



'''simple docstring'''

import collections
import inspect
import unittest

from transformers import SwinvaConfig
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, _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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
	from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor


class 	a__     :


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= parent
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= batch_size
					SCREAMING_SNAKE_CASE  :						List[Any]      					= image_size
					SCREAMING_SNAKE_CASE  :						str      					= patch_size
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_channels
					SCREAMING_SNAKE_CASE  :						int      					= embed_dim
					SCREAMING_SNAKE_CASE  :						Tuple      					= depths
					SCREAMING_SNAKE_CASE  :						List[Any]      					= num_heads
					SCREAMING_SNAKE_CASE  :						List[str]      					= window_size
					SCREAMING_SNAKE_CASE  :						str      					= mlp_ratio
					SCREAMING_SNAKE_CASE  :						List[str]      					= qkv_bias
					SCREAMING_SNAKE_CASE  :						str      					= hidden_dropout_prob
					SCREAMING_SNAKE_CASE  :						int      					= attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE  :						str      					= drop_path_rate
					SCREAMING_SNAKE_CASE  :						List[Any]      					= hidden_act
					SCREAMING_SNAKE_CASE  :						Dict      					= use_absolute_embeddings
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= patch_norm
					SCREAMING_SNAKE_CASE  :						Tuple      					= layer_norm_eps
					SCREAMING_SNAKE_CASE  :						List[str]      					= initializer_range
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= is_training
					SCREAMING_SNAKE_CASE  :						str      					= scope
					SCREAMING_SNAKE_CASE  :						Dict      					= use_labels
					SCREAMING_SNAKE_CASE  :						List[str]      					= type_sequence_label_size
					SCREAMING_SNAKE_CASE  :						List[str]      					= encoder_stride


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

					SCREAMING_SNAKE_CASE  :						str      					= None
					if self.use_labels:
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= ids_tensor([self.batch_size],					self.type_sequence_label_size      )

					SCREAMING_SNAKE_CASE  :						Any      					= self.get_config()

					return config, pixel_values, labels


	def 				lowercase__  (self : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					return SwinvaConfig(
					    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,					)


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= SwinvaModel(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						List[str]      					= model(__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
					SCREAMING_SNAKE_CASE  :						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 				lowercase__  (self : List[str],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= SwinvaForMaskedImageModeling(config=__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= model(__UpperCAmelCase      )
					self.parent.assertEqual(
					    result.logits.shape,					(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

					# test greyscale images
					SCREAMING_SNAKE_CASE  :						Any      					= 1
					SCREAMING_SNAKE_CASE  :						Dict      					= SwinvaForMaskedImageModeling(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()

					SCREAMING_SNAKE_CASE  :						Any      					= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
					SCREAMING_SNAKE_CASE  :						Any      					= model(__UpperCAmelCase      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, 1, self.image_size, self.image_size)      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.type_sequence_label_size
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= SwinvaForImageClassification(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(__UpperCAmelCase,					labels=__UpperCAmelCase      )
					self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.type_sequence_label_size)      )






	def 				lowercase__  (self : Optional[Any]      )     ->			Optional[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.prepare_config_and_inputs()
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= config_and_inputs
					SCREAMING_SNAKE_CASE  :						Tuple      					= {'''pixel_values''': pixel_values}
					return config, inputs_dict



@require_torch
class 	a__     (				_lowercase,			_lowercase,			unittest.TestCase	):
	__magic_name__		:    List[Any]	    =     (
	    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
	)
	__magic_name__		:    Any	    =     (
	    {"feature-extraction": SwinvaModel, "image-classification": SwinvaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__magic_name__		:    List[str]	    =     False
	__magic_name__		:    Optional[int]	    =     False
	__magic_name__		:    List[Any]	    =     False
	__magic_name__		:    List[str]	    =     False


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= SwinvaModelTester(self      )
					SCREAMING_SNAKE_CASE  :						Any      					= ConfigTester(self,					config_class=__UpperCAmelCase,					embed_dim=37      )


	def 				lowercase__  (self : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					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 				lowercase__  (self : Dict      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*__UpperCAmelCase      )


