




"""simple docstring"""





import functools
import operator

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


A   =       logging.get_logger(__name__)

A   =       {
    """facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
    # See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}



class      a__				(	__magic_name__   ):
			lowercase_    =	"wav2vec2"


			def __init__(      self      :    Union[str, Any]      ,  UpperCamelCase_      :    Union[str, Any]=32      ,  UpperCamelCase_      :    Tuple=768      ,  UpperCamelCase_      :    List[Any]=12      ,  UpperCamelCase_      :    Any=12      ,  UpperCamelCase_      :    List[str]=3072      ,  UpperCamelCase_      :    Any="gelu"      ,  UpperCamelCase_      :    List[Any]=0.1      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  UpperCamelCase_      :    List[Any]=0.1      ,  UpperCamelCase_      :    Any=0.0      ,  UpperCamelCase_      :    List[str]=0.0      ,  UpperCamelCase_      :    List[Any]=0.1      ,  UpperCamelCase_      :    Dict=0.1      ,  UpperCamelCase_      :    Any=0.02      ,  UpperCamelCase_      :    List[Any]=1e-5      ,  UpperCamelCase_      :    Tuple="group"      ,  UpperCamelCase_      :    List[str]="gelu"      ,  UpperCamelCase_      :    List[Any]=(512, 512, 512, 512, 512, 512, 512)      ,  UpperCamelCase_      :    Dict=(5, 2, 2, 2, 2, 2, 2)      ,  UpperCamelCase_      :    List[str]=(10, 3, 3, 3, 3, 2, 2)      ,  UpperCamelCase_      :    List[str]=False      ,  UpperCamelCase_      :    int=128      ,  UpperCamelCase_      :    Any=16      ,  UpperCamelCase_      :    Any=False      ,  UpperCamelCase_      :    Dict=True      ,  UpperCamelCase_      :    Optional[int]=0.05      ,  UpperCamelCase_      :    List[str]=10      ,  UpperCamelCase_      :    Any=2      ,  UpperCamelCase_      :    Optional[int]=0.0      ,  UpperCamelCase_      :    List[Any]=10      ,  UpperCamelCase_      :    List[str]=0      ,  UpperCamelCase_      :    str=320      ,  UpperCamelCase_      :    List[str]=2      ,  UpperCamelCase_      :    Optional[int]=0.1      ,  UpperCamelCase_      :    List[str]=100      ,  UpperCamelCase_      :    Tuple=256      ,  UpperCamelCase_      :    List[Any]=256      ,  UpperCamelCase_      :    Dict=0.1      ,  UpperCamelCase_      :    Tuple="sum"      ,  UpperCamelCase_      :    List[str]=False      ,  UpperCamelCase_      :    Optional[int]=False      ,  UpperCamelCase_      :    Optional[Any]=256      ,  UpperCamelCase_      :    Optional[int]=(512, 512, 512, 512, 1500)      ,  UpperCamelCase_      :    int=(5, 3, 3, 1, 1)      ,  UpperCamelCase_      :    Any=(1, 2, 3, 1, 1)      ,  UpperCamelCase_      :    Dict=512      ,  UpperCamelCase_      :    Optional[Any]=0      ,  UpperCamelCase_      :    Optional[int]=1      ,  UpperCamelCase_      :    List[str]=2      ,  UpperCamelCase_      :    Union[str, Any]=False      ,  UpperCamelCase_      :    Dict=3      ,  UpperCamelCase_      :    List[str]=2      ,  UpperCamelCase_      :    int=3      ,  UpperCamelCase_      :    Union[str, Any]=None      ,  UpperCamelCase_      :    Any=None      ,  **UpperCamelCase_      :    Union[str, Any]      ,  ):


						"""simple docstring"""




						super().__init__(**UpperCamelCase_      ,  pad_token_id=UpperCamelCase_      ,  bos_token_id=UpperCamelCase_      ,  eos_token_id=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					hidden_size
						__UpperCAmelCase	: Any		=					feat_extract_norm
						__UpperCAmelCase	: Union[str, Any]		=					feat_extract_activation
						__UpperCAmelCase	: Tuple		=					list(UpperCamelCase_)
						__UpperCAmelCase	: List[Any]		=					list(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					list(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					conv_bias
						__UpperCAmelCase	: str		=					num_conv_pos_embeddings
						__UpperCAmelCase	: Optional[Any]		=					num_conv_pos_embedding_groups
						__UpperCAmelCase	: str		=					len(self.conv_dim)
						__UpperCAmelCase	: Optional[int]		=					num_hidden_layers
						__UpperCAmelCase	: Tuple		=					intermediate_size
						__UpperCAmelCase	: Tuple		=					hidden_act
						__UpperCAmelCase	: Dict		=					num_attention_heads
						__UpperCAmelCase	: str		=					hidden_dropout
						__UpperCAmelCase	: Any		=					attention_dropout
						__UpperCAmelCase	: Union[str, Any]		=					activation_dropout
						__UpperCAmelCase	: Optional[int]		=					feat_proj_dropout
						__UpperCAmelCase	: str		=					final_dropout
						__UpperCAmelCase	: str		=					layerdrop
						__UpperCAmelCase	: int		=					layer_norm_eps
						__UpperCAmelCase	: Any		=					initializer_range
						__UpperCAmelCase	: List[Any]		=					vocab_size
						__UpperCAmelCase	: Optional[Any]		=					do_stable_layer_norm
						__UpperCAmelCase	: str		=					use_weighted_layer_sum

