





"""simple docstring"""


import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
__lowerCamelCase      :List[str]	    =		logging.getLogger(__name__)




def   snake_case		(     UpperCamelCase__       :						str			,				UpperCamelCase__       :						int    )   ->      List[str]:
      lowerCamelCase					:					Optional[int]        =	np.argmax(lowerCAmelCase_			,				axis=1    )
      return np.sum(outputs == labels    )




def   snake_case		(     UpperCamelCase__       :						Dict    )   ->      Any:



      with open(lowerCAmelCase_			,				encoding="""utf_8"""    ) as f:
            lowerCamelCase					:					Dict        =	csv.reader(lowerCAmelCase_    )
            lowerCamelCase					:					Tuple        =	[]
            next(lowerCAmelCase_    )  # skip the first line
            for line in tqdm(lowerCAmelCase_    ):
                  output.append((""" """.join(line[1:5]    ), line[5], line[6], int(line[-1]    ) - 1)    )
      return output




def   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Dict			,				UpperCamelCase__       :						Union[str, Any]			,				UpperCamelCase__       :						int    )   ->      Optional[int]:
      lowerCamelCase					:					Union[str, Any]        =	[]
      for dataset in encoded_datasets:
            lowerCamelCase					:					List[Any]        =	len(lowerCAmelCase_    )
            lowerCamelCase					:					int        =	np.zeros((n_batch, 2, input_len)			,				dtype=np.intaa    )
            lowerCamelCase					:					Optional[Any]        =	np.zeros((n_batch, 2)			,				dtype=np.intaa    )
            lowerCamelCase					:					Optional[Any]        =	np.full((n_batch, 2, input_len)			,				fill_value=-100			,				dtype=np.intaa    )
            lowerCamelCase					:					int        =	np.zeros((n_batch,)			,				dtype=np.intaa    )
            for (
                i,
                (story, conta, conta, mc_label),
            ) in enumerate(lowerCAmelCase_    ):
                  lowerCamelCase					:					Any        =	[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  lowerCamelCase					:					List[Any]        =	[start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
                  lowerCamelCase					:					List[Any]        =	with_conta
                  lowerCamelCase					:					Dict        =	with_conta
                  lowerCamelCase					:					str        =	len(lowerCAmelCase_    ) - 1
                  lowerCamelCase					:					List[Any]        =	len(lowerCAmelCase_    ) - 1
                  lowerCamelCase					:					Optional[Any]        =	with_conta
                  lowerCamelCase					:					List[Any]        =	with_conta
                  lowerCamelCase					:					Optional[Any]        =	mc_label
            lowerCamelCase					:					Optional[Any]        =	(input_ids, mc_token_ids, lm_labels, mc_labels)
            tensor_datasets.append(tuple(torch.tensor(lowerCAmelCase_    ) for t in all_inputs    )    )
      return tensor_datasets




def   snake_case		(     )   ->      List[str]:
      lowerCamelCase					:					List[Any]        =	argparse.ArgumentParser()
      parser.add_argument("""--model_name"""			,				type=lowerCAmelCase_			,				default="""openai-gpt"""			,				help="""pretrained model name"""    )
      parser.add_argument("""--do_train"""			,				action="""store_true"""			,				help="""Whether to run training."""    )
      parser.add_argument("""--do_eval"""			,				action="""store_true"""			,				help="""Whether to run eval on the dev set."""    )
      parser.add_argument(
          """--output_dir"""			,				default=lowerCAmelCase_			,				type=lowerCAmelCase_			,				required=lowerCAmelCase_			,				help="""The output directory where the model predictions and checkpoints will be written."""			,				)
      parser.add_argument("""--train_dataset"""			,				type=lowerCAmelCase_			,				default=""""""    )
      parser.add_argument("""--eval_dataset"""			,				type=lowerCAmelCase_			,				default=""""""    )
      parser.add_argument("""--seed"""			,				type=lowerCAmelCase_			,				default=42    )
      parser.add_argument("""--num_train_epochs"""			,				type=lowerCAmelCase_			,				default=3    )
      parser.add_argument("""--train_batch_size"""			,				type=lowerCAmelCase_			,				default=8    )
      parser.add_argument("""--eval_batch_size"""			,				type=lowerCAmelCase_			,				default=16    )
      parser.add_argument("""--adam_epsilon"""			,				default=1E-8			,				type=lowerCAmelCase_			,				help="""Epsilon for Adam optimizer."""    )
      parser.add_argument("""--max_grad_norm"""			,				type=lowerCAmelCase_			,				default=1    )
      parser.add_argument(
          """--max_steps"""			,				default=-1			,				type=lowerCAmelCase_			,				help=(
              """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
          )			,				)
      parser.add_argument(
          """--gradient_accumulation_steps"""			,				type=lowerCAmelCase_			,				default=1			,				help="""Number of updates steps to accumulate before                        performing a backward/update pass."""			,				)
      parser.add_argument("""--learning_rate"""			,				type=lowerCAmelCase_			,				default=6.25E-5    )
      parser.add_argument("""--warmup_steps"""			,				default=0			,				type=lowerCAmelCase_			,				help="""Linear warmup over warmup_steps."""    )
      parser.add_argument("""--lr_schedule"""			,				type=lowerCAmelCase_			,				default="""warmup_linear"""    )
      parser.add_argument("""--weight_decay"""			,				type=lowerCAmelCase_			,				default=0.0_1    )
      parser.add_argument("""--lm_coef"""			,				type=lowerCAmelCase_			,				default=0.9    )
      parser.add_argument("""--n_valid"""			,				type=lowerCAmelCase_			,				default=374    )