	@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''      )
	def 				lowercase__  (self : Dict      )     ->			List[str]:

					"""simple docstring"""

					pass


	@unittest.skip(reason='''Swinv2 does not use inputs_embeds'''      )
	def 				lowercase__  (self : Optional[int]      )     ->			Optional[Any]:

					"""simple docstring"""

					pass


	def 				lowercase__  (self : Optional[Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

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

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Dict      					= model_class(__UpperCAmelCase      )
									self.assertIsInstance(model.get_input_embeddings(),					(nn.Module)      )
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(__UpperCAmelCase,					nn.Linear      )      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Tuple      					= self.model_tester.prepare_config_and_inputs_for_common()

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

									SCREAMING_SNAKE_CASE  :						List[Any]      					= ['''pixel_values''']
									self.assertListEqual(arg_names[:1],					__UpperCAmelCase      )


	def 				lowercase__  (self : int      )     ->			Dict:

					"""simple docstring"""

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

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= True
									SCREAMING_SNAKE_CASE  :						Tuple      					= False
									SCREAMING_SNAKE_CASE  :						Dict      					= True
									SCREAMING_SNAKE_CASE  :						int      					= model_class(__UpperCAmelCase      )
									model.to(__UpperCAmelCase      )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						Any      					= model(**self._prepare_for_class(__UpperCAmelCase,					__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Dict      					= outputs.attentions
									SCREAMING_SNAKE_CASE  :						int      					= len(self.model_tester.depths      )
									self.assertEqual(len(__UpperCAmelCase      ),					__UpperCAmelCase      )

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

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

									# Check attention is always last and order is fine
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= True
									SCREAMING_SNAKE_CASE  :						Tuple      					= True
									SCREAMING_SNAKE_CASE  :						List[Any]      					= model_class(__UpperCAmelCase      )
									model.to(__UpperCAmelCase      )
									model.eval()
									with torch.no_grad():
													SCREAMING_SNAKE_CASE  :						Dict      					= model(**self._prepare_for_class(__UpperCAmelCase,					__UpperCAmelCase      )      )

									if hasattr(self.model_tester,					'''num_hidden_states_types'''      ):
													SCREAMING_SNAKE_CASE  :						int      					= self.model_tester.num_hidden_states_types
									else:
													# also another +1 for reshaped_hidden_states
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= 2
									self.assertEqual(out_len + added_hidden_states,					len(__UpperCAmelCase      )      )

									SCREAMING_SNAKE_CASE  :						Any      					= outputs.attentions

									self.assertEqual(len(__UpperCAmelCase      ),					__UpperCAmelCase      )

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


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= model_class(__UpperCAmelCase      )
					model.to(__UpperCAmelCase      )
					model.eval()

					with torch.no_grad():
									SCREAMING_SNAKE_CASE  :						List[str]      					= model(**self._prepare_for_class(__UpperCAmelCase,					__UpperCAmelCase      )      )

					SCREAMING_SNAKE_CASE  :						str      					= outputs.hidden_states

					SCREAMING_SNAKE_CASE  :						str      					= getattr(
					    self.model_tester,					'''expected_num_hidden_layers''',					len(self.model_tester.depths      ) + 1      )
					self.assertEqual(len(__UpperCAmelCase      ),					__UpperCAmelCase      )

					# Swinv2 has a different seq_length
					SCREAMING_SNAKE_CASE  :						Tuple      					= (
					    config.patch_size
					    if isinstance(config.patch_size,					collections.abc.Iterable      )
					    else (config.patch_size, config.patch_size)
					)

					SCREAMING_SNAKE_CASE  :						Optional[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],					)

					SCREAMING_SNAKE_CASE  :						List[Any]      					= outputs.reshaped_hidden_states
					self.assertEqual(len(__UpperCAmelCase      ),					__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= reshaped_hidden_states[0].shape
					SCREAMING_SNAKE_CASE  :						Tuple      					= (
					    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 				lowercase__  (self : str      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Tuple      					= self.model_tester.prepare_config_and_inputs_for_common()

