


"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


__SCREAMING_SNAKE_CASE							:			List[Any]						=			re.compile(R'\b(a|an|the)\b', re.UNICODE)

__SCREAMING_SNAKE_CASE							:			List[Any]						=			None







def   _a (					)			-> str:
							snake_case_							=						argparse.ArgumentParser("""Official evaluation script for SQuAD version 2.0.""" )
							parser.add_argument("""data_file""" ,       metavar="""data.json""" ,       help="""Input data JSON file.""" )
							parser.add_argument("""pred_file""" ,       metavar="""pred.json""" ,       help="""Model predictions.""" )
							parser.add_argument(
							    """--out-file""" ,       """-o""" ,       metavar="""eval.json""" ,       help="""Write accuracy metrics to file (default is stdout).""" )
							parser.add_argument(
							    """--na-prob-file""" ,       """-n""" ,       metavar="""na_prob.json""" ,       help="""Model estimates of probability of no answer.""" )
							parser.add_argument(
							    """--na-prob-thresh""" ,       """-t""" ,       type=UpperCamelCase__ ,       default=1.0 ,       help="""Predict \"\" if no-answer probability exceeds this (default = 1.0).""" ,       )
							parser.add_argument(
							    """--out-image-dir""" ,       """-p""" ,       metavar="""out_images""" ,       default=UpperCamelCase__ ,       help="""Save precision-recall curves to directory.""" )
							parser.add_argument("""--verbose""" ,       """-v""" ,       action="""store_true""" )
							if len(sys.argv ) == 1:
														parser.print_help()
														sys.exit(1 )
							return parser.parse_args()







def   _a (					_SCREAMING_SNAKE_CASE )			-> Optional[Any]:
							snake_case_							=						{}
							for article in dataset:
														for p in article["paragraphs"]:
																					for qa in p["qas"]:
																												snake_case_							=						bool(qa["""answers"""]["""text"""] )
							return qid_to_has_ans







def   _a (					_SCREAMING_SNAKE_CASE )			-> Dict:





							def remove_articles(_SCREAMING_SNAKE_CASE ):
														return ARTICLES_REGEX.sub(""" """ ,       UpperCamelCase__ )

							def white_space_fix(_SCREAMING_SNAKE_CASE ):
														return " ".join(text.split() )

							def remove_punc(_SCREAMING_SNAKE_CASE ):
														snake_case_							=						set(string.punctuation )
														return "".join(ch for ch in text if ch not in exclude )

							def lower(_SCREAMING_SNAKE_CASE ):
														return text.lower()

							return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__ ) ) ) )







def   _a (					_SCREAMING_SNAKE_CASE )			-> List[Any]:





							if not s:
														return []
							return normalize_answer(UpperCamelCase__ ).split()







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> int:
							return int(normalize_answer(UpperCamelCase__ ) == normalize_answer(UpperCamelCase__ ) )







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Optional[Any]:
							snake_case_							=						get_tokens(UpperCamelCase__ )
							snake_case_							=						get_tokens(UpperCamelCase__ )
							snake_case_							=						collections.Counter(UpperCamelCase__ ) & collections.Counter(UpperCamelCase__ )
							snake_case_							=						sum(common.values() )
							if len(UpperCamelCase__ ) == 0 or len(UpperCamelCase__ ) == 0:
														# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
														return int(gold_toks == pred_toks )
							if num_same == 0:
														return 0
							snake_case_							=						1.0 * num_same / len(UpperCamelCase__ )
							snake_case_							=						1.0 * num_same / len(UpperCamelCase__ )
							snake_case_							=						(2 * precision * recall) / (precision + recall)
							return fa







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Dict:
							snake_case_							=						{}
							snake_case_							=						{}
							for article in dataset:
														for p in article["paragraphs"]:
																					for qa in p["qas"]:
																												snake_case_							=						qa["""id"""]
																												snake_case_							=						[t for t in qa["""answers"""]["""text"""] if normalize_answer(UpperCamelCase__ )]
																												if not gold_answers:
																																			# For unanswerable questions, only correct answer is empty string
																																			snake_case_							=						[""""""]
																												if qid not in preds:
																																			print(f"""Missing prediction for {qid}""" )
																																			continue
																												snake_case_							=						preds[qid]
																												# Take max over all gold answers
																												snake_case_							=						max(compute_exact(UpperCamelCase__ ,       UpperCamelCase__ ) for a in gold_answers )
																												snake_case_							=						max(compute_fa(UpperCamelCase__ ,       UpperCamelCase__ ) for a in gold_answers )
							return exact_scores, fa_scores







