


"""simple docstring"""







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		,					snake_case__		,					snake_case__=13		,					snake_case__=30		,					snake_case__=2		,					snake_case__=3		,					snake_case__=True		,					snake_case__=True		,					snake_case__=32		,					snake_case__=5		,					snake_case__=4		,					snake_case__=37		,					snake_case__="gelu"		,					snake_case__=0.1		,					snake_case__=0.1		,					snake_case__=10		,					snake_case__=0.02		,					snake_case__=3		,					snake_case__=0.6		,					snake_case__=None		,					):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							parent
         lowerCAmelCase					:					Any								=							batch_size
         lowerCAmelCase					:					List[str]								=							image_size
         lowerCAmelCase					:					Dict								=							patch_size
         lowerCAmelCase					:					Optional[int]								=							num_channels
         lowerCAmelCase					:					Optional[int]								=							is_training
         lowerCAmelCase					:					str								=							use_labels
         lowerCAmelCase					:					Optional[int]								=							hidden_size
         lowerCAmelCase					:					Optional[Any]								=							num_hidden_layers
         lowerCAmelCase					:					str								=							num_attention_heads
         lowerCAmelCase					:					int								=							intermediate_size
         lowerCAmelCase					:					Optional[int]								=							hidden_act
         lowerCAmelCase					:					Dict								=							hidden_dropout_prob
         lowerCAmelCase					:					Dict								=							attention_probs_dropout_prob
         lowerCAmelCase					:					Any								=							type_sequence_label_size
         lowerCAmelCase					:					Optional[Any]								=							initializer_range
         lowerCAmelCase					:					Any								=							mask_ratio
         lowerCAmelCase					:					Optional[Any]								=							scope

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

         lowerCAmelCase					:					Optional[Any]								=							None
         if self.use_labels:
           lowerCAmelCase					:					str								=							ids_tensor([self.batch_size]		,					self.type_sequence_label_size						)

         lowerCAmelCase					:					int								=							self.get_config()

         return config, pixel_values, labels

       def 	lowercase__							(						self						):






         """simple docstring"""





         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=snake_case__		,					initializer_range=self.initializer_range		,					mask_ratio=self.mask_ratio		,					)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[int]								=							ViTMAEModel(config=snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase					:					List[str]								=							model(snake_case__						)
         self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							ViTMAEForPreTraining(snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase					:					str								=							model(snake_case__						)
         lowerCAmelCase					:					List[str]								=							(self.image_size // self.patch_size) ** 2
         lowerCAmelCase					:					List[Any]								=							self.patch_size**2 * self.num_channels
         self.parent.assertEqual(result.logits.shape		,					(self.batch_size, num_patches, expected_num_channels)						)

         # test greyscale images
         lowerCAmelCase					:					int								=							1
         lowerCAmelCase					:					Optional[int]								=							ViTMAEForPreTraining(snake_case__						)
         model.to(snake_case__						)
         model.eval()
         lowerCAmelCase					:					Tuple								=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
         lowerCAmelCase					:					int								=							model(snake_case__						)
         lowerCAmelCase					:					Any								=							self.patch_size**2
         self.parent.assertEqual(result.logits.shape		,					(self.batch_size, num_patches, expected_num_channels)						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Optional[Any]								=							self.prepare_config_and_inputs()
         lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase					:					Tuple								=							config_and_inputs
         lowerCAmelCase					:					Any								=							{"pixel_values": pixel_values}
         return config, inputs_dict


@require_torch
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


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

       a  :    List[str]       =False
       a  :    Union[str, Any]       =False
       a  :    Optional[Any]       =False
       a  :    int       =False

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[Any]								=							ViTMAEModelTester(self						)
         lowerCAmelCase					:					int								=							ConfigTester(self		,					config_class=snake_case__		,					has_text_modality=snake_case__		,					hidden_size=37						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         self.config_tester.run_common_tests()

