

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 _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Optional[Any]   ,   a				:			Optional[Any]   ,   a				:			Dict=3   ,   a				:			Optional[int]=32   ,   a				:			Any=3   ,   a				:			Optional[Any]=10   ,   a				:			List[str]=[10, 20, 30, 40]   ,   a				:			Dict=[1, 1, 2, 1]   ,   a				:			Dict=True   ,   a				:			int=True   ,   a				:			List[Any]="relu"   ,   a				:			List[str]=3   ,   a				:			List[Any]=None   ,   )				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							parent
				SCREAMING_SNAKE_CASE :   int					  =							batch_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							image_size
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							num_channels
				SCREAMING_SNAKE_CASE :   str					  =							embeddings_size
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							hidden_sizes
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							depths
				SCREAMING_SNAKE_CASE :   int					  =							is_training
				SCREAMING_SNAKE_CASE :   Dict					  =							use_labels
				SCREAMING_SNAKE_CASE :   Any					  =							hidden_act
				SCREAMING_SNAKE_CASE :   str					  =							num_labels
				SCREAMING_SNAKE_CASE :   int					  =							scope
				SCREAMING_SNAKE_CASE :   str					  =							len(a						)
			def 				__UpperCamelCase			(      self				:			int						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

				SCREAMING_SNAKE_CASE :   Dict					  =							None
				if self.use_labels:
					SCREAMING_SNAKE_CASE :   int					  =							ids_tensor([self.batch_size]   ,   self.num_labels						)

				SCREAMING_SNAKE_CASE :   str					  =							self.get_config()
				return config, pixel_values, labels
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							str:

				"""simple docstring"""

				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 				__UpperCamelCase			(      self				:			Optional[int]   ,   a				:			Optional[int]   ,   a				:			Optional[int]   ,   a				:			Tuple						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							TFRegNetModel(config=a						)
				SCREAMING_SNAKE_CASE :   int					  =							model(a   ,   training=a						)
				# 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 				__UpperCamelCase			(      self				:			Dict   ,   a				:			Any   ,   a				:			List[str]   ,   a				:			Optional[Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.num_labels
				SCREAMING_SNAKE_CASE :   Tuple					  =							TFRegNetForImageClassification(a						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							model(a   ,   labels=a   ,   training=a						)
				self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)						)






			def 				__UpperCamelCase			(      self				:			str						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   Tuple					  =							config_and_inputs
				SCREAMING_SNAKE_CASE :   Dict					  =							{"pixel_values": pixel_values}
				return config, inputs_dict






@require_tf
class _UpperCamelCase				(				__A				,						__A				,						unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
			lowerCamelCase__							     =(
			    {'feature-extraction': TFRegNetModel, 'image-classification': TFRegNetForImageClassification}
			    if is_tf_available()
			    else {}
			)

			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			lowerCamelCase__							     =False
			def 				__UpperCamelCase			(      self				:			int						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							TFRegNetModelTester(self						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							ConfigTester(self   ,   config_class=a   ,   has_text_modality=a						)
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[str]:

				"""simple docstring"""

				return
			@unittest.skip(reason="RegNet does not use inputs_embeds"						)
			def 				__UpperCamelCase			(      self				:			str						)				->							int:

				"""simple docstring"""

				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 				__UpperCamelCase			(      self				:			Dict						)				->							List[str]:

				"""simple docstring"""

				super().test_keras_fit()
			@unittest.skip(reason="RegNet does not support input and output embeddings"						)
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Optional[Any]:

				"""simple docstring"""

				pass
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

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

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

					SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							["pixel_values"]
					self.assertListEqual(arg_names[:1]   ,   a						)
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a						)
			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Union[str, Any]:

				"""simple docstring"""

				def check_hidden_states_output(a				:			Dict   ,   a				:			Optional[int]   ,   a				:			Any						):
					SCREAMING_SNAKE_CASE :   str					  =							model_class(a						)
					SCREAMING_SNAKE_CASE :   str					  =							model(**self._prepare_for_class(a   ,   a						)   ,   training=a						)

