"""simple docstring"""





import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class      UpperCAmelCase_	(    _a ,							unittest.TestCase):
      lowerCamelCase__		:    int   =  UnCLIPImageVariationPipeline
      lowerCamelCase__		:    int   =  IMAGE_VARIATION_PARAMS - {"height", "width", "guidance_scale"}
      lowerCamelCase__		:    Any   =  IMAGE_VARIATION_BATCH_PARAMS

      lowerCamelCase__		:    Dict   =  [
          "generator",
          "return_dict",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]
      lowerCamelCase__		:    Dict   =  False


      @property
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             return 3_2


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             return 3_2


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             return self.time_input_dim


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


      @property
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             return 1_0_0


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Optional[Any]       =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
             return tokenizer


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             torch.manual_seed(0							)
             lowercase__			:		List[str]       =	CLIPTextConfig(
                 bos_token_id=0		,			eos_token_id=2		,			hidden_size=self.text_embedder_hidden_size		,			projection_dim=self.text_embedder_hidden_size		,			intermediate_size=3_7		,			layer_norm_eps=1e-05		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1_0_0_0		,			)
             return CLIPTextModelWithProjection(a							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             torch.manual_seed(0							)
             lowercase__			:		List[Any]       =	CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size		,			projection_dim=self.text_embedder_hidden_size		,			num_hidden_layers=5		,			num_attention_heads=4		,			image_size=3_2		,			intermediate_size=3_7		,			patch_size=1		,			)
             return CLIPVisionModelWithProjection(a							)


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             torch.manual_seed(0							)

             lowercase__			:		Dict       =	{
                 'clip_embeddings_dim': self.text_embedder_hidden_size,
                 'time_embed_dim': self.time_embed_dim,
                 'cross_attention_dim': self.cross_attention_dim,
             }

             lowercase__			:		Dict       =	UnCLIPTextProjModel(**a							)
             return model


      @property
      def 		_UpperCAmelCase       (   self							)       ->					str:
             torch.manual_seed(0							)

             lowercase__			:		Union[str, Any]       =	{
                 'sample_size': 3_2,
                 # RGB in channels
                 'in_channels': 3,
                 # Out channels is double in channels because predicts mean and variance
                 'out_channels': 6,
                 '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,
                 'cross_attention_dim': self.cross_attention_dim,
                 'attention_head_dim': 4,
                 'resnet_time_scale_shift': 'scale_shift',
                 'class_embed_type': 'identity',
             }

             lowercase__			:		Optional[int]       =	UNetaDConditionModel(**a							)
             return model


      @property
      def 		_UpperCAmelCase       (   self							)       ->					str:
             return {
                 "sample_size": 6_4,
                 "layers_per_block": 1,
                 "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
                 "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
                 "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
                 "in_channels": 6,
                 "out_channels": 3,
             }


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Any:
             torch.manual_seed(0							)

             lowercase__			:		Dict       =	UNetaDModel(**self.dummy_super_res_kwargs							)
             return model


      @property
      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             # seeded differently to get different unet than `self.dummy_super_res_first`
             torch.manual_seed(1							)

             lowercase__			:		Tuple       =	UNetaDModel(**self.dummy_super_res_kwargs							)
             return model


      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Union[str, Any]       =	self.dummy_decoder
             lowercase__			:		Dict       =	self.dummy_text_proj
             lowercase__			:		List[Any]       =	self.dummy_text_encoder
             lowercase__			:		Tuple       =	self.dummy_tokenizer
             lowercase__			:		List[str]       =	self.dummy_super_res_first
             lowercase__			:		List[Any]       =	self.dummy_super_res_last

             lowercase__			:		Dict       =	UnCLIPScheduler(
                 variance_type='learned_range'		,			prediction_type='epsilon'		,			num_train_timesteps=1_0_0_0		,			)

             lowercase__			:		Tuple       =	UnCLIPScheduler(
                 variance_type='fixed_small_log'		,			prediction_type='epsilon'		,			num_train_timesteps=1_0_0_0		,			)

             lowercase__			:		Optional[Any]       =	CLIPImageProcessor(crop_size=3_2		,			size=3_2							)