       @unittest.skip(reason="ViTMAE does not use inputs_embeds"						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase						,  lowerCAmelCase					:					Tuple								=							self.model_tester.prepare_config_and_inputs_for_common()

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

       def 	lowercase__							(						self						):






         """simple docstring"""





         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(snake_case__						)
           lowerCAmelCase					:					List[str]								=							inspect.signature(model.forward						)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           lowerCAmelCase					:					Optional[Any]								=							[*signature.parameters.keys()]

           lowerCAmelCase					:					Tuple								=							["pixel_values"]
           self.assertListEqual(arg_names[:1]		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					Any								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					List[str]								=							self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*snake_case__						)

       def 	lowercase__							(						self		,					snake_case__		,					snake_case__		,					snake_case__						):






         """simple docstring"""





         np.random.seed(2						)

         lowerCAmelCase					:					Optional[Any]								=							int((pt_model.config.image_size // pt_model.config.patch_size) ** 2						)
         lowerCAmelCase					:					Union[str, Any]								=							np.random.uniform(size=(self.model_tester.batch_size, num_patches)						)
         lowerCAmelCase					:					List[str]								=							torch.from_numpy(snake_case__						)

         # Add `noise` argument.
         # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
         lowerCAmelCase					:					List[str]								=							pt_noise

         super().check_pt_tf_models(snake_case__		,					snake_case__		,					snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





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

         for model_class in self.all_model_classes:
           lowerCAmelCase					:					Optional[Any]								=							model_class(snake_case__						)
           model.to(snake_case__						)
           model.eval()
           # make random mask reproducible
           torch.manual_seed(2						)
           with torch.no_grad():
             lowerCAmelCase					:					Union[str, Any]								=							model(**self._prepare_for_class(snake_case__		,					snake_case__						)						)

           lowerCAmelCase					:					Optional[Any]								=							outputs[0].cpu().numpy()
           lowerCAmelCase					:					Optional[Any]								=							0

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

             # Make sure we don't have nans
             lowerCAmelCase					:					Dict								=							after_outputs[0].cpu().numpy()
             lowerCAmelCase					:					Optional[Any]								=							0
             lowerCAmelCase					:					List[str]								=							np.amax(np.abs(out_a - out_a						)						)
             self.assertLessEqual(snake_case__		,					1e-5						)

       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       @unittest.skip(
           reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       @unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass

       @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
       def 	lowercase__							(						self						):






         """simple docstring"""





         pass







       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           lowerCAmelCase					:					List[Any]								=							ViTMAEModel.from_pretrained(snake_case__						)
           self.assertIsNotNone(snake_case__						)

def  a__      (    ):


  '''simple docstring'''







  lowerCAmelCase					:					int								=							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 	lowercase__							(						self						):






         """simple docstring"""





         return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"						) if is_vision_available() else None







       @slow
       def 	lowercase__							(						self						):






         """simple docstring"""





         np.random.seed(2						)

         lowerCAmelCase					:					str								=							ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"						).to(snake_case__						)

         lowerCAmelCase					:					Union[str, Any]								=							self.default_image_processor
         lowerCAmelCase					:					Union[str, Any]								=							prepare_img()
         lowerCAmelCase					:					List[Any]								=							image_processor(images=snake_case__		,					return_tensors="pt"						).to(snake_case__						)

         # 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)
         lowerCAmelCase					:					Union[str, Any]								=							ViTMAEConfig()
         lowerCAmelCase					:					Any								=							int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2						)
         lowerCAmelCase					:					int								=							np.random.uniform(size=(1, num_patches)						)

         # forward pass
         with torch.no_grad():
           lowerCAmelCase					:					Any								=							model(**snake_case__		,					noise=torch.from_numpy(snake_case__						).to(device=snake_case__						)						)

         # verify the logits
         lowerCAmelCase					:					str								=							torch.Size((1, 196, 768)						)
         self.assertEqual(outputs.logits.shape		,					snake_case__						)

         lowerCAmelCase					:					Optional[Any]								=							torch.tensor(
             [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]						)