      parser.add_argument("""--server_ip"""			,				type=lowerCAmelCase_			,				default=""""""			,				help="""Can be used for distant debugging."""    )
      parser.add_argument("""--server_port"""			,				type=lowerCAmelCase_			,				default=""""""			,				help="""Can be used for distant debugging."""    )
      lowerCamelCase					:					Optional[int]        =	parser.parse_args()
      print(lowerCAmelCase_    )

      if args.server_ip and args.server_port:
            # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
            import ptvsd

            print("""Waiting for debugger attach"""    )
            ptvsd.enable_attach(address=(args.server_ip, args.server_port)			,				redirect_output=lowerCAmelCase_    )
            ptvsd.wait_for_attach()

      random.seed(args.seed    )
      np.random.seed(args.seed    )
      torch.manual_seed(args.seed    )
      torch.cuda.manual_seed_all(args.seed    )

      lowerCamelCase					:					Optional[Any]        =	torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
      lowerCamelCase					:					Dict        =	torch.cuda.device_count()
      logger.info("""device: {}, n_gpu {}""".format(lowerCAmelCase_			,				lowerCAmelCase_    )    )

      if not args.do_train and not args.do_eval:
            raise ValueError("""At least one of `do_train` or `do_eval` must be True."""    )

      if not os.path.exists(args.output_dir    ):
            os.makedirs(args.output_dir    )

      # Load tokenizer and model
      # This loading functions also add new tokens and embeddings called `special tokens`
      # These new embeddings will be fine-tuned on the RocStories dataset
      lowerCamelCase					:					Optional[Any]        =	['''_start_''', '''_delimiter_''', '''_classify_''']
      lowerCamelCase					:					Optional[int]        =	OpenAIGPTTokenizer.from_pretrained(args.model_name    )
      tokenizer.add_tokens(lowerCAmelCase_    )
      lowerCamelCase					:					Optional[Any]        =	tokenizer.convert_tokens_to_ids(lowerCAmelCase_    )
      lowerCamelCase					:					List[str]        =	OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
      model.resize_token_embeddings(len(lowerCAmelCase_    )    )
      model.to(lowerCAmelCase_    )

      # Load and encode the datasets
      def tokenize_and_encode(UpperCamelCase__       :						Dict    ):
            if isinstance(lowerCAmelCase_			,				lowerCAmelCase_    ):
                  return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(lowerCAmelCase_    )    )
            elif isinstance(lowerCAmelCase_			,				lowerCAmelCase_    ):
                  return obj
            return [tokenize_and_encode(lowerCAmelCase_    ) for o in obj]

      logger.info("""Encoding dataset..."""    )
      lowerCamelCase					:					Optional[Any]        =	load_rocstories_dataset(args.train_dataset    )
      lowerCamelCase					:					List[Any]        =	load_rocstories_dataset(args.eval_dataset    )
      lowerCamelCase					:					Dict        =	(train_dataset, eval_dataset)
      lowerCamelCase					:					Optional[Any]        =	tokenize_and_encode(lowerCAmelCase_    )