             lowercase__			:		Any       =	self.dummy_image_encoder

             return {
                 "decoder": decoder,
                 "text_encoder": text_encoder,
                 "tokenizer": tokenizer,
                 "text_proj": text_proj,
                 "feature_extractor": feature_extractor,
                 "image_encoder": image_encoder,
                 "super_res_first": super_res_first,
                 "super_res_last": super_res_last,
                 "decoder_scheduler": decoder_scheduler,
                 "super_res_scheduler": super_res_scheduler,
             }


      def 		_UpperCAmelCase       (   self		,			a		,			a=0		,			a=True							)       ->					Union[str, Any]:
             lowercase__			:		Union[str, Any]       =	floats_tensor((1, 3, 3_2, 3_2)		,			rng=random.Random(a							)							).to(a							)
             if str(a							).startswith('mps'							):
                    lowercase__			:		List[Any]       =	torch.manual_seed(a							)
             else:
                    lowercase__			:		Any       =	torch.Generator(device=a							).manual_seed(a							)

             if pil_image:
                    lowercase__			:		Dict       =	input_image * 0.5 + 0.5
                    lowercase__			:		Tuple       =	input_image.clamp(0		,			1							)
                    lowercase__			:		Optional[Any]       =	input_image.cpu().permute(0		,			2		,			3		,			1							).float().numpy()
                    lowercase__			:		List[str]       =	DiffusionPipeline.numpy_to_pil(a							)[0]

             return {
                 "image": input_image,
                 "generator": generator,
                 "decoder_num_inference_steps": 2,
                 "super_res_num_inference_steps": 2,
                 "output_type": "np",
             }


      def 		_UpperCAmelCase       (   self							)       ->					Optional[Any]:
             lowercase__			:		Optional[int]       =	'cpu'

             lowercase__			:		List[str]       =	self.get_dummy_components()

             lowercase__			:		Any       =	self.pipeline_class(**a							)
             lowercase__			:		Optional[Any]       =	pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		Union[str, Any]       =	self.get_dummy_inputs(a		,			pil_image=a							)

             lowercase__			:		Optional[int]       =	pipe(**a							)
             lowercase__			:		int       =	output.images

             lowercase__			:		Tuple       =	self.get_dummy_inputs(a		,			pil_image=a							)

             lowercase__			:		Union[str, Any]       =	pipe(
                 **a		,			return_dict=a		,			)[0]

             lowercase__			:		Dict       =	image[0, -3:, -3:, -1]
             lowercase__			:		Tuple       =	image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             lowercase__			:		Optional[int]       =	np.array(
                 [
                     0.9_997,
                     0.0_002,
                     0.9_997,
                     0.9_997,
                     0.9_969,
                     0.0_023,
                     0.9_997,
                     0.9_969,
                     0.9_970,
                 ]							)

             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 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		Any       =	'cpu'

             lowercase__			:		Optional[int]       =	self.get_dummy_components()

             lowercase__			:		Union[str, Any]       =	self.pipeline_class(**a							)
             lowercase__			:		Union[str, Any]       =	pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		List[str]       =	self.get_dummy_inputs(a		,			pil_image=a							)

             lowercase__			:		List[Any]       =	pipe(**a							)
             lowercase__			:		List[str]       =	output.images

             lowercase__			:		Any       =	self.get_dummy_inputs(a		,			pil_image=a							)

             lowercase__			:		Tuple       =	pipe(
                 **a		,			return_dict=a		,			)[0]

             lowercase__			:		str       =	image[0, -3:, -3:, -1]
             lowercase__			:		Optional[Any]       =	image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (1, 6_4, 6_4, 3)

             lowercase__			:		Any       =	np.array([0.9_997, 0.0_003, 0.9_997, 0.9_997, 0.9_970, 0.0_024, 0.9_997, 0.9_971, 0.9_971]							)