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









"""simple docstring"""







import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class 				SCREAMING_SNAKE_CASE__    (		lowercase       ,   unittest.TestCase    ):
       """simple docstring"""


       a  :    List[str]       =None
       a  :    List[Any]       =BloomTokenizerFast
       a  :    Optional[int]       =BloomTokenizerFast
       a  :    Optional[Any]       =True
       a  :    Dict       =False
       a  :    Optional[Any]       ="tokenizer_file"
       a  :    Optional[int]       ={"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}

       def 	lowercase__							(						self						):






         """simple docstring"""





         super().setUp()
         lowerCAmelCase					:					Tuple								=							BloomTokenizerFast.from_pretrained("bigscience/tokenizer"						)
         tokenizer.save_pretrained(self.tmpdirname						)

       def 	lowercase__							(						self		,					**snake_case__						):






         """simple docstring"""





         kwargs.update(self.special_tokens_map						)
         return BloomTokenizerFast.from_pretrained(self.tmpdirname		,					**snake_case__						)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()

         lowerCAmelCase					:					List[Any]								=							["The quick brown fox</s>", "jumps over the lazy dog</s>"]
         lowerCAmelCase					:					str								=							[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_encode_plus(snake_case__						)["input_ids"]
         self.assertListEqual(snake_case__		,					snake_case__						)

         lowerCAmelCase					:					Optional[int]								=							tokenizer.batch_decode(snake_case__						)
         self.assertListEqual(snake_case__		,					snake_case__						)

       def 	lowercase__							(						self		,					snake_case__=6						):






         """simple docstring"""





         for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
             lowerCAmelCase					:					Tuple								=							self.rust_tokenizer_class.from_pretrained(snake_case__		,					**snake_case__						)
             # tokenizer_r.pad_token = None # Hotfixing padding = None
             # Simple input
             lowerCAmelCase					:					str								=							"This is a simple input"
             lowerCAmelCase					:					Tuple								=							["This is a simple input 1", "This is a simple input 2"]
             lowerCAmelCase					:					Any								=							("This is a simple input", "This is a pair")
             lowerCAmelCase					:					Tuple								=							[
                 ("This is a simple input 1", "This is a simple input 2"),
                 ("This is a simple pair 1", "This is a simple pair 2"),
             ]

             # Simple input tests
             try:
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode_plus(snake_case__		,					max_length=snake_case__						)

               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.encode(snake_case__		,					max_length=snake_case__						)
               tokenizer_r.batch_encode_plus(snake_case__		,					max_length=snake_case__						)
             except ValueError:
               self.fail("Bloom Tokenizer should be able to deal with padding"						)

             lowerCAmelCase					:					Tuple								=							None  # Hotfixing padding = None
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Simple input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(snake_case__		,					tokenizer_r.encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"						)

             # Pair input
             self.assertRaises(
                 snake_case__		,					tokenizer_r.batch_encode_plus		,					snake_case__		,					max_length=snake_case__		,					padding="max_length"		,					)

       def 	lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase					:					int								=							self.get_rust_tokenizer()
         lowerCAmelCase					:					int								=							load_dataset("xnli"		,					"all_languages"		,					split="test"		,					streaming=snake_case__						)

         lowerCAmelCase					:					Tuple								=							next(iter(snake_case__						)						)["premise"]  # pick up one data
         lowerCAmelCase					:					Optional[Any]								=							list(sample_data.values()						)

         lowerCAmelCase					:					int								=							list(map(tokenizer.encode		,					snake_case__						)						)
         lowerCAmelCase					:					List[Any]								=							[tokenizer.decode(snake_case__		,					clean_up_tokenization_spaces=snake_case__						) for x in output_tokens]
         self.assertListEqual(snake_case__		,					snake_case__						)







       def 	lowercase__							(						self						):






         """simple docstring"""





         self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map						)		,					1						)
         self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()						)[0]						)		,					1						)





