'   ,						default=0.7   ,						type=_A   ,						help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'   ,						)
       parser.add_argument('--token_counts'   ,						type=_A   ,						help='The token counts in the data_file for MLM.'					)

       parser.add_argument(
           '--restrict_ce_to_mask'   ,						action='store_true'   ,						help='If true, compute the distillation loss only the [MLM] prediction distribution.'   ,						)
       parser.add_argument(
           '--freeze_pos_embs'   ,						action='store_true'   ,						help='Freeze positional embeddings during distillation. For student_type in [\'roberta\', \'gpt2\'] only.'   ,						)
       parser.add_argument(
           '--freeze_token_type_embds'   ,						action='store_true'   ,						help='Freeze token type embeddings during distillation if existent. For student_type in [\'roberta\'] only.'   ,						)

       parser.add_argument('--n_epoch'   ,						type=_A   ,						default=3   ,						help='Number of pass on the whole dataset.'					)
       parser.add_argument('--batch_size'   ,						type=_A   ,						default=5   ,						help='Batch size (for each process).'					)
       parser.add_argument(
           '--group_by_size'   ,						action='store_false'   ,						help='If true, group sequences that have similar length into the same batch. Default is true.'   ,						)

       parser.add_argument(
           '--gradient_accumulation_steps'   ,						type=_A   ,						default=5_0   ,						help='Gradient accumulation for larger training batches.'   ,						)
       parser.add_argument('--warmup_prop'   ,						default=0.05   ,						type=_A   ,						help='Linear warmup proportion.'					)
       parser.add_argument('--weight_decay'   ,						default=0.0   ,						type=_A   ,						help='Weight decay if we apply some.'					)
       parser.add_argument('--learning_rate'   ,						default=5E-4   ,						type=_A   ,						help='The initial learning rate for Adam.'					)
       parser.add_argument('--adam_epsilon'   ,						default=1E-6   ,						type=_A   ,						help='Epsilon for Adam optimizer.'					)
       parser.add_argument('--max_grad_norm'   ,						default=5.0   ,						type=_A   ,						help='Max gradient norm.'					)
       parser.add_argument('--initializer_range'   ,						default=0.02   ,						type=_A   ,						help='Random initialization range.'					)

       parser.add_argument(
           '--fp16'   ,						action='store_true'   ,						help='Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'   ,						)
       parser.add_argument(
           '--fp16_opt_level'   ,						type=_A   ,						default='O1'   ,						help=(
               'For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\'].'
               'See details at https://nvidia.github.io/apex/amp.html'
           )   ,						)
       parser.add_argument('--n_gpu'   ,						type=_A   ,						default=1   ,						help='Number of GPUs in the node.'					)
       parser.add_argument('--local_rank'   ,						type=_A   ,						default=-1   ,						help='Distributed training - Local rank'					)
       parser.add_argument('--seed'   ,						type=_A   ,						default=5_6   ,						help='Random seed'					)

       parser.add_argument('--log_interval'   ,						type=_A   ,						default=5_0_0   ,						help='Tensorboard logging interval.'					)
       parser.add_argument('--checkpoint_interval'   ,						type=_A   ,						default=4_0_0_0   ,						help='Checkpoint interval.'					)
       _lowerCAmelCase    :							Tuple   			=       parser.parse_args()
       sanity_checks(_A					)

       # ARGS #
       init_gpu_params(_A					)
       set_seed(_A					)
       if args.is_master:
              if os.path.exists(args.dump_path					):
                     if not args.force:
                            raise ValueError(
                                f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
                                ' itUse `--force` if you want to overwrite it'					)
                     else:
                            shutil.rmtree(args.dump_path					)

              if not os.path.exists(args.dump_path					):
                     os.makedirs(args.dump_path					)
              logger.info(f'Experiment will be dumped and logged in {args.dump_path}'					)

              # SAVE PARAMS #
              logger.info(f'Param: {args}'					)
              with open(os.path.join(args.dump_path   ,						'parameters.json'					)   ,						'w'					) as f:
                     json.dump(vars(_A					)   ,						_A   ,						indent=4					)
              git_log(args.dump_path					)

       _lowerCAmelCase    :							List[Any]   			=       MODEL_CLASSES[args.student_type]
       _lowerCAmelCase    :							Tuple   			=       MODEL_CLASSES[args.teacher_type]

       # TOKENIZER #
       _lowerCAmelCase    :							int   			=       teacher_tokenizer_class.from_pretrained(args.teacher_name					)
       _lowerCAmelCase    :							List[Any]   			=       {}
       for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
              _lowerCAmelCase    :							Optional[Any]   			=       tokenizer.all_special_tokens.index(_A					)
              _lowerCAmelCase    :							str   			=       tokenizer.all_special_ids[idx]
       logger.info(f'Special tokens {special_tok_ids}'					)
       _lowerCAmelCase    :							Tuple   			=       special_tok_ids
       _lowerCAmelCase    :							List[Any]   			=       tokenizer.max_model_input_sizes[args.teacher_name]

