





"""simple docstring"""





import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


_UpperCamelCase							=      trt.Logger(trt.Logger.WARNING)
_UpperCamelCase							=      absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

_UpperCamelCase							=      logging.getLogger(__name__)

_UpperCamelCase							=      argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    '--onnx_model_path',
    default=None,
    type=str,
    required=True,
    help='Path to ONNX model: ',
)

parser.add_argument(
    '--output_dir',
    default=None,
    type=str,
    required=True,
    help='The output directory where the model checkpoints and predictions will be written.',
)

# Other parameters

parser.add_argument(
    '--tokenizer_name',
    default='',
    type=str,
    required=True,
    help='Pretrained tokenizer name or path if not the same as model_name',
)

parser.add_argument(
    '--version_2_with_negative',
    action='store_true',
    help='If true, the SQuAD examples contain some that do not have an answer.',
)
parser.add_argument(
    '--null_score_diff_threshold',
    type=float,
    default=0.0,
    help='If null_score - best_non_null is greater than the threshold predict null.',
)

parser.add_argument(
    '--max_seq_length',
    default=384,
    type=int,
    help=(
        'The maximum total input sequence length after WordPiece tokenization. Sequences '
        'longer than this will be truncated, and sequences shorter than this will be padded.'
    ),
)
parser.add_argument(
    '--doc_stride',
    default=128,
    type=int,
    help='When splitting up a long document into chunks, how much stride to take between chunks.',
)

parser.add_argument('--per_device_eval_batch_size', default=8, type=int, help='Batch size per GPU/CPU for evaluation.')

parser.add_argument(
    '--n_best_size',
    default=20,
    type=int,
    help='The total number of n-best predictions to generate in the nbest_predictions.json output file.',
)
parser.add_argument(
    '--max_answer_length',
    default=30,
    type=int,
    help=(
        'The maximum length of an answer that can be generated. This is needed because the start '
        'and end predictions are not conditioned on one another.'
    ),
)

parser.add_argument('--seed', type=int, default=42, help='random seed for initialization')

parser.add_argument(
    '--dataset_name',
    type=str,
    default=None,
    required=True,
    help='The name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--dataset_config_name',
    type=str,
    default=None,
    help='The configuration name of the dataset to use (via the datasets library).',
)
parser.add_argument(
    '--preprocessing_num_workers', type=int, default=4, help='A csv or a json file containing the training data.'
)
parser.add_argument('--overwrite_cache', action='store_true', help='Overwrite the cached training and evaluation sets')
parser.add_argument(
    '--fp16',
    action='store_true',
    help='Whether to use 16-bit (mixed) precision instead of 32-bit',
)
parser.add_argument(
    '--int8',
    action='store_true',
    help='Whether to use INT8',
)

_UpperCamelCase							=      parser.parse_args()

if args.tokenizer_name:
							_UpperCamelCase							=      AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
							raise ValueError(
							    'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
							    'You can do it from another script, save it, and load it from here, using --tokenizer_name.'
							)

logger.info('Training/evaluation parameters %s', args)

_UpperCamelCase							=      args.per_device_eval_batch_size

_UpperCamelCase							=      (args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
_UpperCamelCase							=      True

_UpperCamelCase							=      'temp_engine/bert-fp32.engine'
if args.fpaa:
							_UpperCamelCase							=      'temp_engine/bert-fp16.engine'
if args.inta:
							_UpperCamelCase							=      'temp_engine/bert-int8.engine'

# import ONNX file
if not os.path.exists('temp_engine'):
							os.makedirs('temp_engine')

_UpperCamelCase							=      1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
							with open(args.onnx_model_path, 'rb') as model:
														if not parser.parse(model.read()):
																					for error in range(parser.num_errors):
																												print(parser.get_error(error))

    # Query input names and shapes from parsed TensorRT network
							_UpperCamelCase							=      [network.get_input(i) for i in range(network.num_inputs)]
							_UpperCamelCase							=      [_input.name for _input in network_inputs]  # ex: ["actual_input1"]

							with builder.create_builder_config() as config:
														_UpperCamelCase							=      1 << 50
														if STRICT_TYPES:
																					config.set_flag(trt.BuilderFlag.STRICT_TYPES)
														if args.fpaa:
																					config.set_flag(trt.BuilderFlag.FPaa)
														if args.inta:
																					config.set_flag(trt.BuilderFlag.INTa)
														_UpperCamelCase							=      builder.create_optimization_profile()
														config.add_optimization_profile(profile)
														for i in range(len(input_names)):
																					profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
														_UpperCamelCase							=      builder.build_engine(network, config)