      # Compute the max input length for the Transformer
      lowerCamelCase					:					Tuple        =	model.config.n_positions // 2 - 2
      lowerCamelCase					:					Optional[Any]        =	max(
          len(story[:max_length]    ) + max(len(conta[:max_length]    )			,				len(conta[:max_length]    )    ) + 3
          for dataset in encoded_datasets
          for story, conta, conta, _ in dataset    )
      lowerCamelCase					:					Optional[int]        =	min(lowerCAmelCase_			,				model.config.n_positions    )  # Max size of input for the pre-trained model

      # Prepare inputs tensors and dataloaders
      lowerCamelCase					:					int        =	pre_process_datasets(lowerCAmelCase_			,				lowerCAmelCase_			,				lowerCAmelCase_			,				*lowerCAmelCase_    )
      lowerCamelCase					:					Dict        =	tensor_datasets[0], tensor_datasets[1]

      lowerCamelCase					:					str        =	TensorDataset(*lowerCAmelCase_    )
      lowerCamelCase					:					List[Any]        =	RandomSampler(lowerCAmelCase_    )
      lowerCamelCase					:					List[Any]        =	DataLoader(lowerCAmelCase_			,				sampler=lowerCAmelCase_			,				batch_size=args.train_batch_size    )

      lowerCamelCase					:					str        =	TensorDataset(*lowerCAmelCase_    )
      lowerCamelCase					:					Any        =	SequentialSampler(lowerCAmelCase_    )
      lowerCamelCase					:					str        =	DataLoader(lowerCAmelCase_			,				sampler=lowerCAmelCase_			,				batch_size=args.eval_batch_size    )

      # Prepare optimizer
      if args.do_train:
            if args.max_steps > 0:
                  lowerCamelCase					:					Optional[int]        =	args.max_steps
                  lowerCamelCase					:					Optional[int]        =	args.max_steps // (len(lowerCAmelCase_    ) // args.gradient_accumulation_steps) + 1
            else:
                  lowerCamelCase					:					Dict        =	len(lowerCAmelCase_    ) // args.gradient_accumulation_steps * args.num_train_epochs

            lowerCamelCase					:					Union[str, Any]        =	list(model.named_parameters()    )
            lowerCamelCase					:					str        =	['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
            lowerCamelCase					:					Union[str, Any]        =	[
                {
                    '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay    )],
                    '''weight_decay''': args.weight_decay,
                },
                {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay    )], '''weight_decay''': 0.0},
            ]
            lowerCamelCase					:					Any        =	AdamW(lowerCAmelCase_			,				lr=args.learning_rate			,				eps=args.adam_epsilon    )
            lowerCamelCase					:					str        =	get_linear_schedule_with_warmup(
                lowerCAmelCase_			,				num_warmup_steps=args.warmup_steps			,				num_training_steps=lowerCAmelCase_    )

      if args.do_train:
            lowerCamelCase					:					Optional[int]        =	0, 0, None
            model.train()
            for _ in trange(int(args.num_train_epochs    )			,				desc="""Epoch"""    ):
                  lowerCamelCase					:					str        =	0
                  lowerCamelCase					:					List[str]        =	0
                  lowerCamelCase					:					Tuple        =	tqdm(lowerCAmelCase_			,				desc="""Training"""    )
                  for step, batch in enumerate(lowerCAmelCase_    ):
                        lowerCamelCase					:					Any        =	tuple(t.to(lowerCAmelCase_    ) for t in batch    )
                        lowerCamelCase					:					Optional[Any]        =	batch
                        lowerCamelCase					:					Union[str, Any]        =	model(lowerCAmelCase_			,				mc_token_ids=lowerCAmelCase_			,				lm_labels=lowerCAmelCase_			,				mc_labels=lowerCAmelCase_    )
                        lowerCamelCase					:					List[Any]        =	args.lm_coef * losses[0] + losses[1]
                        loss.backward()
                        optimizer.step()
                        scheduler.step()
                        optimizer.zero_grad()
                        tr_loss += loss.item()
                        lowerCamelCase					:					Optional[Any]        =	(
                            loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
                        )
                        nb_tr_steps += 1
                        lowerCamelCase					:					int        =	'''Training loss: {:.2e} lr: {:.2e}'''.format(lowerCAmelCase_			,				scheduler.get_lr()[0]    )

