



from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
	from PIL import Image

	from transformers import ViTImageProcessor


class 					_lowercase      :
				'''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__=2   ,       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'''


									UpperCamelCase_						     =      parent
									UpperCamelCase_						     =      batch_size
									UpperCamelCase_						     =      image_size
									UpperCamelCase_						     =      patch_size
									UpperCamelCase_						     =      num_channels
									UpperCamelCase_						     =      is_training
									UpperCamelCase_						     =      use_labels
									UpperCamelCase_						     =      hidden_size
									UpperCamelCase_						     =      num_hidden_layers
									UpperCamelCase_						     =      num_attention_heads
									UpperCamelCase_						     =      intermediate_size
									UpperCamelCase_						     =      hidden_act
									UpperCamelCase_						     =      hidden_dropout_prob
									UpperCamelCase_						     =      attention_probs_dropout_prob
									UpperCamelCase_						     =      type_sequence_label_size
									UpperCamelCase_						     =      initializer_range
									UpperCamelCase_						     =      mask_ratio
									UpperCamelCase_						     =      scope

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

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


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

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

									UpperCamelCase_						     =      self.get_config()

									return config, pixel_values, labels

				def 					_lowerCamelCase    (       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   ,       decoder_hidden_size=self.hidden_size   ,       decoder_num_hidden_layers=self.num_hidden_layers   ,       decoder_num_attention_heads=self.num_attention_heads   ,       decoder_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 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__   ,       snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      TFViTMAEModel(config=__snake_case			)
									UpperCamelCase_						     =      model(__snake_case   ,       training=__snake_case			)
									self.parent.assertEqual(result.last_hidden_state.shape   ,       (self.batch_size, self.seq_length, self.hidden_size)			)

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__   ,       snake_case__			):

									'''simple docstring'''


									UpperCamelCase_						     =      TFViTMAEForPreTraining(__snake_case			)
									UpperCamelCase_						     =      model(__snake_case   ,       training=__snake_case			)
									# expected sequence length = num_patches
									UpperCamelCase_						     =      (self.image_size // self.patch_size) ** 2
									UpperCamelCase_						     =      self.patch_size**2 * self.num_channels
									self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, num_patches, expected_num_channels)			)

									# test greyscale images
									UpperCamelCase_						     =      1
									UpperCamelCase_						     =      TFViTMAEForPreTraining(__snake_case			)

									UpperCamelCase_						     =      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
									UpperCamelCase_						     =      model(__snake_case   ,       training=__snake_case			)
									UpperCamelCase_						     =      self.patch_size**2
									self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, num_patches, expected_num_channels)			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.prepare_config_and_inputs()
									((UpperCamelCase_)     ,			(UpperCamelCase_)     ,			(UpperCamelCase_))						     =      config_and_inputs
									UpperCamelCase_						     =      {"pixel_values": pixel_values}
									return config, inputs_dict




@require_tf
class 					_lowercase      (lowerCamelCase_					,      lowerCamelCase_					,      unittest.TestCase					):
				'''simple docstring'''







				lowercase__        =  (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
				lowercase__        =  {'''feature-extraction''': TFViTMAEModel} if is_tf_available() else {}

				lowercase__        =  False
				lowercase__        =  False
				lowercase__        =  False
				lowercase__        =  False

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      TFViTMAEModelTester(self			)
									UpperCamelCase_						     =      ConfigTester(self   ,       config_class=__snake_case   ,       has_text_modality=__snake_case   ,       hidden_size=37			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									self.config_tester.run_common_tests()

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

									'''simple docstring'''


									pass

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_     ,			UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
														UpperCamelCase_						     =      model_class(__snake_case			)
														self.assertIsInstance(model.get_input_embeddings()   ,       (tf.keras.layers.Layer)			)
														UpperCamelCase_						     =      model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__snake_case   ,       tf.keras.layers.Layer			)			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_     ,			UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs_for_common()

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

														UpperCamelCase_						     =      ["pixel_values"]
														self.assertListEqual(arg_names[:1]   ,       __snake_case			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__snake_case			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_pretraining(*__snake_case			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									np.random.seed(2			)

									UpperCamelCase_     ,			UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_						     =      int((config.image_size // config.patch_size) ** 2			)
									UpperCamelCase_						     =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

									for model_class in self.all_model_classes:
														UpperCamelCase_						     =      model_class(__snake_case			)
														UpperCamelCase_						     =      self._prepare_for_class(__snake_case   ,       __snake_case			)

														UpperCamelCase_						     =      model(__snake_case   ,       noise=__snake_case			)

														UpperCamelCase_						     =      copy.deepcopy(self._prepare_for_class(__snake_case   ,       __snake_case			)			)
														UpperCamelCase_						     =      model(**__snake_case   ,       noise=__snake_case			)
														UpperCamelCase_						     =      outputs_dict[0].numpy()
														UpperCamelCase_						     =      outputs_keywords[0].numpy()

														self.assertLess(np.sum(np.abs(output_dict - output_keywords			)			)   ,       1e-6			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									np.random.seed(2			)