					SCREAMING_SNAKE_CASE  :						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:
									SCREAMING_SNAKE_CASE  :						List[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"]
									SCREAMING_SNAKE_CASE  :						str      					= True

									self.check_hidden_states_output(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.model_tester.prepare_config_and_inputs_for_common()
					SCREAMING_SNAKE_CASE  :						Dict      					= 3

					SCREAMING_SNAKE_CASE  :						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)
					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= (
					    config.patch_size
					    if isinstance(config.patch_size,					collections.abc.Iterable      )
					    else (config.patch_size, config.patch_size)
					)

					SCREAMING_SNAKE_CASE  :						str      					= image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
					SCREAMING_SNAKE_CASE  :						str      					= image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						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"]
									SCREAMING_SNAKE_CASE  :						str      					= True
									self.check_hidden_states_output(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					(padded_height, padded_width)      )


	def 				lowercase__  (self : List[Any]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCAmelCase      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*__UpperCAmelCase      )


	@slow
	def 				lowercase__  (self : Any      )     ->			Optional[Any]:

					"""simple docstring"""

					for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE  :						List[str]      					= SwinvaModel.from_pretrained(__UpperCAmelCase      )
									self.assertIsNotNone(__UpperCAmelCase      )






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

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= _config_zero_init(__UpperCAmelCase      )
					for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE  :						List[Any]      					= model_class(config=__UpperCAmelCase      )
									for name, param in model.named_parameters():
													if "embeddings" not in name and "logit_scale" 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 				lowercase__  (self : Dict      )     ->			Tuple:

					"""simple docstring"""

					return (
					    AutoImageProcessor.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      )
					    if is_vision_available()
					    else None
					)






	@slow
	def 				lowercase__  (self : Dict      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= SwinvaForImageClassification.from_pretrained('''microsoft/swinv2-tiny-patch4-window8-256'''      ).to(
					    __UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= self.default_image_processor

					SCREAMING_SNAKE_CASE  :						Tuple      					= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					SCREAMING_SNAKE_CASE  :						int      					= image_processor(images=__UpperCAmelCase,					return_tensors='''pt'''      ).to(__UpperCAmelCase      )

					# forward pass
					with torch.no_grad():
									SCREAMING_SNAKE_CASE  :						List[str]      					= model(**__UpperCAmelCase      )

					# verify the logits
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.Size((1, 1000)      )
					self.assertEqual(outputs.logits.shape,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Any      					= torch.tensor([-0.3947, -0.4306, 0.0026]      ).to(__UpperCAmelCase      )
					self.assertTrue(torch.allclose(outputs.logits[0, :3],					__UpperCAmelCase,					atol=1e-4      )      )









'''simple docstring'''

import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]=32    ,	_SCREAMING_SNAKE_CASE      :List[Any]=10    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]=1_00    ,	_SCREAMING_SNAKE_CASE      :int=10_26    ,	_SCREAMING_SNAKE_CASE      :List[Any]=True    ,	_SCREAMING_SNAKE_CASE      :str="data/tokenized_stories_train_wikitext103.jbl"    ,	_SCREAMING_SNAKE_CASE      :List[Any]="igf_context_pairs.jbl"    ,	):
				set_seed(3       )
				# generate train_data and objective_set
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[int]      					= generate_datasets(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	number=_SCREAMING_SNAKE_CASE    ,	min_len=10_26    ,	trim=_SCREAMING_SNAKE_CASE       )
				# keeps model same across runs
				set_seed(4       )
				# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
				# can we train on GPU?
				SCREAMING_SNAKE_CASE  :						List[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )

				# load pretrained model
				SCREAMING_SNAKE_CASE  :						Any      					= load_gpta('''gpt2'''       ).to(_SCREAMING_SNAKE_CASE       )
				print('''computing perplexity on objective set'''       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= compute_perplexity(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ).item()
				print('''perplexity on objective set:'''    ,	_SCREAMING_SNAKE_CASE       )