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> List[str]:
							snake_case_							=						{}
							for qid, s in scores.items():
														snake_case_							=						na_probs[qid] > na_prob_thresh
														if pred_na:
																					snake_case_							=						float(not qid_to_has_ans[qid] )
														else:
																					snake_case_							=						s
							return new_scores







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE=None )			-> Tuple:





							if not qid_list:
														snake_case_							=						len(UpperCamelCase__ )
														return collections.OrderedDict(
														    [
														        ("""exact""", 100.0 * sum(exact_scores.values() ) / total),
														        ("""f1""", 100.0 * sum(fa_scores.values() ) / total),
														        ("""total""", total),
														    ] )
							else:
														snake_case_							=						len(UpperCamelCase__ )
														return collections.OrderedDict(
														    [
														        ("""exact""", 100.0 * sum(exact_scores[k] for k in qid_list ) / total),
														        ("""f1""", 100.0 * sum(fa_scores[k] for k in qid_list ) / total),
														        ("""total""", total),
														    ] )







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> str:





							for k in new_eval:
														snake_case_							=						new_eval[k]







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Optional[int]:
							plt.step(UpperCamelCase__ ,       UpperCamelCase__ ,       color="""b""" ,       alpha=0.2 ,       where="""post""" )
							plt.fill_between(UpperCamelCase__ ,       UpperCamelCase__ ,       step="""post""" ,       alpha=0.2 ,       color="""b""" )
							plt.xlabel("""Recall""" )
							plt.ylabel("""Precision""" )
							plt.xlim([0.0, 1.05] )
							plt.ylim([0.0, 1.05] )
							plt.title(UpperCamelCase__ )
							plt.savefig(UpperCamelCase__ )
							plt.clf()







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE=None ,       _SCREAMING_SNAKE_CASE=None )			-> Optional[Any]:
							snake_case_							=						sorted(UpperCamelCase__ ,       key=lambda _SCREAMING_SNAKE_CASE : na_probs[k] )
							snake_case_							=						0.0
							snake_case_							=						1.0
							snake_case_							=						0.0
							snake_case_							=						[1.0]
							snake_case_							=						[0.0]
							snake_case_							=						0.0
							for i, qid in enumerate(UpperCamelCase__ ):
														if qid_to_has_ans[qid]:
																					true_pos += scores[qid]
														snake_case_							=						true_pos / float(i + 1 )
														snake_case_							=						true_pos / float(UpperCamelCase__ )
														if i == len(UpperCamelCase__ ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
																					# i.e., if we can put a threshold after this point
																					avg_prec += cur_p * (cur_r - recalls[-1])
																					precisions.append(UpperCamelCase__ )
																					recalls.append(UpperCamelCase__ )
							if out_image:
														plot_pr_curve(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ )
							return {"ap": 100.0 * avg_prec}







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Dict:





							if out_image_dir and not os.path.exists(UpperCamelCase__ ):
														os.makedirs(UpperCamelCase__ )
							snake_case_							=						sum(1 for v in qid_to_has_ans.values() if v )
							if num_true_pos == 0:
														return
							snake_case_							=						make_precision_recall_eval(
							    UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       out_image=os.path.join(UpperCamelCase__ ,       """pr_exact.png""" ) ,       title="""Precision-Recall curve for Exact Match score""" ,       )
							snake_case_							=						make_precision_recall_eval(
							    UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       out_image=os.path.join(UpperCamelCase__ ,       """pr_f1.png""" ) ,       title="""Precision-Recall curve for F1 score""" ,       )
							snake_case_							=						{k: float(UpperCamelCase__ ) for k, v in qid_to_has_ans.items()}
							snake_case_							=						make_precision_recall_eval(
							    UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       out_image=os.path.join(UpperCamelCase__ ,       """pr_oracle.png""" ) ,       title="""Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)""" ,       )
							merge_eval(UpperCamelCase__ ,       UpperCamelCase__ ,       """pr_exact""" )
							merge_eval(UpperCamelCase__ ,       UpperCamelCase__ ,       """pr_f1""" )
							merge_eval(UpperCamelCase__ ,       UpperCamelCase__ ,       """pr_oracle""" )







