




"""simple docstring"""

import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()





class 							lowercase_			(	__lowerCAmelCase     ,      __lowerCAmelCase     ,      __lowerCAmelCase     ,      unittest.TestCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Optional[int]     =      StableUnCLIPPipeline
 UpperCAmelCase       :	List[Any]     =      TEXT_TO_IMAGE_PARAMS
 UpperCAmelCase       :	int     =      TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase       :	Union[str, Any]     =      TEXT_TO_IMAGE_IMAGE_PARAMS
 UpperCAmelCase       :	Union[str, Any]     =      TEXT_TO_IMAGE_IMAGE_PARAMS

 # TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
 UpperCAmelCase       :	Dict     =      False

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       _A     =  32
       _A     =  embedder_hidden_size

       # prior components

       torch.manual_seed(0						)
       _A     =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       torch.manual_seed(0						)
       _A     =  CLIPTextModelWithProjection(
           CLIPTextConfig(
               bos_token_id=0       ,				eos_token_id=2       ,				hidden_size=_UpperCAmelCase       ,				projection_dim=_UpperCAmelCase       ,				intermediate_size=37       ,				layer_norm_eps=1E-0_5       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				pad_token_id=1       ,				vocab_size=1_000       ,				)						)

       torch.manual_seed(0						)
       _A     =  PriorTransformer(
           num_attention_heads=2       ,				attention_head_dim=12       ,				embedding_dim=_UpperCAmelCase       ,				num_layers=1       ,				)

       torch.manual_seed(0						)
       _A     =  DDPMScheduler(
           variance_type='fixed_small_log'       ,				prediction_type='sample'       ,				num_train_timesteps=1_000       ,				clip_sample=_UpperCAmelCase       ,				clip_sample_range=5.0       ,				beta_schedule='squaredcos_cap_v2'       ,				)

       # regular denoising components

       torch.manual_seed(0						)
       _A     =  StableUnCLIPImageNormalizer(embedding_dim=_UpperCAmelCase						)
       _A     =  DDPMScheduler(beta_schedule='squaredcos_cap_v2'						)

       torch.manual_seed(0						)
       _A     =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

       torch.manual_seed(0						)
       _A     =  CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0       ,				eos_token_id=2       ,				hidden_size=_UpperCAmelCase       ,				projection_dim=32       ,				intermediate_size=37       ,				layer_norm_eps=1E-0_5       ,				num_attention_heads=4       ,				num_hidden_layers=5       ,				pad_token_id=1       ,				vocab_size=1_000       ,				)						)

       torch.manual_seed(0						)
       _A     =  UNetaDConditionModel(
           sample_size=32       ,				in_channels=4       ,				out_channels=4       ,				down_block_types=('CrossAttnDownBlock2D', 'DownBlock2D')       ,				up_block_types=('UpBlock2D', 'CrossAttnUpBlock2D')       ,				block_out_channels=(32, 64)       ,				attention_head_dim=(2, 4)       ,				class_embed_type='projection'       ,				projection_class_embeddings_input_dim=embedder_projection_dim * 2       ,				cross_attention_dim=_UpperCAmelCase       ,				layers_per_block=1       ,				upcast_attention=_UpperCAmelCase       ,				use_linear_projection=_UpperCAmelCase       ,				)

       torch.manual_seed(0						)
       _A     =  DDIMScheduler(
           beta_schedule='scaled_linear'       ,				beta_start=0.0_0085       ,				beta_end=0.012       ,				prediction_type='v_prediction'       ,				set_alpha_to_one=_UpperCAmelCase       ,				steps_offset=1       ,				)

       torch.manual_seed(0						)
       _A     =  AutoencoderKL()

       _A     =  {
           # prior components
           'prior_tokenizer': prior_tokenizer,
           'prior_text_encoder': prior_text_encoder,
           'prior': prior,
           'prior_scheduler': prior_scheduler,
           # image noising components
           'image_normalizer': image_normalizer,
           'image_noising_scheduler': image_noising_scheduler,
           # regular denoising components
           'tokenizer': tokenizer,
           'text_encoder': text_encoder,
           'unet': unet,
           'scheduler': scheduler,
           'vae': vae,
       }

       return components

 def      lowerCAmelCase_				(					self			:			int       ,				_UpperCAmelCase			:			Tuple       ,				_UpperCAmelCase			:			List[Any]=0						):
       if str(_UpperCAmelCase						).startswith('mps'						):
             _A     =  torch.manual_seed(_UpperCAmelCase						)
       else:
             _A     =  torch.Generator(device=_UpperCAmelCase						).manual_seed(_UpperCAmelCase						)
       _A     =  {
           'prompt': 'A painting of a squirrel eating a burger',
           'generator': generator,
           'num_inference_steps': 2,
           'prior_num_inference_steps': 2,
           'output_type': 'numpy',
       }
       return inputs

