'''simple docstring'''





import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch
			from torch import nn

			from transformers import SwiftFormerForImageClassification, SwiftFormerModel
			from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor




class 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						Tuple       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						Any=13       ,		lowercase__					:						Union[str, Any]=3       ,		lowercase__					:						Tuple=True       ,		lowercase__					:						Union[str, Any]=True       ,		lowercase__					:						Union[str, Any]=0.1       ,		lowercase__					:						List[Any]=0.1       ,		lowercase__					:						Dict=224       ,		lowercase__					:						Any=1_000       ,		lowercase__					:						int=[3, 3, 6, 4]       ,		lowercase__					:						Optional[int]=[48, 56, 112, 220]       ,		)							->int:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						parent
										_UpperCamelCase			:			Union[str, Any]   			=						batch_size
										_UpperCamelCase			:			str   			=						num_channels
										_UpperCamelCase			:			List[str]   			=						is_training
										_UpperCamelCase			:			str   			=						use_labels
										_UpperCamelCase			:			List[str]   			=						hidden_dropout_prob
										_UpperCamelCase			:			Optional[int]   			=						attention_probs_dropout_prob
										_UpperCamelCase			:			int   			=						num_labels
										_UpperCamelCase			:			Dict   			=						image_size
										_UpperCamelCase			:			Dict   			=						layer_depths
										_UpperCamelCase			:			Optional[int]   			=						embed_dims





				def  snake_case__				(						self					:						Any					)							->Optional[int]:


										'''simple docstring'''




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

										_UpperCamelCase			:			Optional[Any]   			=						None
										if self.use_labels:
																_UpperCamelCase			:			List[Any]   			=						ids_tensor([self.batch_size]       ,		self.num_labels					)

										_UpperCamelCase			:			List[Any]   			=						self.get_config()

										return config, pixel_values, labels





				def  snake_case__				(						self					:						List[Any]					)							->str:


										'''simple docstring'''




										return SwiftFormerConfig(
										    depths=self.layer_depths       ,		embed_dims=self.embed_dims       ,		mlp_ratio=4       ,		downsamples=[True, True, True, True]       ,		hidden_act="gelu"       ,		num_labels=self.num_labels       ,		down_patch_size=3       ,		down_stride=2       ,		down_pad=1       ,		drop_rate=0.0       ,		drop_path_rate=0.0       ,		use_layer_scale=lowercase__       ,		layer_scale_init_value=1e-5       ,		)





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						List[str]       ,		lowercase__					:						Tuple       ,		lowercase__					:						Tuple					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						SwiftFormerModel(config=lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			Optional[Any]   			=						model(lowercase__					)
										self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.embed_dims[-1], 7, 7)					)





				def  snake_case__				(						self					:						int       ,		lowercase__					:						Tuple       ,		lowercase__					:						List[str]       ,		lowercase__					:						Union[str, Any]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Dict   			=						self.num_labels
										_UpperCamelCase			:			int   			=						SwiftFormerForImageClassification(lowercase__					)
										model.to(lowercase__					)
										model.eval()
										_UpperCamelCase			:			Optional[int]   			=						model(lowercase__       ,		labels=lowercase__					)
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)

										_UpperCamelCase			:			Tuple   			=						SwiftFormerForImageClassification(lowercase__					)
										model.to(lowercase__					)
										model.eval()

										_UpperCamelCase			:			Optional[int]   			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)
										_UpperCamelCase			:			int   			=						model(lowercase__					)
										self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)					)







				def  snake_case__				(						self					:						List[str]					)							->List[Any]:


										'''simple docstring'''




										((_UpperCamelCase)			,       (_UpperCamelCase)			,       (_UpperCamelCase))			:			int   			=						self.prepare_config_and_inputs()
										_UpperCamelCase			:			Optional[Any]   			=						{"pixel_values": pixel_values}
										return config, inputs_dict





@require_torch
class 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				UpperCAmelCase__							=							(
				    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False
				UpperCAmelCase__							=							False





				def  snake_case__				(						self					:						Optional[int]					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						SwiftFormerModelTester(self					)
										_UpperCamelCase			:			Tuple   			=						ConfigTester(
										    self       ,		config_class=lowercase__       ,		has_text_modality=lowercase__       ,		hidden_size=37       ,		num_attention_heads=12       ,		num_hidden_layers=12       ,		)





				def  snake_case__				(						self					:						Dict					)							->Tuple:


										'''simple docstring'''




										self.config_tester.run_common_tests()





				@unittest.skip(reason="SwiftFormer does not use inputs_embeds"					)
				def  snake_case__				(						self					:						List[Any]					)							->Optional[Any]:


										'''simple docstring'''




										pass





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


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			List[Any]   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase			:			Optional[Any]   			=						model_class(lowercase__					)
																_UpperCamelCase			:			Dict   			=						model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(lowercase__       ,		nn.Linear					)					)





				def  snake_case__				(						self					:						str					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			List[str]   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase			:			Optional[int]   			=						model_class(lowercase__					)
																_UpperCamelCase			:			Union[str, Any]   			=						inspect.signature(model.forward					)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCamelCase			:			Union[str, Any]   			=						[*signature.parameters.keys()]

