
import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
						import torch

SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   ):
				return importlib.util.find_spec('''deepspeed'''     ) is not None


if is_accelerate_available() and is_deepspeed_available():
						from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
						# Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
						# Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
						from builtins import object as DeepSpeedConfig






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							Dict:





									'''simple docstring'''
									set_hf_deepspeed_config(self						)
									dep_version_check('''accelerate'''						)
									dep_version_check('''deepspeed'''						)
									super().__init__(a__						)






class 					__lowercase		(					A       ):


					def __init__(	self       ,       a__						)		->							List[Any]:





									'''simple docstring'''
									super().__init__(a__						)
									A_       	=  None
									A_       	=  []


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





									'''simple docstring'''
									if self._dtype is None:
													raise ValueError('''trainer_config_process() wasn\'t called yet to tell dtype'''						)
									return self._dtype


					def        lowerCAmelCase_    (	self       ,       a__						)		->							Optional[int]:





									'''simple docstring'''
									A_       	=  self.get_value(a__						)
									if val is None:
													return False
									else:
													return val == "auto"


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__=None       ,       a__=True						)		->							List[str]:





									'''simple docstring'''
									A_	,					A_       	=  self.find_config_node(a__						)
									if config is None:
													return

									if config.get(a__						) == "auto":
													A_       	=  hf_val
													return

									if not must_match:
													return

									A_       	=  config.get(a__						)
									if ds_val is not None and ds_val != hf_val:
													self.mismatches.append(F"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"						)

					__magic_name__						:							str     	=	partialmethod(A							,       must_match=A       )


					def        lowerCAmelCase_    (	self       ,       a__						)		->							str:





									'''simple docstring'''
									A_       	=  args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
									self.fill_match(
									    '''train_micro_batch_size_per_gpu'''       ,       args.per_device_train_batch_size       ,       '''per_device_train_batch_size'''						)
									self.fill_match('''gradient_accumulation_steps'''       ,       args.gradient_accumulation_steps       ,       '''gradient_accumulation_steps'''						)
									self.fill_match('''train_batch_size'''       ,       a__       ,       '''train_batch_size (calculated)'''						)
									self.fill_match('''gradient_clipping'''       ,       args.max_grad_norm       ,       '''max_grad_norm'''						)

									self.fill_match('''optimizer.params.lr'''       ,       args.learning_rate       ,       '''learning_rate'''						)
									self.fill_match('''optimizer.params.betas'''       ,       [args.adam_betaa, args.adam_betaa]       ,       '''adam_beta1+adam_beta2'''						)
									self.fill_match('''optimizer.params.eps'''       ,       args.adam_epsilon       ,       '''adam_epsilon'''						)
									self.fill_match('''optimizer.params.weight_decay'''       ,       args.weight_decay       ,       '''weight_decay'''						)

									self.fill_only('''scheduler.params.warmup_min_lr'''       ,       0						)  # not a trainer arg
									self.fill_match('''scheduler.params.warmup_max_lr'''       ,       args.learning_rate       ,       '''learning_rate'''						)
									# total_num_steps - will get set in trainer_config_finalize

									# fp16
									if args.fpaa or args.fpaa_full_eval:
													A_       	=  '''apex''' if args.fpaa_backend == '''apex''' else '''amp'''
									else:
													A_       	=  None

									if args.save_on_each_node:
													# deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
													A_       	=  self.config.get('''checkpoint'''       ,       {}						)
													A_       	=  args.save_on_each_node

									# amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
									# any here unless the user did the work
									self.fill_match(
									    '''fp16.enabled'''       ,       ((args.fpaa or args.fpaa_full_eval) and fpaa_backend == '''amp''')       ,       '''fp16|fp16_full_eval+fp16_backend(amp)'''       ,       )

									# apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
									# ZeRO features
									self.fill_match('''amp.enabled'''       ,       fpaa_backend == '''apex'''       ,       '''fp16+fp16_backend(apex)'''						)
									self.fill_match('''amp.opt_level'''       ,       args.fpaa_opt_level       ,       '''fp16_opt_level'''						)

									self.fill_match('''bf16.enabled'''       ,       (args.bfaa or args.bfaa_full_eval)       ,       '''bf16|bf16_full_eval'''						)