														# serialize_engine and store in file (can be directly loaded and deserialized):
														with open(engine_name, 'wb') as f:
																					f.write(engine.serialize())



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							List[Any]    ,						SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							int    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Any   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =np.asarray(inputs['''input_ids''']    ,						dtype=np.intaa   )
							__lowerCamelCase      :      List[Any]         =np.asarray(inputs['''attention_mask''']    ,						dtype=np.intaa   )
							__lowerCamelCase      :      Union[str, Any]         =np.asarray(inputs['''token_type_ids''']    ,						dtype=np.intaa   )

							# Copy inputs
							cuda.memcpy_htod_async(d_inputs[0]    ,						input_ids.ravel()    ,						SCREAMING_SNAKE_CASE   )
							cuda.memcpy_htod_async(d_inputs[1]    ,						attention_mask.ravel()    ,						SCREAMING_SNAKE_CASE   )
							cuda.memcpy_htod_async(d_inputs[2]    ,						token_type_ids.ravel()    ,						SCREAMING_SNAKE_CASE   )
							# start time
							__lowerCamelCase      :      Optional[int]         =time.time()
							# Run inference
							context.execute_async(
							    bindings=[int(SCREAMING_SNAKE_CASE   ) for d_inp in d_inputs] + [int(SCREAMING_SNAKE_CASE   ), int(SCREAMING_SNAKE_CASE   )]    ,						stream_handle=stream.handle   )
							# Transfer predictions back from GPU
							cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							cuda.memcpy_dtoh_async(SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE   )
							# Synchronize the stream and take time
							stream.synchronize()
							# end time
							__lowerCamelCase      :      Dict         =time.time()
							__lowerCamelCase      :      Tuple         =end_time - start_time
							__lowerCamelCase      :      str         =(h_outputa, h_outputa)
							# print(outputs)
							return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
_UpperCamelCase							=      Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
    datefmt='%m/%d/%Y %H:%M:%S',
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
							datasets.utils.logging.set_verbosity_warning()
							transformers.utils.logging.set_verbosity_info()
else:
							datasets.utils.logging.set_verbosity_error()
							transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
							set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
							# Downloading and loading a dataset from the hub.
							_UpperCamelCase							=      load_dataset(args.dataset_name, args.dataset_config_name)
else:
							raise ValueError('Evaluation requires a dataset name')
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

_UpperCamelCase							=      raw_datasets['validation'].column_names

_UpperCamelCase							=      'question' if 'question' in column_names else column_names[0]
_UpperCamelCase							=      'context' if 'context' in column_names else column_names[1]
_UpperCamelCase							=      'answers' if 'answers' in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
_UpperCamelCase							=      tokenizer.padding_side == 'right'

if args.max_seq_length > tokenizer.model_max_length:
							logger.warning(
							    f'''The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the'''
							    f'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.'''
							)

_UpperCamelCase							=      min(args.max_seq_length, tokenizer.model_max_length)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Tuple         =[q.lstrip() for q in examples[question_column_name]]

							# Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
							# in one example possible giving several features when a context is long, each of those features having a
							# context that overlaps a bit the context of the previous feature.
							__lowerCamelCase      :      Optional[int]         =tokenizer(
							    examples[question_column_name if pad_on_right else context_column_name]    ,						examples[context_column_name if pad_on_right else question_column_name]    ,						truncation='''only_second''' if pad_on_right else '''only_first'''    ,						max_length=SCREAMING_SNAKE_CASE    ,						stride=args.doc_stride    ,						return_overflowing_tokens=SCREAMING_SNAKE_CASE    ,						return_offsets_mapping=SCREAMING_SNAKE_CASE    ,						padding='''max_length'''    ,						)

							# Since one example might give us several features if it has a long context, we need a map from a feature to
							# its corresponding example. This key gives us just that.
							__lowerCamelCase      :      Tuple         =tokenized_examples.pop('''overflow_to_sample_mapping'''   )

