
import inspect
import unittest

from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel

if is_vision_available():
	from PIL import Image

	from transformers import AutoImageProcessor







class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	def __init__(       self		,    snake_case		,    snake_case=3		,    snake_case=32		,    snake_case=3		,    snake_case=10		,    snake_case=[10, 20, 30, 40]		,    snake_case=[1, 1, 2, 1]		,    snake_case=True		,    snake_case=True		,    snake_case="relu"		,    snake_case=3		,    snake_case=None		,    ):
		lowercase       =    parent
		lowercase       =    batch_size
		lowercase       =    image_size
		lowercase       =    num_channels
		lowercase       =    embeddings_size
		lowercase       =    hidden_sizes
		lowercase       =    depths
		lowercase       =    is_training
		lowercase       =    use_labels
		lowercase       =    hidden_act
		lowercase       =    num_labels
		lowercase       =    scope
		lowercase       =    len(snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

		lowercase       =    self.get_config()

		return config, pixel_values





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return RegNetConfig(
		    num_channels=self.num_channels		,    embeddings_size=self.embeddings_size		,    hidden_sizes=self.hidden_sizes		,    depths=self.depths		,    hidden_act=self.hidden_act		,    num_labels=self.num_labels		,    image_size=self.image_size		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    FlaxRegNetModel(config=snake_case       )
		lowercase       =    model(snake_case       )

		# Output shape (b, c, h, w)
		self.parent.assertEqual(
		    result.last_hidden_state.shape		,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case       ):
		lowercase       =    self.num_labels
		lowercase       =    FlaxRegNetForImageClassification(config=snake_case       )
		lowercase       =    model(snake_case       )
		self.parent.assertEqual(result.logits.shape		,    (self.batch_size, self.num_labels)       )





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







@require_flax
class        A_   (			__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()

	_UpperCamelCase     :     str							 =       False
	_UpperCamelCase     :     str							 =       False
	_UpperCamelCase     :     Tuple							 =       False





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    FlaxRegNetModelTester(self       )
		lowercase       =    ConfigTester(self		,    config_class=snake_case		,    has_text_modality=snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.create_and_test_config_common_properties()
		self.config_tester.create_and_test_config_to_json_string()
		self.config_tester.create_and_test_config_to_json_file()
		self.config_tester.create_and_test_config_from_and_save_pretrained()
		self.config_tester.create_and_test_config_with_num_labels()
		self.config_tester.check_config_can_be_init_without_params()
		self.config_tester.check_config_arguments_init()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*snake_case       )





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





	@unittest.skip(reason='RegNet does not support input and output embeddings'       )
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

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

			lowercase       =    ['pixel_values']
			self.assertListEqual(arg_names[:1]		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		def check_hidden_states_output(snake_case		,    snake_case		,    snake_case       ):
			lowercase       =    model_class(snake_case       )

			lowercase       =    model(**self._prepare_for_class(snake_case		,    snake_case       )       )

			lowercase       =    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

			lowercase       =    self.model_tester.num_stages
			self.assertEqual(len(snake_case       )		,    expected_num_stages + 1       )

		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			lowercase       =    True
			check_hidden_states_output(snake_case		,    snake_case		,    snake_case       )

			# check that output_hidden_states also work using config
			del inputs_dict["output_hidden_states"]
			lowercase       =    True

			check_hidden_states_output(snake_case		,    snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase  ,					lowercase       =    self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__       ):
				lowercase       =    self._prepare_for_class(snake_case		,    snake_case       )
				lowercase       =    model_class(snake_case       )

				@jax.jit
				def model_jitted(snake_case		,    **snake_case       ):
					return model(pixel_values=snake_case		,    **snake_case       )

				with self.subTest('JIT Enabled'       ):
					lowercase       =    model_jitted(**snake_case       ).to_tuple()

				with self.subTest('JIT Disabled'       ):
					with jax.disable_jit():
						lowercase       =    model_jitted(**snake_case       ).to_tuple()

				self.assertEqual(len(snake_case       )		,    len(snake_case       )       )
				for jitted_output, output in zip(snake_case		,    snake_case       ):
					self.assertEqual(jitted_output.shape		,    output.shape       )






def 							UpperCAmelCase_		(							):
	lowercase       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







@require_flax
class        A_   (			unittest.TestCase   ):




	'''simple docstring'''


	@cached_property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return AutoImageProcessor.from_pretrained('facebook/regnet-y-040'       ) if is_vision_available() else None





	@slow
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    FlaxRegNetForImageClassification.from_pretrained('facebook/regnet-y-040'       )

		lowercase       =    self.default_image_processor
		lowercase       =    prepare_img()
		lowercase       =    image_processor(images=snake_case		,    return_tensors='np'       )

		lowercase       =    model(**snake_case       )

		# verify the logits
		lowercase       =    (1, 1000)
		self.assertEqual(outputs.logits.shape		,    snake_case       )

		lowercase       =    jnp.array([-0.4_180, -1.5_051, -3.4_836]       )

		self.assertTrue(jnp.allclose(outputs.logits[0, :3]		,    snake_case		,    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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    1.5
	lowercase       =    int(factor * num_class_images							)
	lowercase       =    ClipClient(
	    url='https://knn.laion.ai/knn-service' ,							indice_name='laion_400m' ,							num_images=__SCREAMING_SNAKE_CASE ,							aesthetic_weight=0.1							)

	os.makedirs(F'''{class_data_dir}/images''' ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	if len(list(Path(F'''{class_data_dir}/images'''							).iterdir()							)							) >= num_class_images:
		return

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

	lowercase       =    0
	lowercase       =    0
	lowercase       =    tqdm(desc='downloading real regularization images' ,							total=__SCREAMING_SNAKE_CASE							)

	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:
			lowercase       =    class_images[count]
			count += 1
			try:
				lowercase       =    requests.get(images['url']							)
				if img.status_code == 200:
					lowercase       =    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 							UpperCAmelCase_		(							):
	lowercase       =    argparse.ArgumentParser('' ,							add_help=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_prompt' ,							help='text prompt to retrieve images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--class_data_dir' ,							help='path to save images' ,							required=__SCREAMING_SNAKE_CASE ,							type=__SCREAMING_SNAKE_CASE							)
	parser.add_argument('--num_class_images' ,							help='number of images to download' ,							default=200 ,							type=__SCREAMING_SNAKE_CASE							)
	return parser.parse_args()


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