									# deepspeed's default mode is fp16 unless there is a config that says differently
									if self.is_true('''bf16.enabled'''						):
													A_       	=  torch.bfloataa
									elif self.is_false('''fp16.enabled'''						):
													A_       	=  torch.floataa
									else:
													A_       	=  torch.floataa


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__						)		->							Tuple:





									'''simple docstring'''
									A_       	=  [
									    '''zero_optimization.reduce_bucket_size''',
									    '''zero_optimization.stage3_prefetch_bucket_size''',
									    '''zero_optimization.stage3_param_persistence_threshold''',
									]
									A_       	=  [x for x in hidden_size_based_keys if self.is_auto(a__						)]

									if len(a__						) > 0:
													if hasattr(model.config       ,       '''hidden_size'''						):
																	A_       	=  model.config.hidden_size
													elif hasattr(model.config       ,       '''hidden_sizes'''						):
																	# if there are many hidden sizes pick the largest one
																	A_       	=  max(model.config.hidden_sizes						)
													else:
																	raise ValueError(
																	    '''The model\'s config file has neither `hidden_size` nor `hidden_sizes` entry, '''
																	    '''therefore it\'s not possible to automatically fill out the following `auto` entries '''
																	    F"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
																	    '''`auto` values for these keys with an integer value of your choice.'''						)

													self.fill_only('''zero_optimization.reduce_bucket_size'''       ,       hidden_size * hidden_size						)
													if self.is_zeroa():
																	# automatically assign the optimal config values based on model config
																	self.fill_only('''zero_optimization.stage3_prefetch_bucket_size'''       ,       0.9 * hidden_size * hidden_size						)
																	self.fill_only('''zero_optimization.stage3_param_persistence_threshold'''       ,       1_0 * hidden_size						)

        # scheduler
									self.fill_match('''scheduler.params.total_num_steps'''       ,       a__       ,       '''num_training_steps (calculated)'''						)
									self.fill_match('''scheduler.params.warmup_num_steps'''       ,       args.get_warmup_steps(a__						)       ,       '''warmup_steps'''						)

									if len(self.mismatches						) > 0:
													A_       	=  '''\n'''.join(self.mismatches						)
													raise ValueError(
													    '''Please correct the following DeepSpeed config values that mismatch TrainingArguments'''
													    F" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."						)
SCREAMING_SNAKE_CASE   :		Optional[Any]					 =       None



def        lowerCamelCase_  (   __UpperCamelCase     ):
				# this is a special weakref global object to allow us to get to Deepspeed config from APIs
				# that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
				global _hf_deepspeed_config_weak_ref
				# will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
				A_       	=  weakref.ref(__UpperCamelCase     )



def        lowerCamelCase_  (   ):
				# useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
				global _hf_deepspeed_config_weak_ref
				A_       	=  None



def        lowerCamelCase_  (   ):
				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().is_zeroa()
				else:
								return False



def        lowerCamelCase_  (   ):
				if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
								return _hf_deepspeed_config_weak_ref().config
				else:
								return None



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				from accelerate.utils import DummyOptim, DummyScheduler

				A_       	=  hf_deepspeed_config.config

				# Optimizer + Scheduler
				# Currently supported combos:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Yes
				# 3. DS scheduler + HF optimizer: Yes
				# 4. HF scheduler + DS optimizer: No
				#
				# Unless Offload is enabled in which case it's:
				# 1. DS scheduler + DS optimizer: Yes
				# 2. HF scheduler + HF optimizer: Mostly*
				# 3. DS scheduler + HF optimizer: Mostly*
				# 4. HF scheduler + DS optimizer: No
				#
				# Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

				A_       	=  None
				if "optimizer" in config:
								if args.adafactor:
												raise ValueError(
												    '''--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. '''
												    '''Only one optimizer can be configured.'''     )
								A_       	=  DummyOptim(params=__UpperCamelCase     )
				else:
								if hf_deepspeed_config.is_offload():
												logger.info(
												    '''Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the'''
												    ''' custom optimizer has both CPU and GPU implementation (except LAMB)'''     )

								# ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
								# But trainer uses AdamW by default.
								A_       	=  trainer.create_optimizer()
								# To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
								A_       	=  True

				A_       	=  None
				if "scheduler" in config:
								A_       	=  DummyScheduler(__UpperCamelCase     )
				else:
								if isinstance(__UpperCamelCase ,						__UpperCamelCase     ):
												raise ValueError(
												    '''Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '''
												    '''Please configure a scheduler in the DeepSpeed config.'''     )
								A_       	=  trainer.create_scheduler(num_training_steps=__UpperCamelCase ,						optimizer=__UpperCamelCase     )

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase=False     ):
				from deepspeed.utils import logger as ds_logger

				A_       	=  trainer.model
				A_       	=  trainer.args

				A_       	=  trainer.accelerator.state.deepspeed_plugin.hf_ds_config

				# resume config update - some bits like `model` and `num_training_steps` only become available during train
				hf_deepspeed_config.trainer_config_finalize(__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# set the Deepspeed log level consistent with the Trainer
				ds_logger.setLevel(args.get_process_log_level()     )

				if inference:
								# only Z3 makes sense for the inference
								if not hf_deepspeed_config.is_zeroa():
												raise ValueError('''ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config'''     )

								# in case the training config is re-used for inference
								hf_deepspeed_config.del_config_sub_tree('''optimizer'''     )
								hf_deepspeed_config.del_config_sub_tree('''lr_scheduler'''     )
								A_	,					A_       	=  None, None
								A_       	=  None
				else:
								A_       	=  None  # important for when deepspeed_init is used as re-init
								A_       	=  list(filter(lambda __UpperCamelCase     : p.requires_grad ,						model.parameters()     )     )
								A_	,					A_       	=  deepspeed_optim_sched(
								    __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     )

				# keep for quick debug:
				# from pprint import pprint; pprint(config)

				return optimizer, lr_scheduler



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				# it's possible that the user is trying to resume from model_path, which doesn't necessarily
				# contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
				# a resume from a checkpoint and not just a local pretrained weight. So we check here if the
				# path contains what looks like a deepspeed checkpoint
				import glob

				A_       	=  sorted(glob.glob(F"{checkpoint_path}/global_step*"     )     )

				if len(__UpperCamelCase     ) > 0:
								logger.info(F"Attempting to resume from {checkpoint_path}"     )
								# this magically updates self.optimizer and self.lr_scheduler
								A_	,					A_       	=  deepspeed_engine.load_checkpoint(
								    __UpperCamelCase ,						load_optimizer_states=__UpperCamelCase ,						load_lr_scheduler_states=__UpperCamelCase     )
								if load_path is None:
												raise ValueError(F"[deepspeed] failed to resume from checkpoint {checkpoint_path}"     )
				else:
								raise ValueError(F"Can't find a valid checkpoint at {checkpoint_path}"     )

import inspect
import unittest

from transformers import DataaVecVisionConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 (
						    MODEL_MAPPING,
						    DataaVecVisionForImageClassification,
						    DataaVecVisionForSemanticSegmentation,
						    DataaVecVisionModel,
						)
						from transformers.models.dataavec.modeling_dataavec_vision import DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor






class 					__lowercase		:


					def __init__(	self       ,       a__       ,       a__=1_0_0       ,       a__=1_3       ,       a__=3_0       ,       a__=2       ,       a__=3       ,       a__=True       ,       a__=True       ,       a__=3_2       ,       a__=4       ,       a__=4       ,       a__=3_7       ,       a__="gelu"       ,       a__=0.1       ,       a__=0.1       ,       a__=1_0       ,       a__=0.02       ,       a__=3       ,       a__=None       ,       a__=[0, 1, 2, 3]       ,       )		->							Optional[int]:





