
"""simple docstring"""

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		, UpperCAmelCase_		, UpperCAmelCase_=100		, UpperCAmelCase_=13		, UpperCAmelCase_=30		, UpperCAmelCase_=2		, UpperCAmelCase_=3		, UpperCAmelCase_=True		, UpperCAmelCase_=True		, UpperCAmelCase_=32		, UpperCAmelCase_=4		, UpperCAmelCase_=4		, UpperCAmelCase_=37		, UpperCAmelCase_="gelu"		, UpperCAmelCase_=0.1		, UpperCAmelCase_=0.1		, UpperCAmelCase_=10		, UpperCAmelCase_=0.02		, UpperCAmelCase_=3		, UpperCAmelCase_=None		, UpperCAmelCase_=[0, 1, 2, 3]		, )		->							Union[str, Any]:
			lowerCamelCase     :			Any		=    parent
			lowerCamelCase     :			Any		=    100
			lowerCamelCase     :			int		=    batch_size
			lowerCamelCase     :			Optional[Any]		=    image_size
			lowerCamelCase     :			List[str]		=    patch_size
			lowerCamelCase     :			Tuple		=    num_channels
			lowerCamelCase     :			int		=    is_training
			lowerCamelCase     :			int		=    use_labels
			lowerCamelCase     :			Optional[int]		=    hidden_size
			lowerCamelCase     :			Tuple		=    num_hidden_layers
			lowerCamelCase     :			Union[str, Any]		=    num_attention_heads
			lowerCamelCase     :			str		=    intermediate_size
			lowerCamelCase     :			List[str]		=    hidden_act
			lowerCamelCase     :			str		=    hidden_dropout_prob
			lowerCamelCase     :			Optional[Any]		=    attention_probs_dropout_prob
			lowerCamelCase     :			int		=    type_sequence_label_size
			lowerCamelCase     :			int		=    initializer_range
			lowerCamelCase     :			List[str]		=    scope
			lowerCamelCase     :			Optional[Any]		=    out_indices
			lowerCamelCase     :			str		=    num_labels

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




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Optional[int]		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			lowerCamelCase     :			Dict		=    None
			lowerCamelCase     :			Union[str, Any]		=    None
			if self.use_labels:
					lowerCamelCase     :			Optional[int]		=    ids_tensor([self.batch_size]		, self.type_sequence_label_size					)
					lowerCamelCase     :			Union[str, Any]		=    ids_tensor([self.batch_size, self.image_size, self.image_size]		, self.num_labels					)

			lowerCamelCase     :			Any		=    self.get_config()

			return config, pixel_values, labels, pixel_labels




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			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=UpperCAmelCase_		, initializer_range=self.initializer_range		, out_indices=self.out_indices		, )




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			Union[str, Any]		=    DataaVecVisionModel(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[str]		=    model(UpperCAmelCase_					)
			# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
			lowerCamelCase     :			str		=    (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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							List[str]:
			lowerCamelCase     :			List[Any]		=    self.type_sequence_label_size
			lowerCamelCase     :			Any		=    DataaVecVisionForImageClassification(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			str		=    model(UpperCAmelCase_		, labels=UpperCAmelCase_					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.type_sequence_label_size)					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Union[str, Any]:
			lowerCamelCase     :			str		=    self.num_labels
			lowerCamelCase     :			int		=    DataaVecVisionForSemanticSegmentation(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    model(UpperCAmelCase_					)
			self.parent.assertEqual(
			    result.logits.shape		, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)
			lowerCamelCase     :			int		=    model(UpperCAmelCase_		, labels=UpperCAmelCase_					)
			self.parent.assertEqual(
			    result.logits.shape		, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Optional[Any]		=    self.prepare_config_and_inputs()
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			List[Any]		=    config_and_inputs
			lowerCamelCase     :			List[Any]		=    {'pixel_values': pixel_values}
			return config, inputs_dict






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

	lowercase_        =	False
	lowercase_        =	False
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase     :			List[Any]		=    DataaVecVisionModelTester(self					)
			lowerCamelCase     :			Optional[Any]		=    ConfigTester(
			    self		, config_class=UpperCAmelCase_		, has_text_modality=UpperCAmelCase_		, hidden_size=37					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			self.config_tester.run_common_tests()




	def 			_UpperCamelCase    ( self					)		->							str:
			# 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 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			pass




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase     :			Dict		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase     :			List[Any]		=    model_class(UpperCAmelCase_					)
					self.assertIsInstance(model.get_input_embeddings()		, (nn.Module)					)
					lowerCamelCase     :			Union[str, Any]		=    model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(UpperCAmelCase_		, nn.Linear					)					)