							# For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
							# corresponding example_id and we will store the offset mappings.
							__lowerCamelCase      :      Union[str, Any]         =[]

							for i in range(len(tokenized_examples['''input_ids''']   )   ):
														# Grab the sequence corresponding to that example (to know what is the context and what is the question).
														__lowerCamelCase      :      Dict         =tokenized_examples.sequence_ids(SCREAMING_SNAKE_CASE   )
														__lowerCamelCase      :      str         =1 if pad_on_right else 0

														# One example can give several spans, this is the index of the example containing this span of text.
														__lowerCamelCase      :      int         =sample_mapping[i]
														tokenized_examples["example_id"].append(examples['''id'''][sample_index]   )

														# Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
														# position is part of the context or not.
														__lowerCamelCase      :      List[str]         =[
														    (o if sequence_ids[k] == context_index else None)
														    for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]   )
														]

							return tokenized_examples


_UpperCamelCase							=      raw_datasets['validation']
# Validation Feature Creation
_UpperCamelCase							=      eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc='Running tokenizer on validation dataset',
)

_UpperCamelCase							=      default_data_collator

_UpperCamelCase							=      eval_dataset.remove_columns(['example_id', 'offset_mapping'])
_UpperCamelCase							=      DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]    ,						SCREAMING_SNAKE_CASE       :							Any    ,						SCREAMING_SNAKE_CASE       :							Optional[Any]    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]="eval"   ):


							'''simple docstring'''







							__lowerCamelCase      :      Any         =postprocess_qa_predictions(
							    examples=SCREAMING_SNAKE_CASE    ,						features=SCREAMING_SNAKE_CASE    ,						predictions=SCREAMING_SNAKE_CASE    ,						version_2_with_negative=args.version_2_with_negative    ,						n_best_size=args.n_best_size    ,						max_answer_length=args.max_answer_length    ,						null_score_diff_threshold=args.null_score_diff_threshold    ,						output_dir=args.output_dir    ,						prefix=SCREAMING_SNAKE_CASE    ,						)
							# Format the result to the format the metric expects.
							if args.version_2_with_negative:
														__lowerCamelCase      :      Dict         =[
														    {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
														]
							else:
														__lowerCamelCase      :      Optional[Any]         =[{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

							__lowerCamelCase      :      str         =[{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
							return EvalPrediction(predictions=SCREAMING_SNAKE_CASE    ,						label_ids=SCREAMING_SNAKE_CASE   )


_UpperCamelCase							=      load_metric('squad_v2' if args.version_2_with_negative else 'squad')

# Evaluation!
logger.info('Loading ONNX model %s for evaluation', args.onnx_model_path)
with open(engine_name, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
							# setup for TRT inferrence
							for i in range(len(input_names)):
														context.set_binding_shape(i, INPUT_SHAPE)
							assert context.all_binding_shapes_specified



							def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							List[Any]   ):


														'''simple docstring'''







														return trt.volume(engine.get_binding_shape(SCREAMING_SNAKE_CASE   )   ) * engine.get_binding_dtype(SCREAMING_SNAKE_CASE   ).itemsize

							# Allocate device memory for inputs and outputs.
							_UpperCamelCase							=      [cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

							# Allocate output buffer
							_UpperCamelCase							=      cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
							_UpperCamelCase							=      cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
							_UpperCamelCase							=      cuda.mem_alloc(h_outputa.nbytes)
							_UpperCamelCase							=      cuda.mem_alloc(h_outputa.nbytes)

							# Create a stream in which to copy inputs/outputs and run inference.
							_UpperCamelCase							=      cuda.Stream()

							# Evaluation
							logger.info('***** Running Evaluation *****')
							logger.info(f'''  Num examples = {len(eval_dataset)}''')
							logger.info(f'''  Batch size = {args.per_device_eval_batch_size}''')

							_UpperCamelCase							=      0.0
							_UpperCamelCase							=      0
							_UpperCamelCase							=      timeit.default_timer()

							_UpperCamelCase							=      None
							for step, batch in enumerate(eval_dataloader):
														_UpperCamelCase			,    _UpperCamelCase							=      model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
														total_time += infer_time
														niter += 1

														_UpperCamelCase			,    _UpperCamelCase							=      outputs
														_UpperCamelCase							=      torch.tensor(start_logits)
														_UpperCamelCase							=      torch.tensor(end_logits)

