


'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch

from parameterized import parameterized

from transformers.testing_utils import (
    CaptureStderr,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_torch_dist_unique_port,
    require_apex,
    require_bitsandbytes,
    require_fairscale,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    require_torch_non_multi_gpu,
    slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed


__snake_case							   =  os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F"""{bindir}/../../examples/pytorch/translation"""):
		from run_translation import main  # noqa


set_seed(42)
__snake_case							   =  '''sshleifer/student_marian_en_ro_6_1'''
__snake_case							   =  '''sshleifer/tiny-mbart'''
@require_torch
class      lowercase			(							A__       ):

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=False					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     UpperCamelCase_=True					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self.run_trainer(
							    eval_steps=1					,     max_len=12					,     model_name=UpperCamelCase_					,     num_train_epochs=1					,     distributed=UpperCamelCase_					,     extra_args_str=UpperCamelCase_					,     predict_with_generate=UpperCamelCase_					,     do_train=UpperCamelCase_					,     do_eval=UpperCamelCase_					,     do_predict=UpperCamelCase_					,     )
							UpperCamelCase__     :Tuple							      =				TrainerState.load_from_json(os.path.join(UpperCamelCase_					,     '''trainer_state.json'''							)							).log_history

							if not do_eval:
												return

							UpperCamelCase__     :List[str]							      =				[log for log in logs if '''eval_loss''' in log.keys()]

							UpperCamelCase__     :Tuple							      =				eval_metrics[0]
							if predict_with_generate:
												assert "eval_bleu" in first_step_stats

												UpperCamelCase__     :Tuple							      =				eval_metrics[-1]
												assert isinstance(last_step_stats['''eval_bleu''']					,     UpperCamelCase_							)
												assert not math.isnan(float(last_step_stats['''eval_loss''']							)							), "eval_loss must not be `nan`"




		@require_torch_non_multi_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick()




		@require_torch_multi_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_							)




		@require_torch_multi_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp simple'''							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp simple --fp16'''							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp zero_dp_2'''					,     predict_with_generate=UpperCamelCase_							)




		@unittest.skip('''Requires an update of the env running those tests'''							)
		@require_torch_multi_gpu
		@require_fairscale
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(
							    distributed=UpperCamelCase_					,     extra_args_str='''--sharded_ddp zero_dp_2 --fp16'''					,     predict_with_generate=UpperCamelCase_							)




		@require_apex
		@require_torch_gpu
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--fp16 --fp16_backend=apex'''							)
							# test 2nd time - was getting eval_loss': nan'
							# to reproduce the problem set distributed=False
							self.run_seqaseq_quick(distributed=UpperCamelCase_					,     extra_args_str='''--fp16 --fp16_backend=apex'''							)




		@parameterized.expand(['''base''', '''low''', '''high''', '''mixed''']							)
		@require_torch_multi_gpu
		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				{
							    # test with the default log_level - should be info and thus log info once
							    '''base''': {'''extra_args_str''': '''''', '''n_matches''': 1},
							    # test with low log_level and log_level_replica - should be noisy on all processes
							    # now the info string should appear twice on 2 processes
							    '''low''': {'''extra_args_str''': '''--log_level debug --log_level_replica debug''', '''n_matches''': 2},
							    # test with high log_level and low log_level_replica
							    # now the info string should appear once only on the replica
							    '''high''': {'''extra_args_str''': '''--log_level error --log_level_replica debug''', '''n_matches''': 1},
							    # test with high log_level and log_level_replica - should be quiet on all processes
							    '''mixed''': {'''extra_args_str''': '''--log_level error --log_level_replica error''', '''n_matches''': 0},
							}

							UpperCamelCase__     :Any							      =				experiments[experiment_id]
							UpperCamelCase__     :Any							      =				{'''distributed''': True, '''predict_with_generate''': False, '''do_eval''': False, '''do_predict''': False}
							UpperCamelCase__     :Optional[Any]							      =				'''Running training'''
							with CaptureStderr() as cl:
												self.run_seqaseq_quick(**UpperCamelCase_					,     extra_args_str=data['''extra_args_str''']							)
							UpperCamelCase__     :Optional[Any]							      =				len(re.findall(UpperCamelCase_					,     cl.err							)							)
							self.assertEqual(UpperCamelCase_					,     data['''n_matches''']							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.run_trainer(
							    eval_steps=2					,     max_len=128					,     model_name=UpperCamelCase_					,     learning_rate=3e-4					,     num_train_epochs=10					,     distributed=UpperCamelCase_					,     )

