



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 :



							def __init__(	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=13  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=32  ,	__SCREAMING_SNAKE_CASE							:  int=2  ,	__SCREAMING_SNAKE_CASE							:  Tuple=3  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=16  ,	__SCREAMING_SNAKE_CASE							:  List[str]=[32, 64, 128]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=[1, 2, 1]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=[2, 2, 4]  ,	__SCREAMING_SNAKE_CASE							:  Any=2  ,	__SCREAMING_SNAKE_CASE							:  str=2.0  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  int=0.0  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=0.0  ,	__SCREAMING_SNAKE_CASE							:  Any=0.1  ,	__SCREAMING_SNAKE_CASE							:  Tuple="gelu"  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=False  ,	__SCREAMING_SNAKE_CASE							:  str=True  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=0.02  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]=1E-5  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Dict=None  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=10  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=8  ,	__SCREAMING_SNAKE_CASE							:  str=["stage1", "stage2"]  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=[1, 2]  ,	):


											'''simple docstring'''

											__a		 =       parent
											__a		 =       batch_size
											__a		 =       image_size
											__a		 =       patch_size
											__a		 =       num_channels
											__a		 =       embed_dim
											__a		 =       hidden_sizes
											__a		 =       depths
											__a		 =       num_heads
											__a		 =       window_size
											__a		 =       mlp_ratio
											__a		 =       qkv_bias
											__a		 =       hidden_dropout_prob
											__a		 =       attention_probs_dropout_prob
											__a		 =       drop_path_rate
											__a		 =       hidden_act
											__a		 =       use_absolute_embeddings
											__a		 =       patch_norm
											__a		 =       layer_norm_eps
											__a		 =       initializer_range
											__a		 =       is_training
											__a		 =       scope
											__a		 =       use_labels
											__a		 =       type_sequence_label_size
											__a		 =       encoder_stride
											__a		 =       out_features
											__a		 =       out_indices



							def 	_lowerCamelCase    (	self							:  Union[str, Any]):


											'''simple docstring'''

											__a		 =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

											__a		 =       None
											if self.use_labels:
															__a		 =       ids_tensor([self.batch_size]  ,	self.type_sequence_label_size)

											__a		 =       self.get_config()

											return config, pixel_values, labels



							def 	_lowerCamelCase    (	self							:  List[str]):


											'''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							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  int):


											'''simple docstring'''

											__a		 =       FocalNetModel(config=__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a		 =       model(__SCREAMING_SNAKE_CASE)

											__a		 =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
											__a		 =       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							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


											'''simple docstring'''

											__a		 =       FocalNetBackbone(config=__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a		 =       model(__SCREAMING_SNAKE_CASE)

											# 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
											__a		 =       None
											__a		 =       FocalNetBackbone(config=__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a		 =       model(__SCREAMING_SNAKE_CASE)

											# 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							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]):


											'''simple docstring'''

											__a		 =       FocalNetForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a		 =       model(__SCREAMING_SNAKE_CASE)
											self.parent.assertEqual(
											    result.reconstruction.shape  ,	(self.batch_size, self.num_channels, self.image_size, self.image_size))

											# test greyscale images
											__a		 =       1
											__a		 =       FocalNetForMaskedImageModeling(__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()

											__a		 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											__a		 =       model(__SCREAMING_SNAKE_CASE)
											self.parent.assertEqual(result.reconstruction.shape  ,	(self.batch_size, 1, self.image_size, self.image_size))



							def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


											'''simple docstring'''

											__a		 =       self.type_sequence_label_size
											__a		 =       FocalNetForImageClassification(__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()
											__a		 =       model(__SCREAMING_SNAKE_CASE  ,	labels=__SCREAMING_SNAKE_CASE)
											self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.type_sequence_label_size))

											# test greyscale images
											__a		 =       1
											__a		 =       FocalNetForImageClassification(__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()

											__a		 =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
											__a		 =       model(__SCREAMING_SNAKE_CASE)
											self.parent.assertEqual(result.logits.shape  ,	(self.batch_size, self.type_sequence_label_size))





							def 	_lowerCamelCase    (	self							:  Optional[Any]):


											'''simple docstring'''

											__a		 =       self.prepare_config_and_inputs()

											__a ,     __a ,     __a		 =       config_and_inputs
											__a		 =       {'''pixel_values''': pixel_values}
											return config, inputs_dict


@require_torch
class 					_A (			__UpperCAmelCase   ,__UpperCAmelCase   ,unittest.TestCase					):
							UpperCamelCase__						:					Optional[int]		      =		(
							    (
							        FocalNetModel,
							        FocalNetForImageClassification,
							        FocalNetForMaskedImageModeling,
							        FocalNetBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase__						:					Optional[Any]		      =		(
							    {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase__						:					Optional[Any]		      =		False

							UpperCamelCase__						:					int		      =		False
							UpperCamelCase__						:					int		      =		False
							UpperCamelCase__						:					Dict		      =		False
							UpperCamelCase__						:					Dict		      =		False



							def 	_lowerCamelCase    (	self							:  str):


											'''simple docstring'''

