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 	__snake_case	:





						'''simple docstring'''






						def __init__(					self     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				List[Any]=13    ,  _UpperCamelCase     :				Optional[Any]=30    ,  _UpperCamelCase     :				List[Any]=2    ,  _UpperCamelCase     :				Dict=3    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				List[str]=32    ,  _UpperCamelCase     :				Optional[Any]=2    ,  _UpperCamelCase     :				int=4    ,  _UpperCamelCase     :				Optional[Any]=37    ,  _UpperCamelCase     :				Union[str, Any]="gelu"    ,  _UpperCamelCase     :				str=0.1    ,  _UpperCamelCase     :				str=0.1    ,  _UpperCamelCase     :				Tuple=10    ,  _UpperCamelCase     :				Tuple=0.0_2    ,  _UpperCamelCase     :				Optional[int]=3    ,  _UpperCamelCase     :				Optional[Any]=0.6    ,  _UpperCamelCase     :				Dict=None    ,  )				->List[str]:


										"""simple docstring"""


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

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



						def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->str:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

										_lowerCamelCase     :		Union[str, Any] 						=						None
										if self.use_labels:
														_lowerCamelCase     :		List[Any] 						=						ids_tensor([self.batch_size]    ,  self.type_sequence_label_size)

										_lowerCamelCase     :		Union[str, Any] 						=						self.get_config()

										return config, pixel_values, labels



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


										"""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=A_    ,  initializer_range=self.initializer_range    ,  mask_ratio=self.mask_ratio    ,  )



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Union[str, Any])				->Tuple:


										"""simple docstring"""


										_lowerCamelCase     :		int 						=						TFViTMAEModel(config=A_)
										_lowerCamelCase     :		List[Any] 						=						model(A_    ,  training=A_)
										self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size))



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				List[str])				->Optional[int]:


										"""simple docstring"""


										_lowerCamelCase     :		Any 						=						TFViTMAEForPreTraining(A_)
										_lowerCamelCase     :		Any 						=						model(A_    ,  training=A_)
										# expected sequence length = num_patches
										_lowerCamelCase     :		Optional[Any] 						=						(self.image_size // self.patch_size) ** 2
										_lowerCamelCase     :		Optional[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     :		Dict 						=						1
										_lowerCamelCase     :		Tuple 						=						TFViTMAEForPreTraining(A_)

										_lowerCamelCase     :		Optional[Any] 						=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
										_lowerCamelCase     :		Dict 						=						model(A_    ,  training=A_)
										_lowerCamelCase     :		List[Any] 						=						self.patch_size**2
										self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, num_patches, expected_num_channels))




						def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->int:


										"""simple docstring"""


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





@require_tf
class 	__snake_case	( _lowercase ,  _lowercase ,  unittest.TestCase		):





						'''simple docstring'''






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

						_snake_case    		=       False
						_snake_case    		=       False
						_snake_case    		=       False
						_snake_case    		=       False



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[int]:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						TFViTMAEModelTester(self)
										_lowerCamelCase     :		Dict 						=						ConfigTester(self    ,  config_class=A_    ,  has_text_modality=A_    ,  hidden_size=37)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Optional[int]:


										"""simple docstring"""


										self.config_tester.run_common_tests()



						@unittest.skip(reason="""ViTMAE does not use inputs_embeds""")
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->Any:


										"""simple docstring"""


										pass



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[str]:


										"""simple docstring"""


										_lowerCamelCase						,							_lowerCamelCase     :		Optional[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														_lowerCamelCase     :		Any 						=						model_class(A_)
														self.assertIsInstance(model.get_input_embeddings()    ,  (tf.keras.layers.Layer))
														_lowerCamelCase     :		Optional[Any] 						=						model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(A_    ,  tf.keras.layers.Layer))



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Optional[int]:


										"""simple docstring"""


										_lowerCamelCase						,							_lowerCamelCase     :		str 						=						self.model_tester.prepare_config_and_inputs_for_common()

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

														_lowerCamelCase     :		List[str] 						=						["""pixel_values"""]
														self.assertListEqual(arg_names[:1]    ,  A_)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->List[str]:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*A_)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->int:


										"""simple docstring"""


										_lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_pretraining(*A_)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Optional[Any]:


										"""simple docstring"""


										np.random.seed(2)

										_lowerCamelCase						,							_lowerCamelCase     :		List[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

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

										for model_class in self.all_model_classes:
														_lowerCamelCase     :		Any 						=						model_class(A_)
														_lowerCamelCase     :		List[Any] 						=						self._prepare_for_class(A_    ,  A_)

														_lowerCamelCase     :		Optional[int] 						=						model(A_    ,  noise=A_)