    # Save a trained model
      if args.do_train:
            # Save a trained model, configuration and tokenizer
            lowerCamelCase					:					Optional[int]        =	model.module if hasattr(lowerCAmelCase_			,				"""module"""    ) else model  # Only save the model itself

            # If we save using the predefined names, we can load using `from_pretrained`
            lowerCamelCase					:					Dict        =	os.path.join(args.output_dir			,				lowerCAmelCase_    )
            lowerCamelCase					:					Dict        =	os.path.join(args.output_dir			,				lowerCAmelCase_    )

            torch.save(model_to_save.state_dict()			,				lowerCAmelCase_    )
            model_to_save.config.to_json_file(lowerCAmelCase_    )
            tokenizer.save_vocabulary(args.output_dir    )

            # Load a trained model and vocabulary that you have fine-tuned
            lowerCamelCase					:					Dict        =	OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
            lowerCamelCase					:					Optional[int]        =	OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
            model.to(lowerCAmelCase_    )

      if args.do_eval:
            model.eval()
            lowerCamelCase					:					Tuple        =	0, 0
            lowerCamelCase					:					str        =	0, 0
            for batch in tqdm(lowerCAmelCase_			,				desc="""Evaluating"""    ):
                  lowerCamelCase					:					Any        =	tuple(t.to(lowerCAmelCase_    ) for t in batch    )
                  lowerCamelCase					:					Dict        =	batch
                  with torch.no_grad():
                        lowerCamelCase					:					List[Any]        =	model(
                            lowerCAmelCase_			,				mc_token_ids=lowerCAmelCase_			,				lm_labels=lowerCAmelCase_			,				mc_labels=lowerCAmelCase_    )

                  lowerCamelCase					:					int        =	mc_logits.detach().cpu().numpy()
                  lowerCamelCase					:					Any        =	mc_labels.to("""cpu"""    ).numpy()
                  lowerCamelCase					:					Optional[Any]        =	accuracy(lowerCAmelCase_			,				lowerCAmelCase_    )

                  eval_loss += mc_loss.mean().item()
                  eval_accuracy += tmp_eval_accuracy

                  nb_eval_examples += input_ids.size(0    )
                  nb_eval_steps += 1

            lowerCamelCase					:					Optional[int]        =	eval_loss / nb_eval_steps
            lowerCamelCase					:					Optional[int]        =	eval_accuracy / nb_eval_examples
            lowerCamelCase					:					List[Any]        =	tr_loss / nb_tr_steps if args.do_train else None
            lowerCamelCase					:					Tuple        =	{'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

            lowerCamelCase					:					str        =	os.path.join(args.output_dir			,				"""eval_results.txt"""    )
            with open(lowerCAmelCase_			,				"""w"""    ) as writer:
                  logger.info("""***** Eval results *****"""    )
                  for key in sorted(result.keys()    ):
                        logger.info("""  %s = %s"""			,				lowerCAmelCase_			,				str(result[key]    )    )
                        writer.write("""%s = %s\n""" % (key, str(result[key]    ))    )


if __name__ == "__main__":
    main()








"""simple docstring"""


import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import (
        FocalNetBackbone,
        FocalNetForImageClassification,
        FocalNetForMaskedImageModeling,
        FocalNetModel,
    )
    from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
    from PIL import Image

    from transformers import AutoImageProcessor


class 			A__	:


     """simple docstring"""