																_UpperCamelCase			:			int   			=						["pixel_values"]
																self.assertListEqual(arg_names[:1]       ,		lowercase__					)





				def  snake_case__				(						self					:						int					)							->Union[str, Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__					)





				def  snake_case__				(						self					:						Tuple					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase__					)





				@slow
				def  snake_case__				(						self					:						Any					)							->str:


										'''simple docstring'''




										for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCamelCase			:			List[Any]   			=						SwiftFormerModel.from_pretrained(lowercase__					)
																self.assertIsNotNone(lowercase__					)





				@unittest.skip(reason="SwiftFormer does not output attentions"					)
				def  snake_case__				(						self					:						List[str]					)							->List[str]:


										'''simple docstring'''




										pass





				def  snake_case__				(						self					:						Optional[int]					)							->Any:


										'''simple docstring'''




										def check_hidden_states_output(lowercase__					:						Dict       ,		lowercase__					:						str       ,		lowercase__					:						Any					):
																_UpperCamelCase			:			str   			=						model_class(lowercase__					)
																model.to(lowercase__					)
																model.eval()

																with torch.no_grad():
																						_UpperCamelCase			:			str   			=						model(**self._prepare_for_class(lowercase__       ,		lowercase__					)					)

																_UpperCamelCase			:			Optional[Any]   			=						outputs.hidden_states

																_UpperCamelCase			:			Any   			=						8
																self.assertEqual(len(lowercase__					)       ,		lowercase__					)  # TODO

																# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																# with the width and height being successively divided by 2, after every 2 blocks
																for i in range(len(lowercase__					)					):
																						self.assertEqual(
																						    hidden_states[i].shape       ,		torch.Size(
																						        [
																						            self.model_tester.batch_size,
																						            self.model_tester.embed_dims[i // 2],
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																						        ]					)       ,		)

										_UpperCamelCase			,       _UpperCamelCase			:			Optional[int]   			=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																_UpperCamelCase			:			Optional[int]   			=						True
																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)

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

																check_hidden_states_output(lowercase__       ,		lowercase__       ,		lowercase__					)





				def  snake_case__				(						self					:						int					)							->str:


										'''simple docstring'''




										def _config_zero_init(lowercase__					:						List[Any]					):
																_UpperCamelCase			:			Any   			=						copy.deepcopy(lowercase__					)
																for key in configs_no_init.__dict__.keys():
																						if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																												setattr(lowercase__       ,		lowercase__       ,		1e-10					)
																						if isinstance(getattr(lowercase__       ,		lowercase__       ,		lowercase__					)       ,		lowercase__					):
																												_UpperCamelCase			:			Dict   			=						_config_zero_init(getattr(lowercase__       ,		lowercase__					)					)
																												setattr(lowercase__       ,		lowercase__       ,		lowercase__					)
																return configs_no_init

										_UpperCamelCase			,       _UpperCamelCase			:			Optional[Any]   			=						self.model_tester.prepare_config_and_inputs_for_common()

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







				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."					)
				def  snake_case__				(						self					:						Optional[int]					)							->Optional[int]:


										'''simple docstring'''




										pass





def 						__A							(     )     ->			Union[str, Any]:




						'''simple docstring'''
						_UpperCamelCase			:			int   			=						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
						return image






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







				'''simple docstring'''





				@cached_property
				def  snake_case__				(						self					:						int					)							->Union[str, Any]:


										'''simple docstring'''




										return ViTImageProcessor.from_pretrained("MBZUAI/swiftformer-xs"					) if is_vision_available() else None







				@slow
				def  snake_case__				(						self					:						List[Any]					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"					).to(lowercase__					)

										_UpperCamelCase			:			str   			=						self.default_image_processor
										_UpperCamelCase			:			int   			=						prepare_img()
										_UpperCamelCase			:			List[Any]   			=						image_processor(images=lowercase__       ,		return_tensors="pt"					).to(lowercase__					)

										# forward pass
										with torch.no_grad():
																_UpperCamelCase			:			Optional[Any]   			=						model(**lowercase__					)

										# verify the logits
										_UpperCamelCase			:			str   			=						torch.Size((1, 1_000)					)
										self.assertEqual(outputs.logits.shape       ,		lowercase__					)

										_UpperCamelCase			:			List[str]   			=						torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]					).to(lowercase__					)
										self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		lowercase__       ,		atol=1e-4					)					)



'''simple docstring'''





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
)
lowerCAmelCase_	:		Optional[int]			=     logging.getLogger(__name__)

if __name__ == "__main__":
			lowerCAmelCase_	:		Optional[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)
			lowerCAmelCase_	:		str			=     parser.parse_args()

			logger.info(f"""Loading data from {args.data_file}""")
			with open(args.data_file, """rb""") as fp:
						lowerCAmelCase_	:		Tuple			=     pickle.load(fp)

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


