




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  :      int				,       snake_case__  :      Dict				,       snake_case__  :      List[str]=1_00				,       snake_case__  :      List[str]=13				,       snake_case__  :      Union[str, Any]=30				,       snake_case__  :      Optional[Any]=2				,       snake_case__  :      Optional[Any]=3				,       snake_case__  :      int=True				,       snake_case__  :      Union[str, Any]=True				,       snake_case__  :      List[str]=32				,       snake_case__  :      Any=4				,       snake_case__  :      List[str]=4				,       snake_case__  :      Optional[Any]=37				,       snake_case__  :      Optional[Any]="gelu"				,       snake_case__  :      Dict=0.1				,       snake_case__  :      List[str]=0.1				,       snake_case__  :      Optional[int]=10				,       snake_case__  :      Optional[int]=0.02				,       snake_case__  :      Optional[Any]=3				,       snake_case__  :      Union[str, Any]=None				,       snake_case__  :      List[Any]=[0, 1, 2, 3]				,       ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       parent
								SCREAMING_SNAKE_CASE_							=       1_00
								SCREAMING_SNAKE_CASE_							=       batch_size
								SCREAMING_SNAKE_CASE_							=       image_size
								SCREAMING_SNAKE_CASE_							=       patch_size
								SCREAMING_SNAKE_CASE_							=       num_channels
								SCREAMING_SNAKE_CASE_							=       is_training
								SCREAMING_SNAKE_CASE_							=       use_labels
								SCREAMING_SNAKE_CASE_							=       hidden_size
								SCREAMING_SNAKE_CASE_							=       num_hidden_layers
								SCREAMING_SNAKE_CASE_							=       num_attention_heads
								SCREAMING_SNAKE_CASE_							=       intermediate_size
								SCREAMING_SNAKE_CASE_							=       hidden_act
								SCREAMING_SNAKE_CASE_							=       hidden_dropout_prob
								SCREAMING_SNAKE_CASE_							=       attention_probs_dropout_prob
								SCREAMING_SNAKE_CASE_							=       type_sequence_label_size
								SCREAMING_SNAKE_CASE_							=       initializer_range
								SCREAMING_SNAKE_CASE_							=       scope
								SCREAMING_SNAKE_CASE_							=       out_indices
								SCREAMING_SNAKE_CASE_							=       num_labels

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



						def   __a				( self  :      int     ):

								"""simple docstring"""

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

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

								SCREAMING_SNAKE_CASE_							=       self.get_config()

								return config, pixel_values, labels, pixel_labels



						def   __a				( 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=snake_case__				,       initializer_range=self.initializer_range				,       out_indices=self.out_indices				,       )



						def   __a				( self  :      Dict				,       snake_case__  :      List[Any]				,       snake_case__  :      Dict				,       snake_case__  :      Union[str, Any]				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       DataaVecVisionModel(config=snake_case__     )
								model.to(snake_case__     )
								model.eval()
								SCREAMING_SNAKE_CASE_							=       model(snake_case__     )
								# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
								SCREAMING_SNAKE_CASE_							=       (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   __a				( self  :      int				,       snake_case__  :      Any				,       snake_case__  :      Optional[int]				,       snake_case__  :      Union[str, Any]				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

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



						def   __a				( self  :      Optional[int]				,       snake_case__  :      Optional[int]				,       snake_case__  :      Any				,       snake_case__  :      List[str]				,       snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.num_labels
								SCREAMING_SNAKE_CASE_							=       DataaVecVisionForSemanticSegmentation(snake_case__     )
								model.to(snake_case__     )
								model.eval()
								SCREAMING_SNAKE_CASE_							=       model(snake_case__     )
								self.parent.assertEqual(
								    result.logits.shape				,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )
								SCREAMING_SNAKE_CASE_							=       model(snake_case__				,       labels=snake_case__     )
								self.parent.assertEqual(
								    result.logits.shape				,       (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)     )






