
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
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 				lowercase		(					SCREAMING_SNAKE_CASE__       ,  unittest.TestCase		):
							lowercase_       :       Optional[int]       					=KandinskyVaaPipeline
							lowercase_       :       List[Any]       					=[
							    '''image_embeds''',
							    '''negative_image_embeds''',
							]
							lowercase_       :       str       					=['''image_embeds''', '''negative_image_embeds''']
							lowercase_       :       Optional[Any]       					=[
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							lowercase_       :       Union[str, Any]       					=False




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return 3_2




							@property
							def  A__      (				self):
												return self.time_input_dim




							@property
							def  A__      (				self):
												return self.time_input_dim * 4




							@property
							def  A__      (				self):
												return 1_0_0




							@property
							def  A__      (				self):
												torch.manual_seed(0)

												lowercase							=						{
												    '''in_channels''': 4,
												    # Out channels is double in channels because predicts mean and variance
												    '''out_channels''': 8,
												    '''addition_embed_type''': '''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''': '''image_proj''',
												    '''cross_attention_dim''': self.cross_attention_dim,
												    '''attention_head_dim''': 4,
												    '''resnet_time_scale_shift''': '''scale_shift''',
												    '''class_embed_type''': None,
												}

												lowercase							=						UNetaDConditionModel(**A__)
												return model




							@property
							def  A__      (				self):
												return {
												    "block_out_channels": [3_2, 6_4],
												    "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": 1_2,
												    "out_channels": 3,
												    "up_block_types": [
												        "AttnUpDecoderBlock2D",
												        "UpDecoderBlock2D",
												    ],
												    "vq_embed_dim": 4,
												}




							@property
							def  A__      (				self):
												torch.manual_seed(0)
												lowercase							=						VQModel(**self.dummy_movq_kwargs)
												return model




							def  A__      (				self):
												lowercase							=						self.dummy_unet
												lowercase							=						self.dummy_movq

												lowercase							=						DDIMScheduler(
												    num_train_timesteps=1_0_0_0   ,beta_schedule='''linear'''   ,beta_start=0.00085   ,beta_end=0.012   ,clip_sample=A__   ,set_alpha_to_one=A__   ,steps_offset=1   ,prediction_type='''epsilon'''   ,thresholding=A__   ,)

												lowercase							=						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''movq''': movq,
												}
												return components




							def  A__      (				self   ,A__   ,A__=0):
												lowercase							=						floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(A__)).to(A__)
												lowercase							=						floats_tensor((1, self.text_embedder_hidden_size)   ,rng=random.Random(seed + 1)).to(
												    A__)
												if str(A__).startswith('''mps'''):
																	lowercase							=						torch.manual_seed(A__)
												else:
																	lowercase							=						torch.Generator(device=A__).manual_seed(A__)
												lowercase							=						{
												    '''image_embeds''': image_embeds,
												    '''negative_image_embeds''': negative_image_embeds,
												    '''generator''': generator,
												    '''height''': 6_4,
												    '''width''': 6_4,
												    '''guidance_scale''': 4.0,
												    '''num_inference_steps''': 2,
												    '''output_type''': '''np''',
												}
												return inputs





							def  A__      (				self):
												lowercase							=						'''cpu'''

												lowercase							=						self.get_dummy_components()

												lowercase							=						self.pipeline_class(**A__)
												lowercase							=						pipe.to(A__)

												pipe.set_progress_bar_config(disable=A__)

												lowercase							=						pipe(**self.get_dummy_inputs(A__))
												lowercase							=						output.images

												lowercase							=						pipe(
												    **self.get_dummy_inputs(A__)   ,return_dict=A__   ,)[0]

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

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

												lowercase							=						np.array(
												    [0.6237976, 1.0, 0.36441332, 1.0, 0.70639634, 0.29877186, 0.85652125, 0.5216843, 0.54454046])

												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 				lowercase		(					unittest.TestCase		):