														_lowerCamelCase     :		List[Any] 						=						copy.deepcopy(self._prepare_for_class(A_    ,  A_))
														_lowerCamelCase     :		Union[str, Any] 						=						model(**A_    ,  noise=A_)
														_lowerCamelCase     :		str 						=						outputs_dict[0].numpy()
														_lowerCamelCase     :		Dict 						=						outputs_keywords[0].numpy()

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



						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Dict:


										"""simple docstring"""


										np.random.seed(2)

										_lowerCamelCase						,							_lowerCamelCase     :		List[Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()

										_lowerCamelCase     :		List[Any] 						=						int((config.image_size // config.patch_size) ** 2)
										_lowerCamelCase     :		Dict 						=						np.random.uniform(size=(self.model_tester.batch_size, num_patches))

										def prepare_numpy_arrays(_UpperCamelCase     :				Union[str, Any]):
														_lowerCamelCase     :		List[str] 						=						{}
														for k, v in inputs_dict.items():
																		if tf.is_tensor(A_):
																						_lowerCamelCase     :		int 						=						v.numpy()
																		else:
																						_lowerCamelCase     :		List[Any] 						=						np.array(A_)

														return inputs_np_dict

										for model_class in self.all_model_classes:
														_lowerCamelCase     :		List[str] 						=						model_class(A_)

														_lowerCamelCase     :		str 						=						self._prepare_for_class(A_    ,  A_)
														_lowerCamelCase     :		Tuple 						=						prepare_numpy_arrays(A_)

														_lowerCamelCase     :		List[str] 						=						model(A_    ,  noise=A_)
														_lowerCamelCase     :		Tuple 						=						model(**A_    ,  noise=A_)
														self.assert_outputs_same(A_    ,  A_)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				Dict    ,  _UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[Any])				->Dict:


										"""simple docstring"""


										np.random.seed(2)

										_lowerCamelCase     :		Tuple 						=						int((tf_model.config.image_size // tf_model.config.patch_size) ** 2)
										_lowerCamelCase     :		Union[str, Any] 						=						np.random.uniform(size=(self.model_tester.batch_size, num_patches))
										_lowerCamelCase     :		str 						=						tf.constant(A_)

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

										super().check_pt_tf_models(A_    ,  A_    ,  A_)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


										"""simple docstring"""


										np.random.seed(2)

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

										_lowerCamelCase     :		Dict 						=						{
										    module_member
										    for model_class in self.all_model_classes
										    for module in (import_module(model_class.__module__),)
										    for module_member_name in dir(A_)
										    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(A_    ,  A_),)
										    if isinstance(A_    ,  A_)
										    and tf.keras.layers.Layer in module_member.__bases__
										    and getattr(A_    ,  """_keras_serializable"""    ,  A_)
										}

										_lowerCamelCase     :		List[str] 						=						int((config.image_size // config.patch_size) ** 2)
										_lowerCamelCase     :		Tuple 						=						np.random.uniform(size=(self.model_tester.batch_size, num_patches))
										_lowerCamelCase     :		List[str] 						=						tf.convert_to_tensor(A_)
										inputs_dict.update({"""noise""": noise})

										for main_layer_class in tf_main_layer_classes:
														_lowerCamelCase     :		Optional[int] 						=						main_layer_class(A_)

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

														_lowerCamelCase     :		Tuple 						=						tf.keras.Model(A_    ,  outputs=main_layer(A_))
														_lowerCamelCase     :		Tuple 						=						model(A_)

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



						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


										"""simple docstring"""


										np.random.seed(2)

										_lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						self.model_tester.prepare_config_and_inputs_for_common()

										_lowerCamelCase     :		Optional[Any] 						=						int((config.image_size // config.patch_size) ** 2)
										_lowerCamelCase     :		Union[str, Any] 						=						np.random.uniform(size=(self.model_tester.batch_size, num_patches))

										for model_class in self.all_model_classes:
														_lowerCamelCase     :		Union[str, Any] 						=						model_class(A_)
														_lowerCamelCase     :		int 						=						self._prepare_for_class(A_    ,  A_)
														_lowerCamelCase     :		Any 						=						model(A_    ,  noise=A_)

														if model_class.__name__ == "TFViTMAEModel":
																		_lowerCamelCase     :		List[Any] 						=						outputs.last_hidden_state.numpy()
																		_lowerCamelCase     :		Union[str, Any] 						=						0
														else:
																		_lowerCamelCase     :		Optional[int] 						=						outputs.logits.numpy()
																		_lowerCamelCase     :		Dict 						=						0

														with tempfile.TemporaryDirectory() as tmpdirname:
																		model.save_pretrained(A_    ,  saved_model=A_)
																		_lowerCamelCase     :		Optional[Any] 						=						model_class.from_pretrained(A_)
																		_lowerCamelCase     :		Dict 						=						model(A_    ,  noise=A_)

																		if model_class.__name__ == "TFViTMAEModel":
																						_lowerCamelCase     :		Any 						=						after_outputs["""last_hidden_state"""].numpy()
																						_lowerCamelCase     :		List[str] 						=						0
																		else:
																						_lowerCamelCase     :		Optional[int] 						=						after_outputs["""logits"""].numpy()
																						_lowerCamelCase     :		Any 						=						0

