





'''simple docstring'''






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						:


   def __init__( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase=13    ,   _UpperCAmelCase=30    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=32    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=4    ,   _UpperCAmelCase=37    ,   _UpperCAmelCase="gelu"    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=10    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=0.6    ,   _UpperCAmelCase=None    ,   ):
       A       :       Union[str, Any]							=						parent
       A       :       Union[str, Any]							=						batch_size
       A       :       str							=						image_size
       A       :       Any							=						patch_size
       A       :       Optional[int]							=						num_channels
       A       :       Optional[int]							=						is_training
       A       :       Tuple							=						use_labels
       A       :       Optional[int]							=						hidden_size
       A       :       List[str]							=						num_hidden_layers
       A       :       Union[str, Any]							=						num_attention_heads
       A       :       Optional[int]							=						intermediate_size
       A       :       List[str]							=						hidden_act
       A       :       Optional[Any]							=						hidden_dropout_prob
       A       :       Tuple							=						attention_probs_dropout_prob
       A       :       List[str]							=						type_sequence_label_size
       A       :       List[str]							=						initializer_range
       A       :       List[Any]							=						mask_ratio
       A       :       Any							=						scope

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


   def 					snake_case				( self					):
       A       :       Tuple							=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

       A       :       List[str]							=						None
       if self.use_labels:
           A       :       Tuple							=						ids_tensor([self.batch_size]    ,   self.type_sequence_label_size					)

       A       :       List[str]							=						self.get_config()

       return config, pixel_values, labels


   def 					snake_case				( self					):
       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=_UpperCAmelCase    ,   initializer_range=self.initializer_range    ,   mask_ratio=self.mask_ratio    ,   )


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       Dict							=						TFViTMAEModel(config=_UpperCAmelCase					)
       A       :       List[Any]							=						model(_UpperCAmelCase    ,   training=_UpperCAmelCase					)
       self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       A       :       int							=						TFViTMAEForPreTraining(_UpperCAmelCase					)
       A       :       Union[str, Any]							=						model(_UpperCAmelCase    ,   training=_UpperCAmelCase					)
       # expected sequence length = num_patches
       A       :       Optional[int]							=						(self.image_size // self.patch_size) ** 2
       A       :       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
       A       :       Any							=						1
       A       :       int							=						TFViTMAEForPreTraining(_UpperCAmelCase					)

       A       :       Union[str, Any]							=						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
       A       :       Union[str, Any]							=						model(_UpperCAmelCase    ,   training=_UpperCAmelCase					)
       A       :       Any							=						self.patch_size**2
       self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, num_patches, expected_num_channels)					)


   def 					snake_case				( self					):
       A       :       Dict							=						self.prepare_config_and_inputs()
       ((A),	(A),	(A))       :       Any							=						config_and_inputs
       A       :       List[str]							=						{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_tf
class   _lowercase						(							a		,					a		,					unittest.TestCase				):
   _UpperCamelCase       		=   (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
   _UpperCamelCase       		=   {"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False
   _UpperCamelCase       		=   False


   def 					snake_case				( self					):
       A       :       Optional[int]							=						TFViTMAEModelTester(self					)
       A       :       Optional[Any]							=						ConfigTester(self    ,   config_class=_UpperCAmelCase    ,   has_text_modality=_UpperCAmelCase    ,   hidden_size=37					)


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


   @unittest.skip(reason='''ViTMAE does not use inputs_embeds'''					)
   def 					snake_case				( self					):
       pass


   def 					snake_case				( self					):
       A,	A       :       Tuple							=						self.model_tester.prepare_config_and_inputs_for_common()

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


   def 					snake_case				( self					):
       A,	A       :       Union[str, Any]							=						self.model_tester.prepare_config_and_inputs_for_common()