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





							def  A__      (				self):
												lowercase							=						load_numpy(
												    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
												    '''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''')

												lowercase							=						KandinskyVaaPriorPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-prior'''   ,torch_dtype=torch.floataa)
												pipe_prior.to(A__)

												lowercase							=						KandinskyVaaPipeline.from_pretrained(
												    '''kandinsky-community/kandinsky-2-2-decoder'''   ,torch_dtype=torch.floataa)
												lowercase							=						pipeline.to(A__)
												pipeline.set_progress_bar_config(disable=A__)

												lowercase							=						'''red cat, 4k photo'''

												lowercase							=						torch.Generator(device='''cuda''').manual_seed(0)
												lowercase	,						lowercase							=						pipe_prior(
												    A__   ,generator=A__   ,num_inference_steps=5   ,negative_prompt=''''''   ,).to_tuple()

												lowercase							=						torch.Generator(device='''cuda''').manual_seed(0)
												lowercase							=						pipeline(
												    image_embeds=A__   ,negative_image_embeds=A__   ,generator=A__   ,num_inference_steps=1_0_0   ,output_type='''np'''   ,)

												lowercase							=						output.images[0]

												assert image.shape == (5_1_2, 5_1_2, 3)

												assert_mean_pixel_difference(A__   ,A__)






import copy
import os
from typing import Union

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


lowercase__		:Dict   =    logging.get_logger(__name__)

lowercase__		:Optional[int]   =    {
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    # See all AltCLIP models at https://huggingface.co/models?filter=altclip
}







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip_text_model'''




							def __init__(				self   ,A__=2_5_0_0_0_2   ,A__=1_0_2_4   ,A__=2_4   ,A__=1_6   ,A__=4_0_9_6   ,A__="gelu"   ,A__=0.1   ,A__=0.1   ,A__=5_1_4   ,A__=1   ,A__=0.02   ,A__=0.02   ,A__=1E-05   ,A__=1   ,A__=0   ,A__=2   ,A__="absolute"   ,A__=True   ,A__=7_6_8   ,**A__   ,):
												super().__init__(pad_token_id=A__   ,bos_token_id=A__   ,eos_token_id=A__   ,**A__)

												lowercase							=						vocab_size
												lowercase							=						hidden_size
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						hidden_act
												lowercase							=						intermediate_size
												lowercase							=						hidden_dropout_prob
												lowercase							=						attention_probs_dropout_prob
												lowercase							=						max_position_embeddings
												lowercase							=						type_vocab_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						layer_norm_eps
												lowercase							=						position_embedding_type
												lowercase							=						use_cache
												lowercase							=						project_dim







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Optional[int]       					='''altclip_vision_model'''




							def __init__(				self   ,A__=7_6_8   ,A__=3_0_7_2   ,A__=5_1_2   ,A__=1_2   ,A__=1_2   ,A__=3   ,A__=2_2_4   ,A__=3_2   ,A__="quick_gelu"   ,A__=1E-5   ,A__=0.0   ,A__=0.02   ,A__=1.0   ,**A__   ,):
												super().__init__(**A__)

												lowercase							=						hidden_size
												lowercase							=						intermediate_size
												lowercase							=						projection_dim
												lowercase							=						num_hidden_layers
												lowercase							=						num_attention_heads
												lowercase							=						num_channels
												lowercase							=						patch_size
												lowercase							=						image_size
												lowercase							=						initializer_range
												lowercase							=						initializer_factor
												lowercase							=						attention_dropout
												lowercase							=						layer_norm_eps
												lowercase							=						hidden_act





							@classmethod
							def  A__      (				cls   ,A__   ,**A__):
												cls._set_token_in_kwargs(A__)

												lowercase	,						lowercase							=						cls.get_config_dict(A__   ,**A__)

												# get the vision config dict if we are loading from AltCLIPConfig
												if config_dict.get('''model_type''') == "altclip":
																	lowercase							=						config_dict['''vision_config''']

												if "model_type" in config_dict and hasattr(cls   ,'''model_type''') and config_dict["model_type"] != cls.model_type:
																	logger.warning(
																	    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
																	    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.')