							# Check metrics
							UpperCamelCase__     :Tuple							      =				TrainerState.load_from_json(os.path.join(UpperCamelCase_					,     '''trainer_state.json'''							)							).log_history
							UpperCamelCase__     :Union[str, Any]							      =				[log for log in logs if '''eval_loss''' in log.keys()]
							UpperCamelCase__     :Optional[Any]							      =				eval_metrics[0]
							UpperCamelCase__     :int							      =				eval_metrics[-1]

							assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
							assert isinstance(last_step_stats['''eval_bleu''']					,     UpperCamelCase_							)

							# test if do_predict saves generations and metrics
							UpperCamelCase__     :Union[str, Any]							      =				os.listdir(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				{os.path.basename(UpperCamelCase_							) for p in contents}
							assert "generated_predictions.txt" in contents
							assert "predict_results.json" in contents




		@slow
		@require_bitsandbytes
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							from transformers.training_args import OptimizerNames

							def train_and_return_metrics(UpperCamelCase_							) -> Tuple[int, float]:
												UpperCamelCase__     :Union[str, Any]							      =				'''--skip_memory_metrics 0'''

												UpperCamelCase__     :Optional[int]							      =				self.run_trainer(
												    max_len=128					,     model_name=UpperCamelCase_					,     learning_rate=3e-4					,     num_train_epochs=1					,     optim=UpperCamelCase_					,     distributed=UpperCamelCase_					,     extra_args_str=UpperCamelCase_					,     do_eval=UpperCamelCase_					,     do_predict=UpperCamelCase_					,     n_gpus_to_use=1					,     )

												# Check metrics
												UpperCamelCase__     :Optional[int]							      =				TrainerState.load_from_json(Path(UpperCamelCase_					,     '''trainer_state.json'''							)							).log_history
												UpperCamelCase__     :Optional[int]							      =				int(logs[0]['''train_mem_gpu_peaked_delta'''] / 2**20							)
												UpperCamelCase__     :int							      =				int(logs[0]['''train_mem_gpu_alloc_delta'''] / 2**20							)

												UpperCamelCase__     :List[Any]							      =				logs[0]['''train_loss''']
												return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss

							UpperCamelCase__     :Union[str, Any]							      =				train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value							)
							UpperCamelCase__     :Any							      =				train_and_return_metrics(OptimizerNames.ADAMW_BNB.value							)

							UpperCamelCase__     :Dict							      =				gpu_alloc_mem_orig - gpu_alloc_mem_bnb

							UpperCamelCase__     :int							      =				gpu_peak_mem_orig + gpu_alloc_mem_orig
							UpperCamelCase__     :Dict							      =				gpu_peak_mem_bnb + gpu_alloc_mem_bnb
							UpperCamelCase__     :Any							      =				gpu_total_mem_orig - gpu_total_mem_bnb

							# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
							# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
							# in 2 bytes and the diff in optim memory usage is derived as so:
							#
							# - normal 25*8=~200MB (8 bytes per param)
							# - bnb    25*2= ~50MB (2 bytes per param)
							#
							# Thus we should expect ~150MB total memory saved.
							#
							# Peak memory should be the same - the total should be different by about that same margin
							#
							# After leaving a small margin to accommodate for differences between gpus let's check
							# that we have at least 120MB in savings
							UpperCamelCase__     :List[str]							      =				120