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  torch_device == 'cpu'

       self._test_attention_slicing_forward_pass(test_max_difference=_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  torch_device in ['cpu', 'mps']

       self._test_inference_batch_single_identical(test_max_difference=_UpperCAmelCase						)





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



 '''simple docstring'''

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

 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'						)

       _A     =  StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,				torch_dtype=torch.floataa						)
       pipe.to(_UpperCAmelCase						)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
       # stable unclip will oom when integration tests are run on a V100,
       # so turn on memory savings
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       _A     =  torch.Generator(device='cpu'						).manual_seed(0						)
       _A     =  pipe('anime turle'       ,				generator=_UpperCAmelCase       ,				output_type='np'						)

       _A     =  output.images[0]

       assert image.shape == (768, 768, 3)

       assert_mean_pixel_difference(_UpperCAmelCase       ,				_UpperCAmelCase						)



 def      lowerCAmelCase_				(					self			:			Tuple						):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       _A     =  StableUnCLIPPipeline.from_pretrained('fusing/stable-unclip-2-1-l'       ,				torch_dtype=torch.floataa						)
       _A     =  pipe.to(_UpperCAmelCase						)
       pipe.set_progress_bar_config(disable=_UpperCAmelCase						)
       pipe.enable_attention_slicing()
       pipe.enable_sequential_cpu_offload()

       _A     =  pipe(
           'anime turtle'       ,				prior_num_inference_steps=2       ,				num_inference_steps=2       ,				output_type='np'       ,				)

       _A     =  torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9







"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/s2t-small-librispeech-asr''': (
        '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	str     =      '''speech_to_text'''
 UpperCAmelCase       :	List[Any]     =      ['''past_key_values''']
 UpperCAmelCase       :	Tuple     =      {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}

 def __init__(					self			:			int       ,				_UpperCAmelCase			:			Union[str, Any]=10_000       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			int=2_048       ,				_UpperCAmelCase			:			Optional[Any]=4       ,				_UpperCAmelCase			:			List[str]=6       ,				_UpperCAmelCase			:			Tuple=2_048       ,				_UpperCAmelCase			:			str=4       ,				_UpperCAmelCase			:			int=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			Optional[int]=True       ,				_UpperCAmelCase			:			Optional[Any]=True       ,				_UpperCAmelCase			:			Union[str, Any]="relu"       ,				_UpperCAmelCase			:			List[Any]=256       ,				_UpperCAmelCase			:			Optional[int]=0.1       ,				_UpperCAmelCase			:			Any=0.0       ,				_UpperCAmelCase			:			Dict=0.0       ,				_UpperCAmelCase			:			str=0.02       ,				_UpperCAmelCase			:			Any=2       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			List[str]=1       ,				_UpperCAmelCase			:			Tuple=0       ,				_UpperCAmelCase			:			Tuple=2       ,				_UpperCAmelCase			:			List[str]=6_000       ,				_UpperCAmelCase			:			Optional[Any]=1_024       ,				_UpperCAmelCase			:			Optional[Any]=2       ,				_UpperCAmelCase			:			Any=(5, 5)       ,				_UpperCAmelCase			:			int=1_024       ,				_UpperCAmelCase			:			str=80       ,				_UpperCAmelCase			:			Any=1       ,				**_UpperCAmelCase			:			Tuple       ,				):
       _A     =  vocab_size
       _A     =  d_model
       _A     =  encoder_ffn_dim
       _A     =  encoder_layers
       _A     =  encoder_attention_heads
       _A     =  decoder_ffn_dim
       _A     =  decoder_layers
       _A     =  decoder_attention_heads
       _A     =  dropout
       _A     =  attention_dropout
       _A     =  activation_dropout
       _A     =  activation_function
       _A     =  init_std
       _A     =  encoder_layerdrop
       _A     =  decoder_layerdrop
       _A     =  use_cache
       _A     =  encoder_layers
       _A     =  scale_embedding  # scale factor will be sqrt(d_model) if True
       _A     =  max_source_positions
       _A     =  max_target_positions
       _A     =  num_conv_layers
       _A     =  list(_UpperCAmelCase						)
       _A     =  conv_channels
       _A     =  input_feat_per_channel
       _A     =  input_channels

       if len(self.conv_kernel_sizes						) != self.num_conv_layers:
             raise ValueError(
                 'Configuration for convolutional module is incorrect. '
                 'It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` '
                 F'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes						)}`, '''
                 F'''`config.num_conv_layers = {self.num_conv_layers}`.'''						)

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