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Tuple:





							if not qid_list:
														return
							snake_case_							=						[na_probs[k] for k in qid_list]
							snake_case_							=						np.ones_like(UpperCamelCase__ ) / float(len(UpperCamelCase__ ) )
							plt.hist(UpperCamelCase__ ,       weights=UpperCamelCase__ ,       bins=20 ,       range=(0.0, 1.0) )
							plt.xlabel("""Model probability of no-answer""" )
							plt.ylabel("""Proportion of dataset""" )
							plt.title(f"""Histogram of no-answer probability: {name}""" )
							plt.savefig(os.path.join(UpperCamelCase__ ,       f"""na_prob_hist_{name}.png""" ) )
							plt.clf()







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> int:
							snake_case_							=						sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
							snake_case_							=						num_no_ans
							snake_case_							=						cur_score
							snake_case_							=						0.0
							snake_case_							=						sorted(UpperCamelCase__ ,       key=lambda _SCREAMING_SNAKE_CASE : na_probs[k] )
							for i, qid in enumerate(UpperCamelCase__ ):
														if qid not in scores:
																					continue
														if qid_to_has_ans[qid]:
																					snake_case_							=						scores[qid]
														else:
																					if preds[qid]:
																												snake_case_							=						-1
																					else:
																												snake_case_							=						0
														cur_score += diff
														if cur_score > best_score:
																					snake_case_							=						cur_score
																					snake_case_							=						na_probs[qid]
							return 100.0 * best_score / len(UpperCamelCase__ ), best_thresh







def   _a (					_SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE ,       _SCREAMING_SNAKE_CASE )			-> Union[str, Any]:
							snake_case_				,  snake_case_							=						find_best_thresh(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ )
							snake_case_				,  snake_case_							=						find_best_thresh(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ )
							snake_case_							=						best_exact
							snake_case_							=						exact_thresh
							snake_case_							=						best_fa
							snake_case_							=						fa_thresh







def   _a (					)			-> Union[str, Any]:





							with open(OPTS.data_file ) as f:
														snake_case_							=						json.load(UpperCamelCase__ )
														snake_case_							=						dataset_json["""data"""]
							with open(OPTS.pred_file ) as f:
														snake_case_							=						json.load(UpperCamelCase__ )
							if OPTS.na_prob_file:
														with open(OPTS.na_prob_file ) as f:
																					snake_case_							=						json.load(UpperCamelCase__ )
							else:
														snake_case_							=						{k: 0.0 for k in preds}
							snake_case_							=						make_qid_to_has_ans(UpperCamelCase__ )  # maps qid to True/False
							snake_case_							=						[k for k, v in qid_to_has_ans.items() if v]
							snake_case_							=						[k for k, v in qid_to_has_ans.items() if not v]
							snake_case_				,  snake_case_							=						get_raw_scores(UpperCamelCase__ ,       UpperCamelCase__ )
							snake_case_							=						apply_no_ans_threshold(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       OPTS.na_prob_thresh )
							snake_case_							=						apply_no_ans_threshold(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       OPTS.na_prob_thresh )
							snake_case_							=						make_eval_dict(UpperCamelCase__ ,       UpperCamelCase__ )
							if has_ans_qids:
														snake_case_							=						make_eval_dict(UpperCamelCase__ ,       UpperCamelCase__ ,       qid_list=UpperCamelCase__ )
														merge_eval(UpperCamelCase__ ,       UpperCamelCase__ ,       """HasAns""" )
							if no_ans_qids:
														snake_case_							=						make_eval_dict(UpperCamelCase__ ,       UpperCamelCase__ ,       qid_list=UpperCamelCase__ )
														merge_eval(UpperCamelCase__ ,       UpperCamelCase__ ,       """NoAns""" )
							if OPTS.na_prob_file:
														find_all_best_thresh(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ )
							if OPTS.na_prob_file and OPTS.out_image_dir:
														run_precision_recall_analysis(UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       UpperCamelCase__ ,       OPTS.out_image_dir )
														histogram_na_prob(UpperCamelCase__ ,       UpperCamelCase__ ,       OPTS.out_image_dir ,       """hasAns""" )
														histogram_na_prob(UpperCamelCase__ ,       UpperCamelCase__ ,       OPTS.out_image_dir ,       """noAns""" )
							if OPTS.out_file:
														with open(OPTS.out_file ,       """w""" ) as f:
																					json.dump(UpperCamelCase__ ,       UpperCamelCase__ )
							else:
														print(json.dumps(UpperCamelCase__ ,       indent=2 ) )