							# uncomment the following if this test starts failing - requires py38 for a new print feature
							# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
							# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
							# print(f" {gpu_alloc_mem_bnb=}MB  {gpu_peak_mem_bnb=}MB  {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
							# print(f"{gpu_alloc_mem_diff=}MB")
							# print(f"{gpu_peak_mem_diff=}MB")
							# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
							# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")

							self.assertGreater(
							    UpperCamelCase_					,     UpperCamelCase_					,     '''should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got'''
							    F''' a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and'''
							    F''' gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB'''					,     )

							self.assertGreater(
							    UpperCamelCase_					,     UpperCamelCase_					,     '''should use ~150MB less total gpu memory with BNB, compared to without it for this model but got'''
							    F''' a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and'''
							    F''' gpu_total_mem_bnb={gpu_total_mem_bnb}MB'''					,     )

							self.assertEqual(
							    UpperCamelCase_					,     UpperCamelCase_					,     F'''loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}'''							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_ = 3e-3					,     UpperCamelCase_ = "adafactor"					,     UpperCamelCase_ = False					,     UpperCamelCase_ = None					,     UpperCamelCase_ = 0					,     UpperCamelCase_ = True					,     UpperCamelCase_ = True					,     UpperCamelCase_ = True					,     UpperCamelCase_ = True					,     UpperCamelCase_ = None					,     ):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.test_file_dir / '''../fixtures/tests_samples/wmt_en_ro'''
							UpperCamelCase__     :Any							      =				self.get_auto_remove_tmp_dir()
							UpperCamelCase__     :Any							      =				F'''
            --model_name_or_path {model_name}
            --train_file {data_dir}/train.json
            --validation_file {data_dir}/val.json
            --test_file {data_dir}/test.json
            --output_dir {output_dir}
            --overwrite_output_dir
            --max_train_samples 8
            --max_source_length {max_len}
            --max_target_length {max_len}
            --do_train
            --num_train_epochs {str(UpperCamelCase_							)}
            --per_device_train_batch_size 4
            --learning_rate {learning_rate}
            --warmup_steps 8
            --logging_steps 0
            --logging_strategy no
            --save_steps {str(UpperCamelCase_							)}
            --group_by_length
            --label_smoothing_factor 0.1
            --target_lang ro_RO
            --source_lang en_XX
        '''.split()

							UpperCamelCase__     :Any							      =				F'''
            --do_eval
            --per_device_eval_batch_size 4
            --max_eval_samples 8
            --val_max_target_length {max_len}
            --evaluation_strategy steps
            --eval_steps {str(UpperCamelCase_							)}
        '''.split()

							UpperCamelCase__     :Optional[Any]							      =				'''
            --do_predict
        '''.split()

							UpperCamelCase__     :int							      =				[]
							if do_train:
												args += args_train

							if do_eval:
												args += args_eval

							if do_predict:
												args += args_predict

							if predict_with_generate:
												args += "--predict_with_generate".split()

							if do_train:
												if optim == "adafactor":
																	args += "--adafactor".split()
												else:
																	args += F'''--optim {optim}'''.split()

							if extra_args_str is not None:
												args += extra_args_str.split()

							if distributed:
												if n_gpus_to_use is None:
																	UpperCamelCase__     :str							      =				get_gpu_count()
												UpperCamelCase__     :str							      =				get_torch_dist_unique_port()
												UpperCamelCase__     :Optional[int]							      =				F'''
                -m torch.distributed.run
                --nproc_per_node={n_gpus_to_use}
                --master_port={master_port}
                {self.examples_dir_str}/pytorch/translation/run_translation.py
            '''.split()
												UpperCamelCase__     :int							      =				[sys.executable] + distributed_args + args
												# keep for quick debug
												# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
												execute_subprocess_async(UpperCamelCase_					,     env=self.get_env()							)
							else:
												UpperCamelCase__     :List[Any]							      =				['''run_translation.py'''] + args
												with patch.object(UpperCamelCase_					,     '''argv'''					,     UpperCamelCase_							):
																	main()

