






import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @property
       def       _a   ( self							):
         torch.manual_seed(0							)
         UpperCamelCase_: str   				=    UNetaDModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=('DownBlock2D', 'AttnDownBlock2D')  ,      up_block_types=('AttnUpBlock2D', 'UpBlock2D')  ,      )
         return model





       @property
       def       _a   ( self							):
         torch.manual_seed(0							)
         UpperCamelCase_: Union[str, Any]   				=    VQModel(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=3  ,      )
         return model





       @property
       def       _a   ( self							):
         torch.manual_seed(0							)
         UpperCamelCase_: str   				=    CLIPTextConfig(
             bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=3_2  ,      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 CLIPTextModel(_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.dummy_uncond_unet
         UpperCamelCase_: List[str]   				=    DDIMScheduler()
         UpperCamelCase_: Union[str, Any]   				=    self.dummy_vq_model

         UpperCamelCase_: List[Any]   				=    LDMPipeline(unet=_lowerCamelCase  ,      vqvae=_lowerCamelCase  ,      scheduler=_lowerCamelCase							)
         ldm.to(_lowerCamelCase							)
         ldm.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: Dict   				=    torch.manual_seed(0							)
         UpperCamelCase_: Dict   				=    ldm(generator=_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'							).images

         UpperCamelCase_: Dict   				=    torch.manual_seed(0							)
         UpperCamelCase_: List[Any]   				=    ldm(generator=_lowerCamelCase  ,      num_inference_steps=2  ,      output_type='numpy'  ,      return_dict=_lowerCamelCase							)[0]

         UpperCamelCase_: Tuple   				=    image[0, -3:, -3:, -1]
         UpperCamelCase_: Optional[Any]   				=    image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 6_4, 6_4, 3)
         UpperCamelCase_: List[str]   				=    np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]							)
         UpperCamelCase_: Union[str, Any]   				=    1e-2 if torch_device != '''mps''' else 3e-2

         assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < tolerance







@slow
@require_torch
class     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    LDMPipeline.from_pretrained('CompVis/ldm-celebahq-256'							)
         ldm.to(_lowerCamelCase							)
         ldm.set_progress_bar_config(disable=_lowerCamelCase							)

         UpperCamelCase_: Tuple   				=    torch.manual_seed(0							)
         UpperCamelCase_: List[Any]   				=    ldm(generator=_lowerCamelCase  ,      num_inference_steps=5  ,      output_type='numpy'							).images

         UpperCamelCase_: Optional[int]   				=    image[0, -3:, -3:, -1]

         assert image.shape == (1, 2_5_6, 2_5_6, 3)
         UpperCamelCase_: List[Any]   				=    np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]							)
         UpperCamelCase_: str   				=    1e-2 if torch_device != '''mps''' else 3e-2

         assert np.abs(image_slice.flatten() - expected_slice							).max() < tolerance









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


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    '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     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

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