						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

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



@require_torch
class       lowercase_		(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__       ,  unittest.TestCase   ):
						lowerCAmelCase__ 			=(
						    (DataaVecVisionModel, DataaVecVisionForImageClassification, DataaVecVisionForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						lowerCAmelCase__ 			=(
						    {
						        "feature-extraction": DataaVecVisionModel,
						        "image-classification": DataaVecVisionForImageClassification,
						        "image-segmentation": DataaVecVisionForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						lowerCAmelCase__ 			=False
						lowerCAmelCase__ 			=False
						lowerCAmelCase__ 			=False



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       DataaVecVisionModelTester(self     )
								SCREAMING_SNAKE_CASE_							=       ConfigTester(
								    self				,       config_class=snake_case__				,       has_text_modality=snake_case__				,       hidden_size=37     )



						def   __a				( self  :      int     ):

								"""simple docstring"""

								self.config_tester.run_common_tests()



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								pass



						@require_torch_multi_gpu
						@unittest.skip(
						    reason='Data2VecVision has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'     )
						def   __a				( self  :      str     ):

								"""simple docstring"""

								pass



						def   __a				( self  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()

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



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()

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

										SCREAMING_SNAKE_CASE_							=       ['pixel_values']
										self.assertListEqual(arg_names[:1]				,       snake_case__     )



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

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



						def   __a				( self  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_segmentation(*snake_case__     )



						def   __a				( self  :      int     ):

								"""simple docstring"""

								if not self.model_tester.is_training:
										return

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

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

										SCREAMING_SNAKE_CASE_							=       model_class(snake_case__     )
										model.to(snake_case__     )
										model.train()
										SCREAMING_SNAKE_CASE_							=       self._prepare_for_class(snake_case__				,       snake_case__				,       return_labels=snake_case__     )
										SCREAMING_SNAKE_CASE_							=       model(**snake_case__     ).loss
										loss.backward()



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()
								if not self.model_tester.is_training:
										return

								SCREAMING_SNAKE_CASE_							=       False
								SCREAMING_SNAKE_CASE_							=       True

								for model_class in self.all_model_classes:
										if model_class in [*get_values(snake_case__     )] 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":
												SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       inputs_dict['pixel_values'].shape
												SCREAMING_SNAKE_CASE_							=       torch.zeros(
												    [self.model_tester.batch_size, height, width]				,       device=snake_case__     ).long()
										SCREAMING_SNAKE_CASE_							=       model_class(snake_case__     )
										model.gradient_checkpointing_enable()
										model.to(snake_case__     )
										model.train()
										SCREAMING_SNAKE_CASE_							=       self._prepare_for_class(snake_case__				,       snake_case__				,       return_labels=snake_case__     )
										SCREAMING_SNAKE_CASE_							=       model(**snake_case__     ).loss
										loss.backward()



						def   __a				( self  :      List[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       self.model_tester.prepare_config_and_inputs_for_common()

								SCREAMING_SNAKE_CASE_							=       _config_zero_init(snake_case__     )
								for model_class in self.all_model_classes:
										SCREAMING_SNAKE_CASE_							=       model_class(config=snake_case__     )
										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   __a				( self  :      List[Any]				,       snake_case__  :      List[str]				,       snake_case__  :      Dict				,       snake_case__  :      Union[str, Any]				,       snake_case__  :      str=2e-4				,       snake_case__  :      int="outputs"				,       snake_case__  :      List[Any]=None     ):

								"""simple docstring"""

								super().check_pt_tf_outputs(snake_case__				,       snake_case__				,       snake_case__				,       snake_case__				,       snake_case__				,       snake_case__     )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

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






						@slow
						def   __a				( self  :      str     ):

								"""simple docstring"""

								for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										SCREAMING_SNAKE_CASE_							=       DataaVecVisionModel.from_pretrained(snake_case__     )
										self.assertIsNotNone(snake_case__     )


def 				_a       (				)->  Optional[Any]:
		SCREAMING_SNAKE_CASE_							=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
		return image




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



						@cached_property
						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

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






						@slow
						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'     ).to(
								    snake_case__     )

								SCREAMING_SNAKE_CASE_							=       self.default_image_processor
								SCREAMING_SNAKE_CASE_							=       prepare_img()
								SCREAMING_SNAKE_CASE_							=       image_processor(images=snake_case__				,       return_tensors='pt'     ).to(snake_case__     )