       # DATA LOADER #
       logger.info(f'Loading data from {args.data_file}'					)
       with open(args.data_file   ,						'rb'					) as fp:
              _lowerCAmelCase    :							Dict   			=       pickle.load(_A					)

       if args.mlm:
              logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)'					)
              with open(args.token_counts   ,						'rb'					) as fp:
                     _lowerCAmelCase    :							int   			=       pickle.load(_A					)

              _lowerCAmelCase    :							List[Any]   			=       np.maximum(_A   ,						1					) ** -args.mlm_smoothing
              for idx in special_tok_ids.values():
                     _lowerCAmelCase    :							Tuple   			=       0.0  # do not predict special tokens
              _lowerCAmelCase    :							Optional[Any]   			=       torch.from_numpy(_A					)
       else:
              _lowerCAmelCase    :							Dict   			=       None

       _lowerCAmelCase    :							List[str]   			=       LmSeqsDataset(params=_A   ,						data=_A					)
       logger.info('Data loader created.'					)

       # STUDENT #
       logger.info(f'Loading student config from {args.student_config}'					)
       _lowerCAmelCase    :							int   			=       student_config_class.from_pretrained(args.student_config					)
       _lowerCAmelCase    :							List[Any]   			=       True

       if args.student_pretrained_weights is not None:
              logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}'					)
              _lowerCAmelCase    :							Optional[int]   			=       student_model_class.from_pretrained(args.student_pretrained_weights   ,						config=_A					)
       else:
              _lowerCAmelCase    :							Optional[Any]   			=       student_model_class(_A					)

       if args.n_gpu > 0:
              student.to(f'cuda:{args.local_rank}'					)
       logger.info('Student loaded.'					)

       # TEACHER #
       _lowerCAmelCase    :							int   			=       teacher_model_class.from_pretrained(args.teacher_name   ,						output_hidden_states=_A					)
       if args.n_gpu > 0:
              teacher.to(f'cuda:{args.local_rank}'					)
       logger.info(f'Teacher loaded from {args.teacher_name}.'					)

       # FREEZING #
       if args.freeze_pos_embs:
              freeze_pos_embeddings(_A   ,						_A					)
       if args.freeze_token_type_embds:
              freeze_token_type_embeddings(_A   ,						_A					)

       # SANITY CHECKS #
       assert student.config.vocab_size == teacher.config.vocab_size
       assert student.config.hidden_size == teacher.config.hidden_size
       assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
       if args.mlm:
              assert token_probs.size(0					) == stu_architecture_config.vocab_size

       # DISTILLER #
       torch.cuda.empty_cache()
       _lowerCAmelCase    :							int   			=       Distiller(
           params=_A   ,						dataset=_A   ,						token_probs=_A   ,						student=_A   ,						teacher=_A					)
       distiller.train()
       logger.info('Let\'s go get some drinks.'					)


if __name__ == "__main__":
       main()





'''simple docstring'''







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    :     str					=						logging.get_logger(__name__)

# General docstring
lowerCAmelCase    :     Optional[Any]					=						"""RegNetConfig"""

# Base docstring
lowerCAmelCase    :     int					=						"""facebook/regnet-y-040"""
lowerCAmelCase    :     Optional[Any]					=						[1, 10_88, 7, 7]

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

lowerCAmelCase    :     Tuple					=						[
    """facebook/regnet-y-040""",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.convolution(self.padding(snake_case__   )   )
            _lowerCAmelCase    :							Union[str, Any]   			=       self.normalization(snake_case__   )
            _lowerCAmelCase    :							int   			=       self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							str   			=       config.num_channels
            _lowerCAmelCase    :							List[Any]   			=       TFRegNetConvLayer(
                out_channels=config.embedding_size      ,	kernel_size=3      ,	stride=2      ,	activation=config.hidden_act      ,	name='embedder'      ,	)







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       shape_list(snake_case__   )[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[Any]   			=       tf.transpose(snake_case__      ,	perm=(0, 2, 3, 1)   )
            _lowerCAmelCase    :							Tuple   			=       self.embedder(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(**snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       tf.keras.layers.ConvaD(
                filters=snake_case__      ,	kernel_size=1      ,	strides=snake_case__      ,	use_bias=snake_case__      ,	name='convolution'   )
            _lowerCAmelCase    :							Tuple   			=       tf.keras.layers.BatchNormalization(epsilon=1E-5      ,	momentum=0.9      ,	name='normalization'   )







     def a	( self      ,	snake_case__      ,	snake_case__ = False   ):