									'''simple docstring'''
									A_       	=  parent
									A_       	=  1_0_0
									A_       	=  batch_size
									A_       	=  image_size
									A_       	=  patch_size
									A_       	=  num_channels
									A_       	=  is_training
									A_       	=  use_labels
									A_       	=  hidden_size
									A_       	=  num_hidden_layers
									A_       	=  num_attention_heads
									A_       	=  intermediate_size
									A_       	=  hidden_act
									A_       	=  hidden_dropout_prob
									A_       	=  attention_probs_dropout_prob
									A_       	=  type_sequence_label_size
									A_       	=  initializer_range
									A_       	=  scope
									A_       	=  out_indices
									A_       	=  num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									A_       	=  (image_size // patch_size) ** 2
									A_       	=  num_patches + 1


					def        lowerCAmelCase_    (	self						)		->							List[Any]:





									'''simple docstring'''
									A_       	=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									A_       	=  None
									A_       	=  None
									if self.use_labels:
													A_       	=  ids_tensor([self.batch_size]       ,       self.type_sequence_label_size						)
													A_       	=  ids_tensor([self.batch_size, self.image_size, self.image_size]       ,       self.num_labels						)

									A_       	=  self.get_config()

									return config, pixel_values, labels, pixel_labels


					def        lowerCAmelCase_    (	self						)		->							int:





									'''simple docstring'''
									return DataaVecVisionConfig(
									    vocab_size=self.vocab_size       ,       image_size=self.image_size       ,       patch_size=self.patch_size       ,       num_channels=self.num_channels       ,       hidden_size=self.hidden_size       ,       num_hidden_layers=self.num_hidden_layers       ,       num_attention_heads=self.num_attention_heads       ,       intermediate_size=self.intermediate_size       ,       hidden_act=self.hidden_act       ,       hidden_dropout_prob=self.hidden_dropout_prob       ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,       is_decoder=a__       ,       initializer_range=self.initializer_range       ,       out_indices=self.out_indices       ,       )


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__						)		->							List[str]:





									'''simple docstring'''
									A_       	=  DataaVecVisionModel(config=a__						)
									model.to(a__						)
									model.eval()
									A_       	=  model(a__						)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									A_       	=  (self.image_size // self.patch_size) ** 2
									self.parent.assertEqual(result.last_hidden_state.shape       ,       (self.batch_size, num_patches + 1, self.hidden_size)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__						)		->							Any:





									'''simple docstring'''
									A_       	=  self.type_sequence_label_size
									A_       	=  DataaVecVisionForImageClassification(a__						)
									model.to(a__						)
									model.eval()
									A_       	=  model(a__       ,       labels=a__						)
									self.parent.assertEqual(result.logits.shape       ,       (self.batch_size, self.type_sequence_label_size)						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__						)		->							int:





									'''simple docstring'''
									A_       	=  self.num_labels
									A_       	=  DataaVecVisionForSemanticSegmentation(a__						)
									model.to(a__						)
									model.eval()
									A_       	=  model(a__						)
									self.parent.assertEqual(
									    result.logits.shape       ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
									A_       	=  model(a__       ,       labels=a__						)
									self.parent.assertEqual(
									    result.logits.shape       ,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)


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





									'''simple docstring'''
									A_       	=  self.prepare_config_and_inputs()
									A_	,					A_	,					A_	,					A_       	=  config_and_inputs
									A_       	=  {'''pixel_values''': pixel_values}
									return config, inputs_dict






@require_torch
class 					__lowercase		(					A							,       A							,       unittest.TestCase       ):
					__magic_name__						:							int     	=	(
					    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
					    if is_torch_available()
					    else ()
					)
					__magic_name__						:							Any     	=	(
					    {
					        '''feature-extraction''': DataaVecVisionModel,
					        '''image-classification''': DataaVecVisionForImageClassification,
					        '''image-segmentation''': DataaVecVisionForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					__magic_name__						:							Union[str, Any]     	=	False
					__magic_name__						:							str     	=	False
					__magic_name__						:							Union[str, Any]     	=	False


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





									'''simple docstring'''
									A_       	=  DataaVecVisionModelTester(self						)
									A_       	=  ConfigTester(
									    self       ,       config_class=a__       ,       has_text_modality=a__       ,       hidden_size=3_7						)


					def        lowerCAmelCase_    (	self						)		->							str:





									'''simple docstring'''
									self.config_tester.run_common_tests()


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





									'''simple docstring'''
									# Data2VecVision does not use inputs_embeds
									pass