				# collect igf pairs and save to file demo.jbl
				collect_objective_set(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				# clean up, delete model and data we don't need anymore
				del model, train_data, objective_set
				torch.cuda.empty_cache()






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Optional[int]=15    ,	_SCREAMING_SNAKE_CASE      :Any=1_28    ,	_SCREAMING_SNAKE_CASE      :Any=1_00    ,	_SCREAMING_SNAKE_CASE      :List[str]="igf_model.pt"    ,	):
				set_seed(42       )

				# Load pre-trained model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )

				# Initialize secondary learner to use embedding weights of model
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= SecondaryLearner(_SCREAMING_SNAKE_CASE       )

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						Any      					= train_secondary_learner(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	max_epochs=_SCREAMING_SNAKE_CASE    ,	batch_size=_SCREAMING_SNAKE_CASE    ,	eval_freq=1_00    ,	igf_model_path=_SCREAMING_SNAKE_CASE    ,	)

				del model, secondary_learner_train_data
				torch.cuda.empty_cache()

				return secondary_learner






def       __lowercase  (_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]    ,	_SCREAMING_SNAKE_CASE      :Union[str, Any]=32    ,	_SCREAMING_SNAKE_CASE      :Tuple=10_00    ,	_SCREAMING_SNAKE_CASE      :int=16    ,	_SCREAMING_SNAKE_CASE      :List[str]=1.0    ,	_SCREAMING_SNAKE_CASE      :Any=recopy_gpta    ,	_SCREAMING_SNAKE_CASE      :Tuple=None    ,	_SCREAMING_SNAKE_CASE      :int=10    ,	_SCREAMING_SNAKE_CASE      :Optional[int]="gpt2_finetuned.pt"    ,	):
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''       )
				SCREAMING_SNAKE_CASE  :						Any      					= RandomSampler(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						List[Any]      					= DataLoader(_SCREAMING_SNAKE_CASE    ,	sampler=_SCREAMING_SNAKE_CASE       )

				SCREAMING_SNAKE_CASE  :						Dict      					= max_steps // (len(_SCREAMING_SNAKE_CASE       )) + 1
				SCREAMING_SNAKE_CASE  :						str      					= 0
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= torch.zeros((1, context_len)    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[Any]      					= recopy_model(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )

				model.train()
				if secondary_learner is not None:
								secondary_learner.to(_SCREAMING_SNAKE_CASE       )
								secondary_learner.eval()
				SCREAMING_SNAKE_CASE  :						str      					= []
				SCREAMING_SNAKE_CASE  :						List[str]      					= 0

				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []

				# Compute the performance of the transformer model at the beginning
				SCREAMING_SNAKE_CASE  :						Any      					= compute_perplexity(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				test_perps.append(_SCREAMING_SNAKE_CASE       )
				print('''Test perplexity, step'''    ,	_SCREAMING_SNAKE_CASE    ,	''':'''    ,	_SCREAMING_SNAKE_CASE       )
				for epoch in range(int(_SCREAMING_SNAKE_CASE       )       ):
								for step, example in enumerate(_SCREAMING_SNAKE_CASE       ):
												torch.cuda.empty_cache()
												SCREAMING_SNAKE_CASE  :						Any      					= random.randint(0    ,	example.size(2       ) - context_len - 1       )
												SCREAMING_SNAKE_CASE  :						int      					= example[0, 0, start : start + context_len]
												lm_optimizer.zero_grad()
												SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model(_SCREAMING_SNAKE_CASE    ,	labels=_SCREAMING_SNAKE_CASE       )
												SCREAMING_SNAKE_CASE  :						int      					= True

												if secondary_learner is not None:
																SCREAMING_SNAKE_CASE  :						str      					= secondary_learner.forward(
																    torch.tensor(_SCREAMING_SNAKE_CASE    ,	dtype=torch.long    ,	device=_SCREAMING_SNAKE_CASE       ).unsqueeze(0       )       )[0].item()
																observed_qs.append(float(_SCREAMING_SNAKE_CASE       )       )