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

           A       :       Tuple							=						['''pixel_values''']
           self.assertListEqual(arg_names[:1]    ,   _UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Optional[Any]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       A       :       Optional[int]							=						self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_pretraining(*_UpperCAmelCase					)


   def 					snake_case				( self					):
       # make the mask reproducible
       np.random.seed(2					)

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

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

       for model_class in self.all_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)
           A       :       Dict							=						self._prepare_for_class(_UpperCAmelCase    ,   _UpperCAmelCase					)

           A       :       str							=						model(_UpperCAmelCase    ,   noise=_UpperCAmelCase					)

           A       :       Tuple							=						copy.deepcopy(self._prepare_for_class(_UpperCAmelCase    ,   _UpperCAmelCase					)					)
           A       :       Union[str, Any]							=						model(**_UpperCAmelCase    ,   noise=_UpperCAmelCase					)
           A       :       Any							=						outputs_dict[0].numpy()
           A       :       List[str]							=						outputs_keywords[0].numpy()

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


   def 					snake_case				( self					):
       # make the mask reproducible
       np.random.seed(2					)

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

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

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

           return inputs_np_dict

       for model_class in self.all_model_classes:
           A       :       str							=						model_class(_UpperCAmelCase					)

           A       :       List[Any]							=						self._prepare_for_class(_UpperCAmelCase    ,   _UpperCAmelCase					)
           A       :       List[str]							=						prepare_numpy_arrays(_UpperCAmelCase					)

           A       :       Any							=						model(_UpperCAmelCase    ,   noise=_UpperCAmelCase					)
           A       :       int							=						model(**_UpperCAmelCase    ,   noise=_UpperCAmelCase					)
           self.assert_outputs_same(_UpperCAmelCase    ,   _UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					):
       # make masks reproducible
       np.random.seed(2					)

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

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

       super().check_pt_tf_models(_UpperCAmelCase    ,   _UpperCAmelCase    ,   _UpperCAmelCase					)


   def 					snake_case				( self					):
       # make mask reproducible
       np.random.seed(2					)

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

       A       :       Union[str, Any]							=						{
           module_member
           for model_class in self.all_model_classes
           for module in (import_module(model_class.__module__					),)
           for module_member_name in dir(_UpperCAmelCase					)
           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(_UpperCAmelCase    ,   _UpperCAmelCase					),)
           if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)
           and tf.keras.layers.Layer in module_member.__bases__
           and getattr(_UpperCAmelCase    ,   '''_keras_serializable'''    ,   _UpperCAmelCase					)
       }

       A       :       Optional[int]							=						int((config.image_size // config.patch_size) ** 2					)
       A       :       Any							=						np.random.uniform(size=(self.model_tester.batch_size, num_patches)					)
       A       :       List[str]							=						tf.convert_to_tensor(_UpperCAmelCase					)
       inputs_dict.update({'''noise''': noise}					)

       for main_layer_class in tf_main_layer_classes:
           A       :       Optional[int]							=						main_layer_class(_UpperCAmelCase					)

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

           A       :       Union[str, Any]							=						tf.keras.Model(_UpperCAmelCase    ,   outputs=main_layer(_UpperCAmelCase					)					)
           A       :       Optional[Any]							=						model(_UpperCAmelCase					)

           with tempfile.TemporaryDirectory() as tmpdirname:
               A       :       List[Any]							=						os.path.join(_UpperCAmelCase    ,   '''keras_model.h5'''					)
               model.save(_UpperCAmelCase					)
               A       :       List[Any]							=						tf.keras.models.load_model(
                   _UpperCAmelCase    ,   custom_objects={main_layer_class.__name__: main_layer_class}					)
               assert isinstance(_UpperCAmelCase    ,   tf.keras.Model					)
               A       :       int							=						model(_UpperCAmelCase					)
               self.assert_outputs_same(_UpperCAmelCase    ,   _UpperCAmelCase					)


   @slow
   def 					snake_case				( self					):
       # make mask reproducible
       np.random.seed(2					)