             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 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		Union[str, Any]       =	'cpu'

             lowercase__			:		Tuple       =	self.get_dummy_components()

             lowercase__			:		List[str]       =	self.pipeline_class(**a							)
             lowercase__			:		Tuple       =	pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		List[Any]       =	self.get_dummy_inputs(a		,			pil_image=a							)
             lowercase__			:		Optional[int]       =	[
                 pipeline_inputs['image'],
                 pipeline_inputs['image'],
             ]

             lowercase__			:		Any       =	pipe(**a							)
             lowercase__			:		List[str]       =	output.images

             lowercase__			:		List[Any]       =	self.get_dummy_inputs(a		,			pil_image=a							)
             lowercase__			:		Optional[Any]       =	[
                 tuple_pipeline_inputs['image'],
                 tuple_pipeline_inputs['image'],
             ]

             lowercase__			:		List[str]       =	pipe(
                 **a		,			return_dict=a		,			)[0]

             lowercase__			:		Optional[int]       =	image[0, -3:, -3:, -1]
             lowercase__			:		Optional[Any]       =	image_from_tuple[0, -3:, -3:, -1]

             assert image.shape == (2, 6_4, 6_4, 3)

             lowercase__			:		Tuple       =	np.array(
                 [
                     0.9_997,
                     0.9_989,
                     0.0_008,
                     0.0_021,
                     0.9_960,
                     0.0_018,
                     0.0_014,
                     0.0_002,
                     0.9_933,
                 ]							)

             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 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		int       =	torch.device('cpu'							)


             class      UpperCAmelCase_	:
                   lowerCamelCase__		:    Union[str, Any]   =  1





             lowercase__			:		str       =	self.get_dummy_components()

             lowercase__			:		str       =	self.pipeline_class(**a							)
             lowercase__			:		Any       =	pipe.to(a							)

             pipe.set_progress_bar_config(disable=a							)

             lowercase__			:		Optional[Any]       =	torch.Generator(device=a							).manual_seed(0							)
             lowercase__			:		int       =	pipe.decoder.dtype
             lowercase__			:		Optional[int]       =	1

             lowercase__			:		Optional[int]       =	(
                 batch_size,
                 pipe.decoder.config.in_channels,
                 pipe.decoder.config.sample_size,
                 pipe.decoder.config.sample_size,
             )
             lowercase__			:		Tuple       =	pipe.prepare_latents(
                 a		,			dtype=a		,			device=a		,			generator=a		,			latents=a		,			scheduler=DummyScheduler()							)

             lowercase__			:		List[Any]       =	(
                 batch_size,
                 pipe.super_res_first.config.in_channels // 2,
                 pipe.super_res_first.config.sample_size,
                 pipe.super_res_first.config.sample_size,
             )
             lowercase__			:		str       =	pipe.prepare_latents(
                 a		,			dtype=a		,			device=a		,			generator=a		,			latents=a		,			scheduler=DummyScheduler()							)

             lowercase__			:		Dict       =	self.get_dummy_inputs(a		,			pil_image=a							)

             lowercase__			:		Optional[Any]       =	pipe(
                 **a		,			decoder_latents=a		,			super_res_latents=a							).images

             lowercase__			:		Union[str, Any]       =	self.get_dummy_inputs(a		,			pil_image=a							)
             # Don't pass image, instead pass embedding
             lowercase__			:		Optional[Any]       =	pipeline_inputs.pop('image'							)
             lowercase__			:		Dict       =	pipe.image_encoder(a							).image_embeds

             lowercase__			:		List[str]       =	pipe(
                 **a		,			decoder_latents=a		,			super_res_latents=a		,			image_embeddings=a		,			).images

             # make sure passing text embeddings manually is identical
             assert np.abs(img_out_a - img_out_a							).max() < 1e-4


      @skip_mps
      def 		_UpperCAmelCase       (   self							)       ->					str:
             lowercase__			:		List[Any]       =	torch_device == 'cpu'

             # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
             lowercase__			:		Optional[int]       =	1e-2

             self._test_attention_slicing_forward_pass(
                 test_max_difference=a		,			expected_max_diff=a							)


      @skip_mps
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		List[Any]       =	torch_device == 'cpu'
             lowercase__			:		List[Any]       =	True
             lowercase__			:		List[Any]       =	[
                 'decoder_num_inference_steps',
                 'super_res_num_inference_steps',
             ]

             self._test_inference_batch_single_identical(
                 test_max_difference=a		,			relax_max_difference=a		,			additional_params_copy_to_batched_inputs=a		,			)


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		Optional[int]       =	[
                 'decoder_num_inference_steps',
                 'super_res_num_inference_steps',
             ]

             if torch_device == "mps":
                    # TODO: MPS errors with larger batch sizes
                    lowercase__			:		Union[str, Any]       =	[2, 3]
                    self._test_inference_batch_consistent(
                        batch_sizes=a		,			additional_params_copy_to_batched_inputs=a		,			)
             else:
                    self._test_inference_batch_consistent(
                        additional_params_copy_to_batched_inputs=a							)


