
"""simple docstring"""


import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 	lowerCamelCase__      (      __magic_name__				,			unittest.TestCase       ):





			'''simple docstring'''
			lowerCamelCase						      =	KandinskyPipeline
			lowerCamelCase						      =	[
			    '''prompt''',
			    '''image_embeds''',
			    '''negative_image_embeds''',
			]
			lowerCamelCase						      =	['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
			lowerCamelCase						      =	[
			    '''generator''',
			    '''height''',
			    '''width''',
			    '''latents''',
			    '''guidance_scale''',
			    '''negative_prompt''',
			    '''num_inference_steps''',
			    '''return_dict''',
			    '''guidance_scale''',
			    '''num_images_per_prompt''',
			    '''output_type''',
			    '''return_dict''',
			]
			lowerCamelCase						      =	False



			@property
			def       _lowerCAmelCase	(							self						)				->  str:
				return 32



			@property
			def       _lowerCAmelCase	(							self						)				->  Any:
				return 32



			@property
			def       _lowerCAmelCase	(							self						)				->  List[Any]:
				return self.time_input_dim



			@property
			def       _lowerCAmelCase	(							self						)				->  Optional[int]:
				return self.time_input_dim * 4



			@property
			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				return 1_00



			@property
			def       _lowerCAmelCase	(							self						)				->  int:
				_lowerCAmelCase			=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""						)
				return tokenizer



			@property
			def       _lowerCAmelCase	(							self						)				->  List[str]:
				torch.manual_seed(0						)
				_lowerCAmelCase			=MCLIPConfig(
				    numDims=self.cross_attention_dim  ,  transformerDimensions=self.text_embedder_hidden_size  ,  hidden_size=self.text_embedder_hidden_size  ,  intermediate_size=37  ,  num_attention_heads=4  ,  num_hidden_layers=5  ,  vocab_size=10_05  ,  )

				_lowerCAmelCase			=MultilingualCLIP(__UpperCAmelCase						)
				_lowerCAmelCase			=text_encoder.eval()

				return text_encoder



			@property
			def       _lowerCAmelCase	(							self						)				->  int:
				torch.manual_seed(0						)

				_lowerCAmelCase			={
				    """in_channels""": 4,
				    # Out channels is double in channels because predicts mean and variance
				    """out_channels""": 8,
				    """addition_embed_type""": """text_image""",
				    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
				    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
				    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
				    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    """layers_per_block""": 1,
				    """encoder_hid_dim""": self.text_embedder_hidden_size,
				    """encoder_hid_dim_type""": """text_image_proj""",
				    """cross_attention_dim""": self.cross_attention_dim,
				    """attention_head_dim""": 4,
				    """resnet_time_scale_shift""": """scale_shift""",
				    """class_embed_type""": None,
				}

				_lowerCAmelCase			=UNetaDConditionModel(**__UpperCAmelCase						)
				return model



			@property
			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				return {
				    "block_out_channels": [32, 64],
				    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
				    "in_channels": 3,
				    "latent_channels": 4,
				    "layers_per_block": 1,
				    "norm_num_groups": 8,
				    "norm_type": "spatial",
				    "num_vq_embeddings": 12,
				    "out_channels": 3,
				    "up_block_types": [
				        "AttnUpDecoderBlock2D",
				        "UpDecoderBlock2D",
				    ],
				    "vq_embed_dim": 4,
				}



			@property
			def       _lowerCAmelCase	(							self						)				->  List[str]:
				torch.manual_seed(0						)
				_lowerCAmelCase			=VQModel(**self.dummy_movq_kwargs						)
				return model



			def       _lowerCAmelCase	(							self						)				->  Union[str, Any]:
				_lowerCAmelCase			=self.dummy_text_encoder
				_lowerCAmelCase			=self.dummy_tokenizer
				_lowerCAmelCase			=self.dummy_unet
				_lowerCAmelCase			=self.dummy_movq

				_lowerCAmelCase			=DDIMScheduler(
				    num_train_timesteps=10_00  ,  beta_schedule="""linear"""  ,  beta_start=0.0_0_0_8_5  ,  beta_end=0.0_1_2  ,  clip_sample=__UpperCAmelCase  ,  set_alpha_to_one=__UpperCAmelCase  ,  steps_offset=1  ,  prediction_type="""epsilon"""  ,  thresholding=__UpperCAmelCase  ,  )

