




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 ):
   __SCREAMING_SNAKE_CASE		:				Tuple      					=       KandinskyPipeline
   __SCREAMING_SNAKE_CASE		:				List[Any]      					=       [
       """prompt""",
       """image_embeds""",
       """negative_image_embeds""",
   ]
   __SCREAMING_SNAKE_CASE		:				Any      					=       ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
   __SCREAMING_SNAKE_CASE		:				Optional[Any]      					=       [
       """generator""",
       """height""",
       """width""",
       """latents""",
       """guidance_scale""",
       """negative_prompt""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   __SCREAMING_SNAKE_CASE		:				List[Any]      					=       False






   @property
   def 			_a    (						self :							Optional[Any]  )->      Dict:
     return 32






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






   @property
   def 			_a    (						self :							Dict  )->      List[str]:
     return self.time_input_dim






   @property
   def 			_a    (						self :							Any  )->      str:
     return self.time_input_dim * 4






   @property
   def 			_a    (						self :							Dict  )->      Any:
     return 1_00






   @property
   def 			_a    (						self :							Optional[int]  )->      Tuple:
     _lowercase:			Union[str, Any]      =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'  )
     return tokenizer






   @property
   def 			_a    (						self :							Union[str, Any]  )->      Union[str, Any]:
     torch.manual_seed(0  )
     _lowercase:			List[str]      =     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    ,       )

     _lowercase:			int      =     MultilingualCLIP(snake_case_  )
     _lowercase:			Optional[Any]      =     text_encoder.eval()

     return text_encoder






   @property
   def 			_a    (						self :							Optional[Any]  )->      str:
     torch.manual_seed(0  )

     _lowercase:			List[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,
     }

     _lowercase:			Union[str, Any]      =     UNetaDConditionModel(**snake_case_  )
     return model






   @property
   def 			_a    (						self :							Optional[int]  )->      Dict:
     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 			_a    (						self :							Dict  )->      List[str]:
     torch.manual_seed(0  )
     _lowercase:			Tuple      =     VQModel(**self.dummy_movq_kwargs  )
     return model






   def 			_a    (						self :							str  )->      Dict:
     _lowercase:			Union[str, Any]      =     self.dummy_text_encoder
     _lowercase:			str      =     self.dummy_tokenizer
     _lowercase:			Tuple      =     self.dummy_unet
     _lowercase:			Union[str, Any]      =     self.dummy_movq

     _lowercase:			Tuple      =     DDIMScheduler(
         num_train_timesteps=10_00    ,       beta_schedule='linear'    ,       beta_start=0.00_085    ,       beta_end=0.012    ,       clip_sample=snake_case_    ,       set_alpha_to_one=snake_case_    ,       steps_offset=1    ,       prediction_type='epsilon'    ,       thresholding=snake_case_    ,       )

     _lowercase:			Tuple      =     {
         'text_encoder': text_encoder,
         'tokenizer': tokenizer,
         'unet': unet,
         'scheduler': scheduler,
         'movq': movq,
     }
     return components






   def 			_a    (						self :							int    ,       UpperCamelCase_ :							str    ,       UpperCamelCase_ :							Any=0  )->      List[str]:
     _lowercase:			Any      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(snake_case_  )  ).to(snake_case_  )
     _lowercase:			Optional[Any]      =     floats_tensor((1, self.cross_attention_dim)    ,       rng=random.Random(seed + 1  )  ).to(snake_case_  )
     if str(snake_case_  ).startswith('mps'  ):
       _lowercase:			Optional[int]      =     torch.manual_seed(snake_case_  )
     else:
       _lowercase:			Any      =     torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )
     _lowercase:			Optional[Any]      =     {
         '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 			_a    (						self :							str  )->      int:
     _lowercase:			Any      =     'cpu'

     _lowercase:			Union[str, Any]      =     self.get_dummy_components()

     _lowercase:			Union[str, Any]      =     self.pipeline_class(**snake_case_  )
     _lowercase:			Dict      =     pipe.to(snake_case_  )

     pipe.set_progress_bar_config(disable=snake_case_  )

     _lowercase:			Optional[int]      =     pipe(**self.get_dummy_inputs(snake_case_  )  )
     _lowercase:			List[str]      =     output.images

     _lowercase:			str      =     pipe(
         **self.get_dummy_inputs(snake_case_  )    ,       return_dict=snake_case_    ,       )[0]