														# necessary to pad predictions and labels for being gathered
														_UpperCamelCase							=      accelerator.pad_across_processes(start_logits, dim=1, pad_index=-100)
														_UpperCamelCase							=      accelerator.pad_across_processes(end_logits, dim=1, pad_index=-100)

														_UpperCamelCase							=      (accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
														_UpperCamelCase							=      logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-100)

							if all_preds is not None:
														_UpperCamelCase							=      nested_truncate(all_preds, len(eval_dataset))

							_UpperCamelCase							=      timeit.default_timer() - start_time
							logger.info('  Evaluation done in total %f secs (%f sec per example)', evalTime, evalTime / len(eval_dataset))
							# Inference time from TRT
							logger.info('Average Inference Time = {:.3f} ms'.format(total_time * 1000 / niter))
							logger.info('Total Inference Time =  {:.3f} ms'.format(total_time * 1000))
							logger.info('Total Number of Inference =  %d', niter)

_UpperCamelCase							=      post_processing_function(eval_examples, eval_dataset, all_preds)
_UpperCamelCase							=      metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f'''Evaluation metrics: {eval_metric}''')











"""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


_UpperCamelCase							=      logging.get_logger(__name__)

# General docstring
_UpperCamelCase							=      'RegNetConfig'

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

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

_UpperCamelCase							=      [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]
class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Dict						,    __lowercase  :int						,    __lowercase  :int = 3						,    __lowercase  :int = 1						,    __lowercase  :int = 1						,    __lowercase  :Optional[str] = "relu"						,    **__lowercase  :int						,    ):
											super().__init__(**__lowercase			)
											# The padding and conv has been verified in
											# https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
											__lowerCamelCase      :      int         =tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2			)
											__lowerCamelCase      :      str         =tf.keras.layers.ConvaD(
											    filters=__lowercase						,    kernel_size=__lowercase						,    strides=__lowercase						,    padding='''VALID'''						,    groups=__lowercase						,    use_bias=__lowercase						,    name='''convolution'''						,    )
											__lowerCamelCase      :      str         =tf.keras.layers.BatchNormalization(epsilon=1e-5						,    momentum=0.9						,    name='''normalization'''			)
											__lowerCamelCase      :      Optional[int]         =ACTaFN[activation] if activation is not None else tf.identity




				def 					__lowercase  (     self  :Optional[int]						,    __lowercase  :Any			):
											__lowerCamelCase      :      str         =self.convolution(self.padding(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =self.normalization(__lowercase			)
											__lowerCamelCase      :      Any         =self.activation(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :RegNetConfig						,    **__lowercase  :Any			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Tuple         =config.num_channels
											__lowerCamelCase      :      Union[str, Any]         =TFRegNetConvLayer(
											    out_channels=config.embedding_size						,    kernel_size=3						,    stride=2						,    activation=config.hidden_act						,    name='''embedder'''						,    )




				def 					__lowercase  (     self  :int						,    __lowercase  :List[str]			):
											__lowerCamelCase      :      int         =shape_list(__lowercase			)[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      :      Union[str, Any]         =tf.transpose(__lowercase						,    perm=(0, 2, 3, 1)			)
											__lowerCamelCase      :      Optional[int]         =self.embedder(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :List[Any]						,    __lowercase  :int						,    __lowercase  :int = 2						,    **__lowercase  :Optional[int]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      int         =tf.keras.layers.ConvaD(
											    filters=__lowercase						,    kernel_size=1						,    strides=__lowercase						,    use_bias=__lowercase						,    name='''convolution'''			)
											__lowerCamelCase      :      List[str]         =tf.keras.layers.BatchNormalization(epsilon=1e-5						,    momentum=0.9						,    name='''normalization'''			)




				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :tf.Tensor						,    __lowercase  :bool = False			):
											return self.normalization(self.convolution(__lowercase			)						,    training=__lowercase			)