     def __init__(	self:						List[Any]     ,		__a:						List[str]     ,		__a:						Optional[int]=13     ,		__a:						List[str]=32     ,		__a:						int=2     ,		__a:						List[str]=3     ,		__a:						Union[str, Any]=16     ,		__a:						int=[32, 64, 128]     ,		__a:						Optional[Any]=[1, 2, 1]     ,		__a:						Optional[int]=[2, 2, 4]     ,		__a:						Tuple=2     ,		__a:						Dict=2.0     ,		__a:						List[str]=True     ,		__a:						Optional[Any]=0.0     ,		__a:						Any=0.0     ,		__a:						List[Any]=0.1     ,		__a:						List[str]="gelu"     ,		__a:						Tuple=False     ,		__a:						Union[str, Any]=True     ,		__a:						Optional[int]=0.02     ,		__a:						Tuple=1e-5     ,		__a:						int=True     ,		__a:						List[Any]=None     ,		__a:						Optional[int]=True     ,		__a:						Dict=10     ,		__a:						List[str]=8     ,		__a:						Any=["stage1", "stage2"]     ,		__a:						Union[str, Any]=[1, 2]     ,		)->    Dict:
           lowerCamelCase					:					Dict        =	parent
           lowerCamelCase					:					Optional[Any]        =	batch_size
           lowerCamelCase					:					Union[str, Any]        =	image_size
           lowerCamelCase					:					Optional[int]        =	patch_size
           lowerCamelCase					:					Any        =	num_channels
           lowerCamelCase					:					Any        =	embed_dim
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					List[Any]        =	depths
           lowerCamelCase					:					Tuple        =	num_heads
           lowerCamelCase					:					List[Any]        =	window_size
           lowerCamelCase					:					str        =	mlp_ratio
           lowerCamelCase					:					str        =	qkv_bias
           lowerCamelCase					:					str        =	hidden_dropout_prob
           lowerCamelCase					:					Dict        =	attention_probs_dropout_prob
           lowerCamelCase					:					Tuple        =	drop_path_rate
           lowerCamelCase					:					Dict        =	hidden_act
           lowerCamelCase					:					Tuple        =	use_absolute_embeddings
           lowerCamelCase					:					List[str]        =	patch_norm
           lowerCamelCase					:					List[str]        =	layer_norm_eps
           lowerCamelCase					:					str        =	initializer_range
           lowerCamelCase					:					Tuple        =	is_training
           lowerCamelCase					:					int        =	scope
           lowerCamelCase					:					Union[str, Any]        =	use_labels
           lowerCamelCase					:					List[str]        =	type_sequence_label_size
           lowerCamelCase					:					str        =	encoder_stride
           lowerCamelCase					:					List[str]        =	out_features
           lowerCamelCase					:					Optional[int]        =	out_indices

     def 		a__						(	self:						Optional[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

           lowerCamelCase					:					str        =	None
           if self.use_labels:
                 lowerCamelCase					:					List[str]        =	ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)

           lowerCamelCase					:					str        =	self.get_config()

           return config, pixel_values, labels

     def 		a__						(	self:						List[Any]	)->    Optional[int]:
           return FocalNetConfig(
               image_size=self.image_size     ,		patch_size=self.patch_size     ,		num_channels=self.num_channels     ,		embed_dim=self.embed_dim     ,		hidden_sizes=self.hidden_sizes     ,		depths=self.depths     ,		num_heads=self.num_heads     ,		window_size=self.window_size     ,		mlp_ratio=self.mlp_ratio     ,		qkv_bias=self.qkv_bias     ,		hidden_dropout_prob=self.hidden_dropout_prob     ,		attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,		drop_path_rate=self.drop_path_rate     ,		hidden_act=self.hidden_act     ,		use_absolute_embeddings=self.use_absolute_embeddings     ,		path_norm=self.patch_norm     ,		layer_norm_eps=self.layer_norm_eps     ,		initializer_range=self.initializer_range     ,		encoder_stride=self.encoder_stride     ,		out_features=self.out_features     ,		out_indices=self.out_indices     ,		)

     def 		a__						(	self:						Tuple     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetModel(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Tuple        =	model(__a	)

           lowerCamelCase					:					Any        =	((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths	) - 1))
           lowerCamelCase					:					List[Any]        =	int(config.embed_dim * 2 ** (len(config.depths	) - 1)	)

           self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, expected_seq_len, expected_dim)	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Dict     ,		__a:						Tuple     ,		__a:						List[Any]	)->    int:
           lowerCamelCase					:					List[Any]        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Optional[Any]        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.image_size, 8, 8]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		len(config.out_features	)	)
           self.parent.assertListEqual(model.channels     ,		config.hidden_sizes[:-1]	)

           # verify backbone works with out_features=None
           lowerCamelCase					:					Dict        =	None
           lowerCamelCase					:					Dict        =	FocalNetBackbone(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					Any        =	model(__a	)

           # verify feature maps
           self.parent.assertEqual(len(result.feature_maps	)     ,		1	)
           self.parent.assertListEqual(list(result.feature_maps[0].shape	)     ,		[self.batch_size, self.image_size * 2, 4, 4]	)

           # verify channels
           self.parent.assertEqual(len(model.channels	)     ,		1	)
           self.parent.assertListEqual(model.channels     ,		[config.hidden_sizes[-1]]	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]     ,		__a:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	FocalNetForMaskedImageModeling(config=__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a	)
           self.parent.assertEqual(
               result.reconstruction.shape     ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

