
'''simple docstring'''
from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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 TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor







class   a_ :




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




			def 					lowerCAmelCase__					(       self	):
				a_      				=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

				a_      				=						None
				if self.use_labels:
					a_      				=						ids_tensor([self.batch_size]					,   self.num_labels	)

				a_      				=						self.get_config()
				return config, pixel_values, labels




			def 					lowerCAmelCase__					(       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					,   )




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						TFRegNetModel(config=UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase					,   training=UpperCAmelCase	)
				# expected last hidden states: B, C, H // 32, W // 32
				self.parent.assertEqual(
				    result.last_hidden_state.shape					,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)					,   )




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
				a_      				=						self.num_labels
				a_      				=						TFRegNetForImageClassification(UpperCAmelCase	)
				a_      				=						model(UpperCAmelCase					,   labels=UpperCAmelCase					,   training=UpperCAmelCase	)
				self.parent.assertEqual(result.logits.shape					,   (self.batch_size, self.num_labels)	)






			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.prepare_config_and_inputs()
				a_				,  a_				,  a_      				=						config_and_inputs
				a_      				=						{"""pixel_values""": pixel_values}
				return config, inputs_dict







@require_tf
class   a_ (      UpperCamelCase__		,  UpperCamelCase__		,  unittest.TestCase					):
			lowerCamelCase__	:			Dict       =    (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
			lowerCamelCase__	:			List[Any]       =    (
			    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__	:			Dict       =    False
			lowerCamelCase__	:			int       =    False
			lowerCamelCase__	:			int       =    False
			lowerCamelCase__	:			Optional[int]       =    False
			lowerCamelCase__	:			Tuple       =    False




			def 					lowerCAmelCase__					(       self	):
				a_      				=						TFRegNetModelTester(self	)
				a_      				=						ConfigTester(self					,   config_class=UpperCAmelCase					,   has_text_modality=UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				return




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




			@unittest.skipIf(
			    not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""	)	) == 0					,   reason="""TF does not support backprop for grouped convolutions on CPU."""					,   )
			@slow
			def 					lowerCAmelCase__					(       self	):
				super().test_keras_fit()




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




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()

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

					a_      				=						["""pixel_values"""]
					self.assertListEqual(arg_names[:1]					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				def check_hidden_states_output(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	):
					a_      				=						model_class(UpperCAmelCase	)
					a_      				=						model(**self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase	)					,   training=UpperCAmelCase	)

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

					a_      				=						self.model_tester.num_stages
					self.assertEqual(len(UpperCAmelCase	)					,   expected_num_stages + 1	)

					# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
					self.assertListEqual(
					    list(hidden_states[0].shape[-2:]	)					,   [self.model_tester.image_size // 2, self.model_tester.image_size // 2]					,   )

				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()
				a_      				=						["""basic""", """bottleneck"""]
				for model_class in self.all_model_classes:
					for layer_type in layers_type:
						a_      				=						layer_type
						a_      				=						True
						check_hidden_states_output(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

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

						check_hidden_states_output(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)




			def 					lowerCAmelCase__					(       self	):
				a_				,  a_      				=						self.model_tester.prepare_config_and_inputs_for_common()

				def check_equivalence(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase={}	):
					a_      				=						model(UpperCAmelCase					,   return_dict=UpperCAmelCase					,   **UpperCAmelCase	)
					a_      				=						model(UpperCAmelCase					,   return_dict=UpperCAmelCase					,   **UpperCAmelCase	).to_tuple()

					def recursive_check(UpperCAmelCase					,   UpperCAmelCase	):
						if isinstance(UpperCAmelCase					,   (List, Tuple)	):
							for tuple_iterable_value, dict_iterable_value in zip(UpperCAmelCase					,   UpperCAmelCase	):
								recursive_check(UpperCAmelCase					,   UpperCAmelCase	)
						elif tuple_object is None:
							return
						else:
							self.assertTrue(
							    all(tf.equal(UpperCAmelCase					,   UpperCAmelCase	)	)					,   msg=(
							        """Tuple and dict output are not equal. Difference:"""
							        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object	)	)}'''
							    )					,   )

						recursive_check(UpperCAmelCase					,   UpperCAmelCase	)

				for model_class in self.all_model_classes:
					a_      				=						model_class(UpperCAmelCase	)

					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase	)
					check_equivalence(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase					,   return_labels=UpperCAmelCase	)
					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase					,   return_labels=UpperCAmelCase	)
					check_equivalence(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase	)

					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase	)
					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase	)
					check_equivalence(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   {"""output_hidden_states""": True}	)

					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase					,   return_labels=UpperCAmelCase	)
					a_      				=						self._prepare_for_class(UpperCAmelCase					,   UpperCAmelCase					,   return_labels=UpperCAmelCase	)
					check_equivalence(UpperCAmelCase					,   UpperCAmelCase					,   UpperCAmelCase					,   {"""output_hidden_states""": True}	)




			def 					lowerCAmelCase__					(       self	):
				a_      				=						self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase	)






			@slow
			def 					lowerCAmelCase__					(       self	):
				for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					a_      				=						TFRegNetModel.from_pretrained(UpperCAmelCase	)
					self.assertIsNotNone(UpperCAmelCase	)







def      UpperCamelCase_				(      ):
	a_      				=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
	return image







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




			@cached_property
			def 					lowerCAmelCase__					(       self	):
				return (
				    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
				    if is_vision_available()
				    else None
				)






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)

				a_      				=						self.default_image_processor
				a_      				=						prepare_img()
				a_      				=						image_processor(images=UpperCAmelCase					,   return_tensors="""tf"""	)

				# forward pass
				a_      				=						model(**UpperCAmelCase					,   training=UpperCAmelCase	)

				# verify the logits
				a_      				=						tf.TensorShape((1, 10_00)	)
				self.assertEqual(outputs.logits.shape					,   UpperCAmelCase	)

				a_      				=						tf.constant([-0.41_80, -1.50_51, -3.48_36]	)

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


'''simple docstring'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)