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase	,    lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs_for_common()

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

					lowerCamelCase     :			Union[str, Any]		=    ['pixel_values']
					self.assertListEqual(arg_names[:1]		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase     :			Dict		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Dict		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_segmentation(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							str:
			if not self.model_tester.is_training:
					return

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

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

					lowerCamelCase     :			List[Any]		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.train()
					lowerCamelCase     :			Dict		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			str		=    model(**UpperCAmelCase_					).loss
					loss.backward()




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase	,    lowerCamelCase     :			int		=    self.model_tester.prepare_config_and_inputs_for_common()
			if not self.model_tester.is_training:
					return

			lowerCamelCase     :			int		=    False
			lowerCamelCase     :			Optional[Any]		=    True

			for model_class in self.all_model_classes:
					if model_class in [*get_values(UpperCAmelCase_					)] 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":
							lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Optional[int]		=    inputs_dict['pixel_values'].shape
							lowerCamelCase     :			Dict		=    torch.zeros(
							    [self.model_tester.batch_size, height, width]		, device=UpperCAmelCase_					).long()
					lowerCamelCase     :			Any		=    model_class(UpperCAmelCase_					)
					model.gradient_checkpointing_enable()
					model.to(UpperCAmelCase_					)
					model.train()
					lowerCamelCase     :			List[str]		=    self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_		, return_labels=UpperCAmelCase_					)
					lowerCamelCase     :			Any		=    model(**UpperCAmelCase_					).loss
					loss.backward()




	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase	,    lowerCamelCase     :			List[str]		=    self.model_tester.prepare_config_and_inputs_for_common()

			lowerCamelCase     :			Tuple		=    _config_zero_init(UpperCAmelCase_					)
			for model_class in self.all_model_classes:
					lowerCamelCase     :			Union[str, Any]		=    model_class(config=UpperCAmelCase_					)
					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 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_=2E-4		, UpperCAmelCase_="outputs"		, UpperCAmelCase_=None					)		->							List[Any]:
			# We override with a slightly higher tol value, as semseg models tend to diverge a bit more
			super().check_pt_tf_outputs(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			Union[str, Any]		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_					)




	@slow
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase     :			Union[str, Any]		=    DataaVecVisionModel.from_pretrained(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)


def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			str		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image






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




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			return (
			    BeitImageProcessor.from_pretrained('facebook/data2vec-vision-base-ft1k'					) if is_vision_available() else None
			)




	@slow
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			Optional[Any]		=    DataaVecVisionForImageClassification.from_pretrained('facebook/data2vec-vision-base-ft1k'					).to(
			    UpperCAmelCase_					)

			lowerCamelCase     :			List[Any]		=    self.default_image_processor
			lowerCamelCase     :			Optional[Any]		=    prepare_img()
			lowerCamelCase     :			Optional[Any]		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			Tuple		=    model(**UpperCAmelCase_					)
			lowerCamelCase     :			Optional[Any]		=    outputs.logits

			# verify the logits
			lowerCamelCase     :			List[str]		=    torch.Size((1, 1000)					)
			self.assertEqual(logits.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			Any		=    torch.tensor([0.3277, -0.1395, 0.0911]					).to(UpperCAmelCase_					)

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

			lowerCamelCase     :			Optional[int]		=    [model.config.labelaid[i] for i in ['remote control, remote', 'tabby, tabby cat']]
			self.assertEqual(logits[0].topk(2					).indices.cpu().tolist()		, UpperCAmelCase_					)








"""simple docstring"""

import numpy as np
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    bin(int.from_bytes(text.encode(a_,						a_			),						'big'			)			)[2:]
		return bits.zfill(8 * ((len(a_			) + 7) // 8)			)
def 		UpperCAmelCase					(		a_,						a_="utf-8",						a_="surrogatepass"			):





		'''simple docstring'''


		lowerCamelCase     :			List[Any]		=    int(a_,						2			)
		return n.to_bytes((n.bit_length() + 7) // 8,						'big'			).decode(a_,						a_			) or "\0"
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		if size_par + len(a_			) <= 2**size_par - (len(a_			) - 1):
				raise ValueError('size of parity don\'t match with size of data'			)

		lowerCamelCase     :			List[str]		=    []
		lowerCamelCase     :			str		=    []
		lowerCamelCase     :			Any		=    [bin(a_			)[2:] for x in range(1,						size_par + len(a_			) + 1			)]