           # test greyscale images
           lowerCamelCase					:					List[str]        =	1
           lowerCamelCase					:					Any        =	FocalNetForMaskedImageModeling(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					str        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Tuple        =	model(__a	)
           self.parent.assertEqual(result.reconstruction.shape     ,		(self.batch_size, 1, self.image_size, self.image_size)	)

     def 		a__						(	self:						str     ,		__a:						Optional[Any]     ,		__a:						Optional[Any]     ,		__a:						Tuple	)->    str:
           lowerCamelCase					:					Optional[Any]        =	self.type_sequence_label_size
           lowerCamelCase					:					Optional[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()
           lowerCamelCase					:					List[str]        =	model(__a     ,		labels=__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)

           # test greyscale images
           lowerCamelCase					:					int        =	1
           lowerCamelCase					:					List[Any]        =	FocalNetForImageClassification(__a	)
           model.to(__a	)
           model.eval()

           lowerCamelCase					:					Union[str, Any]        =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]	)
           lowerCamelCase					:					Optional[Any]        =	model(__a	)
           self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.type_sequence_label_size)	)







     def 		a__						(	self:						int	)->    Optional[int]:
           lowerCamelCase					:					str        =	self.prepare_config_and_inputs()

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	config_and_inputs
           lowerCamelCase					:					List[Any]        =	{"""pixel_values""": pixel_values}
           return config, inputs_dict


@require_torch
class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					List[str]													=(
         (
             FocalNetModel,
             FocalNetForImageClassification,
             FocalNetForMaskedImageModeling,
             FocalNetBackbone,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					Optional[int]													=(
         {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )
     snake_case__	:					Tuple													=False

     snake_case__	:					Dict													=False
     snake_case__	:					Dict													=False
     snake_case__	:					Tuple													=False
     snake_case__	:					Optional[int]													=False

     def 		a__						(	self:						Union[str, Any]	)->    Optional[int]:
           lowerCamelCase					:					List[str]        =	FocalNetModelTester(self	)
           lowerCamelCase					:					Optional[Any]        =	ConfigTester(self     ,		config_class=__a     ,		embed_dim=37     ,		has_text_modality=__a	)

     def 		a__						(	self:						List[str]	)->    List[str]:
           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()

     def 		a__						(	self:						List[str]	)->    Union[str, Any]:
           return

     def 		a__						(	self:						Tuple	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__a	)

     def 		a__						(	self:						List[Any]	)->    Dict:
           lowerCamelCase					:					int        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__a	)

     def 		a__						(	self:						List[Any]	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__a	)

     def 		a__						(	self:						List[str]	)->    Dict:
           lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__a	)

     @unittest.skip(reason="""FocalNet does not use inputs_embeds"""	)
     def 		a__						(	self:						Optional[Any]	)->    str:
           pass

     @unittest.skip(reason="""FocalNet does not use feedforward chunking"""	)
     def 		a__						(	self:						Optional[Any]	)->    Dict:
           pass

     def 		a__						(	self:						Optional[Any]	)->    Dict:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					Any        =	model_class(__a	)
                 self.assertIsInstance(model.get_input_embeddings()     ,		(nn.Module)	)
                 lowerCamelCase					:					Dict        =	model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(__a     ,		nn.Linear	)	)

     def 		a__						(	self:						Tuple	)->    Optional[int]:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					int        =	model_class(__a	)
                 lowerCamelCase					:					int        =	inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 lowerCamelCase					:					Any        =	[*signature.parameters.keys()]

                 lowerCamelCase					:					List[Any]        =	["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]     ,		__a	)

     def 		a__						(	self:						str     ,		__a:						Union[str, Any]     ,		__a:						int     ,		__a:						Tuple     ,		__a:						List[str]	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	model_class(__a	)
           model.to(__a	)
           model.eval()

           with torch.no_grad():
                 lowerCamelCase					:					List[str]        =	model(**self._prepare_for_class(__a     ,		__a	)	)

           lowerCamelCase					:					List[str]        =	outputs.hidden_states

           lowerCamelCase					:					Tuple        =	getattr(
               self.model_tester     ,		"""expected_num_hidden_layers"""     ,		len(self.model_tester.depths	) + 1	)
           self.assertEqual(len(__a	)     ,		__a	)

