

"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


a    :Any			=       False

class    __a							(unittest.TestCase):






     '''simple docstring'''

     def     _a	(  self     )     ->					int:


           """simple docstring"""


           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     @property
     def     _a	(  self     )     ->					List[str]:


           """simple docstring"""


           return 12

     @property
     def     _a	(  self     )     ->					str:


           """simple docstring"""


           return 12

     @property
     def     _a	(  self     )     ->					Optional[int]:


           """simple docstring"""


           return 32

     @property
     def     _a	(  self     )     ->					Optional[Any]:


           """simple docstring"""


           torch.manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		VQModel(
               block_out_channels=[32, 64]		,			in_channels=3		,			out_channels=3		,			down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]		,			up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]		,			latent_channels=3		,			num_vq_embeddings=self.num_embed		,			vq_embed_dim=3		,			)
           return model

     @property
     def     _a	(  self     )     ->					int:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					str											=		CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )
           return tokenizer

     @property
     def     _a	(  self     )     ->					List[Any]:


           """simple docstring"""


           torch.manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		CLIPTextConfig(
               bos_token_id=0		,			eos_token_id=2		,			hidden_size=self.text_embedder_hidden_size		,			intermediate_size=37		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_000		,			)
           return CLIPTextModel(__a     )

     @property
     def     _a	(  self     )     ->					int:


           """simple docstring"""


           torch.manual_seed(0     )

           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		12
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		12

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		{
               """attention_bias""": True,
               """cross_attention_dim""": 32,
               """attention_head_dim""": height * width,
               """num_attention_heads""": 1,
               """num_vector_embeds""": self.num_embed,
               """num_embeds_ada_norm""": self.num_embeds_ada_norm,
               """norm_num_groups""": 32,
               """sample_size""": width,
               """activation_fn""": """geglu-approximate""",
           }

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		TransformeraDModel(**__a     )
           return model

     def     _a	(  self     )     ->					Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		"""cpu"""

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		self.dummy_vqvae
           SCREAMING_SNAKE_CASE__      :					Any											=		self.dummy_text_encoder
           SCREAMING_SNAKE_CASE__      :					str											=		self.dummy_tokenizer
           SCREAMING_SNAKE_CASE__      :					List[str]											=		self.dummy_transformer
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		VQDiffusionScheduler(self.num_embed     )
           SCREAMING_SNAKE_CASE__      :					List[str]											=		LearnedClassifierFreeSamplingEmbeddings(learnable=__a     )

           SCREAMING_SNAKE_CASE__      :					Dict											=		VQDiffusionPipeline(
               vqvae=__a		,			text_encoder=__a		,			tokenizer=__a		,			transformer=__a		,			scheduler=__a		,			learned_classifier_free_sampling_embeddings=__a		,			)
           SCREAMING_SNAKE_CASE__      :					Optional[Any]											=		pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )

           SCREAMING_SNAKE_CASE__      :					Tuple											=		"""teddy bear playing in the pool"""

           SCREAMING_SNAKE_CASE__      :					int											=		torch.Generator(device=__a     ).manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Dict											=		pipe([prompt]		,			generator=__a		,			num_inference_steps=2		,			output_type="""np"""     )
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		output.images

           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		torch.Generator(device=__a     ).manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		pipe(
               [prompt]		,			generator=__a		,			output_type="""np"""		,			return_dict=__a		,			num_inference_steps=2     )[0]

           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 24, 24, 3)

           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]     )

           assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2





     def     _a	(  self     )     ->					Any:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		"""cpu"""

           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.dummy_vqvae
           SCREAMING_SNAKE_CASE__      :					Dict											=		self.dummy_text_encoder
           SCREAMING_SNAKE_CASE__      :					List[Any]											=		self.dummy_tokenizer
           SCREAMING_SNAKE_CASE__      :					Tuple											=		self.dummy_transformer
           SCREAMING_SNAKE_CASE__      :					str											=		VQDiffusionScheduler(self.num_embed     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		LearnedClassifierFreeSamplingEmbeddings(
               learnable=__a		,			hidden_size=self.text_embedder_hidden_size		,			length=tokenizer.model_max_length     )

           SCREAMING_SNAKE_CASE__      :					str											=		VQDiffusionPipeline(
               vqvae=__a		,			text_encoder=__a		,			tokenizer=__a		,			transformer=__a		,			scheduler=__a		,			learned_classifier_free_sampling_embeddings=__a		,			)
           SCREAMING_SNAKE_CASE__      :					Union[str, Any]											=		pipe.to(__a     )
           pipe.set_progress_bar_config(disable=__a     )

           SCREAMING_SNAKE_CASE__      :					Dict											=		"""teddy bear playing in the pool"""

           SCREAMING_SNAKE_CASE__      :					Any											=		torch.Generator(device=__a     ).manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Any											=		pipe([prompt]		,			generator=__a		,			num_inference_steps=2		,			output_type="""np"""     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		output.images

           SCREAMING_SNAKE_CASE__      :					Dict											=		torch.Generator(device=__a     ).manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		pipe(
               [prompt]		,			generator=__a		,			output_type="""np"""		,			return_dict=__a		,			num_inference_steps=2     )[0]

           SCREAMING_SNAKE_CASE__      :					List[Any]											=		image[0, -3:, -3:, -1]
           SCREAMING_SNAKE_CASE__      :					int											=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 24, 24, 3)

