'''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 			UpperCAmelCase  :





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=13	,   __lowerCAmelCase=30	,   __lowerCAmelCase=2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=True	,   __lowerCAmelCase=True	,   __lowerCAmelCase=32	,   __lowerCAmelCase=2	,   __lowerCAmelCase=4	,   __lowerCAmelCase=37	,   __lowerCAmelCase="gelu"	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=10	,   __lowerCAmelCase=0.0_2	,   __lowerCAmelCase=3	,   __lowerCAmelCase=0.6	,   __lowerCAmelCase=None	,   )  ->  List[str]:
        lowercase__				:    int  =					parent
        lowercase__				:    Union[str, Any]  =					batch_size
        lowercase__				:    Tuple  =					image_size
        lowercase__				:    Optional[Any]  =					patch_size
        lowercase__				:    int  =					num_channels
        lowercase__				:    Optional[int]  =					is_training
        lowercase__				:    Optional[int]  =					use_labels
        lowercase__				:    Union[str, Any]  =					hidden_size
        lowercase__				:    Union[str, Any]  =					num_hidden_layers
        lowercase__				:    Dict  =					num_attention_heads
        lowercase__				:    List[str]  =					intermediate_size
        lowercase__				:    Dict  =					hidden_act
        lowercase__				:    Union[str, Any]  =					hidden_dropout_prob
        lowercase__				:    List[str]  =					attention_probs_dropout_prob
        lowercase__				:    Union[str, Any]  =					type_sequence_label_size
        lowercase__				:    Optional[int]  =					initializer_range
        lowercase__				:    Dict  =					mask_ratio
        lowercase__				:    List[Any]  =					scope

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

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__				:    str  =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

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

        return config, pixel_values, labels

       def _lowerCAmelCase(						self )  ->  Tuple:
        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=__lowerCAmelCase	,   initializer_range=self.initializer_range	,   mask_ratio=self.mask_ratio	,   )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    Tuple  =					TFViTMAEModel(config=__lowerCAmelCase )
        lowercase__				:    Dict  =					model(__lowerCAmelCase	,   training=__lowerCAmelCase )
        self.parent.assertEqual(result.last_hidden_state.shape	,   (self.batch_size, self.seq_length, self.hidden_size) )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Any:
        lowercase__				:    Optional[int]  =					TFViTMAEForPreTraining(__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					model(__lowerCAmelCase	,   training=__lowerCAmelCase )
        # expected sequence length = num_patches
        lowercase__				:    Optional[Any]  =					(self.image_size // self.patch_size) ** 2
        lowercase__				:    Optional[int]  =					self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, num_patches, expected_num_channels) )

        # test greyscale images
        lowercase__				:    Any  =					1
        lowercase__				:    Optional[int]  =					TFViTMAEForPreTraining(__lowerCAmelCase )

        lowercase__				:    str  =					floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
        lowercase__				:    Dict  =					model(__lowerCAmelCase	,   training=__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					self.patch_size**2
        self.parent.assertEqual(result.logits.shape	,   (self.batch_size, num_patches, expected_num_channels) )


       def _lowerCAmelCase(						self )  ->  Union[str, Any]:
        lowercase__				:    Union[str, Any]  =					self.prepare_config_and_inputs()
        ((lowercase__) ,							(lowercase__) ,							(lowercase__))				:    Union[str, Any]  =					config_and_inputs
        lowercase__				:    Union[str, Any]  =					{'''pixel_values''': pixel_values}
        return config, inputs_dict



@require_tf
class 			UpperCAmelCase  (						a__    ,						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    (TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
       SCREAMING_SNAKE_CASE							=    {"feature-extraction": TFViTMAEModel} if is_tf_available() else {}

       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  List[str]:
        lowercase__				:    Any  =					TFViTMAEModelTester(self )
        lowercase__				:    str  =					ConfigTester(self	,   config_class=__lowerCAmelCase	,   has_text_modality=__lowerCAmelCase	,   hidden_size=37 )

       def _lowerCAmelCase(						self )  ->  str:
        self.config_tester.run_common_tests()