									UpperCamelCase_     ,			UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_						     =      int((config.image_size // config.patch_size) ** 2			)
									UpperCamelCase_						     =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

									def prepare_numpy_arrays(snake_case__			):
														UpperCamelCase_						     =      {}
														for k, v in inputs_dict.items():
																			if tf.is_tensor(__snake_case			):
																								UpperCamelCase_						     =      v.numpy()
																			else:
																								UpperCamelCase_						     =      np.array(__snake_case			)

														return inputs_np_dict

									for model_class in self.all_model_classes:
														UpperCamelCase_						     =      model_class(__snake_case			)

														UpperCamelCase_						     =      self._prepare_for_class(__snake_case   ,       __snake_case			)
														UpperCamelCase_						     =      prepare_numpy_arrays(__snake_case			)

														UpperCamelCase_						     =      model(__snake_case   ,       noise=__snake_case			)
														UpperCamelCase_						     =      model(**__snake_case   ,       noise=__snake_case			)
														self.assert_outputs_same(__snake_case   ,       __snake_case			)

				def 					_lowerCamelCase    (       self   ,       snake_case__   ,       snake_case__   ,       snake_case__			):

									'''simple docstring'''


									np.random.seed(2			)

									UpperCamelCase_						     =      int((tf_model.config.image_size // tf_model.config.patch_size) ** 2			)
									UpperCamelCase_						     =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
									UpperCamelCase_						     =      tf.constant(__snake_case			)

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

									super().check_pt_tf_models(__snake_case   ,       __snake_case   ,       __snake_case			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									np.random.seed(2			)

									UpperCamelCase_     ,			UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_						     =      {
									    module_member
									    for model_class in self.all_model_classes
									    for module in (import_module(model_class.__module__			),)
									    for module_member_name in dir(__snake_case			)
									    if module_member_name.endswith("MainLayer"			)
									    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
									    and module_member_name[: -len("MainLayer"			)] == model_class.__name__[: -len("Model"			)]
									    for module_member in (getattr(__snake_case   ,       __snake_case			),)
									    if isinstance(__snake_case   ,       __snake_case			)
									    and tf.keras.layers.Layer in module_member.__bases__
									    and getattr(__snake_case   ,       "_keras_serializable"   ,       __snake_case			)
									}

									UpperCamelCase_						     =      int((config.image_size // config.patch_size) ** 2			)
									UpperCamelCase_						     =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
									UpperCamelCase_						     =      tf.convert_to_tensor(__snake_case			)
									inputs_dict.update({"noise": noise}			)

									for main_layer_class in tf_main_layer_classes:
														UpperCamelCase_						     =      main_layer_class(__snake_case			)

														UpperCamelCase_						     =      {
														    name: tf.keras.Input(tensor.shape[1:]   ,       dtype=tensor.dtype			) for name, tensor in inputs_dict.items()
														}

														UpperCamelCase_						     =      tf.keras.Model(__snake_case   ,       outputs=main_layer(__snake_case			)			)
														UpperCamelCase_						     =      model(__snake_case			)

														with tempfile.TemporaryDirectory() as tmpdirname:
																			UpperCamelCase_						     =      os.path.join(__snake_case   ,       "keras_model.h5"			)
																			model.save(__snake_case			)
																			UpperCamelCase_						     =      tf.keras.models.load_model(
																			    __snake_case   ,       custom_objects={main_layer_class.__name__: main_layer_class}			)
																			assert isinstance(__snake_case   ,       tf.keras.Model			)
																			UpperCamelCase_						     =      model(__snake_case			)
																			self.assert_outputs_same(__snake_case   ,       __snake_case			)

				@slow
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									np.random.seed(2			)

									UpperCamelCase_     ,			UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_						     =      int((config.image_size // config.patch_size) ** 2			)
									UpperCamelCase_						     =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

									for model_class in self.all_model_classes:
														UpperCamelCase_						     =      model_class(__snake_case			)
														UpperCamelCase_						     =      self._prepare_for_class(__snake_case   ,       __snake_case			)
														UpperCamelCase_						     =      model(__snake_case   ,       noise=__snake_case			)

														if model_class.__name__ == "TFViTMAEModel":
																			UpperCamelCase_						     =      outputs.last_hidden_state.numpy()
																			UpperCamelCase_						     =      0
														else:
																			UpperCamelCase_						     =      outputs.logits.numpy()
																			UpperCamelCase_						     =      0

														with tempfile.TemporaryDirectory() as tmpdirname:
																			model.save_pretrained(__snake_case   ,       saved_model=__snake_case			)
																			UpperCamelCase_						     =      model_class.from_pretrained(__snake_case			)
																			UpperCamelCase_						     =      model(__snake_case   ,       noise=__snake_case			)

																			if model_class.__name__ == "TFViTMAEModel":
																								UpperCamelCase_						     =      after_outputs["last_hidden_state"].numpy()
																								UpperCamelCase_						     =      0
																			else:
																								UpperCamelCase_						     =      after_outputs["logits"].numpy()
																								UpperCamelCase_						     =      0