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:			Union[str, Any]						=			parse_args()
				if OPTS.out_image_dir:
								import matplotlib

								matplotlib.use('Agg')
								import matplotlib.pyplot as plt
				main()




"""simple docstring"""




from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__lowerCamelCase							 =     logging.get_logger(__name__)

# General docstring
__lowerCamelCase							 =     "ResNetConfig"

# Base docstring
__lowerCamelCase							 =     "microsoft/resnet-50"
__lowerCamelCase							 =     [1, 20_48, 7, 7]

# Image classification docstring
__lowerCamelCase							 =     "microsoft/resnet-50"
__lowerCamelCase							 =     "tiger cat"

__lowerCamelCase							 =     [
    "microsoft/resnet-50",
    # See all resnet models at https://huggingface.co/models?filter=resnet
]
class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 3						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"   )    -> Any:
													super().__init__()
													A__		   =							nn.Convad(
													    __UpperCAmelCase						,__UpperCAmelCase						,kernel_size=__UpperCAmelCase						,stride=__UpperCAmelCase						,padding=kernel_size // 2						,bias=__UpperCAmelCase   )
													A__		   =							nn.BatchNormad(__UpperCAmelCase   )
													A__		   =							ACTaFN[activation] if activation is not None else nn.Identity()





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							self.convolution(__UpperCAmelCase   )
													A__		   =							self.normalization(__UpperCAmelCase   )
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Any:
													super().__init__()
													A__		   =							ResNetConvLayer(
													    config.num_channels						,config.embedding_size						,kernel_size=7						,stride=2						,activation=config.hidden_act   )
													A__		   =							nn.MaxPoolad(kernel_size=3						,stride=2						,padding=1   )
													A__		   =							config.num_channels





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							pixel_values.shape[1]
													if num_channels != self.num_channels:
																				raise ValueError(
																				    'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'   )
													A__		   =							self.embedder(__UpperCAmelCase   )
													A__		   =							self.pooler(__UpperCAmelCase   )
													return embedding






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 2   )    -> Optional[Any]:
													super().__init__()
													A__		   =							nn.Convad(__UpperCAmelCase						,__UpperCAmelCase						,kernel_size=1						,stride=__UpperCAmelCase						,bias=__UpperCAmelCase   )
													A__		   =							nn.BatchNormad(__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							self.convolution(__UpperCAmelCase   )
													A__		   =							self.normalization(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"   )    -> int:
													super().__init__()
													A__		   =							in_channels != out_channels or stride != 1
													A__		   =							(
													    ResNetShortCut(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   ) if should_apply_shortcut else nn.Identity()
													)
													A__		   =							nn.Sequential(
													    ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   )						,ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,activation=__UpperCAmelCase   )						,)
													A__		   =							ACTaFN[activation]





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													A__		   =							hidden_state
													A__		   =							self.layer(__UpperCAmelCase   )
													A__		   =							self.shortcut(__UpperCAmelCase   )
													hidden_state += residual
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 1						,__UpperCAmelCase = "relu"						,__UpperCAmelCase = 4   )    -> int:
													super().__init__()
													A__		   =							in_channels != out_channels or stride != 1
													A__		   =							out_channels // reduction
													A__		   =							(
													    ResNetShortCut(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   ) if should_apply_shortcut else nn.Identity()
													)
													A__		   =							nn.Sequential(
													    ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,kernel_size=1   )						,ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase   )						,ResNetConvLayer(__UpperCAmelCase						,__UpperCAmelCase						,kernel_size=1						,activation=__UpperCAmelCase   )						,)
													A__		   =							ACTaFN[activation]





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													A__		   =							hidden_state
													A__		   =							self.layer(__UpperCAmelCase   )
													A__		   =							self.shortcut(__UpperCAmelCase   )
													hidden_state += residual
													A__		   =							self.activation(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = 2						,__UpperCAmelCase = 2						,)    -> Any:
													super().__init__()