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

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

       for model_class in self.all_model_classes:
           A       :       Optional[Any]							=						model_class(_UpperCAmelCase					)
           A       :       Dict							=						self._prepare_for_class(_UpperCAmelCase    ,   _UpperCAmelCase					)
           A       :       List[str]							=						model(_UpperCAmelCase    ,   noise=_UpperCAmelCase					)

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

           with tempfile.TemporaryDirectory() as tmpdirname:
               model.save_pretrained(_UpperCAmelCase    ,   saved_model=_UpperCAmelCase					)
               A       :       Union[str, Any]							=						model_class.from_pretrained(_UpperCAmelCase					)
               A       :       Optional[int]							=						model(_UpperCAmelCase    ,   noise=_UpperCAmelCase					)

               if model_class.__name__ == "TFViTMAEModel":
                   A       :       int							=						after_outputs['''last_hidden_state'''].numpy()
                   A       :       int							=						0
               else:
                   A       :       int							=						after_outputs['''logits'''].numpy()
                   A       :       Tuple							=						0

               A       :       Tuple							=						np.amax(np.abs(out_a - out_a					)					)
               self.assertLessEqual(_UpperCAmelCase    ,   1E-5					)


   def 					snake_case				( self					):
       # make mask reproducible
       np.random.seed(2					)

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

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

       for model_class in self.all_model_classes:
           A       :       int							=						model_class(_UpperCAmelCase					)
           A       :       int							=						self._prepare_for_class(_UpperCAmelCase    ,   _UpperCAmelCase					)

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

           self.assert_outputs_same(_UpperCAmelCase    ,   _UpperCAmelCase					)


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


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


   @slow
   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''					)
       self.assertIsNotNone(_UpperCAmelCase					)







def 							_lowerCamelCase(  )		->  List[str]:
    A       :       Any							=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
    return image





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


   @cached_property
   def 					snake_case				( self					):
       return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''					) if is_vision_available() else None


   @slow
   def 					snake_case				( self					):
       # make random mask reproducible across the PT and TF model
       np.random.seed(2					)

       A       :       Union[str, Any]							=						TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''					)

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

       # forward pass
       A       :       List[str]							=						model(**_UpperCAmelCase    ,   noise=_UpperCAmelCase					)

       # verify the logits
       A       :       Dict							=						tf.convert_to_tensor([1, 196, 768]					)
       self.assertEqual(outputs.logits.shape    ,   _UpperCAmelCase					)

       A       :       Any							=						tf.convert_to_tensor(
           [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]					)

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







'''simple docstring'''






# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

snake_case_     =	"""tiny-wmt19-en-ru"""

# Build

# borrowed from a test
snake_case_     =	[
    """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>""",
]
snake_case_     =	dict(zip(vocab, range(len(vocab))))
snake_case_     =	["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

with tempfile.TemporaryDirectory() as tmpdirname:
     snake_case_     =	Path(tmpdirname)
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
     snake_case_     =	build_dir / VOCAB_FILES_NAMES["""merges_file"""]
     with open(src_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(tgt_vocab_file, """w""") as fp:
          fp.write(json.dumps(vocab_tokens))
     with open(merges_file, """w""") as fp:
          fp.write("""\n""".join(merges))

     snake_case_     =	FSMTTokenizer(
         langs=["""en""", """ru"""],
         src_vocab_size=len(vocab),
         tgt_vocab_size=len(vocab),
         src_vocab_file=src_vocab_file,
         tgt_vocab_file=tgt_vocab_file,
         merges_file=merges_file,
     )

snake_case_     =	FSMTConfig(
    langs=["""ru""", """en"""],
    src_vocab_size=10_00,
    tgt_vocab_size=10_00,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

snake_case_     =	FSMTForConditionalGeneration(config)
print(f'''num of params {tiny_model.num_parameters()}''')

# Test
snake_case_     =	tokenizer(["""Making tiny model"""], return_tensors="""pt""")
snake_case_     =	tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru
