



import logging
import os
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import classification_report, fa_score, precision_score, recall_score
from utils_ner import Split, TFTokenClassificationDataset, TokenClassificationTask

from transformers import (
    AutoConfig,
    AutoTokenizer,
    EvalPrediction,
    HfArgumentParser,
    TFAutoModelForTokenClassification,
    TFTrainer,
    TFTrainingArguments,
)
from transformers.utils import logging as hf_logging


hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()


UpperCAmelCase :				Tuple							=logging.getLogger(__name__)


@dataclass
class    _lowercase :
				'''simple docstring'''







				lowercase__     =    field(
				    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}   )
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
				lowercase__     =    field(
				    default="""NER"""   , metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}   )
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}   )
				lowercase__     =    field(default=a_   , metadata={"""help""": """Set this flag to use fast tokenization."""}   )
				# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
				# or just modify its tokenizer_config.json.
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}   , )




@dataclass
class    _lowercase :
				'''simple docstring'''







				lowercase__     =    field(
				    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}   )
				lowercase__     =    field(
				    metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}   )
				lowercase__     =    field(
				    default=1_28   , metadata={
				        """help""": (
				            """The maximum total input sequence length after tokenization. Sequences longer """
				            """than this will be truncated, sequences shorter will be padded."""
				        )
				    }   , )
				lowercase__     =    field(
				    default=a_   , metadata={"""help""": """Overwrite the cached training and evaluation sets"""}   )




def  _lowerCAmelCase     ():
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.
					UpperCamelCase_			= HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
					UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= parser.parse_args_into_dataclasses()

					if (
					    os.path.exists(training_args.output_dir)
					    and os.listdir(training_args.output_dir)
					    and training_args.do_train
					    and not training_args.overwrite_output_dir
					):
										raise ValueError(
										    f"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
										    " --overwrite_output_dir to overcome.")

					UpperCamelCase_			= import_module("tasks")

					try:
										UpperCamelCase_			= getattr(_lowerCAmelCase ,						model_args.task_type)
										UpperCamelCase_			= token_classification_task_clazz()
					except AttributeError:
										raise ValueError(
										    f"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
										    f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""")

					# Setup logging
					logging.basicConfig(
					    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" ,						datefmt="%m/%d/%Y %H:%M:%S" ,						level=logging.INFO ,						)
					logger.info(
					    "n_replicas: %s, distributed training: %s, 16-bits training: %s" ,						training_args.n_replicas ,						bool(training_args.n_replicas > 1) ,						training_args.fpaa ,						)
					logger.info("Training/evaluation parameters %s" ,						_lowerCAmelCase)

					# Prepare Token Classification task
					UpperCamelCase_			= token_classification_task.get_labels(data_args.labels)
					UpperCamelCase_			= dict(enumerate(_lowerCAmelCase))
					UpperCamelCase_			= len(_lowerCAmelCase)

					# Load pretrained model and tokenizer
					#
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.

					UpperCamelCase_			= AutoConfig.from_pretrained(
					    model_args.config_name if model_args.config_name else model_args.model_name_or_path ,						num_labels=_lowerCAmelCase ,						idalabel=_lowerCAmelCase ,						labelaid={label: i for i, label in enumerate(_lowerCAmelCase)} ,						cache_dir=model_args.cache_dir ,						)
					UpperCamelCase_			= AutoTokenizer.from_pretrained(
					    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path ,						cache_dir=model_args.cache_dir ,						use_fast=model_args.use_fast ,						)

					with training_args.strategy.scope():
										UpperCamelCase_			= TFAutoModelForTokenClassification.from_pretrained(
										    model_args.model_name_or_path ,						from_pt=bool(".bin" in model_args.model_name_or_path) ,						config=_lowerCAmelCase ,						cache_dir=model_args.cache_dir ,						)