													A__		   =							ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

													A__		   =							nn.Sequential(
													    # downsampling is done in the first layer with stride of 2
													    layer(__UpperCAmelCase						,__UpperCAmelCase						,stride=__UpperCAmelCase						,activation=config.hidden_act   )						,*[layer(__UpperCAmelCase						,__UpperCAmelCase						,activation=config.hidden_act   ) for _ in range(depth - 1   )]						,)





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> Tensor:
													A__		   =							input
													for layer in self.layers:
																				A__		   =							layer(__UpperCAmelCase   )
													return hidden_state






class 			UpperCamelCase__(	nn.Module       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													super().__init__()
													A__		   =							nn.ModuleList([]   )
													# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
													self.stages.append(
													    ResNetStage(
													        __UpperCAmelCase						,config.embedding_size						,config.hidden_sizes[0]						,stride=2 if config.downsample_in_first_stage else 1						,depth=config.depths[0]						,)   )
													A__		   =							zip(config.hidden_sizes						,config.hidden_sizes[1:]   )
													for (in_channels, out_channels), depth in zip(__UpperCAmelCase						,config.depths[1:]   ):
																				self.stages.append(ResNetStage(__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase						,depth=__UpperCAmelCase   )   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = False						,__UpperCAmelCase = True   )    -> BaseModelOutputWithNoAttention:
													A__		   =							() if output_hidden_states else None

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

																				A__		   =							stage_module(__UpperCAmelCase   )

													if output_hidden_states:
																				A__		   =							hidden_states + (hidden_state,)

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

													return BaseModelOutputWithNoAttention(
													    last_hidden_state=__UpperCAmelCase						,hidden_states=__UpperCAmelCase						,)






class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  str  							=						ResNetConfig
						lowerCAmelCase__     :  str  							=						'resnet'
						lowerCAmelCase__     :  int  							=						'pixel_values'
						lowerCAmelCase__     :  Any  							=						True





						def  snake_case__		(				self						,__UpperCAmelCase   )    -> List[Any]:
													if isinstance(__UpperCAmelCase						,nn.Convad   ):
																				nn.init.kaiming_normal_(module.weight						,mode='fan_out'						,nonlinearity='relu'   )
													elif isinstance(__UpperCAmelCase						,(nn.BatchNormad, nn.GroupNorm)   ):
																				nn.init.constant_(module.weight						,1   )
																				nn.init.constant_(module.bias						,0   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase=False   )    -> Any:
													if isinstance(__UpperCAmelCase						,__UpperCAmelCase   ):
																				A__		   =							value






__lowerCamelCase							 =     R"\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`ResNetConfig`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

__lowerCamelCase							 =     R"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConvNextImageProcessor.__call__`] for details.\n\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 ResNet model outputting raw features without any specific head on top.'						,  __A						,  )
class 			UpperCamelCase__(	__A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Union[str, Any]:
													super().__init__(__UpperCAmelCase   )
													A__		   =							config
													A__		   =							ResNetEmbeddings(__UpperCAmelCase   )
													A__		   =							ResNetEncoder(__UpperCAmelCase   )
													A__		   =							nn.AdaptiveAvgPoolad((1, 1)   )
													# Initialize weights and apply final processing
													self.post_init()





						@add_start_docstrings_to_model_forward(__UpperCAmelCase   )
						@add_code_sample_docstrings(
						    checkpoint=_CHECKPOINT_FOR_DOC						,output_type=__UpperCAmelCase						,config_class=_CONFIG_FOR_DOC						,modality='vision'						,expected_output=_EXPECTED_OUTPUT_SHAPE						,)
						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None   )    -> BaseModelOutputWithPoolingAndNoAttention:
													A__		   =							(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)
													A__		   =							return_dict if return_dict is not None else self.config.use_return_dict

													A__		   =							self.embedder(__UpperCAmelCase   )

													A__		   =							self.encoder(
													    __UpperCAmelCase						,output_hidden_states=__UpperCAmelCase						,return_dict=__UpperCAmelCase   )

													A__		   =							encoder_outputs[0]

													A__		   =							self.pooler(__UpperCAmelCase   )