																# Here we implement the simple non-constant threshold for the predicted IG(X) value
																# We will decay the selectivity of our secondary learner filter from
																# 1 standard deviation above average to 1 below average after 10 batches.

																if global_step == 10:
																				SCREAMING_SNAKE_CASE  :						Dict      					= -1
																if predicted_q < threshold:
																				SCREAMING_SNAKE_CASE  :						Dict      					= False

            # If we passed the filter, add the context to the batch!
												if do_backprop:
																contexts.append(np.array(context.cpu()       )       )
																SCREAMING_SNAKE_CASE  :						Tuple      					= outputs[0]
																lm_loss.backward()
																examples += 1

												del outputs

												# Once the batch is filled with enough contexts, backprop on the batch.
												if examples == batch_size:
																torch.cuda.empty_cache()
																SCREAMING_SNAKE_CASE  :						int      					= 0
																# Do LM backprop
																torch.nn.utils.clip_grad_norm_(model.parameters()    ,	3.0       )
																lm_optimizer.step()
																lm_scheduler.step()  # Update learning rate schedule
																global_step += 1
																# Compute the performance of the transformer model at this batch
																if global_step % eval_interval == 0:
																				SCREAMING_SNAKE_CASE  :						Any      					= compute_perplexity(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
																				test_perps.append(_SCREAMING_SNAKE_CASE       )

																				print('''Test perplexity, step'''    ,	_SCREAMING_SNAKE_CASE    ,	''':'''    ,	_SCREAMING_SNAKE_CASE       )
            # Break out of the loop after 60 batches
												if max_steps > 0 and global_step > 60:
																break
								if max_steps > 0 and global_step > 60:
												break

    # save finetuned transformer model
				torch.save(model.state_dict()    ,	_SCREAMING_SNAKE_CASE       )
				torch.cuda.empty_cache()
				# Do some cleaning up so we can reinitialize for the next run of this function
				del lm_optimizer
				del lm_scheduler
				return model






def       __lowercase  ():
				SCREAMING_SNAKE_CASE  :						Dict      					= argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''       )

				# Required parameters
				parser.add_argument(
				    '''--data_dir'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_SNAKE_CASE    ,	required=_SCREAMING_SNAKE_CASE    ,	help='''The input data dir. Should contain data files for WikiText.'''    ,	)
				parser.add_argument(
				    '''--model_name_or_path'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_SNAKE_CASE    ,	required=_SCREAMING_SNAKE_CASE    ,	help='''Path to pretrained model or model identifier from huggingface.co/models'''    ,	)
				parser.add_argument(
				    '''--data_file'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=_SCREAMING_SNAKE_CASE    ,	help=(
				        '''A jbl file containing tokenized data which can be split as objective dataset, '''
				        '''train_dataset and test_dataset.'''
				    )    ,	)

				parser.add_argument(
				    '''--igf_data_file'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=_SCREAMING_SNAKE_CASE    ,	help='''A jbl file containing the context and information gain pairs to train secondary learner.'''    ,	)

				parser.add_argument(
				    '''--output_dir'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_SNAKE_CASE    ,	required=_SCREAMING_SNAKE_CASE    ,	help='''The output directory where the final fine-tuned model is stored.'''    ,	)

				parser.add_argument(
				    '''--tokenizer_name'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''Pretrained tokenizer name or path if not the same as model_name'''    ,	)
				parser.add_argument('''--seed'''    ,	type=_SCREAMING_SNAKE_CASE    ,	default=_SCREAMING_SNAKE_CASE    ,	help='''A seed for reproducible training.'''       )

				parser.add_argument(
				    '''--context_len'''    ,	default=32    ,	type=_SCREAMING_SNAKE_CASE    ,	help=(
				        '''The maximum total input sequence length after tokenization. Sequences longer '''
				        '''than this will be truncated, sequences shorter will be padded.'''
				    )    ,	)

