
'''simple docstring'''


import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowercase							:      List[Any]		 =   False

class 		__lowercase (     unittest.TestCase			):

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

			@property
			def 				UpperCAmelCase__       (self					):
								return 1_2

			@property
			def 				UpperCAmelCase__       (self					):
								return 1_2

			@property
			def 				UpperCAmelCase__       (self					):
								return 3_2

			@property
			def 				UpperCAmelCase__       (self					):
								torch.manual_seed(0					)
								lowerCamelCase_    :					Optional[int]   =						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	,      num_vq_embeddings=self.num_embed	,      vq_embed_dim=3	,      )
								return model

			@property
			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					List[str]   =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
								return tokenizer

			@property
			def 				UpperCAmelCase__       (self					):
								torch.manual_seed(0					)
								lowerCamelCase_    :					Tuple   =						CLIPTextConfig(
								    bos_token_id=0	,      eos_token_id=2	,      hidden_size=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 CLIPTextModel(lowerCAmelCase_					)

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

								lowerCamelCase_    :					Dict   =						1_2
								lowerCamelCase_    :					Tuple   =						1_2

								lowerCamelCase_    :					int   =						{
								    '''attention_bias''': True,
								    '''cross_attention_dim''': 3_2,
								    '''attention_head_dim''': height * width,
								    '''num_attention_heads''': 1,
								    '''num_vector_embeds''': self.num_embed,
								    '''num_embeds_ada_norm''': self.num_embeds_ada_norm,
								    '''norm_num_groups''': 3_2,
								    '''sample_size''': width,
								    '''activation_fn''': '''geglu-approximate''',
								}

								lowerCamelCase_    :					Any   =						TransformeraDModel(**lowerCAmelCase_					)
								return model

			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					str   =						'''cpu'''

								lowerCamelCase_    :					Tuple   =						self.dummy_vqvae
								lowerCamelCase_    :					int   =						self.dummy_text_encoder
								lowerCamelCase_    :					Optional[int]   =						self.dummy_tokenizer
								lowerCamelCase_    :					str   =						self.dummy_transformer
								lowerCamelCase_    :					Optional[int]   =						VQDiffusionScheduler(self.num_embed					)
								lowerCamelCase_    :					Optional[Any]   =						LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase_					)

								lowerCamelCase_    :					Tuple   =						VQDiffusionPipeline(
								    vqvae=lowerCAmelCase_	,      text_encoder=lowerCAmelCase_	,      tokenizer=lowerCAmelCase_	,      transformer=lowerCAmelCase_	,      scheduler=lowerCAmelCase_	,      learned_classifier_free_sampling_embeddings=lowerCAmelCase_	,      )
								lowerCamelCase_    :					Optional[int]   =						pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								lowerCamelCase_    :					List[Any]   =						'''teddy bear playing in the pool'''

								lowerCamelCase_    :					Tuple   =						torch.Generator(device=lowerCAmelCase_					).manual_seed(0					)
								lowerCamelCase_    :					Dict   =						pipe([prompt]	,      generator=lowerCAmelCase_	,      num_inference_steps=2	,      output_type='''np'''					)
								lowerCamelCase_    :					List[str]   =						output.images

								lowerCamelCase_    :					Dict   =						torch.Generator(device=lowerCAmelCase_					).manual_seed(0					)
								lowerCamelCase_    :					List[str]   =						pipe(
								    [prompt]	,      generator=lowerCAmelCase_	,      output_type='''np'''	,      return_dict=lowerCAmelCase_	,      num_inference_steps=2					)[0]

								lowerCamelCase_    :					Optional[int]   =						image[0, -3:, -3:, -1]
								lowerCamelCase_    :					Dict   =						image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								lowerCamelCase_    :					Tuple   =						np.array([0.65_51, 0.61_68, 0.50_08, 0.56_76, 0.56_59, 0.42_95, 0.60_73, 0.55_99, 0.49_92]					)

								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					):
								lowerCamelCase_    :					Tuple   =						'''cpu'''

								lowerCamelCase_    :					List[str]   =						self.dummy_vqvae
								lowerCamelCase_    :					Optional[Any]   =						self.dummy_text_encoder
								lowerCamelCase_    :					int   =						self.dummy_tokenizer
								lowerCamelCase_    :					str   =						self.dummy_transformer
								lowerCamelCase_    :					str   =						VQDiffusionScheduler(self.num_embed					)
								lowerCamelCase_    :					str   =						LearnedClassifierFreeSamplingEmbeddings(
								    learnable=lowerCAmelCase_	,      hidden_size=self.text_embedder_hidden_size	,      length=tokenizer.model_max_length					)

								lowerCamelCase_    :					Optional[int]   =						VQDiffusionPipeline(
								    vqvae=lowerCAmelCase_	,      text_encoder=lowerCAmelCase_	,      tokenizer=lowerCAmelCase_	,      transformer=lowerCAmelCase_	,      scheduler=lowerCAmelCase_	,      learned_classifier_free_sampling_embeddings=lowerCAmelCase_	,      )
								lowerCamelCase_    :					Any   =						pipe.to(lowerCAmelCase_					)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_					)

