






import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler





@dataclass
class 	snake_case__ (							_lowerCAmelCase				):
	lowercase__						:					int											=   0
	lowercase__						:					bool											=   False
	lowercase__						:					float											=   3.0





class 	snake_case__ (							unittest.TestCase				):
	def       __magic_name__    (    self			)							->				Optional[Any]:
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							self.assertDictEqual(MockClass().to_kwargs()      ,       {}			)
							self.assertDictEqual(MockClass(a=2			).to_kwargs()      ,       {"""a""": 2}			)
							self.assertDictEqual(MockClass(a=2      ,       b=lowerCAmelCase__			).to_kwargs()      ,       {"""a""": 2, """b""": True}			)
							self.assertDictEqual(MockClass(a=2      ,       c=2.2_5			).to_kwargs()      ,       {"""a""": 2, """c""": 2.2_5}			)
	@require_cuda
	def       __magic_name__    (    self			)							->				str:
							# If no defaults are changed, `to_kwargs` returns an empty dict.
							__magic_name__      :       Tuple       						= GradScalerKwargs(init_scale=10_24      ,       growth_factor=2			)
							AcceleratorState._reset_state()
							__magic_name__      :       List[str]       						= Accelerator(mixed_precision="""fp16"""      ,       kwargs_handlers=[scaler_handler]			)
							print(accelerator.use_fpaa			)
							__magic_name__      :       Tuple       						= accelerator.scaler

							# Check the kwargs have been applied
							self.assertEqual(scaler._init_scale      ,       1_0_2_4.0			)
							self.assertEqual(scaler._growth_factor      ,       2.0			)

							# Check the other values are at the default
							self.assertEqual(scaler._backoff_factor      ,       0.5			)
							self.assertEqual(scaler._growth_interval      ,       20_00			)
							self.assertEqual(scaler._enabled      ,       lowerCAmelCase__			)







	@require_multi_gpu
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Optional[Any]       						= ["""torchrun""", F'--nproc_per_node={torch.cuda.device_count()}', inspect.getfile(self.__class__			)]
							execute_subprocess_async(lowerCAmelCase__      ,       env=os.environ.copy()			)


if __name__ == "__main__":
		__magic_name__:					Union[str, Any]							=     DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
		__magic_name__:					Optional[Any]							=     Accelerator(kwargs_handlers=[ddp_scaler])
		__magic_name__:					Optional[Any]							=     torch.nn.Linear(100, 200)
		__magic_name__:					Dict							=     accelerator.prepare(model)

		# Check the values changed in kwargs
		__magic_name__:					str							=     ""
		__magic_name__:					Optional[Any]							=     model.bucket_bytes_cap // (1_024 * 1_024)
		if observed_bucket_cap_map != 15:
				error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
		if model.find_unused_parameters is not True:
				error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

		# Check the values of the defaults
		if model.dim != 0:
				error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
		if model.broadcast_buffers is not True:
				error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
		if model.gradient_as_bucket_view is not False:
				error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

		# Raise error at the end to make sure we don't stop at the first failure.
		if len(error_msg) > 0:
				raise ValueError(error_msg)








from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFDeiTForImageClassification,
		    TFDeiTForImageClassificationWithTeacher,
		    TFDeiTForMaskedImageModeling,
		    TFDeiTModel,
		)
		from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image

		from transformers import DeiTImageProcessor





class 	snake_case__ :
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=13      ,       lowerCAmelCase__=30      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=32      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=37      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=10      ,       lowerCAmelCase__=0.0_2      ,       lowerCAmelCase__=3      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=2      ,       )							->				Optional[Any]:
							__magic_name__      :       Optional[Any]       						= parent
							__magic_name__      :       List[str]       						= batch_size
							__magic_name__      :       Union[str, Any]       						= image_size
							__magic_name__      :       Optional[Any]       						= patch_size
							__magic_name__      :       Union[str, Any]       						= num_channels
							__magic_name__      :       Union[str, Any]       						= is_training
							__magic_name__      :       Union[str, Any]       						= use_labels
							__magic_name__      :       Tuple       						= hidden_size
							__magic_name__      :       List[str]       						= num_hidden_layers
							__magic_name__      :       Optional[Any]       						= num_attention_heads
							__magic_name__      :       Tuple       						= intermediate_size
							__magic_name__      :       Union[str, Any]       						= hidden_act
							__magic_name__      :       str       						= hidden_dropout_prob
							__magic_name__      :       List[str]       						= attention_probs_dropout_prob
							__magic_name__      :       Tuple       						= type_sequence_label_size
							__magic_name__      :       Any       						= initializer_range
							__magic_name__      :       str       						= scope
							__magic_name__      :       str       						= encoder_stride

							# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
							__magic_name__      :       Dict       						= (image_size // patch_size) ** 2
							__magic_name__      :       int       						= num_patches + 2
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__      :       Union[str, Any]       						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

							__magic_name__      :       Tuple       						= None
							if self.use_labels:
													__magic_name__      :       Any       						= ids_tensor([self.batch_size]      ,       self.type_sequence_label_size			)