            '''simple docstring'''

            return self.normalization(self.convolution(snake_case__   )      ,	training=snake_case__   )
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Any   			=       self.pooler(snake_case__   )
            for layer_module in self.attention:
                   _lowerCAmelCase    :							Tuple   			=       layer_module(snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       hidden_state * pooled
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       hidden_state
            for layer_module in self.layers:
                   _lowerCAmelCase    :							int   			=       layer_module(snake_case__   )
            _lowerCAmelCase    :							int   			=       self.shortcut(snake_case__   )
            hidden_state += residual
            _lowerCAmelCase    :							Tuple   			=       self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Dict   			=       hidden_state
            for layer_module in self.layers:
                   _lowerCAmelCase    :							List[Any]   			=       layer_module(snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       self.shortcut(snake_case__   )
            hidden_state += residual
            _lowerCAmelCase    :							str   			=       self.activation(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(**snake_case__   )

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







     def a	( self      ,	snake_case__   ):

            '''simple docstring'''

            for layer_module in self.layers:
                   _lowerCAmelCase    :							int   			=       layer_module(snake_case__   )
            return hidden_state
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self      ,	snake_case__      ,	snake_case__ = False      ,	snake_case__ = True   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[Any]   			=       () if output_hidden_states else None

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

                   _lowerCAmelCase    :							List[str]   			=       stage_module(snake_case__   )

            if output_hidden_states:
                   _lowerCAmelCase    :							Dict   			=       hidden_states + (hidden_state,)

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

            return TFBaseModelOutputWithNoAttention(last_hidden_state=snake_case__      ,	hidden_states=snake_case__   )
@keras_serializable
class 	UpperCamelCase__					( tf.keras.layers.Layer	):



     """simple docstring"""






     __magic_name__    			=    RegNetConfig







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

            '''simple docstring'''

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







     @unpack_inputs
     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = False      ,	):

            '''simple docstring'''

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

            _lowerCAmelCase    :							int   			=       self.embedder(snake_case__      ,	training=snake_case__   )

            _lowerCAmelCase    :							List[str]   			=       self.encoder(
                snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__   )

            _lowerCAmelCase    :							List[Any]   			=       encoder_outputs[0]
            _lowerCAmelCase    :							Tuple   			=       self.pooler(snake_case__   )

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

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



     """simple docstring"""






     __magic_name__    			=    RegNetConfig
     __magic_name__    			=    "regnet"
     __magic_name__    			=    "pixel_values"







     @property
     def a	( self   ):

            '''simple docstring'''

            return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 224, 224)      ,	dtype=tf.floataa   )}
lowerCAmelCase    :     List[Any]					=						r"""
    Parameters:
    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.
        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowerCAmelCase    :     Dict					=						r"""
    Args:
        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConveNextImageProcessor.__call__`] for details.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
    "The bare RegNet model outputting raw features without any specific head on top."    ,					SCREAMING_SNAKE_CASE_    ,					)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            _lowerCAmelCase    :							List[str]   			=       TFRegNetMainLayer(snake_case__      ,	name='regnet'   )







     @unpack_inputs
     @add_start_docstrings_to_model_forward(snake_case__   )
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC      ,	output_type=snake_case__      ,	config_class=_CONFIG_FOR_DOC      ,	modality='vision'      ,	expected_output=_EXPECTED_OUTPUT_SHAPE      ,	)
     def a	( self      ,	snake_case__      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__=False      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							Optional[Any]   			=       (
                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    :							str   			=       self.regnet(
                pixel_values=snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__      ,	)
            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(
    "\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "    ,					SCREAMING_SNAKE_CASE_    ,					)
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					SCREAMING_SNAKE_CASE_	):



     """simple docstring"""






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

            '''simple docstring'''

            super().__init__(snake_case__      ,	*snake_case__      ,	**snake_case__   )
            _lowerCAmelCase    :							Optional[Any]   			=       config.num_labels
            _lowerCAmelCase    :							Optional[Any]   			=       TFRegNetMainLayer(snake_case__      ,	name='regnet'   )
            # classification head
            _lowerCAmelCase    :							Optional[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(snake_case__   )
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT      ,	output_type=snake_case__      ,	config_class=_CONFIG_FOR_DOC      ,	expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT      ,	)
     def a	( self      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__ = None      ,	snake_case__=False      ,	):

            '''simple docstring'''

            _lowerCAmelCase    :							int   			=       (
                output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
            )
            _lowerCAmelCase    :							List[str]   			=       return_dict if return_dict is not None else self.config.use_return_dict

            _lowerCAmelCase    :							Dict   			=       self.regnet(
                snake_case__      ,	output_hidden_states=snake_case__      ,	return_dict=snake_case__      ,	training=snake_case__   )

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

            _lowerCAmelCase    :							List[Any]   			=       self.classifier[0](snake_case__   )
            _lowerCAmelCase    :							Tuple   			=       self.classifier[1](snake_case__   )

            _lowerCAmelCase    :							int   			=       None if labels is None else self.hf_compute_loss(labels=snake_case__      ,	logits=snake_case__   )

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

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

