

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 SCREAMING_SNAKE_CASE  :






			"""simple docstring"""




			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 ,				):

				"""simple docstring"""


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

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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

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

				snake_case_      		= 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=__UpperCamelCase ,				initializer_range=self.initializer_range ,				mask_ratio=self.mask_ratio ,				)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


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

				# test greyscale images
				snake_case_      		= 1
				snake_case_      		= TFViTMAEForPreTraining(__UpperCamelCase							)

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







			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.prepare_config_and_inputs()
				((snake_case_)		,       (snake_case_)		,       (snake_case_))      		= config_and_inputs
				snake_case_      		= {'pixel_values': pixel_values}
				return config, inputs_dict







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






			"""simple docstring"""




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

			__A						  =  False
			__A						  =  False
			__A						  =  False
			__A						  =  False


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= TFViTMAEModelTester(self							)
				snake_case_      		= ConfigTester(self ,				config_class=__UpperCamelCase ,				has_text_modality=__UpperCamelCase ,				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"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

					snake_case_      		= ['pixel_values']
					self.assertListEqual(arg_names[:1] ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_pretraining(*__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				np.random.seed(2							)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(__UpperCamelCase							)
					snake_case_      		= self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)

					snake_case_      		= model(__UpperCamelCase ,				noise=__UpperCamelCase							)

					snake_case_      		= copy.deepcopy(self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)							)
					snake_case_      		= model(**__UpperCamelCase ,				noise=__UpperCamelCase							)
					snake_case_      		= outputs_dict[0].numpy()
					snake_case_      		= 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							)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

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

					return inputs_np_dict

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(__UpperCamelCase							)

					snake_case_      		= self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)
					snake_case_      		= prepare_numpy_arrays(__UpperCamelCase							)

					snake_case_      		= model(__UpperCamelCase ,				noise=__UpperCamelCase							)
					snake_case_      		= model(**__UpperCamelCase ,				noise=__UpperCamelCase							)
					self.assert_outputs_same(__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):

				"""simple docstring"""


				np.random.seed(2							)

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

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

				super().check_pt_tf_models(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				np.random.seed(2							)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

				snake_case_      		= {
				    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							)
				}

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

				for main_layer_class in tf_main_layer_classes:
					snake_case_      		= main_layer_class(__UpperCamelCase							)

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

					snake_case_      		= tf.keras.Model(__UpperCamelCase ,				outputs=main_layer(__UpperCamelCase							)							)
					snake_case_      		= model(__UpperCamelCase							)

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


			@slow
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				np.random.seed(2							)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(__UpperCamelCase							)
					snake_case_      		= self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)
					snake_case_      		= model(__UpperCamelCase ,				noise=__UpperCamelCase							)

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

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

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

						snake_case_      		= np.amax(np.abs(out_a - out_a							)							)
						self.assertLessEqual(__UpperCamelCase ,				1E-5							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				np.random.seed(2							)

				snake_case_		,       snake_case_      		= self.model_tester.prepare_config_and_inputs_for_common()

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

				for model_class in self.all_model_classes:
					snake_case_      		= model_class(__UpperCamelCase							)
					snake_case_      		= self._prepare_for_class(__UpperCamelCase ,				__UpperCamelCase							)

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


				snake_case_      		= TFViTMAEModel.from_pretrained('google/vit-base-patch16-224'							)
				self.assertIsNotNone(__UpperCamelCase							)







def a():





	'''simple docstring'''
	snake_case_      		= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image






@require_tf
@require_vision
class SCREAMING_SNAKE_CASE  (		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							)

				snake_case_      		= TFViTMAEForPreTraining.from_pretrained('facebook/vit-mae-base'							)

				snake_case_      		= self.default_image_processor
				snake_case_      		= prepare_img()
				snake_case_      		= 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)
				snake_case_      		= ViTMAEConfig()
				snake_case_      		= int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
				snake_case_      		= np.random.uniform(size=(1, num_patches)							)

				# forward pass
				snake_case_      		= model(**__UpperCamelCase ,				noise=__UpperCamelCase							)

				# verify the logits
				snake_case_      		= tf.convert_to_tensor([1, 1_96, 7_68]							)
				self.assertEqual(outputs.logits.shape ,				__UpperCamelCase							)

				snake_case_      		= 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							)




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def a(lowercase__			, lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= 1.5
	snake_case_      		= int(factor * num_class_images	)
	snake_case_      		= ClipClient(
	    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1	)

	os.makedirs(f"""{class_data_dir}/images"""			, exist_ok=lowercase__	)
	if len(list(Path(f"""{class_data_dir}/images"""	).iterdir()	)	) >= num_class_images:
		return

	while True:
		snake_case_      		= client.query(text=lowercase__	)
		if len(lowercase__	) >= factor * num_class_images or num_images > 1e4:
			break
		else:
			snake_case_      		= int(factor * num_images	)
			snake_case_      		= ClipClient(
			    url='https://knn.laion.ai/knn-service'			, indice_name='laion_400m'			, num_images=lowercase__			, aesthetic_weight=0.1			, )

	snake_case_      		= 0
	snake_case_      		= 0
	snake_case_      		= tqdm(desc='downloading real regularization images'			, total=lowercase__	)

	with open(f"""{class_data_dir}/caption.txt"""			, 'w'	) as fa, open(f"""{class_data_dir}/urls.txt"""			, 'w'	) as fa, open(
	    f"""{class_data_dir}/images.txt"""			, 'w'	) as fa:
		while total < num_class_images:
			snake_case_      		= class_images[count]
			count += 1
			try:
				snake_case_      		= requests.get(images['url']	)
				if img.status_code == 200:
					snake_case_      		= Image.open(BytesIO(img.content	)	)
					with open(f"""{class_data_dir}/images/{total}.jpg"""			, 'wb'	) as f:
						f.write(img.content	)
					fa.write(images['caption'] + '\n'	)
					fa.write(images['url'] + '\n'	)
					fa.write(f"""{class_data_dir}/images/{total}.jpg""" + '\n'	)
					total += 1
					pbar.update(1	)
				else:
					continue
			except Exception:
				continue
	return





def a():





	'''simple docstring'''
	snake_case_      		= argparse.ArgumentParser(''			, add_help=lowercase__	)
	parser.add_argument('--class_prompt'			, help='text prompt to retrieve images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--class_data_dir'			, help='path to save images'			, required=lowercase__			, type=lowercase__	)
	parser.add_argument('--num_class_images'			, help='number of images to download'			, default=200			, type=lowercase__	)
	return parser.parse_args()


if __name__ == "__main__":
		A											=     parse_args()
		retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
