





'''simple docstring'''


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Any         =					StableDiffusionXLImgaImgPipeline
 __lowerCamelCase						:							List[Any]         =					TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
 __lowerCamelCase						:							Union[str, Any]         =					PipelineTesterMixin.required_optional_params - {"latents"}
 __lowerCamelCase						:							Optional[Any]         =					TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 __lowerCamelCase						:							List[Any]         =					IMAGE_TO_IMAGE_IMAGE_PARAMS
 __lowerCamelCase						:							List[str]         =					IMAGE_TO_IMAGE_IMAGE_PARAMS




 def 							_snake_case   (	self		)    ->							List[str]:
     torch.manual_seed(0		)
     _lowerCAmelCase				=      UNetaDConditionModel(
         block_out_channels=(32, 64)			,     layers_per_block=2			,     sample_size=32			,     in_channels=4			,     out_channels=4			,     down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,     up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,     attention_head_dim=(2, 4)			,     use_linear_projection=_lowerCAmelCase			,     addition_embed_type="text_time"			,     addition_time_embed_dim=8			,     transformer_layers_per_block=(1, 2)			,     projection_class_embeddings_input_dim=80			,     cross_attention_dim=64			,     )
     _lowerCAmelCase				=      EulerDiscreteScheduler(
         beta_start=0.00085			,     beta_end=0.012			,     steps_offset=1			,     beta_schedule="scaled_linear"			,     timestep_spacing="leading"			,     )
     torch.manual_seed(0		)
     _lowerCAmelCase				=      AutoencoderKL(
         block_out_channels=[32, 64]			,     in_channels=3			,     out_channels=3			,     down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,     up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,     latent_channels=4			,     sample_size=128			,     )
     torch.manual_seed(0		)
     _lowerCAmelCase				=      CLIPTextConfig(
         bos_token_id=0			,     eos_token_id=2			,     hidden_size=32			,     intermediate_size=37			,     layer_norm_eps=1E-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=1000			,     hidden_act="gelu"			,     projection_dim=32			,     )
     _lowerCAmelCase				=      CLIPTextModel(_lowerCAmelCase		)
     _lowerCAmelCase				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,     local_files_only=_lowerCAmelCase		)

     _lowerCAmelCase				=      CLIPTextModelWithProjection(_lowerCAmelCase		)
     _lowerCAmelCase				=      CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"			,     local_files_only=_lowerCAmelCase		)

     _lowerCAmelCase				=      {
         "unet": unet,
         "scheduler": scheduler,
         "vae": vae,
         "text_encoder": text_encoder,
         "tokenizer": tokenizer,
         "text_encoder_2": text_encoder_a,
         "tokenizer_2": tokenizer_a,
         # "safety_checker": None,
         # "feature_extractor": None,
     }
     return components




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase=0		)    ->							List[Any]:
     _lowerCAmelCase				=      floats_tensor((1, 3, 32, 32)			,     rng=random.Random(_lowerCAmelCase		)		).to(_lowerCAmelCase		)
     _lowerCAmelCase				=      image / 2 + 0.5
     if str(_lowerCAmelCase		).startswith("mps"		):
         _lowerCAmelCase				=      torch.manual_seed(_lowerCAmelCase		)
     else:
         _lowerCAmelCase				=      torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
     _lowerCAmelCase				=      {
         "prompt": "A painting of a squirrel eating a burger",
         "image": image,
         "generator": generator,
         "num_inference_steps": 2,
         "guidance_scale": 5.0,
         "output_type": "numpy",
         "strength": 0.75,
     }
     return inputs




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     _lowerCAmelCase				=      "cpu"  # ensure determinism for the device-dependent torch.Generator
     _lowerCAmelCase				=      self.get_dummy_components()
     _lowerCAmelCase				=      StableDiffusionXLImgaImgPipeline(**_lowerCAmelCase		)
     _lowerCAmelCase				=      sd_pipe.to(_lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_dummy_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      sd_pipe(**_lowerCAmelCase		).images
     _lowerCAmelCase				=      image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)

     _lowerCAmelCase				=      np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]		)