		# sorted information data for the size of the output data
		lowerCamelCase     :			int		=    []
		# data position template + parity
		lowerCamelCase     :			Union[str, Any]		=    []
		# parity bit counter
		lowerCamelCase     :			Optional[int]		=    0
		# counter position of data bits
		lowerCamelCase     :			Any		=    0

		for x in range(1,						size_par + len(a_			) + 1			):
				# Performs a template of bit positions - who should be given,
				# and who should be parity
				if qtd_bp < size_par:
						if (np.log(a_			) / np.log(2			)).is_integer():
								data_out_gab.append('P'			)
								lowerCamelCase     :			List[Any]		=    qtd_bp + 1
						else:
								data_out_gab.append('D'			)
				else:
						data_out_gab.append('D'			)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data[cont_data]			)
						cont_data += 1
				else:
						data_ord.append(a_			)

    # Calculates parity
		lowerCamelCase     :			Optional[Any]		=    0  # parity bit counter
		for bp in range(1,						size_par + 1			):
				# Bit counter one for a given parity
				lowerCamelCase     :			Union[str, Any]		=    0
				# counter to control the loop reading
				lowerCamelCase     :			Optional[int]		=    0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase     :			Union[str, Any]		=    (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase     :			str		=    '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(cont_bo % 2			)

				qtd_bp += 1

		# Mount the message
		lowerCamelCase     :			Tuple		=    0  # parity bit counter
		for x in range(0,						size_par + len(a_			)			):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]			)			)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]			)

		return data_out
def 		UpperCAmelCase					(		a_,						a_			):





		'''simple docstring'''


		lowerCamelCase     :			str		=    []
		# Parity bit counter
		lowerCamelCase     :			Dict		=    0
		# Counter p data bit reading
		lowerCamelCase     :			int		=    0
		# list of parity received
		lowerCamelCase     :			Optional[int]		=    []
		lowerCamelCase     :			Any		=    []

		for x in range(1,						len(a_			) + 1			):
				# Performs a template of bit positions - who should be given,
				#  and who should be parity
				if qtd_bp < size_par and (np.log(a_			) / np.log(2			)).is_integer():
						data_out_gab.append('P'			)
						lowerCamelCase     :			List[Any]		=    qtd_bp + 1
				else:
						data_out_gab.append('D'			)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_output.append(data[cont_data]			)
				else:
						parity_received.append(data[cont_data]			)
				cont_data += 1

		# -----------calculates the parity with the data
		lowerCamelCase     :			Dict		=    []
		lowerCamelCase     :			Optional[Any]		=    []
		lowerCamelCase     :			Dict		=    [bin(a_			)[2:] for x in range(1,						size_par + len(a_			) + 1			)]

		#  sorted information data for the size of the output data
		lowerCamelCase     :			Any		=    []
		# Data position feedback + parity
		lowerCamelCase     :			List[str]		=    []
		# Parity bit counter
		lowerCamelCase     :			List[str]		=    0
		# Counter p data bit reading
		lowerCamelCase     :			int		=    0

		for x in range(1,						size_par + len(a_			) + 1			):
				# Performs a template position of bits - who should be given,
				# and who should be parity
				if qtd_bp < size_par and (np.log(a_			) / np.log(2			)).is_integer():
						data_out_gab.append('P'			)
						lowerCamelCase     :			Tuple		=    qtd_bp + 1
				else:
						data_out_gab.append('D'			)

				# Sorts the data to the new output size
				if data_out_gab[-1] == "D":
						data_ord.append(data_output[cont_data]			)
						cont_data += 1
				else:
						data_ord.append(a_			)

    # Calculates parity
		lowerCamelCase     :			List[str]		=    0  # parity bit counter
		for bp in range(1,						size_par + 1			):
				# Bit counter one for a certain parity
				lowerCamelCase     :			List[Any]		=    0
				# Counter to control loop reading
				lowerCamelCase     :			List[Any]		=    0
				for x in data_ord:
						if x is not None:
								try:
										lowerCamelCase     :			Dict		=    (bin_pos[cont_loop])[-1 * (bp)]
								except IndexError:
										lowerCamelCase     :			List[Any]		=    '0'
								if aux == "1" and x == "1":
										cont_bo += 1
						cont_loop += 1
				parity.append(str(cont_bo % 2			)			)

				qtd_bp += 1

		# Mount the message
		lowerCamelCase     :			Tuple		=    0  # Parity bit counter
		for x in range(0,						size_par + len(a_			)			):
				if data_ord[x] is None:
						data_out.append(str(parity[cont_bp]			)			)
						cont_bp += 1
				else:
						data_out.append(data_ord[x]			)

		lowerCamelCase     :			Union[str, Any]		=    parity_received == parity
		return data_output, ack