																			UpperCamelCase_						     =      np.amax(np.abs(out_a - out_a			)			)
																			self.assertLessEqual(__snake_case   ,       1e-5			)

				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									np.random.seed(2			)

									UpperCamelCase_     ,			UpperCamelCase_						     =      self.model_tester.prepare_config_and_inputs_for_common()

									UpperCamelCase_						     =      int((config.image_size // config.patch_size) ** 2			)
									UpperCamelCase_						     =      np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)

									for model_class in self.all_model_classes:
														UpperCamelCase_						     =      model_class(__snake_case			)
														UpperCamelCase_						     =      self._prepare_for_class(__snake_case   ,       __snake_case			)

														UpperCamelCase_						     =      model(__snake_case   ,       noise=__snake_case			)
														UpperCamelCase_						     =      model.get_config()
														# make sure that returned config is jsonifiable, which is required by keras
														json.dumps(__snake_case			)
														UpperCamelCase_						     =      model_class.from_config(model.get_config()			)
														# make sure it also accepts a normal config
														UpperCamelCase_						     =      model_class.from_config(model.config			)
														UpperCamelCase_						     =      new_model(__snake_case			)  # Build model
														new_model.set_weights(model.get_weights()			)
														UpperCamelCase_						     =      new_model(__snake_case   ,       noise=__snake_case			)

														self.assert_outputs_same(__snake_case   ,       __snake_case			)

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

									'''simple docstring'''


									pass

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

									'''simple docstring'''


									pass

				@slow
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									UpperCamelCase_						     =      TFViTMAEModel.from_pretrained("google/vit-base-patch16-224"			)
									self.assertIsNotNone(__snake_case			)




def       _lowerCAmelCase			():
					UpperCamelCase_						     =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
					return image






@require_tf
@require_vision
class 					_lowercase      (unittest.TestCase					):
				'''simple docstring'''







				@cached_property
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


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

				@slow
				def 					_lowerCamelCase    (       self			):

									'''simple docstring'''


									np.random.seed(2			)

									UpperCamelCase_						     =      TFViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"			)

									UpperCamelCase_						     =      self.default_image_processor
									UpperCamelCase_						     =      prepare_img()
									UpperCamelCase_						     =      image_processor(images=__snake_case   ,       return_tensors="tf"			)

									# 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)
									UpperCamelCase_						     =      ViTMAEConfig()
									UpperCamelCase_						     =      int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
									UpperCamelCase_						     =      np.random.uniform(size=(1, num_patches)			)

									# forward pass
									UpperCamelCase_						     =      model(**__snake_case   ,       noise=__snake_case			)

									# verify the logits
									UpperCamelCase_						     =      tf.convert_to_tensor([1, 196, 768]			)
									self.assertEqual(outputs.logits.shape   ,       __snake_case			)

									UpperCamelCase_						     =      tf.convert_to_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]]			)

									tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,       __snake_case   ,       atol=1e-4			)




import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class        snake_case_   (lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Tuple          =			XLMTokenizer
					UpperCAmelCase__					:    List[str]          =			False
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												super().setUp()

												# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
												a__             =     [
												    'l',
												    'o',
												    'w',
												    'e',
												    'r',
												    's',
												    't',
												    'i',
												    'd',
												    'n',
												    'w</w>',
												    'r</w>',
												    't</w>',
												    'lo',
												    'low',
												    'er</w>',
												    'low</w>',
												    'lowest</w>',
												    'newer</w>',
												    'wider</w>',
												    '<unk>',
												]
												a__             =     dict(zip(__snake_case			,range(len(__snake_case      )      )      )      )
												a__             =     ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

												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'      ) as fp:
																			fp.write(json.dumps(__snake_case      )      )
												with open(self.merges_file			,'w'      ) as fp:
																			fp.write('\n'.join(__snake_case      )      )
					def  lowerCamelCase__(      self   :Any			,__snake_case   :int      )					->    Optional[int]:
												a__             =     'lower newer'
												a__             =     'lower newer'
												return input_text, output_text
					def  lowerCamelCase__(      self   :Tuple      )					->    Tuple:
												a__             =     XLMTokenizer(self.vocab_file			,self.merges_file      )

												a__             =     'lower'
												a__             =     ['low', 'er</w>']
												a__             =     tokenizer.tokenize(__snake_case      )
												self.assertListEqual(__snake_case			,__snake_case      )

												a__             =     tokens + ['<unk>']
												a__             =     [14, 15, 20]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case      )			,__snake_case      )



					@slow
					def  lowerCamelCase__(      self   :List[str]      )					->    Optional[Any]:
												a__             =     XLMTokenizer.from_pretrained('xlm-mlm-en-2048'      )

												a__             =     tokenizer.encode('sequence builders'			,add_special_tokens=__snake_case      )
												a__             =     tokenizer.encode('multi-sequence build'			,add_special_tokens=__snake_case      )

												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case      )
												a__             =     tokenizer.build_inputs_with_special_tokens(__snake_case			,__snake_case      )

												assert encoded_sentence == [0] + text + [1]
												assert encoded_pair == [0] + text + [1] + text_a + [1]