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




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     super().test_attention_slicing_forward_pass(expected_max_diff=3E-3		)




 def 							_snake_case   (	self		)    ->							Dict:
     super().test_inference_batch_single_identical(expected_max_diff=3E-3		)




 def 							_snake_case   (	self		)    ->							Any:
     pass





 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.get_dummy_components()
     _lowerCAmelCase				=      StableDiffusionXLImgaImgPipeline(**_lowerCAmelCase		)
     _lowerCAmelCase				=      sd_pipe.to(_lowerCAmelCase		)
     _lowerCAmelCase				=      sd_pipe.to(_lowerCAmelCase		)
     sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     # forward without prompt embeds
     _lowerCAmelCase				=      self.get_dummy_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      3 * ["this is a negative prompt"]
     _lowerCAmelCase				=      negative_prompt
     _lowerCAmelCase				=      3 * [inputs["prompt"]]

     _lowerCAmelCase				=      sd_pipe(**_lowerCAmelCase		)
     _lowerCAmelCase				=      output.images[0, -3:, -3:, -1]

     # forward with prompt embeds
     _lowerCAmelCase				=      self.get_dummy_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      3 * ["this is a negative prompt"]
     _lowerCAmelCase				=      3 * [inputs.pop("prompt"		)]

     (
         (
         _lowerCAmelCase      
     )			,			(
         _lowerCAmelCase      
     )			,			(
         _lowerCAmelCase      
     )			,			(
         _lowerCAmelCase      
     )			,			      
     )				=      sd_pipe.encode_prompt(_lowerCAmelCase			,     negative_prompt=_lowerCAmelCase		)

     _lowerCAmelCase				=      sd_pipe(
         **_lowerCAmelCase			,     prompt_embeds=_lowerCAmelCase			,     negative_prompt_embeds=_lowerCAmelCase			,     pooled_prompt_embeds=_lowerCAmelCase			,     negative_pooled_prompt_embeds=_lowerCAmelCase			,     )
     _lowerCAmelCase				=      output.images[0, -3:, -3:, -1]

     # make sure that it's equal
     assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()		).max() < 1E-4







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




 def 							_snake_case   (	self		)    ->							List[str]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase="cpu"			,     _lowerCAmelCase=torch.floataa			,     _lowerCAmelCase=0		)    ->							Union[str, Any]:
     _lowerCAmelCase				=      torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
     _lowerCAmelCase				=      np.random.RandomState(_lowerCAmelCase		).standard_normal((1, 4, 64, 64)		)
     _lowerCAmelCase				=      torch.from_numpy(_lowerCAmelCase		).to(device=_lowerCAmelCase			,     dtype=_lowerCAmelCase		)
     _lowerCAmelCase				=      {
         "prompt": "a photograph of an astronaut riding a horse",
         "latents": latents,
         "generator": generator,
         "num_inference_steps": 3,
         "guidance_scale": 7.5,
         "output_type": "numpy",
     }
     return inputs





 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base"		)
     pipe.to(_lowerCAmelCase		)
     pipe.set_progress_bar_config(disable=_lowerCAmelCase		)

     _lowerCAmelCase				=      self.get_inputs(_lowerCAmelCase		)
     _lowerCAmelCase				=      pipe(**_lowerCAmelCase		).images
     _lowerCAmelCase				=      image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 512, 512, 3)
     _lowerCAmelCase				=      np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]		)
     assert np.abs(image_slice - expected_slice		).max() < 7E-3









'''simple docstring'''


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


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					"speech_to_text_2"
 __lowerCamelCase						:							Union[str, Any]         =					["past_key_values"]
 __lowerCamelCase						:							Tuple         =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}




 def __init__(	self			,     _lowerCAmelCase=10000			,     _lowerCAmelCase=6			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=4			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=True			,     _lowerCAmelCase="relu"			,     _lowerCAmelCase=256			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=2			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1024			,     **_lowerCAmelCase			,     )    ->							Dict:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      d_model
     _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				=      decoder_layerdrop
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      scale_embedding  # scale factor will be sqrt(d_model) if True
     _lowerCAmelCase				=      max_target_positions

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


