




"""simple docstring"""

import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


_lowerCamelCase     :	List[Any]         =				'''true'''

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase=82     ,					_UpperCAmelCase=16		):




	"""simple docstring"""






	set_seed(42		)
	A_     : Optional[Any]		      =						RegressionModel()
	A_     : List[str]		      =						deepcopy(__lowerCAmelCase		)
	A_     : List[str]		      =						RegressionDataset(length=__lowerCAmelCase		)
	A_     : Optional[Any]		      =						DataLoader(__lowerCAmelCase     ,					batch_size=__lowerCAmelCase		)
	model.to(accelerator.device		)
	A_     : List[Any]		      =						accelerator.prepare(__lowerCAmelCase     ,					__lowerCAmelCase		)
	return model, ddp_model, dataloader

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase=False		):




	"""simple docstring"""






	A_     : List[str]		      =						AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''		)
	A_     : Tuple		      =						load_dataset('''glue'''     ,					'''mrpc'''     ,					split='''validation'''		)

	def tokenize_function(_UpperCAmelCase		):
		A_     : str		      =						tokenizer(examples['''sentence1''']     ,					examples['''sentence2''']     ,					truncation=__lowerCAmelCase     ,					max_length=__lowerCAmelCase		)
		return outputs

	with accelerator.main_process_first():
		A_     : List[Any]		      =						dataset.map(
		    __lowerCAmelCase     ,					batched=__lowerCAmelCase     ,					remove_columns=['''idx''', '''sentence1''', '''sentence2''']     ,					)

	A_     : Union[str, Any]		      =						tokenized_datasets.rename_column('''label'''     ,					'''labels'''		)

	def collate_fn(_UpperCAmelCase		):
		if use_longest:
			return tokenizer.pad(__lowerCAmelCase     ,					padding='''longest'''     ,					return_tensors='''pt'''		)
		return tokenizer.pad(__lowerCAmelCase     ,					padding='''max_length'''     ,					max_length=128     ,					return_tensors='''pt'''		)

	return DataLoader(__lowerCAmelCase     ,					shuffle=__lowerCAmelCase     ,					collate_fn=__lowerCAmelCase     ,					batch_size=16		)

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase		):




	"""simple docstring"""






	A_     : List[Any]		      =						Accelerator(dispatch_batches=__lowerCAmelCase     ,					split_batches=__lowerCAmelCase		)
	A_     : Tuple		      =						get_dataloader(__lowerCAmelCase     ,					not dispatch_batches		)
	A_     : Optional[Any]		      =						AutoModelForSequenceClassification.from_pretrained(
	    '''hf-internal-testing/mrpc-bert-base-cased'''     ,					return_dict=__lowerCAmelCase		)
	A_     : List[Any]		      =						accelerator.prepare(__lowerCAmelCase     ,					__lowerCAmelCase		)
	return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase     ,					_UpperCAmelCase		):




	"""simple docstring"""






	A_     : Union[str, Any]		      =						[]
	for batch in dataloader:
		A_     : Union[str, Any]		      =						batch.values()
		with torch.no_grad():
			A_     : List[Any]		      =						model(__lowerCAmelCase		)
			A_     : Optional[Any]		      =						accelerator.gather_for_metrics((logit, target)		)
			logits_and_targets.append((logit, target)		)
	A_     : List[str]		      =						[], []
	for logit, targ in logits_and_targets:
		logits.append(__lowerCAmelCase		)
		targs.append(__lowerCAmelCase		)
	A_     : Optional[Any]		      =						torch.cat(__lowerCAmelCase		), torch.cat(__lowerCAmelCase		)
	return logits, targs

def 						lowercase_					(						_UpperCAmelCase     ,					_UpperCAmelCase=82     ,					_UpperCAmelCase=False     ,					_UpperCAmelCase=False     ,					_UpperCAmelCase=16		):




	"""simple docstring"""






	A_     : int		      =						get_basic_setup(__lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		)
	A_     : Any		      =						generate_predictions(__lowerCAmelCase     ,					__lowerCAmelCase     ,					__lowerCAmelCase		)
	assert (
	    len(__lowerCAmelCase		) == num_samples
	), f"""Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(__lowerCAmelCase		)}"""

def 						lowercase_					(						_UpperCAmelCase = False     ,					_UpperCAmelCase = False		):




	"""simple docstring"""