																		_lowerCamelCase     :		str 						=						np.amax(np.abs(out_a - out_a))
																		self.assertLessEqual(A_    ,  1E-5)



						def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Any:


										"""simple docstring"""


										np.random.seed(2)

										_lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						self.model_tester.prepare_config_and_inputs_for_common()

										_lowerCamelCase     :		Optional[Any] 						=						int((config.image_size // config.patch_size) ** 2)
										_lowerCamelCase     :		List[str] 						=						np.random.uniform(size=(self.model_tester.batch_size, num_patches))

										for model_class in self.all_model_classes:
														_lowerCamelCase     :		Union[str, Any] 						=						model_class(A_)
														_lowerCamelCase     :		int 						=						self._prepare_for_class(A_    ,  A_)

														_lowerCamelCase     :		Dict 						=						model(A_    ,  noise=A_)
														_lowerCamelCase     :		int 						=						model.get_config()
														# make sure that returned config is jsonifiable, which is required by keras
														json.dumps(A_)
														_lowerCamelCase     :		Any 						=						model_class.from_config(model.get_config())
														# make sure it also accepts a normal config
														_lowerCamelCase     :		str 						=						model_class.from_config(model.config)
														_lowerCamelCase     :		List[Any] 						=						new_model(A_)  # Build model
														new_model.set_weights(model.get_weights())
														_lowerCamelCase     :		Optional[Any] 						=						new_model(A_    ,  noise=A_)

														self.assert_outputs_same(A_    ,  A_)



						@unittest.skip(
						    reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results.""")
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Optional[Any]:


										"""simple docstring"""


										pass



						@unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load""")
						def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Dict:


										"""simple docstring"""


										pass




						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->List[str]:


										"""simple docstring"""


										_lowerCamelCase     :		List[str] 						=						TFViTMAEModel.from_pretrained("""google/vit-base-patch16-224""")
										self.assertIsNotNone(A_)







def 						A__  (					):







				'''simple docstring'''
				_lowerCamelCase     :		str 						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
				return image




@require_tf
@require_vision
class 	__snake_case	( unittest.TestCase		):





						'''simple docstring'''






						@cached_property
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Union[str, Any]:


										"""simple docstring"""


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




						@slow
						def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Optional[int]:


										"""simple docstring"""


										np.random.seed(2)

										_lowerCamelCase     :		Tuple 						=						TFViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base""")

										_lowerCamelCase     :		Dict 						=						self.default_image_processor
										_lowerCamelCase     :		Tuple 						=						prepare_img()
										_lowerCamelCase     :		Union[str, Any] 						=						image_processor(images=A_    ,  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)
										_lowerCamelCase     :		str 						=						ViTMAEConfig()
										_lowerCamelCase     :		int 						=						int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2)
										_lowerCamelCase     :		int 						=						np.random.uniform(size=(1, num_patches))

										# forward pass
										_lowerCamelCase     :		Optional[Any] 						=						model(**A_    ,  noise=A_)

										# verify the logits
										_lowerCamelCase     :		Tuple 						=						tf.convert_to_tensor([1, 196, 768])
										self.assertEqual(outputs.logits.shape    ,  A_)

										_lowerCamelCase     :		List[str] 						=						tf.convert_to_tensor(
										    [[-0.0_5_4_8, -1.7_0_2_3, -0.9_3_2_5], [0.3_7_2_1, -0.5_6_7_0, -0.2_2_3_3], [0.8_2_3_5, -1.3_8_7_8, -0.3_5_2_4]])

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





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available


if is_torch_available():
    import torch


if is_vision_available():
    from PIL import Image

    from transformers import MgpstrProcessor, ViTImageProcessor




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





      '''simple docstring'''






      _snake_case    		=       ViTImageProcessor if is_vision_available() else None



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          return self.image_processor_tester.prepare_image_processor_dict()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						(3, 32, 128)
          _lowerCamelCase     :		str 						=						tempfile.mkdtemp()