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

       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__ ,							lowercase__				:    int  =					self.model_tester.prepare_config_and_inputs_for_common()

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

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        lowercase__ ,							lowercase__				:    List[str]  =					self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         lowercase__				:    Union[str, Any]  =					model_class(__lowerCAmelCase )
         lowercase__				:    List[Any]  =					inspect.signature(model.call )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         lowercase__				:    List[str]  =					[*signature.parameters.keys()]

         lowercase__				:    Optional[Any]  =					['''pixel_values''']
         self.assertListEqual(arg_names[:1]	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[Any]:
        lowercase__				:    Optional[int]  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    str  =					self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*__lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  List[str]:
        # make the mask reproducible
        np.random.seed(2 )

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

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

        for model_class in self.all_model_classes:
         lowercase__				:    List[str]  =					model_class(__lowerCAmelCase )
         lowercase__				:    Tuple  =					self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase )

         lowercase__				:    Any  =					model(__lowerCAmelCase	,   noise=__lowerCAmelCase )

         lowercase__				:    Any  =					copy.deepcopy(self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase ) )
         lowercase__				:    Dict  =					model(**__lowerCAmelCase	,   noise=__lowerCAmelCase )
         lowercase__				:    str  =					outputs_dict[0].numpy()
         lowercase__				:    Optional[int]  =					outputs_keywords[0].numpy()

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

       def _lowerCAmelCase(						self )  ->  List[Any]:
        # make the mask reproducible
        np.random.seed(2 )

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

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

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

         return inputs_np_dict

        for model_class in self.all_model_classes:
         lowercase__				:    Optional[Any]  =					model_class(__lowerCAmelCase )

         lowercase__				:    Optional[int]  =					self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase )
         lowercase__				:    str  =					prepare_numpy_arrays(__lowerCAmelCase )

         lowercase__				:    Any  =					model(__lowerCAmelCase	,   noise=__lowerCAmelCase )
         lowercase__				:    Tuple  =					model(**__lowerCAmelCase	,   noise=__lowerCAmelCase )
         self.assert_outputs_same(__lowerCAmelCase	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        # make masks reproducible
        np.random.seed(2 )

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

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

        super().check_pt_tf_models(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        # make mask reproducible
        np.random.seed(2 )

        lowercase__ ,							lowercase__				:    Union[str, Any]  =					self.model_tester.prepare_config_and_inputs_for_common()

        lowercase__				:    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(__lowerCAmelCase )
            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(__lowerCAmelCase	,   __lowerCAmelCase ),)
            if isinstance(__lowerCAmelCase	,   __lowerCAmelCase )
            and tf.keras.layers.Layer in module_member.__bases__
            and getattr(__lowerCAmelCase	,   '''_keras_serializable'''	,   __lowerCAmelCase )
        }

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

        for main_layer_class in tf_main_layer_classes:
         lowercase__				:    Optional[int]  =					main_layer_class(__lowerCAmelCase )

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

         lowercase__				:    Any  =					tf.keras.Model(__lowerCAmelCase	,   outputs=main_layer(__lowerCAmelCase ) )
         lowercase__				:    Union[str, Any]  =					model(__lowerCAmelCase )

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

       @slow
       def _lowerCAmelCase(						self )  ->  List[str]:
        # make mask reproducible
        np.random.seed(2 )

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

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

        for model_class in self.all_model_classes:
         lowercase__				:    List[str]  =					model_class(__lowerCAmelCase )
         lowercase__				:    int  =					self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase )
         lowercase__				:    Optional[int]  =					model(__lowerCAmelCase	,   noise=__lowerCAmelCase )

         if model_class.__name__ == "TFViTMAEModel":
          lowercase__				:    List[Any]  =					outputs.last_hidden_state.numpy()
          lowercase__				:    Dict  =					0
         else:
          lowercase__				:    Tuple  =					outputs.logits.numpy()
          lowercase__				:    Dict  =					0