     _lowercase:			str      =     image[0, -3:, -3:, -1]
     _lowercase:			List[str]      =     image_from_tuple[0, -3:, -3:, -1]

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

     _lowercase:			List[Any]      =     np.array(
         [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]  )

     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 ):






   def 			_a    (						self :							Optional[int]  )->      Any:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()






   def 			_a    (						self :							Any  )->      Optional[int]:
     _lowercase:			Optional[int]      =     load_numpy(
         'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
         '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

     _lowercase:			Union[str, Any]      =     KandinskyPriorPipeline.from_pretrained(
         'kandinsky-community/kandinsky-2-1-prior'    ,       torch_dtype=torch.floataa  )
     pipe_prior.to(snake_case_  )

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

     _lowercase:			Union[str, Any]      =     'red cat, 4k photo'

     _lowercase:			Optional[Any]      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase  ,       _lowercase:			List[str]      =     pipe_prior(
         snake_case_    ,       generator=snake_case_    ,       num_inference_steps=5    ,       negative_prompt=''    ,       ).to_tuple()

     _lowercase:			int      =     torch.Generator(device='cuda'  ).manual_seed(0  )
     _lowercase:			Union[str, Any]      =     pipeline(
         snake_case_    ,       image_embeds=snake_case_    ,       negative_image_embeds=snake_case_    ,       generator=snake_case_    ,       num_inference_steps=1_00    ,       output_type='np'    ,       )

     _lowercase:			Union[str, Any]      =     output.images[0]

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

     assert_mean_pixel_difference(snake_case_    ,       snake_case_  )








"""simple docstring"""





import functools
import operator

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


A__       :			int=						logging.get_logger(__name__)

A__       :			Optional[int]=						{
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class  __lowerCamelCase     (	_a	):
   a   :					Dict			    ="""hubert"""

   def __init__(       self			,					snake_case_=32			,					snake_case_=768			,					snake_case_=12			,					snake_case_=12			,					snake_case_=3072			,					snake_case_="gelu"			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=True			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.02			,					snake_case_=1E-5			,					snake_case_="group"			,					snake_case_="gelu"			,					snake_case_=(512, 512, 512, 512, 512, 512, 512)			,					snake_case_=(5, 2, 2, 2, 2, 2, 2)			,					snake_case_=(10, 3, 3, 3, 3, 2, 2)			,					snake_case_=False			,					snake_case_=128			,					snake_case_=16			,					snake_case_=False			,					snake_case_=True			,					snake_case_=0.05			,					snake_case_=10			,					snake_case_=2			,					snake_case_=0.0			,					snake_case_=10			,					snake_case_=0			,					snake_case_="sum"			,					snake_case_=False			,					snake_case_=False			,					snake_case_=256			,					snake_case_=0			,					snake_case_=1			,					snake_case_=2			,					**snake_case_			,					)		->							Any:
       super().__init__(**snake_case_			,					pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_   )
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     feat_extract_norm
       UpperCamelCase__								=     feat_extract_activation
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     conv_bias
       UpperCamelCase__								=     num_conv_pos_embeddings
       UpperCamelCase__								=     num_conv_pos_embedding_groups
       UpperCamelCase__								=     len(self.conv_dim   )
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     hidden_dropout
       UpperCamelCase__								=     attention_dropout
       UpperCamelCase__								=     activation_dropout
       UpperCamelCase__								=     feat_proj_layer_norm
       UpperCamelCase__								=     feat_proj_dropout
       UpperCamelCase__								=     final_dropout
       UpperCamelCase__								=     layerdrop
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     do_stable_layer_norm
       UpperCamelCase__								=     use_weighted_layer_sum
       UpperCamelCase__								=     classifier_proj_size

       if (
           (len(self.conv_stride   ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
           or (len(self.conv_dim   ) != self.num_feat_extract_layers)
       ):
           raise ValueError(
               'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
               ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
               F' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'
               F' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'   )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       UpperCamelCase__								=     apply_spec_augment
       UpperCamelCase__								=     mask_time_prob
       UpperCamelCase__								=     mask_time_length
       UpperCamelCase__								=     mask_time_min_masks
       UpperCamelCase__								=     mask_feature_prob
       UpperCamelCase__								=     mask_feature_length
       UpperCamelCase__								=     mask_feature_min_masks

       # ctc loss
       UpperCamelCase__								=     ctc_loss_reduction
       UpperCamelCase__								=     ctc_zero_infinity







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       return functools.reduce(operator.mul			,					self.conv_stride			,					1   )