					@require_torch_multi_gpu
					@unittest.skip(
					    reason='''Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''						)
					def        lowerCAmelCase_    (	self						)		->							Optional[int]:





									'''simple docstring'''
									pass


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()

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


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()

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

													A_       	=  ['''pixel_values''']
													self.assertListEqual(arg_names[:1]       ,       a__						)


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





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a__						)


					def        lowerCAmelCase_    (	self						)		->							Tuple:





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_segmentation(*a__						)


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





									'''simple docstring'''
									if not self.model_tester.is_training:
													return

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

									for model_class in self.all_model_classes:
													if model_class in [*get_values(a__						)]:
																	continue

													A_       	=  model_class(a__						)
													model.to(a__						)
													model.train()
													A_       	=  self._prepare_for_class(a__       ,       a__       ,       return_labels=a__						)
													A_       	=  model(**a__						).loss
													loss.backward()


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





									'''simple docstring'''
									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
													return

									A_       	=  False
									A_       	=  True

									for model_class in self.all_model_classes:
													if model_class in [*get_values(a__						)] or not model_class.supports_gradient_checkpointing:
																	continue
													# TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING
													# this can then be incorporated into _prepare_for_class in test_modeling_common.py
													elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation":
																	A_	,					A_	,					A_	,					A_       	=  inputs_dict['''pixel_values'''].shape
																	A_       	=  torch.zeros(
																	    [self.model_tester.batch_size, height, width]       ,       device=a__						).long()
													A_       	=  model_class(a__						)
													model.gradient_checkpointing_enable()
													model.to(a__						)
													model.train()
													A_       	=  self._prepare_for_class(a__       ,       a__       ,       return_labels=a__						)
													A_       	=  model(**a__						).loss
													loss.backward()


					def        lowerCAmelCase_    (	self						)		->							List[Any]:





									'''simple docstring'''
									A_	,					A_       	=  self.model_tester.prepare_config_and_inputs_for_common()

									A_       	=  _config_zero_init(a__						)
									for model_class in self.all_model_classes:
													A_       	=  model_class(config=a__						)
													for name, param in model.named_parameters():
																	# we skip lambda parameters as these require special initial values
																	# determined by config.layer_scale_init_value
																	if "lambda" in name:
																					continue
																	if 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"       ,       )


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__=2E-4       ,       a__="outputs"       ,       a__=None						)		->							Dict:





									'''simple docstring'''
									# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
									super().check_pt_tf_outputs(a__       ,       a__       ,       a__       ,       a__       ,       a__       ,       a__						)


					def        lowerCAmelCase_    (	self						)		->							Dict:





									'''simple docstring'''
									A_       	=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*a__						)


					@slow
					def        lowerCAmelCase_    (	self						)		->							Any:





									'''simple docstring'''
									for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													A_       	=  DataaVecVisionModel.from_pretrained(a__						)
													self.assertIsNotNone(a__						)



def        lowerCamelCase_  (   ):
				A_       	=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
				return image






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


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





									'''simple docstring'''
									return (
									    BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k'''						) if is_vision_available() else None
									)


					@slow
					def        lowerCAmelCase_    (	self						)		->							Union[str, Any]:





									'''simple docstring'''
									A_       	=  DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k'''						).to(
									    a__						)

									A_       	=  self.default_image_processor
									A_       	=  prepare_img()
									A_       	=  image_processor(images=a__       ,       return_tensors='''pt'''						).to(a__						)

									# forward pass
									with torch.no_grad():
													A_       	=  model(**a__						)
									A_       	=  outputs.logits

									# verify the logits
									A_       	=  torch.Size((1, 1_0_0_0)						)
									self.assertEqual(logits.shape       ,       a__						)

									A_       	=  torch.tensor([0.32_77, -0.13_95, 0.09_11]						).to(a__						)

									self.assertTrue(torch.allclose(logits[0, :3]       ,       a__       ,       atol=1E-4						)						)

									A_       	=  [model.config.labelaid[i] for i in ['''remote control, remote''', '''tabby, tabby cat''']]
									self.assertEqual(logits[0].topk(2						).indices.cpu().tolist()       ,       a__						)