
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]  ,):
												lowercase		      =		parent
												lowercase		      =		1_0_0
												lowercase		      =		batch_size
												lowercase		      =		image_size
												lowercase		      =		patch_size
												lowercase		      =		num_channels
												lowercase		      =		is_training
												lowercase		      =		use_labels
												lowercase		      =		hidden_size
												lowercase		      =		num_hidden_layers
												lowercase		      =		num_attention_heads
												lowercase		      =		intermediate_size
												lowercase		      =		hidden_act
												lowercase		      =		hidden_dropout_prob
												lowercase		      =		attention_probs_dropout_prob
												lowercase		      =		type_sequence_label_size
												lowercase		      =		initializer_range
												lowercase		      =		scope
												lowercase		      =		out_indices
												lowercase		      =		num_labels

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




							def 					A__ (							self):
												lowercase		      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

												lowercase		      =		self.get_config()

												return config, pixel_values, labels, pixel_labels




							def 					A__ (							self):
												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 					A__ (							self  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		DataaVecVisionModel(config=A__)
												model.to(A__)
												model.eval()
												lowercase		      =		model(A__)
												# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
												lowercase		      =		(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  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		self.type_sequence_label_size
												lowercase		      =		DataaVecVisionForImageClassification(A__)
												model.to(A__)
												model.eval()
												lowercase		      =		model(A__  ,labels=A__)
												self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.type_sequence_label_size))




							def 					A__ (							self  ,A__  ,A__  ,A__  ,A__):
												lowercase		      =		self.num_labels
												lowercase		      =		DataaVecVisionForSemanticSegmentation(A__)
												model.to(A__)
												model.eval()
												lowercase		      =		model(A__)
												self.parent.assertEqual(
												    result.logits.shape  ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
												lowercase		      =		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 					A__ (							self):
												lowercase		      =		self.prepare_config_and_inputs()
												lowercase      ,		lowercase      ,		lowercase      ,		lowercase		      =		config_and_inputs
												lowercase		      =		{'''pixel_values''': pixel_values}
												return config, inputs_dict







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

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




							def 					A__ (							self):
												lowercase		      =		DataaVecVisionModelTester(self)
												lowercase		      =		ConfigTester(
												    self  ,config_class=A__  ,has_text_modality=A__  ,hidden_size=3_7)




							def 					A__ (							self):
												self.config_tester.run_common_tests()




							def 					A__ (							self):
												# 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 					A__ (							self):
												pass




							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()

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




							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()

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

																	lowercase		      =		['''pixel_values''']
																	self.assertListEqual(arg_names[:1]  ,A__)




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*A__)




							def 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_segmentation(*A__)




							def 					A__ (							self):
												if not self.model_tester.is_training:
																	return

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

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

																	lowercase		      =		model_class(A__)
																	model.to(A__)
																	model.train()
																	lowercase		      =		self._prepare_for_class(A__  ,A__  ,return_labels=A__)
																	lowercase		      =		model(**A__).loss
																	loss.backward()




							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																	return

												lowercase		      =		False
												lowercase		      =		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":
																						lowercase      ,		lowercase      ,		lowercase      ,		lowercase		      =		inputs_dict['''pixel_values'''].shape
																						lowercase		      =		torch.zeros(
																						    [self.model_tester.batch_size, height, width]  ,device=A__).long()
																	lowercase		      =		model_class(A__)
																	model.gradient_checkpointing_enable()
																	model.to(A__)
																	model.train()
																	lowercase		      =		self._prepare_for_class(A__  ,A__  ,return_labels=A__)
																	lowercase		      =		model(**A__).loss
																	loss.backward()




							def 					A__ (							self):
												lowercase      ,		lowercase		      =		self.model_tester.prepare_config_and_inputs_for_common()

												lowercase		      =		_config_zero_init(A__)
												for model_class in self.all_model_classes:
																	lowercase		      =		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 					A__ (							self  ,A__  ,A__  ,A__  ,A__=2E-4  ,A__="outputs"  ,A__=None):
												# 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 					A__ (							self):
												lowercase		      =		self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*A__)





							@slow
							def 					A__ (							self):
												for model_name in DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowercase		      =		DataaVecVisionModel.from_pretrained(A__)
																	self.assertIsNotNone(A__)



def 			UpperCamelCase       (				):
					'''simple docstring'''
					lowercase		      =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
					return image







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




							@cached_property
							def 					A__ (							self):
												return (
												    BeitImageProcessor.from_pretrained('''facebook/data2vec-vision-base-ft1k''') if is_vision_available() else None
												)





							@slow
							def 					A__ (							self):
												lowercase		      =		DataaVecVisionForImageClassification.from_pretrained('''facebook/data2vec-vision-base-ft1k''').to(
												    A__)

												lowercase		      =		self.default_image_processor
												lowercase		      =		prepare_img()
												lowercase		      =		image_processor(images=A__  ,return_tensors='''pt''').to(A__)

												# forward pass
												with torch.no_grad():
																	lowercase		      =		model(**A__)
												lowercase		      =		outputs.logits

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

												lowercase		      =		torch.tensor([0.3277, -0.1395, 0.0911]).to(A__)

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

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






from __future__ import annotations

import random
import string







class 							lowercase						:




							def __init__(							self  ,A__ = None):
												lowercase		      =		passcode or self.__passcode_creator()
												lowercase		      =		self.__make_key_list()
												lowercase		      =		self.__make_shift_key()




							def __str__(							self):
												return "".join(self.__passcode)




							def 					A__ (							self  ,A__):
												for i in range(1  ,len(A__)  ,2):
																	iterlist[i] *= -1
												return iterlist




							def 					A__ (							self):
												lowercase		      =		string.ascii_letters + string.digits
												lowercase		      =		[random.choice(A__) for _ in range(random.randint(1_0  ,2_0))]
												return password




							def 					A__ (							self):
												lowercase		      =		(
												    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
												)

												lowercase		      =		[]

												# creates points known as breakpoints to break the key_list_options at those
												# points and pivot each substring
												lowercase		      =		sorted(set(self.__passcode))
												lowercase		      =		[]

												# algorithm for creating a new shuffled list, keys_l, out of key_list_options
												for i in key_list_options:
																	temp_list.extend(A__)

																	# checking breakpoints at which to pivot temporary sublist and add it into
																	# keys_l
																	if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1])
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
												return keys_l




							def 					A__ (							self):
												lowercase		      =		sum(self.__neg_pos([ord(A__) for x in self.__passcode]))
												return num if num > 0 else len(self.__passcode)




							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# decoding shift like Caesar cipher algorithm implementing negative shift or
												# reverse shift or left shift
												for i in encoded_message:
																	lowercase		      =		self.__key_list.index(A__)
																	decoded_message += self.__key_list[
																	    (position - self.__shift_key) % -len(self.__key_list)
																	]

												return decoded_message





							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# encoding shift like Caesar cipher algorithm implementing positive shift or
												# forward shift or right shift
												for i in plaintext:
																	lowercase		      =		self.__key_list.index(A__)
																	encoded_message += self.__key_list[
																	    (position + self.__shift_key) % len(self.__key_list)
																	]

												return encoded_message



def 			UpperCamelCase       (				lowerCAmelCase__ = "Hello, this is a modified Caesar cipher"						):
					'''simple docstring'''
					lowercase		      =		ShuffledShiftCipher()
					return cipa.decrypt(cipa.encrypt(lowerCAmelCase__						)						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