								lowerCamelCase_    :					Optional[int]   =						'''teddy bear playing in the pool'''

								lowerCamelCase_    :					List[str]   =						torch.Generator(device=lowerCAmelCase_					).manual_seed(0					)
								lowerCamelCase_    :					Dict   =						pipe([prompt]	,      generator=lowerCAmelCase_	,      num_inference_steps=2	,      output_type='''np'''					)
								lowerCamelCase_    :					List[Any]   =						output.images

								lowerCamelCase_    :					Union[str, Any]   =						torch.Generator(device=lowerCAmelCase_					).manual_seed(0					)
								lowerCamelCase_    :					str   =						pipe(
								    [prompt]	,      generator=lowerCAmelCase_	,      output_type='''np'''	,      return_dict=lowerCAmelCase_	,      num_inference_steps=2					)[0]

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

								assert image.shape == (1, 2_4, 2_4, 3)

								lowerCamelCase_    :					Tuple   =						np.array([0.66_93, 0.60_75, 0.49_59, 0.57_01, 0.55_83, 0.43_33, 0.61_71, 0.56_84, 0.49_88]					)

								assert np.abs(image_slice.flatten() - expected_slice					).max() < 2.0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2

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

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






			def 				UpperCAmelCase__       (self					):
								lowerCamelCase_    :					List[Any]   =						load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
								    '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'''					)

								lowerCamelCase_    :					List[str]   =						VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq'''					)
								lowerCamelCase_    :					Tuple   =						pipeline.to(lowerCAmelCase_					)
								pipeline.set_progress_bar_config(disable=lowerCAmelCase_					)

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								lowerCamelCase_    :					Optional[Any]   =						torch.Generator(device=lowerCAmelCase_					).manual_seed(0					)
								lowerCamelCase_    :					Any   =						pipeline(
								    '''teddy bear playing in the pool'''	,      num_images_per_prompt=1	,      generator=lowerCAmelCase_	,      output_type='''np'''	,      )

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

								assert image.shape == (2_5_6, 2_5_6, 3)
								assert np.abs(expected_image - image					).max() < 2.0









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


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    Any						    =  {
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """pegasus"""
  a_        = ["""past_key_values"""]
  a_        = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






  def __init__(				self :		List[str]					,  lowerCAmelCase_ :		Union[str, Any]=5_0_2_6_5					,  lowerCAmelCase_ :		Union[str, Any]=1_0_2_4					,  lowerCAmelCase_ :		Union[str, Any]=1_2					,  lowerCAmelCase_ :		Dict=4_0_9_6					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		List[Any]=1_2					,  lowerCAmelCase_ :		Union[str, Any]=4_0_9_6					,  lowerCAmelCase_ :		Union[str, Any]=1_6					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Optional[int]=0.0					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Any=True					,  lowerCAmelCase_ :		Dict="gelu"					,  lowerCAmelCase_ :		Optional[int]=1_0_2_4					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		str=0.0					,  lowerCAmelCase_ :		Union[str, Any]=0.0					,  lowerCAmelCase_ :		Optional[int]=0.02					,  lowerCAmelCase_ :		Tuple=0					,  lowerCAmelCase_ :		Tuple=False					,  lowerCAmelCase_ :		Union[str, Any]=0					,  lowerCAmelCase_ :		int=1					,  lowerCAmelCase_ :		Tuple=1					,  **lowerCAmelCase_ :		Tuple					,  )							->						List[str]:
     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	max_position_embeddings
     __lowerCAmelCase								=	d_model
     __lowerCAmelCase								=	encoder_ffn_dim
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	encoder_attention_heads
     __lowerCAmelCase								=	decoder_ffn_dim
     __lowerCAmelCase								=	decoder_layers
     __lowerCAmelCase								=	decoder_attention_heads
     __lowerCAmelCase								=	dropout
     __lowerCAmelCase								=	attention_dropout
     __lowerCAmelCase								=	activation_dropout
     __lowerCAmelCase								=	activation_function
     __lowerCAmelCase								=	init_std
     __lowerCAmelCase								=	encoder_layerdrop
     __lowerCAmelCase								=	decoder_layerdrop
     __lowerCAmelCase								=	use_cache
     __lowerCAmelCase								=	encoder_layers
     __lowerCAmelCase								=	scale_embedding  # scale factor will be sqrt(d_model) if True
     super().__init__(
         pad_token_id=lowerCAmelCase_					,  eos_token_id=lowerCAmelCase_					,  is_encoder_decoder=lowerCAmelCase_					,  decoder_start_token_id=lowerCAmelCase_					,  forced_eos_token_id=lowerCAmelCase_					,  **lowerCAmelCase_					,  )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						int:
     return self.encoder_attention_heads






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						int:
     return self.d_model