					# Get datasets
					UpperCamelCase_			= (
					    TFTokenClassificationDataset(
					        token_classification_task=_lowerCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.train ,						)
					    if training_args.do_train
					    else None
					)
					UpperCamelCase_			= (
					    TFTokenClassificationDataset(
					        token_classification_task=_lowerCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.dev ,						)
					    if training_args.do_eval
					    else None
					)

					def align_predictions(_lowerCAmelCase ,						_lowerCAmelCase) -> Tuple[List[int], List[int]]:
										UpperCamelCase_			= np.argmax(_lowerCAmelCase ,						axis=2)
										UpperCamelCase_	,     UpperCamelCase_			= preds.shape
										UpperCamelCase_			= [[] for _ in range(_lowerCAmelCase)]
										UpperCamelCase_			= [[] for _ in range(_lowerCAmelCase)]

										for i in range(_lowerCAmelCase):
															for j in range(_lowerCAmelCase):
																				if label_ids[i, j] != -1_00:
																									out_label_list[i].append(label_map[label_ids[i][j]])
																									preds_list[i].append(label_map[preds[i][j]])

										return preds_list, out_label_list

					def compute_metrics(_lowerCAmelCase) -> Dict:
										UpperCamelCase_	,     UpperCamelCase_			= align_predictions(p.predictions ,						p.label_ids)

										return {
										    "precision": precision_score(_lowerCAmelCase ,						_lowerCAmelCase),
										    "recall": recall_score(_lowerCAmelCase ,						_lowerCAmelCase),
										    "f1": fa_score(_lowerCAmelCase ,						_lowerCAmelCase),
										}

					# Initialize our Trainer
					UpperCamelCase_			= TFTrainer(
					    model=_lowerCAmelCase ,						args=_lowerCAmelCase ,						train_dataset=train_dataset.get_dataset() if train_dataset else None ,						eval_dataset=eval_dataset.get_dataset() if eval_dataset else None ,						compute_metrics=_lowerCAmelCase ,						)

					# Training
					if training_args.do_train:
										trainer.train()
										trainer.save_model()
										tokenizer.save_pretrained(training_args.output_dir)

					# Evaluation
					UpperCamelCase_			= {}
					if training_args.do_eval:
										logger.info("*** Evaluate ***")

										UpperCamelCase_			= trainer.evaluate()
										UpperCamelCase_			= os.path.join(training_args.output_dir ,						"eval_results.txt")

										with open(_lowerCAmelCase ,						"w") as writer:
															logger.info("***** Eval results *****")

															for key, value in result.items():
																				logger.info("  %s = %s" ,						_lowerCAmelCase ,						_lowerCAmelCase)
																				writer.write("%s = %s\n" % (key, value))

															results.update(_lowerCAmelCase)

    # Predict
					if training_args.do_predict:
										UpperCamelCase_			= TFTokenClassificationDataset(
										    token_classification_task=_lowerCAmelCase ,						data_dir=data_args.data_dir ,						tokenizer=_lowerCAmelCase ,						labels=_lowerCAmelCase ,						model_type=config.model_type ,						max_seq_length=data_args.max_seq_length ,						overwrite_cache=data_args.overwrite_cache ,						mode=Split.test ,						)

										UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= trainer.predict(test_dataset.get_dataset())
										UpperCamelCase_	,     UpperCamelCase_			= align_predictions(_lowerCAmelCase ,						_lowerCAmelCase)
										UpperCamelCase_			= classification_report(_lowerCAmelCase ,						_lowerCAmelCase)

										logger.info("\n%s" ,						_lowerCAmelCase)

										UpperCamelCase_			= os.path.join(training_args.output_dir ,						"test_results.txt")

										with open(_lowerCAmelCase ,						"w") as writer:
															writer.write("%s\n" % report)

										# Save predictions
										UpperCamelCase_			= os.path.join(training_args.output_dir ,						"test_predictions.txt")

										with open(_lowerCAmelCase ,						"w") as writer:
															with open(os.path.join(data_args.data_dir ,						"test.txt") ,						"r") as f:
																				UpperCamelCase_			= 0

																				for line in f:
																									if line.startswith("-DOCSTART-") or line == "" or line == "\n":
																														writer.write(_lowerCAmelCase)

																														if not preds_list[example_id]:
																																			example_id += 1
																									elif preds_list[example_id]:
																														UpperCamelCase_			= line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"