					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.num_stages
					self.assertEqual(len(a						)   ,   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]   ,   )

				SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE :   List[str]					  =							self.model_tester.prepare_config_and_inputs_for_common()
				SCREAMING_SNAKE_CASE :   int					  =							["basic", "bottleneck"]
				for model_class in self.all_model_classes:
					for layer_type in layers_type:
						SCREAMING_SNAKE_CASE :   int					  =							layer_type
						SCREAMING_SNAKE_CASE :   Optional[Any]					  =							True
						check_hidden_states_output(a   ,   a   ,   a						)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						SCREAMING_SNAKE_CASE :   List[Any]					  =							True

						check_hidden_states_output(a   ,   a   ,   a						)
			def 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

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

				def check_equivalence(a				:			int   ,   a				:			Union[str, Any]   ,   a				:			int   ,   a				:			Dict={}						):
					SCREAMING_SNAKE_CASE :   Optional[Any]					  =							model(a   ,   return_dict=a   ,   **a						)
					SCREAMING_SNAKE_CASE :   Any					  =							model(a   ,   return_dict=a   ,   **a						).to_tuple()

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

						recursive_check(a   ,   a						)

				for model_class in self.all_model_classes:
					SCREAMING_SNAKE_CASE :   int					  =							model_class(a						)

					SCREAMING_SNAKE_CASE :   List[str]					  =							self._prepare_for_class(a   ,   a						)
					SCREAMING_SNAKE_CASE :   int					  =							self._prepare_for_class(a   ,   a						)
					check_equivalence(a   ,   a   ,   a						)

					SCREAMING_SNAKE_CASE :   Dict					  =							self._prepare_for_class(a   ,   a   ,   return_labels=a						)
					SCREAMING_SNAKE_CASE :   List[Any]					  =							self._prepare_for_class(a   ,   a   ,   return_labels=a						)
					check_equivalence(a   ,   a   ,   a						)

					SCREAMING_SNAKE_CASE :   str					  =							self._prepare_for_class(a   ,   a						)
					SCREAMING_SNAKE_CASE :   List[str]					  =							self._prepare_for_class(a   ,   a						)
					check_equivalence(a   ,   a   ,   a   ,   {"output_hidden_states": True}						)

					SCREAMING_SNAKE_CASE :   int					  =							self._prepare_for_class(a   ,   a   ,   return_labels=a						)
					SCREAMING_SNAKE_CASE :   Dict					  =							self._prepare_for_class(a   ,   a   ,   return_labels=a						)
					check_equivalence(a   ,   a   ,   a   ,   {"output_hidden_states": True}						)
			def 				__UpperCamelCase			(      self				:			int						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a						)






			@slow
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							str:

				"""simple docstring"""

				for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					SCREAMING_SNAKE_CASE :   str					  =							TFRegNetModel.from_pretrained(a						)
					self.assertIsNotNone(a						)

def 			lowerCamelCase__			(       ):
	SCREAMING_SNAKE_CASE :   List[str]					  =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
	return image






@require_tf
@require_vision
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			@cached_property
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				return (
				    AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
				    if is_vision_available()
				    else None
				)






			@slow
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							self.default_image_processor
				SCREAMING_SNAKE_CASE :   List[str]					  =							prepare_img()
				SCREAMING_SNAKE_CASE :   Any					  =							image_processor(images=a   ,   return_tensors="tf"						)

				# forward pass
				SCREAMING_SNAKE_CASE :   Tuple					  =							model(**a   ,   training=a						)

				# verify the logits
				SCREAMING_SNAKE_CASE :   int					  =							tf.TensorShape((1, 1000)						)
				self.assertEqual(outputs.logits.shape   ,   a						)

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							tf.constant([-0.4180, -1.5051, -3.4836]						)

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


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     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.',
			)

			a_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

			a_      =     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)