

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

    """simple docstring"""

    return 32
   @property
   def 							__UpperCamelCase	(     self							:	List[Any]		)							->    Optional[Any]:

    """simple docstring"""

    return 32
   @property
   def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Optional[Any]:

    """simple docstring"""

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

    """simple docstring"""

    return self.time_input_dim * 4
   @property
   def 							__UpperCamelCase	(     self							:	List[Any]		)							->    Union[str, Any]:

    """simple docstring"""

    return 100
   @property
   def 							__UpperCamelCase	(     self							:	Any		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Any			       =    XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"		)
    return tokenizer
   @property
   def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Dict:

    """simple docstring"""

    torch.manual_seed(0		)
    SCREAMING_SNAKE_CASE				: Dict			       =    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=1005 , )

    SCREAMING_SNAKE_CASE				: List[str]			       =    MultilingualCLIP(a		)
    SCREAMING_SNAKE_CASE				: int			       =    text_encoder.eval()

    return text_encoder
   @property
   def 							__UpperCamelCase	(     self							:	Dict		)							->    Tuple:

    """simple docstring"""

    torch.manual_seed(0		)

    SCREAMING_SNAKE_CASE				: str			       =    {
        "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,
    }

    SCREAMING_SNAKE_CASE				: Tuple			       =    UNetaDConditionModel(**a		)
    return model
   @property
   def 							__UpperCamelCase	(     self							:	Dict		)							->    Any:

    """simple docstring"""

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

    """simple docstring"""

    torch.manual_seed(0		)
    SCREAMING_SNAKE_CASE				: int			       =    VQModel(**self.dummy_movq_kwargs		)
    return model
   def 							__UpperCamelCase	(     self							:	str		)							->    Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[Any]			       =    self.dummy_text_encoder
    SCREAMING_SNAKE_CASE				: str			       =    self.dummy_tokenizer
    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    self.dummy_unet
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    self.dummy_movq

    SCREAMING_SNAKE_CASE				: Dict			       =    DDIMScheduler(
        num_train_timesteps=1000 , beta_schedule="linear" , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=a , set_alpha_to_one=a , steps_offset=1 , prediction_type="epsilon" , thresholding=a , )

    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "unet": unet,
        "scheduler": scheduler,
        "movq": movq,
    }
    return components
   def 							__UpperCamelCase	(     self							:	Optional[Any] , a							:	int , a							:	str=0		)							->    Optional[Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    floats_tensor((1, self.cross_attention_dim) , rng=random.Random(a		)		).to(a		)
    SCREAMING_SNAKE_CASE				: int			       =    floats_tensor((1, self.cross_attention_dim) , rng=random.Random(seed + 1		)		).to(a		)
    if str(a		).startswith("mps"		):
     SCREAMING_SNAKE_CASE				: Any			       =    torch.manual_seed(a		)
    else:
     SCREAMING_SNAKE_CASE				: Any			       =    torch.Generator(device=a		).manual_seed(a		)
    SCREAMING_SNAKE_CASE				: Tuple			       =    {
        "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 							__UpperCamelCase	(     self							:	List[str]		)							->    Dict:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    "cpu"

    SCREAMING_SNAKE_CASE				: str			       =    self.get_dummy_components()

    SCREAMING_SNAKE_CASE				: Tuple			       =    self.pipeline_class(**a		)
    SCREAMING_SNAKE_CASE				: List[Any]			       =    pipe.to(a		)

    pipe.set_progress_bar_config(disable=a		)

    SCREAMING_SNAKE_CASE				: Tuple			       =    pipe(**self.get_dummy_inputs(a		)		)
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    output.images

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    pipe(
        **self.get_dummy_inputs(a		) , return_dict=a , )[0]

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    image[0, -3:, -3:, -1]
    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    image_from_tuple[0, -3:, -3:, -1]

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

    SCREAMING_SNAKE_CASE				: Dict			       =    np.array(
        [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953]		)

    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 _UpperCamelCase							(							unittest.TestCase				):
   '''simple docstring'''







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

    """simple docstring"""

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






   def 							__UpperCamelCase	(     self							:	Any		)							->    Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    load_numpy(
        "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        "/kandinsky/kandinsky_text2img_cat_fp16.npy"		)

    SCREAMING_SNAKE_CASE				: int			       =    KandinskyPriorPipeline.from_pretrained(
        "kandinsky-community/kandinsky-2-1-prior" , torch_dtype=torch.floataa		)
    pipe_prior.to(a		)

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1" , torch_dtype=torch.floataa		)
    SCREAMING_SNAKE_CASE				: List[str]			       =    pipeline.to(a		)
    pipeline.set_progress_bar_config(disable=a		)

    SCREAMING_SNAKE_CASE				: List[str]			       =    "red cat, 4k photo"

    SCREAMING_SNAKE_CASE				: Any			       =    torch.Generator(device="cuda"		).manual_seed(0		)
    SCREAMING_SNAKE_CASE				: int			       =    pipe_prior(
        a , generator=a , num_inference_steps=5 , negative_prompt="" , ).to_tuple()

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.Generator(device="cuda"		).manual_seed(0		)
    SCREAMING_SNAKE_CASE				: List[Any]			       =    pipeline(
        a , image_embeds=a , negative_image_embeds=a , generator=a , num_inference_steps=100 , output_type="np" , )

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    output.images[0]

    assert image.shape == (512, 512, 3)

    assert_mean_pixel_difference(a , a		)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			a_         =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)