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

													return BaseModelOutputWithPoolingAndNoAttention(
													    last_hidden_state=__UpperCAmelCase						,pooler_output=__UpperCAmelCase						,hidden_states=encoder_outputs.hidden_states						,)






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





						def __init__(				self						,__UpperCAmelCase   )    -> Tuple:
													super().__init__(__UpperCAmelCase   )
													A__		   =							config.num_labels
													A__		   =							ResNetModel(__UpperCAmelCase   )
													# classification head
													A__		   =							nn.Sequential(
													    nn.Flatten()						,nn.Linear(config.hidden_sizes[-1]						,config.num_labels   ) if config.num_labels > 0 else nn.Identity()						,)
													# initialize weights and apply final processing
													self.post_init()





						@add_start_docstrings_to_model_forward(__UpperCAmelCase   )
						@add_code_sample_docstrings(
						    checkpoint=_IMAGE_CLASS_CHECKPOINT						,output_type=__UpperCAmelCase						,config_class=_CONFIG_FOR_DOC						,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT						,)
						def  snake_case__		(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,)    -> ImageClassifierOutputWithNoAttention:
													A__		   =							return_dict if return_dict is not None else self.config.use_return_dict

													A__		   =							self.resnet(__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase						,return_dict=__UpperCAmelCase   )

													A__		   =							outputs.pooler_output if return_dict else outputs[1]

													A__		   =							self.classifier(__UpperCAmelCase   )

													A__		   =							None

													if labels is not None:
																				if self.config.problem_type is None:
																											if self.num_labels == 1:
																																		A__		   =							'regression'
																											elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																		A__		   =							'single_label_classification'
																											else:
																																		A__		   =							'multi_label_classification'
																				if self.config.problem_type == "regression":
																											A__		   =							MSELoss()
																											if self.num_labels == 1:
																																		A__		   =							loss_fct(logits.squeeze()						,labels.squeeze()   )
																											else:
																																		A__		   =							loss_fct(__UpperCAmelCase						,__UpperCAmelCase   )
																				elif self.config.problem_type == "single_label_classification":
																											A__		   =							CrossEntropyLoss()
																											A__		   =							loss_fct(logits.view(-1						,self.num_labels   )						,labels.view(-1   )   )
																				elif self.config.problem_type == "multi_label_classification":
																											A__		   =							BCEWithLogitsLoss()
																											A__		   =							loss_fct(__UpperCAmelCase						,__UpperCAmelCase   )

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

													return ImageClassifierOutputWithNoAttention(loss=__UpperCAmelCase						,logits=__UpperCAmelCase						,hidden_states=outputs.hidden_states   )






@add_start_docstrings(
    '\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '						,  __A						,  )
class 			UpperCamelCase__(	__A						,  __A       ):





						def __init__(				self						,__UpperCAmelCase   )    -> Optional[Any]:
													super().__init__(__UpperCAmelCase   )
													super()._init_backbone(__UpperCAmelCase   )

													A__		   =							[config.embedding_size] + config.hidden_sizes
													A__		   =							ResNetEmbeddings(__UpperCAmelCase   )
													A__		   =							ResNetEncoder(__UpperCAmelCase   )

													# initialize weights and apply final processing
													self.post_init()





						@add_start_docstrings_to_model_forward(__UpperCAmelCase   )
						@replace_return_docstrings(output_type=__UpperCAmelCase						,config_class=_CONFIG_FOR_DOC   )
						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None   )    -> BackboneOutput:
													A__		   =							return_dict if return_dict is not None else self.config.use_return_dict
													A__		   =							(
													    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
													)

													A__		   =							self.embedder(__UpperCAmelCase   )

													A__		   =							self.encoder(__UpperCAmelCase						,output_hidden_states=__UpperCAmelCase						,return_dict=__UpperCAmelCase   )

													A__		   =							outputs.hidden_states

													A__		   =							()
													for idx, stage in enumerate(self.stage_names   ):
																				if stage in self.out_features:
																											feature_maps += (hidden_states[idx],)

													if not return_dict:
																				A__		   =							(feature_maps,)
																				if output_hidden_states:
																											output += (outputs.hidden_states,)
																				return output

													return BackboneOutput(
													    feature_maps=__UpperCAmelCase						,hidden_states=outputs.hidden_states if output_hidden_states else None						,attentions=__UpperCAmelCase						,)





