


'''simple docstring'''
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


__snake_case							   =  datasets.utils.logging.get_logger(__name__)
@dataclass
class      lowercase			(							datasets.BuilderConfig       ):

		"""simple docstring"""

		_a									=       None
		_a									=       "utf-8"
		_a									=       None
		_a									=       None
		_a									=       True  # deprecated
		_a									=       None  # deprecated
		_a									=       10 << 20  # 10MB
		_a									=       None



class      lowercase			(							datasets.ArrowBasedBuilder       ):

		"""simple docstring"""

		_a									=       JsonConfig




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if self.config.block_size is not None:
												logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''							)
												UpperCamelCase__     :Any							      =				self.config.block_size
							if self.config.use_threads is not True:
												logger.warning(
												    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''							)
							if self.config.newlines_in_values is not None:
												raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''							)
							return datasets.DatasetInfo(features=self.config.features							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if not self.config.data_files:
												raise ValueError(F'''At least one data file must be specified, but got data_files={self.config.data_files}'''							)
							UpperCamelCase__     :List[str]							      =				dl_manager.download_and_extract(self.config.data_files							)
							if isinstance(lowerCamelCase_					,     (str, list, tuple)							):
												UpperCamelCase__     :Optional[int]							      =				data_files
												if isinstance(lowerCamelCase_					,     lowerCamelCase_							):
																	UpperCamelCase__     :Union[str, Any]							      =				[files]
												UpperCamelCase__     :Tuple							      =				[dl_manager.iter_files(lowerCamelCase_							) for file in files]
												return [datasets.SplitGenerator(name=datasets.Split.TRAIN					,     gen_kwargs={'''files''': files}							)]
							UpperCamelCase__     :Union[str, Any]							      =				[]
							for split_name, files in data_files.items():
												if isinstance(lowerCamelCase_					,     lowerCamelCase_							):
																	UpperCamelCase__     :Any							      =				[files]
												UpperCamelCase__     :Tuple							      =				[dl_manager.iter_files(lowerCamelCase_							) for file in files]
												splits.append(datasets.SplitGenerator(name=lowerCamelCase_					,     gen_kwargs={'''files''': files}							)							)
							return splits




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.config.features is not None:
												# adding missing columns
												for column_name in set(self.config.features							) - set(pa_table.column_names							):
																	UpperCamelCase__     :str							      =				self.config.features.arrow_schema.field(lowerCamelCase_							).type
																	UpperCamelCase__     :Optional[Any]							      =				pa_table.append_column(lowerCamelCase_					,     pa.array([None] * len(lowerCamelCase_							)					,     type=lowerCamelCase_							)							)
												# more expensive cast to support nested structures with keys in a different order
												# allows str <-> int/float or str to Audio for example
												UpperCamelCase__     :List[Any]							      =				table_cast(lowerCamelCase_					,     self.config.features.arrow_schema							)
							return pa_table




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase_							)							):
												# If the file is one json object and if we need to look at the list of items in one specific field
												if self.config.field is not None:
																	with open(lowerCamelCase_					,     encoding=self.config.encoding					,     errors=self.config.encoding_errors							) as f:
																						UpperCamelCase__     :Dict							      =				json.load(lowerCamelCase_							)

																	# We keep only the field we are interested in
																	UpperCamelCase__     :Any							      =				dataset[self.config.field]

																	# We accept two format: a list of dicts or a dict of lists
																	if isinstance(lowerCamelCase_					,     (list, tuple)							):
																						UpperCamelCase__     :Tuple							      =				set().union(*[row.keys() for row in dataset]							)
																						UpperCamelCase__     :str							      =				{col: [row.get(lowerCamelCase_							) for row in dataset] for col in keys}
																	else:
																						UpperCamelCase__     :int							      =				dataset
																	UpperCamelCase__     :Optional[Any]							      =				pa.Table.from_pydict(lowerCamelCase_							)
																	yield file_idx, self._cast_table(lowerCamelCase_							)

												# If the file has one json object per line
												else:
																	with open(lowerCamelCase_					,     '''rb'''							) as f:
																						UpperCamelCase__     :Dict							      =				0
																						# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																						# Set a default minimum value of 16kB if the chunk size is really small
																						UpperCamelCase__     :str							      =				max(self.config.chunksize // 32					,     16 << 10							)
																						UpperCamelCase__     :str							      =				(
																						    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																						)
																						while True:
																											UpperCamelCase__     :Optional[int]							      =				f.read(self.config.chunksize							)
																											if not batch:
																																break
																											# Finish current line
																											try:
																																batch += f.readline()
																											except (AttributeError, io.UnsupportedOperation):
																																batch += readline(lowerCamelCase_							)
																											# PyArrow only accepts utf-8 encoded bytes
																											if self.config.encoding != "utf-8":
																																UpperCamelCase__     :Optional[int]							      =				batch.decode(self.config.encoding					,     errors=lowerCamelCase_							).encode('''utf-8'''							)
																											try:
																																while True:
																																					try:
																																										UpperCamelCase__     :Union[str, Any]							      =				paj.read_json(
																																										    io.BytesIO(lowerCamelCase_							)					,     read_options=paj.ReadOptions(block_size=lowerCamelCase_							)							)
																																										break
																																					except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																										if (
																																										    isinstance(lowerCamelCase_					,     pa.ArrowInvalid							)
																																										    and "straddling" not in str(lowerCamelCase_							)
																																										    or block_size > len(lowerCamelCase_							)
																																										):
																																															raise
																																										else:
																																															# Increase the block size in case it was too small.
																																															# The block size will be reset for the next file.
																																															logger.debug(
																																															    F'''Batch of {len(lowerCamelCase_							)} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.'''							)
																																															block_size *= 2
																											except pa.ArrowInvalid as e:
																																try:
																																					with open(
																																					    lowerCamelCase_					,     encoding=self.config.encoding					,     errors=self.config.encoding_errors							) as f:
																																										UpperCamelCase__     :str							      =				json.load(lowerCamelCase_							)
																																except json.JSONDecodeError:
																																					logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCamelCase_							)}: {e}'''							)
																																					raise e
																																# If possible, parse the file as a list of json objects and exit the loop
																																if isinstance(lowerCamelCase_					,     lowerCamelCase_							):  # list is the only sequence type supported in JSON
																																					try:
																																										UpperCamelCase__     :int							      =				set().union(*[row.keys() for row in dataset]							)
																																										UpperCamelCase__     :Tuple							      =				{col: [row.get(lowerCamelCase_							) for row in dataset] for col in keys}
																																										UpperCamelCase__     :int							      =				pa.Table.from_pydict(lowerCamelCase_							)
																																					except (pa.ArrowInvalid, AttributeError) as e:
																																										logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCamelCase_							)}: {e}'''							)
																																										raise ValueError(F'''Not able to read records in the JSON file at {file}.'''							) from None
																																					yield file_idx, self._cast_table(lowerCamelCase_							)
																																					break
																																else:
																																					logger.error(F'''Failed to read file \'{file}\' with error {type(lowerCamelCase_							)}: {e}'''							)
																																					raise ValueError(
																																					    F'''Not able to read records in the JSON file at {file}. '''
																																					    F'''You should probably indicate the field of the JSON file containing your records. '''
																																					    F'''This JSON file contain the following fields: {str(list(dataset.keys()							)							)}. '''
																																					    F'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. '''							) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield (file_idx, batch_idx), self._cast_table(lowerCamelCase_							)
																											batch_idx += 1



'''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							)