      @skip_mps
      def 		_UpperCAmelCase       (   self							)       ->					Any:
             return super().test_dict_tuple_outputs_equivalent()


      @skip_mps
      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             return super().test_save_load_local()


      @skip_mps
      def 		_UpperCAmelCase       (   self							)       ->					Tuple:
             return super().test_save_load_optional_components()





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


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             # clean up the VRAM after each test
             super().tearDown()
             gc.collect()
             torch.cuda.empty_cache()


      def 		_UpperCAmelCase       (   self							)       ->					List[Any]:
             lowercase__			:		Union[str, Any]       =	load_image(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'							)
             lowercase__			:		Tuple       =	load_numpy(
                 'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                 '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'							)

             lowercase__			:		Optional[Any]       =	UnCLIPImageVariationPipeline.from_pretrained(
                 'kakaobrain/karlo-v1-alpha-image-variations'		,			torch_dtype=torch.floataa							)
             lowercase__			:		int       =	pipeline.to(a							)
             pipeline.set_progress_bar_config(disable=a							)

             lowercase__			:		Any       =	torch.Generator(device='cpu'							).manual_seed(0							)
             lowercase__			:		Dict       =	pipeline(
                 a		,			generator=a		,			output_type='np'		,			)

             lowercase__			:		List[Any]       =	output.images[0]

             assert image.shape == (2_5_6, 2_5_6, 3)

             assert_mean_pixel_difference(a		,			a		,			1_5							)


"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__  :  int						     =	logging.get_logger(__name__)

lowerCAmelCase__  :  str						     =	{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}
class 				snake_case       ( __UpperCAmelCase				):




					"""simple docstring"""

					snake_case__				    = "xglm"
					snake_case__				    = ["past_key_values"]

					snake_case__				    = {
					    "num_attention_heads": "attention_heads",
					    "hidden_size": "d_model",
					    "num_hidden_layers": "num_layers",
					}







					def __init__(				self   :	Any    ,lowerCamelCase__   :	Any=256_008    ,lowerCamelCase__   :	Optional[Any]=2_048    ,lowerCamelCase__   :	List[str]=1_024    ,lowerCamelCase__   :	List[str]=4_096    ,lowerCamelCase__   :	Tuple=24    ,lowerCamelCase__   :	Optional[int]=16    ,lowerCamelCase__   :	int="gelu"    ,lowerCamelCase__   :	Dict=0.1    ,lowerCamelCase__   :	int=0.1    ,lowerCamelCase__   :	List[Any]=0.0    ,lowerCamelCase__   :	List[str]=0.0    ,lowerCamelCase__   :	Optional[Any]=0.0_2    ,lowerCamelCase__   :	List[str]=True    ,lowerCamelCase__   :	Optional[Any]=True    ,lowerCamelCase__   :	str=2    ,lowerCamelCase__   :	Dict=1    ,lowerCamelCase__   :	Optional[int]=0    ,lowerCamelCase__   :	Tuple=2    ,**lowerCamelCase__   :	List[Any]    ,):
									UpperCAmelCase__             =							vocab_size
									UpperCAmelCase__             =							max_position_embeddings
									UpperCAmelCase__             =							d_model
									UpperCAmelCase__             =							ffn_dim
									UpperCAmelCase__             =							num_layers
									UpperCAmelCase__             =							attention_heads
									UpperCAmelCase__             =							activation_function
									UpperCAmelCase__             =							dropout
									UpperCAmelCase__             =							attention_dropout
									UpperCAmelCase__             =							activation_dropout
									UpperCAmelCase__             =							layerdrop
									UpperCAmelCase__             =							init_std
									UpperCAmelCase__             =							scale_embedding  # scale factor will be sqrt(d_model) if True
									UpperCAmelCase__             =							use_cache

									super().__init__(
									    pad_token_id=lowerCamelCase__    ,bos_token_id=lowerCamelCase__    ,eos_token_id=lowerCamelCase__    ,decoder_start_token_id=lowerCamelCase__    ,**lowerCamelCase__    ,)



