






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import ViTMAEForPreTraining, ViTMAEModel
				from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor





class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							def __init__(  self:   Union[str, Any]		,     __A:   int		,     __A:   Any=13		,     __A:   Optional[int]=30		,     __A:   Union[str, Any]=2		,     __A:   str=3		,     __A:   Tuple=True		,     __A:   Dict=True		,     __A:   int=32		,     __A:   Optional[int]=5		,     __A:   Optional[int]=4		,     __A:   str=37		,     __A:   Any="gelu"		,     __A:   Union[str, Any]=0.1		,     __A:   List[str]=0.1		,     __A:   Optional[int]=10		,     __A:   Tuple=0.02		,     __A:   str=3		,     __A:   Union[str, Any]=0.6		,     __A:   Dict=None		,     )    ->				Any:
												_A								= parent
												_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								= mask_ratio
												_A								= scope

												# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
												# (we add 1 for the [CLS] token)
												_A								= (image_size // patch_size) ** 2
												_A								= int(math.ceil((1 - mask_ratio) * (num_patches + 1)   )   )

							def       __A (  self:   int   )    ->				Tuple:
												_A								= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

												_A								= None
												if self.use_labels:
																	_A								= ids_tensor([self.batch_size]		,     self.type_sequence_label_size   )

												_A								= self.get_config()

												return config, pixel_values, labels

							def       __A (  self:   str   )    ->				str:
												return ViTMAEConfig(
												    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		,     mask_ratio=self.mask_ratio		,     )

							def       __A (  self:   int		,     __A:   List[str]		,     __A:   List[str]		,     __A:   List[str]   )    ->				List[Any]:
												_A								= ViTMAEModel(config=__A   )
												model.to(__A   )
												model.eval()
												_A								= model(__A   )
												self.parent.assertEqual(result.last_hidden_state.shape		,     (self.batch_size, self.seq_length, self.hidden_size)   )

							def       __A (  self:   str		,     __A:   Tuple		,     __A:   Dict		,     __A:   int   )    ->				Optional[int]:
												_A								= ViTMAEForPreTraining(__A   )
												model.to(__A   )
												model.eval()
												_A								= model(__A   )
												_A								= (self.image_size // self.patch_size) ** 2
												_A								= self.patch_size**2 * self.num_channels
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, num_patches, expected_num_channels)   )

												# test greyscale images
												_A								= 1
												_A								= ViTMAEForPreTraining(__A   )
												model.to(__A   )
												model.eval()
												_A								= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
												_A								= model(__A   )
												_A								= self.patch_size**2
												self.parent.assertEqual(result.logits.shape		,     (self.batch_size, num_patches, expected_num_channels)   )



							def       __A (  self:   Any   )    ->				str:
												_A								= self.prepare_config_and_inputs()
												_A							,_A							,_A								= config_and_inputs
												_A								= {'''pixel_values''': pixel_values}
												return config, inputs_dict





@require_torch
class 						SCREAMING_SNAKE_CASE				( snake_case  ,	snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
							A_						  =					{"feature-extraction": ViTMAEModel} if is_torch_available() else {}

							A_						  =					False
							A_						  =					False
							A_						  =					False
							A_						  =					False

							def       __A (  self:   Optional[int]   )    ->				Union[str, Any]:
												_A								= ViTMAEModelTester(self   )
												_A								= ConfigTester(self		,     config_class=__A		,     has_text_modality=__A		,     hidden_size=37   )

							def       __A (  self:   List[str]   )    ->				str:
												self.config_tester.run_common_tests()

							@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''   )
							def       __A (  self:   Optional[Any]   )    ->				int:
												pass

							def       __A (  self:   List[Any]   )    ->				Any:
												_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       __A (  self:   Optional[Any]   )    ->				Dict:
												_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       __A (  self:   str   )    ->				Any:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A   )

							def       __A (  self:   Optional[Any]   )    ->				List[str]:
												_A								= self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_pretraining(*__A   )

							def       __A (  self:   Any		,     __A:   List[Any]		,     __A:   Tuple		,     __A:   Optional[int]   )    ->				List[str]:
												# make masks reproducible
												np.random.seed(2   )