				_lowerCAmelCase			={
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """unet""": unet,
				    """scheduler""": scheduler,
				    """movq""": movq,
				}
				return components



			def       _lowerCAmelCase	(							self  ,  __UpperCAmelCase  ,  __UpperCAmelCase=0						)				->  Any:
				_lowerCAmelCase			=floats_tensor((1, self.cross_attention_dim)  ,  rng=random.Random(__UpperCAmelCase						)						).to(__UpperCAmelCase						)
				_lowerCAmelCase			=floats_tensor((1, self.cross_attention_dim)  ,  rng=random.Random(seed + 1						)						).to(__UpperCAmelCase						)
				if str(__UpperCAmelCase						).startswith("""mps"""						):
					_lowerCAmelCase			=torch.manual_seed(__UpperCAmelCase						)
				else:
					_lowerCAmelCase			=torch.Generator(device=__UpperCAmelCase						).manual_seed(__UpperCAmelCase						)
				_lowerCAmelCase			={
				    """prompt""": """horse""",
				    """image_embeds""": image_embeds,
				    """negative_image_embeds""": negative_image_embeds,
				    """generator""": generator,
				    """height""": 64,
				    """width""": 64,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs



			def       _lowerCAmelCase	(							self						)				->  Any:
				_lowerCAmelCase			="""cpu"""

				_lowerCAmelCase			=self.get_dummy_components()

				_lowerCAmelCase			=self.pipeline_class(**__UpperCAmelCase						)
				_lowerCAmelCase			=pipe.to(__UpperCAmelCase						)

				pipe.set_progress_bar_config(disable=__UpperCAmelCase						)

				_lowerCAmelCase			=pipe(**self.get_dummy_inputs(__UpperCAmelCase						)						)
				_lowerCAmelCase			=output.images

				_lowerCAmelCase			=pipe(
				    **self.get_dummy_inputs(__UpperCAmelCase						)  ,  return_dict=__UpperCAmelCase  ,  )[0]

				_lowerCAmelCase			=image[0, -3:, -3:, -1]
				_lowerCAmelCase			=image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)

				_lowerCAmelCase			=np.array(
				    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]						)

				assert (
				    np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2
				), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
				assert (
				    np.abs(image_from_tuple_slice.flatten() - expected_slice						).max() < 1e-2
				), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







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





			'''simple docstring'''



			def       _lowerCAmelCase	(							self						)				->  Dict:
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



			def       _lowerCAmelCase	(							self						)				->  List[str]:
				_lowerCAmelCase			=load_numpy(
				    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
				    """/kandinsky/kandinsky_text2img_cat_fp16.npy"""						)

				_lowerCAmelCase			=KandinskyPriorPipeline.from_pretrained(
				    """kandinsky-community/kandinsky-2-1-prior"""  ,  torch_dtype=torch.floataa						)
				pipe_prior.to(__UpperCAmelCase						)

				_lowerCAmelCase			=KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""  ,  torch_dtype=torch.floataa						)
				_lowerCAmelCase			=pipeline.to(__UpperCAmelCase						)
				pipeline.set_progress_bar_config(disable=__UpperCAmelCase						)

				_lowerCAmelCase			="""red cat, 4k photo"""

				_lowerCAmelCase			=torch.Generator(device="""cuda"""						).manual_seed(0						)
				_lowerCAmelCase		,							_lowerCAmelCase			=pipe_prior(
				    __UpperCAmelCase  ,  generator=__UpperCAmelCase  ,  num_inference_steps=5  ,  negative_prompt=""""""  ,  ).to_tuple()

				_lowerCAmelCase			=torch.Generator(device="""cuda"""						).manual_seed(0						)
				_lowerCAmelCase			=pipeline(
				    __UpperCAmelCase  ,  image_embeds=__UpperCAmelCase  ,  negative_image_embeds=__UpperCAmelCase  ,  generator=__UpperCAmelCase  ,  num_inference_steps=1_00  ,  output_type="""np"""  ,  )

				_lowerCAmelCase			=output.images[0]

				assert image.shape == (5_12, 5_12, 3)

				assert_mean_pixel_difference(__UpperCAmelCase  ,  __UpperCAmelCase						)





"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__A 		= {
    'configuration_convnextv2': [
        'CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConvNextV2Config',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A 		= [
			    'CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConvNextV2ForImageClassification',
			    'ConvNextV2Model',
			    'ConvNextV2PreTrainedModel',
			    'ConvNextV2Backbone',
			]


if TYPE_CHECKING:
			from .configuration_convnextva import (
			    CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConvNextVaConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_convnextva import (
						    CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConvNextVaBackbone,
						    ConvNextVaForImageClassification,
						    ConvNextVaModel,
						    ConvNextVaPreTrainedModel,
						)

else:
			import sys

			__A 		= _LazyModule(__name__, globals()['__file__'], _import_structure)