												return cls.from_dict(A__   ,**A__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Any       					='''altclip'''
							lowercase_       :       List[Any]       					=True




							def __init__(				self   ,A__=None   ,A__=None   ,A__=7_6_8   ,A__=2.6592   ,**A__):
												# If `_config_dict` exist, we use them for the backward compatibility.
												# We pop out these 2 attributes before calling `super().__init__` to avoid them being saved (which causes a lot
												# of confusion!).
												lowercase							=						kwargs.pop('''text_config_dict'''   ,A__)
												lowercase							=						kwargs.pop('''vision_config_dict'''   ,A__)

												super().__init__(**A__)

												# Instead of simply assigning `[text|vision]_config_dict` to `[text|vision]_config`, we use the values in
												# `[text|vision]_config_dict` to update the values in `[text|vision]_config`. The values should be same in most
												# cases, but we don't want to break anything regarding `_config_dict` that existed before commit `8827e1b2`.
												if text_config_dict is not None:
																	if text_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `text_config_dict`.
																	lowercase							=						AltCLIPTextConfig(**A__).to_dict()

																	# Give a warning if the values exist in both `_text_config_dict` and `text_config` but being different.
																	for key, value in _text_config_dict.items():
																						if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
																											# If specified in `text_config_dict`
																											if key in text_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `text_config_dict` and `text_config` but with different values. '
																																    f'The value `text_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`text_config_dict` is provided which will be used to initialize `AltCLIPTextConfig`. The '
																																    f'value `text_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `text_config` with the ones in `_text_config_dict`.
																	text_config.update(_text_config_dict)

												if vision_config_dict is not None:
																	if vision_config is None:
																						lowercase							=						{}

																	# This is the complete result when using `vision_config_dict`.
																	lowercase							=						AltCLIPVisionConfig(**A__).to_dict()
																	# convert keys to string instead of integer
																	if "id2label" in _vision_config_dict:
																						lowercase							=						{
																						    str(A__): value for key, value in _vision_config_dict['''id2label'''].items()
																						}

																	# Give a warning if the values exist in both `_vision_config_dict` and `vision_config` but being different.
																	for key, value in _vision_config_dict.items():
																						if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
																											# If specified in `vision_config_dict`
																											if key in vision_config_dict:
																																lowercase							=						(
																																    f'`{key}` is found in both `vision_config_dict` and `vision_config` but with different '
																																    f'values. The value `vision_config_dict["{key}"]` will be used instead.'
																																)
																											# If inferred from default argument values (just to be super careful)
																											else:
																																lowercase							=						(
																																    f'`vision_config_dict` is provided which will be used to initialize `AltCLIPVisionConfig`. '
																																    f'The value `vision_config["{key}"]` will be overriden.'
																																)
																											logger.warning(A__)

            # Update all values in `vision_config` with the ones in `_vision_config_dict`.
																	vision_config.update(_vision_config_dict)

												if text_config is None:
																	lowercase							=						{}
																	logger.info('''`text_config` is `None`. Initializing the `AltCLIPTextConfig` with default values.''')

												if vision_config is None:
																	lowercase							=						{}
																	logger.info('''`vision_config` is `None`. initializing the `AltCLIPVisionConfig` with default values.''')

												lowercase							=						AltCLIPTextConfig(**A__)
												lowercase							=						AltCLIPVisionConfig(**A__)

												lowercase							=						projection_dim
												lowercase							=						logit_scale_init_value
												lowercase							=						1.0




							@classmethod
							def  A__      (				cls   ,A__   ,A__   ,**A__):
												return cls(text_config=text_config.to_dict()   ,vision_config=vision_config.to_dict()   ,**A__)





							def  A__      (				self):
												lowercase							=						copy.deepcopy(self.__dict__)
												lowercase							=						self.text_config.to_dict()
												lowercase							=						self.vision_config.to_dict()
												lowercase							=						self.__class__.model_type
												return output




