



'''simple docstring'''


import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowercase_      :				int   					=    {
    "cola": 2,
    "mnli": 3,
    "mrpc": 2,
    "sst-2": 2,
    "sts-b": 1,
    "qqp": 2,
    "qnli": 2,
    "rte": 2,
    "wnli": 2,
}


logging.set_verbosity_info()







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      str ,  lowercase_    :      List[Any] ,  lowercase_    :      List[Any] ,  lowercase_    :      Union[str, Any]=None						):
							lowercase        =							XLNetConfig.from_json_file(__A						)

							lowercase        =							finetuning_task.lower() if finetuning_task is not None else """"""
							if finetuning_task in GLUE_TASKS_NUM_LABELS:
														print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""						)
														lowercase        =							finetuning_task
														lowercase        =							GLUE_TASKS_NUM_LABELS[finetuning_task]
														lowercase        =							XLNetForSequenceClassification(__A						)
							elif "squad" in finetuning_task:
														lowercase        =							finetuning_task
														lowercase        =							XLNetForQuestionAnswering(__A						)
							else:
														lowercase        =							XLNetLMHeadModel(__A						)

							# Load weights from tf checkpoint
							load_tf_weights_in_xlnet(__A ,  __A ,  __A						)

							# Save pytorch-model
							lowercase        =							os.path.join(__A ,  __A						)
							lowercase        =							os.path.join(__A ,  __A						)
							print(F"""Save PyTorch model to {os.path.abspath(__A						)}"""						)
							torch.save(model.state_dict() ,  __A						)
							print(F"""Save configuration file to {os.path.abspath(__A						)}"""						)
							with open(__A ,  """w""" ,  encoding="""utf-8"""						) as f:
														f.write(config.to_json_string()						)


if __name__ == "__main__":
						lowercase_      :				Dict   					=    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
						)
						parser.add_argument(
						    '''--xlnet_config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        '''The config json file corresponding to the pre-trained XLNet model. \n'''
						        '''This specifies the model architecture.'''
						    ),
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''Path to the folder to store the PyTorch model or dataset/vocab.''',
						)
						parser.add_argument(
						    '''--finetuning_task''',
						    default=None,
						    type=str,
						    help='''Name of a task on which the XLNet TensorFlow model was fine-tuned''',
						)
						lowercase_      :				Union[str, Any]   					=    parser.parse_args()
						print(args)

						convert_xlnet_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
						)







'''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      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=13 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=1000 ,				_lowerCAmelCase=[3, 3, 6, 4] ,				_lowerCAmelCase=[48, 56, 112, 220] ,				)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							is_training
							lowercase        =							use_labels
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							num_labels
							lowercase        =							image_size
							lowercase        =							layer_depths
							lowercase        =							embed_dims







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






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

							lowercase        =							None
							if self.use_labels:
													lowercase        =							ids_tensor([self.batch_size] ,				self.num_labels						)

							lowercase        =							self.get_config()

							return config, pixel_values, labels







	def   _a   (    self						)    ->    int:
							'''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=_lowerCAmelCase ,				layer_scale_init_value=1E-5 ,				)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.embed_dims[-1], 7, 7)						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.num_labels
							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase ,				labels=_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)						)

							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()

							lowercase        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)
							lowercase        =							model(_lowerCAmelCase						)
							self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels)						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






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




@require_torch
class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__A			  =					(
	    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							SwiftFormerModelTester(self						)
							lowercase        =							ConfigTester(
							    self ,				config_class=_lowerCAmelCase ,				has_text_modality=_lowerCAmelCase ,				hidden_size=37 ,				num_attention_heads=12 ,				num_hidden_layers=12 ,				)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""						)
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							model_class(_lowerCAmelCase						)
													lowercase        =							model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(_lowerCAmelCase ,				nn.Linear						)						)







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase        =							["""pixel_values"""]
													self.assertListEqual(arg_names[:1] ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_lowerCAmelCase						)







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase						)







	@slow
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase        =							SwiftFormerModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)







	@unittest.skip(reason="""SwiftFormer does not output attentions"""						)
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






							def check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						):
													lowercase        =							model_class(_lowerCAmelCase						)
													model.to(_lowerCAmelCase						)
													model.eval()

													with torch.no_grad():
																			lowercase        =							model(**self._prepare_for_class(_lowerCAmelCase ,				_lowerCAmelCase						)						)

													lowercase        =							outputs.hidden_states

													lowercase        =							8
													self.assertEqual(len(_lowerCAmelCase						) ,				_lowerCAmelCase						)  # 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(_lowerCAmelCase						)						):
																			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),
																			        ]						) ,				)

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							True
													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)

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

													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							def _config_zero_init(_lowerCAmelCase						):
													lowercase        =							copy.deepcopy(_lowerCAmelCase						)
													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(_lowerCAmelCase ,				_lowerCAmelCase ,				1E-10						)
																			if isinstance(getattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						) ,				_lowerCAmelCase						):
																									lowercase        =							_config_zero_init(getattr(_lowerCAmelCase ,				_lowerCAmelCase						)						)
																									setattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)
													return configs_no_init

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							lowercase        =							_config_zero_init(_lowerCAmelCase						)
							for model_class in self.all_model_classes:
													lowercase        =							model_class(config=_lowerCAmelCase						)
													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   _a   (    self						)    ->    Any:
							'''simple docstring'''






							pass







def      SCREAMING_SNAKE_CASE				(				):
						lowercase        =							Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




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







	@cached_property
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






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







	@slow
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""						).to(_lowerCAmelCase						)

							lowercase        =							self.default_image_processor
							lowercase        =							prepare_img()
							lowercase        =							image_processor(images=_lowerCAmelCase ,				return_tensors="""pt"""						).to(_lowerCAmelCase						)

							# forward pass
							with torch.no_grad():
													lowercase        =							model(**_lowerCAmelCase						)

							# verify the logits
							lowercase        =							torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape ,				_lowerCAmelCase						)

							lowercase        =							torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)