           SCREAMING_SNAKE_CASE__      :					str											=		np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]     )

           assert np.abs(image_slice.flatten() - expected_slice     ).max() < 2.0
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < 1E-2





@slow
@require_torch_gpu
class    __a							(unittest.TestCase):






     '''simple docstring'''

     def     _a	(  self     )     ->					Optional[int]:


           """simple docstring"""


           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()





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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__      :					Any											=		load_numpy(
               """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
               """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""     )

           SCREAMING_SNAKE_CASE__      :					str											=		VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""     )
           SCREAMING_SNAKE_CASE__      :					Optional[int]											=		pipeline.to(__a     )
           pipeline.set_progress_bar_config(disable=__a     )

           # requires GPU generator for gumbel softmax
           # don't use GPU generator in tests though
           SCREAMING_SNAKE_CASE__      :					Any											=		torch.Generator(device=__a     ).manual_seed(0     )
           SCREAMING_SNAKE_CASE__      :					Tuple											=		pipeline(
               """teddy bear playing in the pool"""		,			num_images_per_prompt=1		,			generator=__a		,			output_type="""np"""		,			)

           SCREAMING_SNAKE_CASE__      :					Any											=		output.images[0]

           assert image.shape == (256, 256, 3)
           assert np.abs(expected_image - image     ).max() < 2.0








"""simple docstring"""


import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							def __init__(     self    ,   __a    ,   __a=None    ,   __a=True    ,   __a=None    ,   **__a	):
										__lowerCAmelCase				    =			parent
										__lowerCAmelCase				    =			config_class
										__lowerCAmelCase				    =			has_text_modality
										__lowerCAmelCase				    =			kwargs
										__lowerCAmelCase				    =			common_properties

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			(
										    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
										    if self.common_properties is None
										    else self.common_properties
										)

										# Add common fields for text models
										if self.has_text_modality:
													common_properties.extend(["vocab_size"]	)

										# Test that config has the common properties as getters
										for prop in common_properties:
													self.parent.assertTrue(hasattr(__a    ,   __a	)    ,   msg=f"`{prop}` does not exist"	)

										# Test that config has the common properties as setter
										for idx, name in enumerate(__a	):
													try:
																setattr(__a    ,   __a    ,   __a	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

        # Test if config class can be called with Config(prop_name=..)
										for idx, name in enumerate(__a	):
													try:
																__lowerCAmelCase				    =			self.config_class(**{name: idx}	)
																self.parent.assertEqual(
																    getattr(__a    ,   __a	)    ,   __a    ,   msg=f"`{name} value {idx} expected, but was {getattr(__a    ,   __a	)}"	)
													except NotImplementedError:
																# Some models might not be able to implement setters for common_properties
																# In that case, a NotImplementedError is raised
																pass

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)
										__lowerCAmelCase				    =			json.loads(config.to_json_string()	)
										for key, value in self.inputs_dict.items():
													self.parent.assertEqual(obj[key]    ,   __a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   "config.json"	)
													config_first.to_json_file(__a	)
													__lowerCAmelCase				    =			self.config_class.from_json_file(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										with tempfile.TemporaryDirectory() as tmpdirname:
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict	)

										__lowerCAmelCase				    =			"test"
										with tempfile.TemporaryDirectory() as tmpdirname:
													__lowerCAmelCase				    =			os.path.join(__a    ,   __a	)
													config_first.save_pretrained(__a	)
													__lowerCAmelCase				    =			self.config_class.from_pretrained(__a    ,   subfolder=__a	)

										self.parent.assertEqual(config_second.to_dict()    ,   config_first.to_dict()	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			self.config_class(**self.inputs_dict    ,   num_labels=5	)
										self.parent.assertEqual(len(config.idalabel	)    ,   5	)
										self.parent.assertEqual(len(config.labelaid	)    ,   5	)

										__lowerCAmelCase				    =			3
										self.parent.assertEqual(len(config.idalabel	)    ,   3	)
										self.parent.assertEqual(len(config.labelaid	)    ,   3	)

							def 			snake_case				(     self	):
										if self.config_class.is_composition:
													return
										__lowerCAmelCase				    =			self.config_class()
										self.parent.assertIsNotNone(__a	)

							def 			snake_case				(     self	):
										__lowerCAmelCase				    =			copy.deepcopy(__a	)
										__lowerCAmelCase				    =			self.config_class(**__a	)
										__lowerCAmelCase				    =			[]
										for key, value in config_common_kwargs.items():
													if key == "torch_dtype":
																if not is_torch_available():
																			continue
																else:
																			import torch

																			if config.torch_dtype != torch.floataa:
																						wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa)	)
													elif getattr(__a    ,   __a	) != value:
																wrong_values.append((key, getattr(__a    ,   __a	), value)	)

										if len(__a	) > 0:
													__lowerCAmelCase				    =			"\n".join([f"- {v[0]}: got {v[1]} instead of {v[2]}" for v in wrong_values]	)
													raise ValueError(f"The following keys were not properly set in the config:\n{errors}"	)



							def 			snake_case				(     self	):
										self.create_and_test_config_common_properties()
										self.create_and_test_config_to_json_string()
										self.create_and_test_config_to_json_file()
										self.create_and_test_config_from_and_save_pretrained()
										self.create_and_test_config_from_and_save_pretrained_subfolder()
										self.create_and_test_config_with_num_labels()
										self.check_config_can_be_init_without_params()
										self.check_config_arguments_init()