				parser.add_argument(
				    '''--size_objective_set'''    ,	default=1_00    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''number of articles that are long enough to be used as our objective set'''    ,	)
				parser.add_argument(
				    '''--eval_freq'''    ,	default=1_00    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''secondary model evaluation is triggered at eval_freq'''       )

				parser.add_argument('''--max_steps'''    ,	default=10_00    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''To calculate training epochs'''       )

				parser.add_argument(
				    '''--secondary_learner_batch_size'''    ,	default=1_28    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''batch size of training data for secondary learner'''    ,	)

				parser.add_argument(
				    '''--batch_size'''    ,	default=16    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''batch size of training data of language model(gpt2) '''       )

				parser.add_argument(
				    '''--eval_interval'''    ,	default=10    ,	type=_SCREAMING_SNAKE_CASE    ,	help=(
				        '''decay the selectivity of our secondary learner filter from'''
				        '''1 standard deviation above average to 1 below average after 10 batches'''
				    )    ,	)

				parser.add_argument(
				    '''--number'''    ,	default=1_00    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''The number of examples split to be used as objective_set/test_data'''       )

				parser.add_argument(
				    '''--min_len'''    ,	default=10_26    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''The minimum length of the article to be used as objective set'''       )

				parser.add_argument(
				    '''--secondary_learner_max_epochs'''    ,	default=15    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''number of epochs to train secondary learner'''       )

				parser.add_argument('''--trim'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''truncate the example if it exceeds context length'''       )

				parser.add_argument(
				    '''--threshold'''    ,	default=1.0    ,	type=_SCREAMING_SNAKE_CASE    ,	help=(
				        '''The threshold value used by secondary learner to filter the train_data and allow only'''
				        ''' informative data as input to the model'''
				    )    ,	)

				parser.add_argument('''--finetuned_model_name'''    ,	default='''gpt2_finetuned.pt'''    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''finetuned_model_name'''       )

				parser.add_argument(
				    '''--recopy_model'''    ,	default=_SCREAMING_SNAKE_CASE    ,	type=_SCREAMING_SNAKE_CASE    ,	help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''    ,	)

				# function calls
				# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
				generate_n_pairs(
				    context_len=32    ,	max_steps=10    ,	size_objective_set=1_00    ,	min_len=10_26    ,	trim=_SCREAMING_SNAKE_CASE    ,	data_file='''data/tokenized_stories_train_wikitext103.jbl'''    ,	igf_data_file='''igf_context_pairs.jbl'''    ,	)

				# Load train data for secondary learner
				SCREAMING_SNAKE_CASE  :						int      					= joblib.load('''data/IGF_values.jbl'''       )

				# Train secondary learner
				SCREAMING_SNAKE_CASE  :						List[Any]      					= training_secondary_learner(
				    _SCREAMING_SNAKE_CASE    ,	secondary_learner_max_epochs=15    ,	secondary_learner_batch_size=1_28    ,	eval_freq=1_00    ,	igf_model_path='''igf_model.pt'''    ,	)

				# load pretrained gpt2 model
				SCREAMING_SNAKE_CASE  :						List[Any]      					= GPTaLMHeadModel.from_pretrained('''gpt2'''       )
				set_seed(42       )

				# Generate train and test data to train and evaluate gpt2 model
				SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						str      					= generate_datasets(
				    context_len=32    ,	file='''data/tokenized_stories_train_wikitext103.jbl'''    ,	number=1_00    ,	min_len=10_26    ,	trim=_SCREAMING_SNAKE_CASE       )

				# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
				finetune(
				    _SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	context_len=32    ,	max_steps=10_00    ,	batch_size=16    ,	threshold=1.0    ,	recopy_model=_SCREAMING_SNAKE_CASE    ,	secondary_learner=_SCREAMING_SNAKE_CASE    ,	eval_interval=10    ,	finetuned_model_name='''gpt2_finetuned.pt'''    ,	)


if __name__ == "__main__":
	main()