												_A								= int((pt_model.config.image_size // pt_model.config.patch_size) ** 2   )
												_A								= np.random.uniform(size=(self.model_tester.batch_size, num_patches)   )
												_A								= torch.from_numpy(__A   )

												# Add `noise` argument.
												# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
												_A								= pt_noise

												super().check_pt_tf_models(__A		,     __A		,     __A   )

							def       __A (  self:   int   )    ->				Dict:
												_A							,_A								= self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_A								= model_class(__A   )
																	model.to(__A   )
																	model.eval()
																	# make random mask reproducible
																	torch.manual_seed(2   )
																	with torch.no_grad():
																						_A								= model(**self._prepare_for_class(__A		,     __A   )   )

																	_A								= outputs[0].cpu().numpy()
																	_A								= 0

																	with tempfile.TemporaryDirectory() as tmpdirname:
																						model.save_pretrained(__A   )
																						_A								= model_class.from_pretrained(__A   )
																						model.to(__A   )
																						# make random mask reproducible
																						torch.manual_seed(2   )
																						with torch.no_grad():
																											_A								= model(**self._prepare_for_class(__A		,     __A   )   )

																						# Make sure we don't have nans
																						_A								= after_outputs[0].cpu().numpy()
																						_A								= 0
																						_A								= np.amax(np.abs(out_a - out_a   )   )
																						self.assertLessEqual(__A		,     1e-5   )

							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''   )
							def       __A (  self:   Any   )    ->				int:
												pass

							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''   )
							def       __A (  self:   Any   )    ->				List[str]:
												pass

							@unittest.skip(
							    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''   )
							def       __A (  self:   Union[str, Any]   )    ->				Optional[Any]:
												pass

							@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''   )
							def       __A (  self:   Optional[int]   )    ->				Any:
												pass

							@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''   )
							def       __A (  self:   List[Any]   )    ->				Optional[int]:
												pass



							@slow
							def       __A (  self:   Union[str, Any]   )    ->				Dict:
												for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_A								= ViTMAEModel.from_pretrained(__A   )
																	self.assertIsNotNone(__A   )




def      __A					(     ):

					'''simple docstring'''







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





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





							"""simple docstring"""

							@cached_property
							def       __A (  self:   Union[str, Any]   )    ->				Any:
												return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''   ) if is_vision_available() else None



							@slow
							def       __A (  self:   int   )    ->				Union[str, Any]:
												# make random mask reproducible across the PT and TF model
												np.random.seed(2   )

												_A								= ViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''   ).to(__A   )

												_A								= self.default_image_processor
												_A								= prepare_img()
												_A								= image_processor(images=__A		,     return_tensors='''pt'''   ).to(__A   )

												# prepare a noise vector that will be also used for testing the TF model
												# (this way we can ensure that the PT and TF models operate on the same inputs)
												_A								= ViTMAEConfig()
												_A								= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2   )
												_A								= np.random.uniform(size=(1, num_patches)   )

												# forward pass
												with torch.no_grad():
																	_A								= model(**__A		,     noise=torch.from_numpy(__A   ).to(device=__A   )   )

												# verify the logits
												_A								= torch.Size((1, 1_96, 7_68)   )
												self.assertEqual(outputs.logits.shape		,     __A   )

												_A								= torch.tensor(
												    [[-0.0_548, -1.7_023, -0.9_325], [0.3_721, -0.5_670, -0.2_233], [0.8_235, -1.3_878, -0.3_524]]   )

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











import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''merges_file''']   )
												with open(self.vocab_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write(json.dumps(__A   ) + '''\n'''   )
												with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write('''\n'''.join(__A   )   )

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

												assert encoded[-1] == encoded_dot[0]