											__a		 =       FocalNetModelTester(self)
											__a		 =       ConfigTester(self  ,	config_class=__SCREAMING_SNAKE_CASE  ,	embed_dim=37  ,	has_text_modality=__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  List[Any]):


											'''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							:  Optional[int]):


											'''simple docstring'''

											return



							def 	_lowerCamelCase    (	self							:  Tuple):


											'''simple docstring'''

											__a		 =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  str):


											'''simple docstring'''

											__a		 =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  List[Any]):


											'''simple docstring'''

											__a		 =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  List[Any]):


											'''simple docstring'''

											__a		 =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)



							@unittest.skip(reason='''FocalNet does not use inputs_embeds''')
							def 	_lowerCamelCase    (	self							:  Optional[Any]):


											'''simple docstring'''

											pass



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


											'''simple docstring'''

											pass



							def 	_lowerCamelCase    (	self							:  Any):


											'''simple docstring'''

											__a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

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



							def 	_lowerCamelCase    (	self							:  List[Any]):


											'''simple docstring'''

											__a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

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

															__a		 =       ['''pixel_values''']
															self.assertListEqual(arg_names[:1]  ,	__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


											'''simple docstring'''

											__a		 =       model_class(__SCREAMING_SNAKE_CASE)
											model.to(__SCREAMING_SNAKE_CASE)
											model.eval()

											with torch.no_grad():
															__a		 =       model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE))

											__a		 =       outputs.hidden_states

											__a		 =       getattr(
											    self.model_tester  ,	'''expected_num_hidden_layers'''  ,	len(self.model_tester.depths) + 1)
											self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	__SCREAMING_SNAKE_CASE)

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

											__a		 =       (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]  ,	)

											__a		 =       outputs.reshaped_hidden_states
											self.assertEqual(len(__SCREAMING_SNAKE_CASE)  ,	__SCREAMING_SNAKE_CASE)

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



							def 	_lowerCamelCase    (	self							:  Tuple):


											'''simple docstring'''

											__a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

											__a		 =       (
											    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]:
															__a		 =       True
															self.check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															__a		 =       True

															self.check_hidden_states_output(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE)



							def 	_lowerCamelCase    (	self							:  List[str]):


											'''simple docstring'''

											__a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()
											__a		 =       3

											__a		 =       (
											    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)
											)
											__a		 =       (
											    config.patch_size
											    if isinstance(config.patch_size  ,	collections.abc.Iterable)
											    else (config.patch_size, config.patch_size)
											)

											__a		 =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
											__a		 =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

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

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



							@slow
							def 	_lowerCamelCase    (	self							:  str):


											'''simple docstring'''

											for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															__a		 =       FocalNetModel.from_pretrained(__SCREAMING_SNAKE_CASE)
															self.assertIsNotNone(__SCREAMING_SNAKE_CASE)





							def 	_lowerCamelCase    (	self							:  Optional[Any]):


											'''simple docstring'''

											__a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

											__a		 =       _config_zero_init(__SCREAMING_SNAKE_CASE)
											for model_class in self.all_model_classes:
															__a		 =       model_class(config=__SCREAMING_SNAKE_CASE)
															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					):



							@cached_property
							def 	_lowerCamelCase    (	self							:  Optional[Any]):


											'''simple docstring'''

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





							@slow
							def 	_lowerCamelCase    (	self							:  Union[str, Any]):


											'''simple docstring'''

											__a		 =       FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny''').to(__SCREAMING_SNAKE_CASE)
											__a		 =       self.default_image_processor

											__a		 =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
											__a		 =       image_processor(images=__SCREAMING_SNAKE_CASE  ,	return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

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

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


@require_torch
class 					_A (			__UpperCAmelCase   ,unittest.TestCase					):
							UpperCamelCase__						:					List[Any]		      =		(FocalNetBackbone,) if is_torch_available() else ()
							UpperCamelCase__						:					int		      =		FocalNetConfig

							UpperCamelCase__						:					Dict		      =		False



							def 	_lowerCamelCase    (	self							:  Union[str, Any]):


											'''simple docstring'''

											__a		 =       FocalNetModelTester(self)










import argparse
import logging
import pickle
from collections import Counter


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

if __name__ == "__main__":
 __snake_case		:Union[str, Any]   	=  argparse.ArgumentParser(
     description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
 )
 parser.add_argument(
     '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
 )
 parser.add_argument(
     '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
 )
 parser.add_argument('''--vocab_size''', default=3_0522, type=int)
 __snake_case		:List[str]   	=  parser.parse_args()

 logger.info(f'Loading data from {args.data_file}')
 with open(args.data_file, '''rb''') as fp:
  __snake_case		:Optional[Any]   	=  pickle.load(fp)

 logger.info('''Counting occurrences for MLM.''')
 __snake_case		:Dict   	=  Counter()
 for tk_ids in data:
  counter.update(tk_ids)
 __snake_case		:Optional[Any]   	=  [0] * args.vocab_size
 for k, v in counter.items():
  __snake_case		:Any   	=  v

 logger.info(f'Dump to {args.token_counts_dump}')
 with open(args.token_counts_dump, '''wb''') as handle:
  pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)