								# forward pass
								with torch.no_grad():
										SCREAMING_SNAKE_CASE_							=       model(**snake_case__     )
								SCREAMING_SNAKE_CASE_							=       outputs.logits

								# verify the logits
								SCREAMING_SNAKE_CASE_							=       torch.Size((1, 10_00)     )
								self.assertEqual(logits.shape				,       snake_case__     )

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

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

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





SCREAMING_SNAKE_CASE:    Optional[Any]											=   [0.33, 0.44, 0.55, 0.44, 0.33]
SCREAMING_SNAKE_CASE:    int											=   3
SCREAMING_SNAKE_CASE:    str											=   5

# Buffer Space (with Parameters Space)
SCREAMING_SNAKE_CASE:    list[float]											=   []
SCREAMING_SNAKE_CASE:    list[float]											=   []

# Machine Time
SCREAMING_SNAKE_CASE:    str											=   0


def 				_a       (				lowerCAmelCase   )->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# Choosing Dynamical Systems (All)
		for key, value in enumerate(lowerCAmelCase   ):
				# Evolution Parameter
				SCREAMING_SNAKE_CASE_							=       float(seed / value   )

				# Control Theory: Orbit Change
				SCREAMING_SNAKE_CASE_							=       (buffer_space[(key + 1) % m] + e) % 1

				# Control Theory: Trajectory Change
				SCREAMING_SNAKE_CASE_							=       (params_space[key] + e) % 1 + 3

				# Modification (Transition Function) - Jumps
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       r  # Saving to Parameters Space

		# Logistic Map
		assert max(lowerCAmelCase   ) < 1
		assert max(lowerCAmelCase   ) < 4

		# Machine Time
		machine_time += 1


def 				_a       (				)->  int:
		global buffer_space, params_space, machine_time, K, m, t

		# PRNG (Xorshift by George Marsaglia)
		def xorshift(lowerCAmelCase						,	lowerCAmelCase   ):
				x ^= y >> 13
				y ^= x << 17
				x ^= y >> 5
				return x

		# Choosing Dynamical Systems (Increment)
		SCREAMING_SNAKE_CASE_							=       machine_time % m

		# Evolution (Time Length)
		for _ in range(0						,	lowerCAmelCase   ):
				# Variables (Position + Parameters)
				SCREAMING_SNAKE_CASE_							=       params_space[key]
				SCREAMING_SNAKE_CASE_							=       buffer_space[key]

				# Modification (Transition Function) - Flow
				SCREAMING_SNAKE_CASE_							=       round(float(r * value * (1 - value)   )						,	10   )
				SCREAMING_SNAKE_CASE_							=       (machine_time * 0.0_1 + r * 1.0_1) % 1 + 3

		# Choosing Chaotic Data
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key + 2) % m] * (10**10)   )
		SCREAMING_SNAKE_CASE_							=       int(buffer_space[(key - 2) % m] * (10**10)   )

		# Machine Time
		machine_time += 1

		return xorshift(lowerCAmelCase						,	lowerCAmelCase   ) % 0xFFFF_FFFF


def 				_a       (				)->  Optional[Any]:
		global buffer_space, params_space, machine_time, K, m, t

		SCREAMING_SNAKE_CASE_							=       K
		SCREAMING_SNAKE_CASE_							=       [0] * m
		SCREAMING_SNAKE_CASE_							=       0


if __name__ == "__main__":
						# Initialization
						reset()

						# Pushing Data (Input)
						import random

						SCREAMING_SNAKE_CASE:    List[Any]											=   random.sample(range(0xff_ff_ff_ff), 1_0_0)
						for chunk in message:
												push(chunk)

						# for controlling
						SCREAMING_SNAKE_CASE:    List[str]											=   ''''''

						# Pulling Data (Output)
						while inp in ("e", "E"):
												print(f"""{format(pull(), '#04x')}""")
												print(buffer_space)
												print(params_space)
												SCREAMING_SNAKE_CASE:    List[Any]											=   input('''(e)exit? ''').strip()