           # FocalNet has a different seq_length
           lowerCamelCase					:					Tuple        =	(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase					:					Dict        =	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]	)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

           lowerCamelCase					:					Optional[Any]        =	outputs.reshaped_hidden_states
           self.assertEqual(len(__a	)     ,		__a	)

           lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	reshaped_hidden_states[0].shape
           lowerCamelCase					:					Tuple        =	(
               reshaped_hidden_states[0].view(__a     ,		__a     ,		height * width	).permute(0     ,		2     ,		1	)
           )
           self.assertListEqual(
               list(reshaped_hidden_states.shape[-2:]	)     ,		[num_patches, self.model_tester.embed_dim]     ,		)

     def 		a__						(	self:						Any	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					Union[str, Any]        =	(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable	)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					List[str]        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		__a	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase					:					List[Any]        =	True

                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		__a	)

     def 		a__						(	self:						str	)->    Union[str, Any]:
           lowerCamelCase     ,			lowerCamelCase					:					Tuple        =	self.model_tester.prepare_config_and_inputs_for_common()
           lowerCamelCase					:					List[str]        =	3

           lowerCamelCase					:					Any        =	(
               self.model_tester.image_size
               if isinstance(self.model_tester.image_size     ,		collections.abc.Iterable	)
               else (self.model_tester.image_size, self.model_tester.image_size)
           )
           lowerCamelCase					:					Optional[int]        =	(
               config.patch_size
               if isinstance(config.patch_size     ,		collections.abc.Iterable	)
               else (config.patch_size, config.patch_size)
           )

           lowerCamelCase					:					Optional[Any]        =	image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
           lowerCamelCase					:					List[str]        =	image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

           for model_class in self.all_model_classes[:-1]:
                 lowerCamelCase					:					str        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		(padded_height, padded_width)	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowerCamelCase					:					Union[str, Any]        =	True
                 self.check_hidden_states_output(__a     ,		__a     ,		__a     ,		(padded_height, padded_width)	)

     @slow
     def 		a__						(	self:						Optional[int]	)->    List[Any]:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowerCamelCase					:					List[str]        =	FocalNetModel.from_pretrained(__a	)
                 self.assertIsNotNone(__a	)







     def 		a__						(	self:						str	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Optional[Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           lowerCamelCase					:					int        =	_config_zero_init(__a	)
           for model_class in self.all_model_classes:
                 lowerCamelCase					:					int        =	model_class(config=__a	)
                 for name, param in model.named_parameters():
                       if "embeddings" not in name and param.requires_grad:
                             self.assertIn(
                                 ((param.data.mean() * 1e9).round() / 1e9).item()     ,		[0.0, 1.0]     ,		msg=f'Parameter {name} of model {model_class} seems not properly initialized'     ,		)


@require_vision
@require_torch
class 			A__	(						unittest.TestCase):


     """simple docstring"""

     @cached_property
     def 		a__						(	self:						Optional[int]	)->    Optional[Any]:
           # TODO update organization
           return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny"""	) if is_vision_available() else None







     @slow
     def 		a__						(	self:						int	)->    Optional[Any]:
           lowerCamelCase					:					Tuple        =	FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny"""	).to(__a	)
           lowerCamelCase					:					Any        =	self.default_image_processor

           lowerCamelCase					:					Union[str, Any]        =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
           lowerCamelCase					:					int        =	image_processor(images=__a     ,		return_tensors="""pt"""	).to(__a	)

           # forward pass
           with torch.no_grad():
                 lowerCamelCase					:					Any        =	model(**__a	)

           # verify the logits
           lowerCamelCase					:					Tuple        =	torch.Size((1, 1_000)	)
           self.assertEqual(outputs.logits.shape     ,		__a	)
           lowerCamelCase					:					List[str]        =	torch.tensor([0.21_66, -0.43_68, 0.21_91]	).to(__a	)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]     ,		__a     ,		atol=1e-4	)	)
           self.assertTrue(outputs.logits.argmax(dim=-1	).item()     ,		281	)


@require_torch
class 			A__	(						__lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=(FocalNetBackbone,) if is_torch_available() else ()
     snake_case__	:					Optional[int]													=FocalNetConfig

     snake_case__	:					str													=False

     def 		a__						(	self:						Union[str, Any]	)->    Tuple:
           lowerCamelCase					:					str        =	FocalNetModelTester(self	)