	A_     : Optional[Any]		      =						evaluate.load('''glue'''     ,					'''mrpc'''		)
	A_     : List[Any]		      =						get_mrpc_setup(__lowerCAmelCase     ,					__lowerCAmelCase		)
	# First do baseline
	A_     : int		      =						setup["no"]
	model.to(__lowerCAmelCase		)
	model.eval()
	for batch in dataloader:
		batch.to(__lowerCAmelCase		)
		with torch.inference_mode():
			A_     : Union[str, Any]		      =						model(**__lowerCAmelCase		)
		A_     : Tuple		      =						outputs.logits.argmax(dim=-1		)
		metric.add_batch(predictions=__lowerCAmelCase     ,					references=batch['''labels''']		)
	A_     : int		      =						metric.compute()

	# Then do distributed
	A_     : List[Any]		      =						setup["ddp"]
	model.eval()
	for batch in dataloader:
		with torch.inference_mode():
			A_     : List[Any]		      =						model(**__lowerCAmelCase		)
		A_     : Union[str, Any]		      =						outputs.logits.argmax(dim=-1		)
		A_     : Dict		      =						batch["labels"]
		A_     : Optional[int]		      =						accelerator.gather_for_metrics((preds, references)		)
		metric.add_batch(predictions=__lowerCAmelCase     ,					references=__lowerCAmelCase		)
	A_     : str		      =						metric.compute()

	for key in "accuracy f1".split():
		assert math.isclose(
		    baseline[key]     ,					distributed[key]		), f"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""

def 						lowercase_					(						):




	"""simple docstring"""






	A_     : List[str]		      =						Accelerator(split_batches=__lowerCAmelCase     ,					dispatch_batches=__lowerCAmelCase		)
	if accelerator.is_local_main_process:
		datasets.utils.logging.set_verbosity_warning()
		transformers.utils.logging.set_verbosity_warning()
	else:
		datasets.utils.logging.set_verbosity_error()
		transformers.utils.logging.set_verbosity_error()
	# These are a bit slower so they should only be ran on the GPU or TPU
	if torch.cuda.is_available() or is_tpu_available():
		if accelerator.is_local_main_process:
			print('''**Testing gather_for_metrics**'''		)
		for split_batches in [True, False]:
			for dispatch_batches in [True, False]:
				if accelerator.is_local_main_process:
					print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`"""		)
				test_mrpc(__lowerCAmelCase     ,					__lowerCAmelCase		)
				accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test torch metrics**'''		)
	for split_batches in [True, False]:
		for dispatch_batches in [True, False]:
			A_     : Tuple		      =						Accelerator(split_batches=__lowerCAmelCase     ,					dispatch_batches=__lowerCAmelCase		)
			if accelerator.is_local_main_process:
				print(f"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99"""		)
			test_torch_metrics(__lowerCAmelCase     ,					99		)
			accelerator.state._reset_state()
	if accelerator.is_local_main_process:
		print('''**Test last batch is not dropped when perfectly divisible**'''		)
	A_     : List[str]		      =						Accelerator()
	test_torch_metrics(__lowerCAmelCase     ,					512		)
	accelerator.state._reset_state()

def 						lowercase_					(						_UpperCAmelCase		):




	"""simple docstring"""






	main()


if __name__ == "__main__":
		main()













import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowerCamelCase	:							Tuple				=logging.get_logger(__name__)

lowerCamelCase	:							Optional[Any]				={
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Optional[Any]        = '''conditional_detr'''
 _lowerCAmelCase       :       List[Any]        = ['''past_key_values''']
 _lowerCAmelCase       :       List[str]        = {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }







 def __init__(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Tuple=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Tuple=3							,   SCREAMING_SNAKE_CASE					:   Any=3_00							,   SCREAMING_SNAKE_CASE					:   Tuple=6							,   SCREAMING_SNAKE_CASE					:   int=20_48							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=8							,   SCREAMING_SNAKE_CASE					:   int=6							,   SCREAMING_SNAKE_CASE					:   Dict=20_48							,   SCREAMING_SNAKE_CASE					:   Optional[int]=8							,   SCREAMING_SNAKE_CASE					:   Dict=0.0							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   int="relu"							,   SCREAMING_SNAKE_CASE					:   Any=2_56							,   SCREAMING_SNAKE_CASE					:   int=0.1							,   SCREAMING_SNAKE_CASE					:   int=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=0.0							,   SCREAMING_SNAKE_CASE					:   List[str]=0.0_2							,   SCREAMING_SNAKE_CASE					:   Optional[int]=1.0							,   SCREAMING_SNAKE_CASE					:   List[str]=False							,   SCREAMING_SNAKE_CASE					:   int="sine"							,   SCREAMING_SNAKE_CASE					:   str="resnet50"							,   SCREAMING_SNAKE_CASE					:   int=True							,   SCREAMING_SNAKE_CASE					:   Tuple=False							,   SCREAMING_SNAKE_CASE					:   Tuple=2							,   SCREAMING_SNAKE_CASE					:   Tuple=5							,   SCREAMING_SNAKE_CASE					:   List[str]=2							,   SCREAMING_SNAKE_CASE					:   List[str]=1							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=1							,   SCREAMING_SNAKE_CASE					:   str=2							,   SCREAMING_SNAKE_CASE					:   Any=5							,   SCREAMING_SNAKE_CASE					:   int=2							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.2_5							,   **SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   ):


      '''simple docstring'''






      if backbone_config is not None and use_timm_backbone:
           raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."						)

      if not use_timm_backbone:
           if backbone_config is None:
                logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING["resnet"](out_features=["stage4"]						)
           elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                UpperCamelCase__			:   Union[str, Any]       				=  backbone_config.get("model_type"						)
                UpperCamelCase__			:   Tuple       				=  CONFIG_MAPPING[backbone_model_type]
                UpperCamelCase__			:   Any       				=  config_class.from_dict(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Optional[Any]       				=  use_timm_backbone
      UpperCamelCase__			:   List[Any]       				=  backbone_config
      UpperCamelCase__			:   Tuple       				=  num_channels
      UpperCamelCase__			:   List[Any]       				=  num_queries
      UpperCamelCase__			:   Dict       				=  d_model
      UpperCamelCase__			:   Any       				=  encoder_ffn_dim
      UpperCamelCase__			:   List[str]       				=  encoder_layers
      UpperCamelCase__			:   List[str]       				=  encoder_attention_heads
      UpperCamelCase__			:   Optional[int]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  decoder_attention_heads
      UpperCamelCase__			:   int       				=  dropout
      UpperCamelCase__			:   Optional[int]       				=  attention_dropout
      UpperCamelCase__			:   Any       				=  activation_dropout
      UpperCamelCase__			:   int       				=  activation_function
      UpperCamelCase__			:   int       				=  init_std
      UpperCamelCase__			:   List[Any]       				=  init_xavier_std
      UpperCamelCase__			:   List[str]       				=  encoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  decoder_layerdrop
      UpperCamelCase__			:   List[Any]       				=  encoder_layers
      UpperCamelCase__			:   Optional[Any]       				=  auxiliary_loss
      UpperCamelCase__			:   List[str]       				=  position_embedding_type
      UpperCamelCase__			:   Optional[Any]       				=  backbone
      UpperCamelCase__			:   Optional[int]       				=  use_pretrained_backbone
      UpperCamelCase__			:   List[Any]       				=  dilation
      # Hungarian matcher
      UpperCamelCase__			:   List[str]       				=  class_cost
      UpperCamelCase__			:   Union[str, Any]       				=  bbox_cost
      UpperCamelCase__			:   int       				=  giou_cost
      # Loss coefficients
      UpperCamelCase__			:   str       				=  mask_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  dice_loss_coefficient
      UpperCamelCase__			:   int       				=  cls_loss_coefficient
      UpperCamelCase__			:   Tuple       				=  bbox_loss_coefficient
      UpperCamelCase__			:   Any       				=  giou_loss_coefficient
      UpperCamelCase__			:   List[Any]       				=  focal_alpha
      super().__init__(is_encoder_decoder=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE						)







 @property
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return self.encoder_attention_heads







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return self.d_model







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  copy.deepcopy(self.__dict__						)
      if self.backbone_config is not None:
           UpperCamelCase__			:   List[str]       				=  self.backbone_config.to_dict()
      UpperCamelCase__			:   int       				=  self.__class__.model_type
      return output







class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = version.parse('''1.11'''       )







 @property
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      return OrderedDict(
          [
              ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ("pixel_mask", {0: "batch"}),
          ]						)







 @property
 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return 1e-5







 @property
 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      return 12