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Dict						,    __lowercase  :int						,    __lowercase  :int						,    **__lowercase  :List[str]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      int         =tf.keras.layers.GlobalAveragePoolingaD(keepdims=__lowercase						,    name='''pooler'''			)
											__lowerCamelCase      :      int         =[
											    tf.keras.layers.ConvaD(filters=__lowercase						,    kernel_size=1						,    activation='''relu'''						,    name='''attention.0'''			),
											    tf.keras.layers.ConvaD(filters=__lowercase						,    kernel_size=1						,    activation='''sigmoid'''						,    name='''attention.2'''			),
											]




				def 					__lowercase  (     self  :Dict						,    __lowercase  :Union[str, Any]			):
											# [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
											__lowerCamelCase      :      Any         =self.pooler(__lowercase			)
											for layer_module in self.attention:
																		__lowerCamelCase      :      Any         =layer_module(__lowercase			)
											__lowerCamelCase      :      Dict         =hidden_state * pooled
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :Optional[int]						,    __lowercase  :RegNetConfig						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :int = 1						,    **__lowercase  :str			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Dict         =in_channels != out_channels or stride != 1
											__lowerCamelCase      :      int         =max(1						,    out_channels // config.groups_width			)
											__lowerCamelCase      :      List[str]         =(
											    TFRegNetShortCut(__lowercase						,    stride=__lowercase						,    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      :      str         =[
											    TFRegNetConvLayer(__lowercase						,    kernel_size=1						,    activation=config.hidden_act						,    name='''layer.0'''			),
											    TFRegNetConvLayer(
											        __lowercase						,    stride=__lowercase						,    groups=__lowercase						,    activation=config.hidden_act						,    name='''layer.1'''			),
											    TFRegNetConvLayer(__lowercase						,    kernel_size=1						,    activation=__lowercase						,    name='''layer.2'''			),
											]
											__lowerCamelCase      :      Optional[int]         =ACTaFN[config.hidden_act]




				def 					__lowercase  (     self  :int						,    __lowercase  :Optional[int]			):
											__lowerCamelCase      :      List[Any]         =hidden_state
											for layer_module in self.layers:
																		__lowerCamelCase      :      str         =layer_module(__lowercase			)
											__lowerCamelCase      :      List[Any]         =self.shortcut(__lowercase			)
											hidden_state += residual
											__lowerCamelCase      :      Optional[int]         =self.activation(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




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




				def 					__lowercase  (     self  :Tuple						,    __lowercase  :Tuple			):
											__lowerCamelCase      :      List[Any]         =hidden_state
											for layer_module in self.layers:
																		__lowerCamelCase      :      int         =layer_module(__lowercase			)
											__lowerCamelCase      :      List[str]         =self.shortcut(__lowercase			)
											hidden_state += residual
											__lowerCamelCase      :      List[str]         =self.activation(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :int						,    __lowercase  :RegNetConfig						,    __lowercase  :int						,    __lowercase  :int						,    __lowercase  :int = 2						,    __lowercase  :int = 2						,    **__lowercase  :Union[str, Any]			):
											super().__init__(**__lowercase			)

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




				def 					__lowercase  (     self  :int						,    __lowercase  :List[str]			):
											for layer_module in self.layers:
																		__lowerCamelCase      :      int         =layer_module(__lowercase			)
											return hidden_state

class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""




				def __init__(     self  :List[Any]						,    __lowercase  :RegNetConfig						,    **__lowercase  :List[str]			):
											super().__init__(**__lowercase			)
											__lowerCamelCase      :      Optional[int]         =[]
											# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
											self.stages.append(
											    TFRegNetStage(
											        __lowercase						,    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      :      Any         =zip(config.hidden_sizes						,    config.hidden_sizes[1:]			)
											for i, ((in_channels, out_channels), depth) in enumerate(zip(__lowercase						,    config.depths[1:]			)			):
																		self.stages.append(TFRegNetStage(__lowercase						,    __lowercase						,    __lowercase						,    depth=__lowercase						,    name=f'stages.{i+1}'			)			)




				def 					__lowercase  (     self  :str						,    __lowercase  :tf.Tensor						,    __lowercase  :bool = False						,    __lowercase  :bool = True			):
											__lowerCamelCase      :      Optional[Any]         =() if output_hidden_states else None

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

																		__lowerCamelCase      :      List[Any]         =stage_module(__lowercase			)

											if output_hidden_states:
																		__lowerCamelCase      :      Union[str, Any]         =hidden_states + (hidden_state,)

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

											return TFBaseModelOutputWithNoAttention(last_hidden_state=__lowercase						,    hidden_states=__lowercase			)

@keras_serializable
class 		SCREAMING_SNAKE_CASE_							(    tf.keras.layers.Layer   ):
				"""simple docstring"""
				__snake_case   :       Optional[int]	 =		RegNetConfig