																														writer.write(_lowerCAmelCase)
																									else:
																														logger.warning("Maximum sequence length exceeded: No prediction for '%s'." ,						line.split()[0])

					return results


if __name__ == "__main__":
	main()





import collections
import inspect
import unittest

from transformers import SwinConfig
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 SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
	from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class    _lowercase :
				'''simple docstring'''







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

									'''simple docstring'''


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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

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

									UpperCamelCase_			= self.get_config()

									return config, pixel_values, labels

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return SwinConfig(
									    image_size=self.image_size  ,			patch_size=self.patch_size  ,			num_channels=self.num_channels  ,			embed_dim=self.embed_dim  ,			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  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModel(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									UpperCamelCase_			= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths     ) - 1))
									UpperCamelCase_			= 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  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinBackbone(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps     )  ,			len(config.out_features     )     )
									self.parent.assertListEqual(list(result.feature_maps[0].shape     )  ,			[self.batch_size, model.channels[0], 16, 16]     )

									# verify channels
									self.parent.assertEqual(len(model.channels     )  ,			len(config.out_features     )     )

									# verify backbone works with out_features=None
									UpperCamelCase_			= None
									UpperCamelCase_			= SwinBackbone(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(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, model.channels[-1], 4, 4]     )

									# verify channels
									self.parent.assertEqual(len(model.channels     )  ,			1     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinForMaskedImageModeling(config=snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(
									    result.logits.shape  ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)     )

									# test greyscale images
									UpperCamelCase_			= 1
									UpperCamelCase_			= SwinForMaskedImageModeling(snake_case__     )
									model.to(snake_case__     )
									model.eval()

									UpperCamelCase_			= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]     )
									UpperCamelCase_			= model(snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, 1, self.image_size, self.image_size)     )

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= self.type_sequence_label_size
									UpperCamelCase_			= SwinForImageClassification(snake_case__     )
									model.to(snake_case__     )
									model.eval()
									UpperCamelCase_			= model(snake_case__  ,			labels=snake_case__     )
									self.parent.assertEqual(result.logits.shape  ,			(self.batch_size, self.type_sequence_label_size)     )

									# test greyscale images
									UpperCamelCase_			= 1
									UpperCamelCase_			= SwinForImageClassification(snake_case__     )
									model.to(snake_case__     )
									model.eval()

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

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.prepare_config_and_inputs()
									(
									    (
									    UpperCamelCase_
									)	,     (
									    UpperCamelCase_
									)	,     (
									    UpperCamelCase_
									)	,     
									)			= config_and_inputs
									UpperCamelCase_			= {"pixel_values": pixel_values}
									return config, inputs_dict




@require_torch
class    _lowercase (a_   , a_   , unittest.TestCase   ):
				'''simple docstring'''







				lowercase__     =    (
				    (
				        SwinModel,
				        SwinBackbone,
				        SwinForImageClassification,
				        SwinForMaskedImageModeling,
				    )
				    if is_torch_available()
				    else ()
				)
				lowercase__     =    (
				    {"""feature-extraction""": SwinModel, """image-classification""": SwinForImageClassification}
				    if is_torch_available()
				    else {}
				)
				lowercase__     =    True

				lowercase__     =    False
				lowercase__     =    False
				lowercase__     =    False

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModelTester(self     )
									UpperCamelCase_			= ConfigTester(self  ,			config_class=snake_case__  ,			embed_dim=37     )

				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_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case__     )

				@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0."     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									super().test_training_gradient_checkpointing()

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case__     )

				@unittest.skip(reason="Swin does not use inputs_embeds"     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				@unittest.skip(reason="Swin Transformer does not use feedforward chunking"     )
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									pass

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														UpperCamelCase_			= model_class(snake_case__     )
														self.assertIsInstance(model.get_input_embeddings()  ,			(nn.Module)     )
														UpperCamelCase_			= model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(snake_case__  ,			nn.Linear     )     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCamelCase_			= ["pixel_values"]
														self.assertListEqual(arg_names[:1]  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()
									UpperCamelCase_			= True