							__magic_name__      :       Union[str, Any]       						= self.get_config()

							return config, pixel_values, labels
	def       __magic_name__    (    self			)							->				List[str]:
							return DeiTConfig(
							    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=lowerCAmelCase__      ,       initializer_range=self.initializer_range      ,       encoder_stride=self.encoder_stride      ,       )
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Any:
							__magic_name__      :       int       						= TFDeiTModel(config=lowerCAmelCase__			)
							__magic_name__      :       List[Any]       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Optional[Any]:
							__magic_name__      :       int       						= TFDeiTForMaskedImageModeling(config=lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(
							    result.reconstruction.shape      ,       (self.batch_size, self.num_channels, self.image_size, self.image_size)			)

							# test greyscale images
							__magic_name__      :       Tuple       						= 1
							__magic_name__      :       List[Any]       						= TFDeiTForMaskedImageModeling(lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
							__magic_name__      :       Any       						= model(lowerCAmelCase__			)
							self.parent.assertEqual(result.reconstruction.shape      ,       (self.batch_size, 1, self.image_size, self.image_size)			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__			)							->				Tuple:
							__magic_name__      :       Union[str, Any]       						= self.type_sequence_label_size
							__magic_name__      :       str       						= TFDeiTForImageClassification(lowerCAmelCase__			)
							__magic_name__      :       List[str]       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)

							# test greyscale images
							__magic_name__      :       int       						= 1
							__magic_name__      :       List[str]       						= TFDeiTForImageClassification(lowerCAmelCase__			)

							__magic_name__      :       List[str]       						= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
							__magic_name__      :       Dict       						= model(lowerCAmelCase__      ,       labels=lowerCAmelCase__			)
							self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)			)







	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Union[str, Any]       						= self.prepare_config_and_inputs()
							__magic_name__						,__magic_name__						,__magic_name__      :       Union[str, Any]       						= config_and_inputs
							__magic_name__      :       str       						= {"""pixel_values""": pixel_values}
							return config, inputs_dict





@require_tf
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					int											=   (
	    (
	        TFDeiTModel,
	        TFDeiTForImageClassification,
	        TFDeiTForImageClassificationWithTeacher,
	        TFDeiTForMaskedImageModeling,
	    )
	    if is_tf_available()
	    else ()
	)
	lowercase__						:					Any											=   (
	    {
	        '''feature-extraction''': TFDeiTModel,
	        '''image-classification''': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
	    }
	    if is_tf_available()
	    else {}
	)

	lowercase__						:					int											=   False
	lowercase__						:					List[Any]											=   False
	lowercase__						:					Tuple											=   False
	lowercase__						:					int											=   False
	def       __magic_name__    (    self			)							->				str:
							__magic_name__      :       str       						= TFDeiTModelTester(self			)
							__magic_name__      :       Optional[Any]       						= ConfigTester(self      ,       config_class=lowerCAmelCase__      ,       has_text_modality=lowerCAmelCase__      ,       hidden_size=37			)
	def       __magic_name__    (    self			)							->				Tuple:
							self.config_tester.run_common_tests()
	@unittest.skip(reason="""DeiT does not use inputs_embeds"""			)
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							pass
	def       __magic_name__    (    self			)							->				Union[str, Any]:
							__magic_name__						,__magic_name__      :       Optional[Any]       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       List[Any]       						= model_class(lowerCAmelCase__			)
													self.assertIsInstance(model.get_input_embeddings()      ,       (tf.keras.layers.Layer)			)
													__magic_name__      :       Tuple       						= model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCAmelCase__      ,       tf.keras.layers.Dense			)			)
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__						,__magic_name__      :       str       						= self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													__magic_name__      :       Any       						= model_class(lowerCAmelCase__			)
													__magic_name__      :       List[Any]       						= inspect.signature(model.call			)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__magic_name__      :       int       						= [*signature.parameters.keys()]

													__magic_name__      :       List[str]       						= ["""pixel_values"""]
													self.assertListEqual(arg_names[:1]      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       Any       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Union[str, Any]       						= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__=False			)							->				str:
							__magic_name__      :       Any       						= super()._prepare_for_class(lowerCAmelCase__      ,       lowerCAmelCase__      ,       return_labels=lowerCAmelCase__			)

							if return_labels:
													if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call			).parameters:
																			del inputs_dict["labels"]

							return inputs_dict







	@slow
	def       __magic_name__    (    self			)							->				Dict:
							for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__      :       int       						= TFDeiTModel.from_pretrained(lowerCAmelCase__			)
													self.assertIsNotNone(lowerCAmelCase__			)



def    UpperCamelCase						(	):






						"""simple docstring"""







						__magic_name__      :       List[Any]       						= Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
						return image





@require_tf
@require_vision
class 	snake_case__ (							unittest.TestCase				):
	@cached_property
	def       __magic_name__    (    self			)							->				Optional[int]:
							return (
							    DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224"""			)
							    if is_vision_available()
							    else None
							)







	@slow
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Optional[int]       						= TFDeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224"""			)

							__magic_name__      :       Any       						= self.default_image_processor
							__magic_name__      :       Tuple       						= prepare_img()
							__magic_name__      :       Tuple       						= image_processor(images=lowerCAmelCase__      ,       return_tensors="""tf"""			)

							# forward pass
							__magic_name__      :       Dict       						= model(**lowerCAmelCase__			)

							# verify the logits
							__magic_name__      :       Optional[Any]       						= tf.TensorShape((1, 10_00)			)
							self.assertEqual(outputs.logits.shape      ,       lowerCAmelCase__			)

							__magic_name__      :       List[Any]       						= tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			)

							self.assertTrue(np.allclose(outputs.logits[0, :3]      ,       lowerCAmelCase__      ,       atol=1e-4			)			)