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




				@unpack_inputs
				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :tf.Tensor						,    __lowercase  :Optional[bool] = None						,    __lowercase  :Optional[bool] = None						,    __lowercase  :bool = False						,    ):
											__lowerCamelCase      :      Union[str, Any]         =(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCamelCase      :      Tuple         =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase      :      Tuple         =self.embedder(__lowercase						,    training=__lowercase			)

											__lowerCamelCase      :      Optional[Any]         =self.encoder(
											    __lowercase						,    output_hidden_states=__lowercase						,    return_dict=__lowercase						,    training=__lowercase			)

											__lowerCamelCase      :      str         =encoder_outputs[0]
											__lowerCamelCase      :      Tuple         =self.pooler(__lowercase			)

											# Change to NCHW output format have uniformity in the modules
											__lowerCamelCase      :      int         =tf.transpose(__lowercase						,    perm=(0, 3, 1, 2)			)
											__lowerCamelCase      :      Any         =tf.transpose(__lowercase						,    perm=(0, 3, 1, 2)			)

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

class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[int]	 =		RegNetConfig
				__snake_case   :       int	 =		"""regnet"""
				__snake_case   :       int	 =		"""pixel_values"""




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

_UpperCamelCase							=      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'

_UpperCamelCase							=      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."""       ,  snake_case__       ,  )
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :RegNetConfig						,    *__lowercase  :List[str]						,    **__lowercase  :int			):
											super().__init__(__lowercase						,    *__lowercase						,    **__lowercase			)
											__lowerCamelCase      :      Tuple         =TFRegNetMainLayer(__lowercase						,    name='''regnet'''			)




				@unpack_inputs
				@add_start_docstrings_to_model_forward(__lowercase			)
				@add_code_sample_docstrings(
				    checkpoint=_CHECKPOINT_FOR_DOC						,    output_type=__lowercase						,    config_class=_CONFIG_FOR_DOC						,    modality='''vision'''						,    expected_output=_EXPECTED_OUTPUT_SHAPE						,    )
				def 					__lowercase  (     self  :Optional[Any]						,    __lowercase  :tf.Tensor						,    __lowercase  :Optional[bool] = None						,    __lowercase  :Optional[bool] = None						,    __lowercase  :Optional[int]=False						,    ):
											__lowerCamelCase      :      List[Any]         =(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCamelCase      :      Optional[int]         =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase      :      Dict         =self.regnet(
											    pixel_values=__lowercase						,    output_hidden_states=__lowercase						,    return_dict=__lowercase						,    training=__lowercase						,    )
											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.
    """       ,  snake_case__       ,  )
class 		SCREAMING_SNAKE_CASE_							(    snake_case__       ,  snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Union[str, Any]						,    __lowercase  :RegNetConfig						,    *__lowercase  :List[Any]						,    **__lowercase  :Dict			):
											super().__init__(__lowercase						,    *__lowercase						,    **__lowercase			)
											__lowerCamelCase      :      Optional[int]         =config.num_labels
											__lowerCamelCase      :      Optional[int]         =TFRegNetMainLayer(__lowercase						,    name='''regnet'''			)
											# classification head
											__lowerCamelCase      :      Union[str, Any]         =[
											    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(__lowercase			)
				@add_code_sample_docstrings(
				    checkpoint=_IMAGE_CLASS_CHECKPOINT						,    output_type=__lowercase						,    config_class=_CONFIG_FOR_DOC						,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,    )
				def 					__lowercase  (     self  :List[Any]						,    __lowercase  :tf.Tensor = None						,    __lowercase  :tf.Tensor = None						,    __lowercase  :bool = None						,    __lowercase  :bool = None						,    __lowercase  :int=False						,    ):
											__lowerCamelCase      :      str         =(
											    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
											)
											__lowerCamelCase      :      Optional[int]         =return_dict if return_dict is not None else self.config.use_return_dict

											__lowerCamelCase      :      str         =self.regnet(
											    __lowercase						,    output_hidden_states=__lowercase						,    return_dict=__lowercase						,    training=__lowercase			)

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

											__lowerCamelCase      :      List[str]         =self.classifier[0](__lowercase			)
											__lowerCamelCase      :      str         =self.classifier[1](__lowercase			)

											__lowerCamelCase      :      str         =None if labels is None else self.hf_compute_loss(labels=__lowercase						,    logits=__lowercase			)

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

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