          # fmt: off
          _lowerCamelCase     :		Dict 						=						["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
          # fmt: on
          _lowerCamelCase     :		str 						=						dict(zip(_UpperCamelCase    ,  range(len(_UpperCamelCase))))

          _lowerCamelCase     :		int 						=						os.path.join(self.tmpdirname    ,  VOCAB_FILES_NAMES["""vocab_file"""])
          with open(self.vocab_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              fp.write(json.dumps(_UpperCamelCase) + """\n""")

          _lowerCamelCase     :		Any 						=						{
              """do_normalize""": False,
              """do_resize""": True,
              """image_processor_type""": """ViTImageProcessor""",
              """resample""": 3,
              """size""": {"""height""": 32, """width""": 128},
          }
          _lowerCamelCase     :		Union[str, Any] 						=						os.path.join(self.tmpdirname    ,  _UpperCamelCase)
          with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8""") as fp:
              json.dump(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  **_UpperCamelCase     :				Any)				->Tuple:


          """simple docstring"""


          return MgpstrTokenizer.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  **_UpperCamelCase     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          return ViTImageProcessor.from_pretrained(self.tmpdirname    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          shutil.rmtree(self.tmpdirname)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						np.random.randint(255    ,  size=(3, 30, 400)    ,  dtype=np.uinta)

          _lowerCamelCase     :		int 						=						Image.fromarray(np.moveaxis(_UpperCamelCase    ,  0    ,  -1))

          return image_input



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()
          _lowerCamelCase     :		Tuple 						=						self.get_image_processor()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)
          _lowerCamelCase     :		int 						=						MgpstrProcessor.from_pretrained(self.tmpdirname    ,  use_fast=_UpperCamelCase)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_tokenizer()
          _lowerCamelCase     :		Optional[Any] 						=						self.get_image_processor()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)
          processor.save_pretrained(self.tmpdirname)

          _lowerCamelCase     :		Tuple 						=						self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)""")
          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor(do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          _lowerCamelCase     :		Tuple 						=						MgpstrProcessor.from_pretrained(
              self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=_UpperCamelCase    ,  padding_value=1.0)

          self.assertEqual(processor.char_tokenizer.get_vocab()    ,  tokenizer_add_kwargs.get_vocab())
          self.assertIsInstance(processor.char_tokenizer    ,  _UpperCamelCase)

          self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor_add_kwargs.to_json_string())
          self.assertIsInstance(processor.image_processor    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->int:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[str] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Optional[int] 						=						image_processor(_UpperCamelCase    ,  return_tensors="""np""")
          _lowerCamelCase     :		int 						=						processor(images=_UpperCamelCase    ,  return_tensors="""np""")

          for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()    ,  input_processor[key].sum()    ,  delta=1E-2)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						"""test"""

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase)

          _lowerCamelCase     :		Dict 						=						tokenizer(_UpperCamelCase)
          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key])



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[Any] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Any 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						"""test"""
          _lowerCamelCase     :		List[str] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		int 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  ["""pixel_values""", """labels"""])

          # test if it raises when no input is passed
          with pytest.raises(_UpperCamelCase):
              processor()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						self.get_image_processor()
          _lowerCamelCase     :		List[str] 						=						self.get_tokenizer()

          _lowerCamelCase     :		Dict 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]

          _lowerCamelCase     :		Any 						=						processor.char_decode(_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						tokenizer.batch_decode(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						[seq.replace(""" """    ,  """""") for seq in decoded_tok]

          self.assertListEqual(_UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.get_image_processor()
          _lowerCamelCase     :		str 						=						self.get_tokenizer()

          _lowerCamelCase     :		List[Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		int 						=						None
          _lowerCamelCase     :		Union[str, Any] 						=						self.prepare_image_inputs()

          _lowerCamelCase     :		Union[str, Any] 						=						processor(text=_UpperCamelCase    ,  images=_UpperCamelCase)

          self.assertListEqual(list(inputs.keys())    ,  processor.model_input_names)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						self.get_image_processor()
          _lowerCamelCase     :		int 						=						self.get_tokenizer()

          _lowerCamelCase     :		Union[str, Any] 						=						MgpstrProcessor(tokenizer=_UpperCamelCase    ,  image_processor=_UpperCamelCase)

          _lowerCamelCase     :		Any 						=						torch.randn(1    ,  27    ,  38)
          _lowerCamelCase     :		List[Any] 						=						torch.randn(1    ,  27    ,  5_0257)
          _lowerCamelCase     :		List[str] 						=						torch.randn(1    ,  27    ,  3_0522)

          _lowerCamelCase     :		int 						=						processor.batch_decode([char_input, bpe_input, wp_input])

          self.assertListEqual(list(results.keys())    ,  ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""])