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														UpperCamelCase_			= False
														UpperCamelCase_			= True
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )
														UpperCamelCase_			= outputs.attentions
														UpperCamelCase_			= len(self.model_tester.depths     )
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														# check that output_attentions also work using config
														del inputs_dict["output_attentions"]
														UpperCamelCase_			= True
														UpperCamelCase_			= config.window_size**2
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )
														UpperCamelCase_			= outputs.attentions
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														self.assertListEqual(
														    list(attentions[0].shape[-3:]     )  ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,			)
														UpperCamelCase_			= len(snake_case__     )

														# Check attention is always last and order is fine
														UpperCamelCase_			= True
														UpperCamelCase_			= True
														UpperCamelCase_			= model_class(snake_case__     )
														model.to(snake_case__     )
														model.eval()
														with torch.no_grad():
																			UpperCamelCase_			= model(**self._prepare_for_class(snake_case__  ,			snake_case__     )     )

														# also another +1 for reshaped_hidden_states
														UpperCamelCase_			= 1 if model_class.__name__ == "SwinBackbone" else 2
														self.assertEqual(out_len + added_hidden_states  ,			len(snake_case__     )     )

														UpperCamelCase_			= outputs.attentions

														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														self.assertListEqual(
														    list(self_attentions[0].shape[-3:]     )  ,			[self.model_tester.num_heads[0], window_size_squared, window_size_squared]  ,			)

				def    _lowerCamelCase       (			self  ,			snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     ):

									'''simple docstring'''


									UpperCamelCase_			= model_class(snake_case__     )
									model.to(snake_case__     )
									model.eval()

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

									UpperCamelCase_			= outputs.hidden_states

									UpperCamelCase_			= getattr(
									    self.model_tester  ,			"expected_num_hidden_layers"  ,			len(self.model_tester.depths     ) + 1     )
									self.assertEqual(len(snake_case__     )  ,			snake_case__     )

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

									UpperCamelCase_			= (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]  ,			)

									if not model_class.__name__ == "SwinBackbone":
														UpperCamelCase_			= outputs.reshaped_hidden_states
														self.assertEqual(len(snake_case__     )  ,			snake_case__     )

														UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_	,     UpperCamelCase_			= reshaped_hidden_states[0].shape
														UpperCamelCase_			= (
														    reshaped_hidden_states[0].view(snake_case__  ,			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     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_			= (
									    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:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

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

														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


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

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

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

									for model_class in self.all_model_classes:
														UpperCamelCase_			= True
														self.check_hidden_states_output(snake_case__  ,			snake_case__  ,			snake_case__  ,			(padded_height, padded_width)     )

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

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														UpperCamelCase_			= SwinModel.from_pretrained(snake_case__     )
														self.assertIsNotNone(snake_case__     )

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_	,     UpperCamelCase_			= self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_			= _config_zero_init(snake_case__     )
									for model_class in self.all_model_classes:
														UpperCamelCase_			= model_class(config=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    _lowercase (unittest.TestCase   ):
				'''simple docstring'''







				@cached_property
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									return (
									    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"     )
									    if is_vision_available()
									    else None
									)

				@slow
				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"     ).to(snake_case__     )
									UpperCamelCase_			= self.default_image_processor

									UpperCamelCase_			= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
									UpperCamelCase_			= image_processor(images=snake_case__  ,			return_tensors="pt"     ).to(snake_case__     )

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

									# verify the logits
									UpperCamelCase_			= torch.Size((1, 1000)     )
									self.assertEqual(outputs.logits.shape  ,			snake_case__     )
									UpperCamelCase_			= torch.tensor([-0.0_948, -0.6_454, -0.0_921]     ).to(snake_case__     )
									self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,			snake_case__  ,			atol=1e-4     )     )




@require_torch
class    _lowercase (unittest.TestCase   , a_   ):
				'''simple docstring'''







				lowercase__     =    (SwinBackbone,) if is_torch_available() else ()
				lowercase__     =    SwinConfig

				def    _lowerCamelCase       (			self     ):

									'''simple docstring'''


									UpperCamelCase_			= SwinModelTester(self     )