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

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

          lowercase__				:    Optional[Any]  =					np.amax(np.abs(out_a - out_a ) )
          self.assertLessEqual(__lowerCAmelCase	,   1E-5 )

       def _lowerCAmelCase(						self )  ->  Tuple:
        # make mask reproducible
        np.random.seed(2 )

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

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

        for model_class in self.all_model_classes:
         lowercase__				:    int  =					model_class(__lowerCAmelCase )
         lowercase__				:    Optional[Any]  =					self._prepare_for_class(__lowerCAmelCase	,   __lowerCAmelCase )

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

         self.assert_outputs_same(__lowerCAmelCase	,   __lowerCAmelCase )

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

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


       @slow
       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    Optional[int]  =					TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224''' )
        self.assertIsNotNone(__lowerCAmelCase )


def 			__UpperCamelCase       (   ):
 lowercase__				:    List[Any]  =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''     )
 return image





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





       '''simple docstring'''



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


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

        lowercase__				:    Dict  =					TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base''' )

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

        # forward pass
        lowercase__				:    Optional[int]  =					model(**__lowerCAmelCase	,   noise=__lowerCAmelCase )

        # verify the logits
        lowercase__				:    Any  =					tf.convert_to_tensor([1, 196, 768] )
        self.assertEqual(outputs.logits.shape	,   __lowerCAmelCase )

        lowercase__				:    Optional[int]  =					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]	,   __lowerCAmelCase	,   atol=1E-4 )


'''simple docstring'''

import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__a: Optional[int]          =   4
__a: Optional[Any]          =   3



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       pass


def 			__UpperCamelCase       (   UpperCAmelCase     ):
 for shard in shards:
  for i in range(UpperCAmelCase     ):
   yield {"i": i, "shard": shard}
def 			__UpperCamelCase       (   ):
 lowercase__				:    Tuple  =					int(os.environ['''RANK''']     )
 lowercase__				:    List[str]  =					int(os.environ['''WORLD_SIZE''']     )

 lowercase__				:    Optional[Any]  =					ArgumentParser()
 parser.add_argument('''--streaming'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--local_rank'''		,     type=UpperCAmelCase     )
 parser.add_argument('''--num_workers'''		,     type=UpperCAmelCase		,     default=0     )
 lowercase__				:    List[Any]  =					parser.parse_args()
 lowercase__				:    List[str]  =					args.streaming
 lowercase__				:    str  =					args.num_workers

 lowercase__				:    Optional[int]  =					{'''shards''': [F"""shard_{shard_idx}""" for shard_idx in range(UpperCAmelCase     )]}
 lowercase__				:    Tuple  =					IterableDataset.from_generator(UpperCAmelCase		,     gen_kwargs=UpperCAmelCase     )
 if not streaming:
  lowercase__				:    int  =					Dataset.from_list(list(UpperCAmelCase     )     )

 lowercase__				:    str  =					split_dataset_by_node(UpperCAmelCase		,     rank=UpperCAmelCase		,     world_size=UpperCAmelCase     )
 lowercase__				:    Optional[int]  =					torch.utils.data.DataLoader(UpperCAmelCase		,     num_workers=UpperCAmelCase     )

 lowercase__				:    Dict  =					NUM_SHARDS * NUM_ITEMS_PER_SHARD
 lowercase__				:    Optional[Any]  =					full_size // world_size
 expected_local_size += int(rank < (full_size % world_size)     )

 lowercase__				:    Union[str, Any]  =					sum(1 for _ in dataloader     )
 if local_size != expected_local_size:
  raise FailedTestError(F"""local_size {local_size} != expected_local_size {expected_local_size}"""     )


if __name__ == "__main__":
  main()