							return output_dir



'''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      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_					,     UpperCamelCase_=13					,     UpperCamelCase_=32					,     UpperCamelCase_=2					,     UpperCamelCase_=3					,     UpperCamelCase_=16					,     UpperCamelCase_=[32, 64, 128]					,     UpperCamelCase_=[1, 2, 1]					,     UpperCamelCase_=[2, 2, 4]					,     UpperCamelCase_=2					,     UpperCamelCase_=2.0					,     UpperCamelCase_=True					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.0					,     UpperCamelCase_=0.1					,     UpperCamelCase_="gelu"					,     UpperCamelCase_=False					,     UpperCamelCase_=True					,     UpperCamelCase_=0.02					,     UpperCamelCase_=1e-5					,     UpperCamelCase_=True					,     UpperCamelCase_=None					,     UpperCamelCase_=True					,     UpperCamelCase_=10					,     UpperCamelCase_=8					,     UpperCamelCase_=["stage1", "stage2"]					,     UpperCamelCase_=[1, 2]					,     ):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				parent
							UpperCamelCase__     :Dict							      =				batch_size
							UpperCamelCase__     :Tuple							      =				image_size
							UpperCamelCase__     :Any							      =				patch_size
							UpperCamelCase__     :Tuple							      =				num_channels
							UpperCamelCase__     :int							      =				embed_dim
							UpperCamelCase__     :Optional[int]							      =				hidden_sizes
							UpperCamelCase__     :List[str]							      =				depths
							UpperCamelCase__     :List[str]							      =				num_heads
							UpperCamelCase__     :Dict							      =				window_size
							UpperCamelCase__     :str							      =				mlp_ratio
							UpperCamelCase__     :List[str]							      =				qkv_bias
							UpperCamelCase__     :Optional[Any]							      =				hidden_dropout_prob
							UpperCamelCase__     :List[Any]							      =				attention_probs_dropout_prob
							UpperCamelCase__     :List[str]							      =				drop_path_rate
							UpperCamelCase__     :str							      =				hidden_act
							UpperCamelCase__     :Optional[Any]							      =				use_absolute_embeddings
							UpperCamelCase__     :str							      =				patch_norm
							UpperCamelCase__     :List[Any]							      =				layer_norm_eps
							UpperCamelCase__     :Dict							      =				initializer_range
							UpperCamelCase__     :int							      =				is_training
							UpperCamelCase__     :List[Any]							      =				scope
							UpperCamelCase__     :Dict							      =				use_labels
							UpperCamelCase__     :Dict							      =				type_sequence_label_size
							UpperCamelCase__     :Tuple							      =				encoder_stride
							UpperCamelCase__     :Optional[int]							      =				out_features
							UpperCamelCase__     :Optional[int]							      =				out_indices




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

							UpperCamelCase__     :Any							      =				None
							if self.use_labels:
												UpperCamelCase__     :int							      =				ids_tensor([self.batch_size]					,     self.type_sequence_label_size							)

							UpperCamelCase__     :Tuple							      =				self.get_config()

							return config, pixel_values, labels




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModel(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :str							      =				model(UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
							UpperCamelCase__     :Optional[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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :Any							      =				model(UpperCamelCase_							)

							# 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
							UpperCamelCase__     :Tuple							      =				None
							UpperCamelCase__     :Optional[Any]							      =				FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[str]							      =				model(UpperCamelCase_							)

							# 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 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				FocalNetForMaskedImageModeling(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :List[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(
							    result.reconstruction.shape					,     (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

							# test greyscale images
							UpperCamelCase__     :Optional[int]							      =				1
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForMaskedImageModeling(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :str							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Optional[Any]							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.reconstruction.shape					,     (self.batch_size, 1, self.image_size, self.image_size)							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				self.type_sequence_label_size
							UpperCamelCase__     :Optional[Any]							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__     :int							      =				model(UpperCamelCase_					,     labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)

							# test greyscale images
							UpperCamelCase__     :Tuple							      =				1
							UpperCamelCase__     :Tuple							      =				FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__     :Optional[int]							      =				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__     :Dict							      =				model(UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape					,     (self.batch_size, self.type_sequence_label_size)							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				self.prepare_config_and_inputs()

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Optional[int]							      =				config_and_inputs
							UpperCamelCase__     :Any							      =				{'''pixel_values''': pixel_values}
							return config, inputs_dict



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

		"""simple docstring"""

		_a									=       (
		    (
		        FocalNetModel,
		        FocalNetForImageClassification,
		        FocalNetForMaskedImageModeling,
		        FocalNetBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		_a									=       (
		    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
		    if is_torch_available()
		    else {}
		)
		_a									=       False

		_a									=       False
		_a									=       False
		_a									=       False
		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Any							      =				FocalNetModelTester(self							)
							UpperCamelCase__     :Optional[int]							      =				ConfigTester(self					,     config_class=UpperCamelCase_					,     embed_dim=37					,     has_text_modality=UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :List[str]							      =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_							)




		@unittest.skip(reason='''FocalNet does not use inputs_embeds'''							)
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		@unittest.skip(reason='''FocalNet does not use feedforward chunking'''							)
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							pass




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :str							      =				self.model_tester.prepare_config_and_inputs_for_common()

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




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__     :List[str]							      =				model_class(UpperCamelCase_							)
												UpperCamelCase__     :Dict							      =				inspect.signature(model.forward							)
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCamelCase__     :Union[str, Any]							      =				[*signature.parameters.keys()]

												UpperCamelCase__     :Tuple							      =				['''pixel_values''']
												self.assertListEqual(arg_names[:1]					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				model_class(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							with torch.no_grad():
												UpperCamelCase__     :str							      =				model(**self._prepare_for_class(UpperCamelCase_					,     UpperCamelCase_							)							)

							UpperCamelCase__     :List[Any]							      =				outputs.hidden_states

							UpperCamelCase__     :Optional[int]							      =				getattr(
							    self.model_tester					,     '''expected_num_hidden_layers'''					,     len(self.model_tester.depths							) + 1							)
							self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

							# FocalNet has a different seq_length
							UpperCamelCase__     :List[str]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				(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]					,     )

							UpperCamelCase__     :List[Any]							      =				outputs.reshaped_hidden_states
							self.assertEqual(len(UpperCamelCase_							)					,     UpperCamelCase_							)

							UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__      ,       UpperCamelCase__     :Dict							      =				reshaped_hidden_states[0].shape
							UpperCamelCase__     :List[str]							      =				(
							    reshaped_hidden_states[0].view(UpperCamelCase_					,     UpperCamelCase_					,     height * width							).permute(0					,     2					,     1							)
							)
							self.assertListEqual(
							    list(reshaped_hidden_states.shape[-2:]							)					,     [num_patches, self.model_tester.embed_dim]					,     )




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Any							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :List[str]							      =				(
							    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]:
												UpperCamelCase__     :Union[str, Any]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)

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

												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Tuple							      =				self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__     :Optional[int]							      =				3

							UpperCamelCase__     :str							      =				(
							    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)
							)
							UpperCamelCase__     :Optional[int]							      =				(
							    config.patch_size
							    if isinstance(config.patch_size					,     collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

							UpperCamelCase__     :List[Any]							      =				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
							UpperCamelCase__     :Dict							      =				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCamelCase__     :List[str]							      =				True
												self.check_hidden_states_output(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     (padded_height, padded_width)							)




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__     :Union[str, Any]							      =				FocalNetModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__      ,       UpperCamelCase__     :Union[str, Any]							      =				self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__     :Dict							      =				_config_zero_init(UpperCamelCase_							)
							for model_class in self.all_model_classes:
												UpperCamelCase__     :int							      =				model_class(config=UpperCamelCase_							)
												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      lowercase			(							unittest.TestCase      ):

		"""simple docstring"""

		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''							) if is_vision_available() else None




		@slow
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''							).to(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				self.default_image_processor

							UpperCamelCase__     :str							      =				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							UpperCamelCase__     :Any							      =				image_processor(images=UpperCamelCase_					,     return_tensors='''pt'''							).to(UpperCamelCase_							)

							# forward pass
							with torch.no_grad():
												UpperCamelCase__     :Tuple							      =				model(**UpperCamelCase_							)

							# verify the logits
							UpperCamelCase__     :Any							      =				torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape					,     UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				torch.tensor([0.2166, -0.4368, 0.2191]							).to(UpperCamelCase_							)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]					,     UpperCamelCase_					,     atol=1e-4							)							)
							self.assertTrue(outputs.logits.argmax(dim=-1							).item()					,     281							)



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

		"""simple docstring"""

		_a									=       (FocalNetBackbone,) if is_torch_available() else ()
		_a									=       FocalNetConfig

		_a									=       False




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							UpperCamelCase__     :str							      =				FocalNetModelTester(self							)