						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	: Union[str, Any]		=					apply_spec_augment
						__UpperCAmelCase	: Optional[Any]		=					mask_time_prob
						__UpperCAmelCase	: List[str]		=					mask_time_length
						__UpperCAmelCase	: str		=					mask_time_min_masks
						__UpperCAmelCase	: Optional[int]		=					mask_feature_prob
						__UpperCAmelCase	: Union[str, Any]		=					mask_feature_length
						__UpperCAmelCase	: str		=					mask_feature_min_masks

						# parameters for pretraining with codevector quantized representations
						__UpperCAmelCase	: int		=					num_codevectors_per_group
						__UpperCAmelCase	: Dict		=					num_codevector_groups
						__UpperCAmelCase	: List[str]		=					contrastive_logits_temperature
						__UpperCAmelCase	: int		=					feat_quantizer_dropout
						__UpperCAmelCase	: Optional[Any]		=					num_negatives
						__UpperCAmelCase	: str		=					codevector_dim
						__UpperCAmelCase	: Dict		=					proj_codevector_dim
						__UpperCAmelCase	: Union[str, Any]		=					diversity_loss_weight

						# ctc loss
						__UpperCAmelCase	: Optional[int]		=					ctc_loss_reduction
						__UpperCAmelCase	: Optional[int]		=					ctc_zero_infinity

						# adapter
						__UpperCAmelCase	: Tuple		=					add_adapter
						__UpperCAmelCase	: Any		=					adapter_kernel_size
						__UpperCAmelCase	: Union[str, Any]		=					adapter_stride
						__UpperCAmelCase	: Union[str, Any]		=					num_adapter_layers
						__UpperCAmelCase	: Dict		=					output_hidden_size or hidden_size
						__UpperCAmelCase	: str		=					adapter_attn_dim

						# SequenceClassification-specific parameter. Feel free to ignore for other classes.
						__UpperCAmelCase	: List[str]		=					classifier_proj_size

						# XVector-specific parameters. Feel free to ignore for other classes.
						__UpperCAmelCase	: List[str]		=					list(UpperCamelCase_)
						__UpperCAmelCase	: Optional[int]		=					list(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					list(UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					xvector_output_dim


			@property
			def       a_     (      self      :    List[str]):


						"""simple docstring"""




						return functools.reduce(operator.mul      ,  self.conv_stride      ,  1)








"""simple docstring"""





import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()



class      a__				(	__magic_name__							,    unittest.TestCase   ):
			lowercase_    =	LDMTextToImagePipeline
			lowercase_    =	TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "negative_prompt_embeds",
			    "cross_attention_kwargs",
			    "prompt_embeds",
			}
			lowercase_    =	PipelineTesterMixin.required_optional_params - {
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			lowercase_    =	TEXT_TO_IMAGE_BATCH_PARAMS
			lowercase_    =	False


			def       a_     (      self      :    Union[str, Any]):


						"""simple docstring"""




						torch.manual_seed(0)
						__UpperCAmelCase	: str		=					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")      ,  cross_attention_dim=32      ,  )
						__UpperCAmelCase	: Dict		=					DDIMScheduler(
						    beta_start=0.00085      ,  beta_end=0.012      ,  beta_schedule="scaled_linear"      ,  clip_sample=UpperCamelCase_      ,  set_alpha_to_one=UpperCamelCase_      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: Optional[int]		=					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      ,  )
						torch.manual_seed(0)
						__UpperCAmelCase	: List[str]		=					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      ,  )
						__UpperCAmelCase	: List[str]		=					CLIPTextModel(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

						__UpperCAmelCase	: Optional[int]		=					{
						    "unet": unet,
						    "scheduler": scheduler,
						    "vqvae": vae,
						    "bert": text_encoder,
						    "tokenizer": tokenizer,
						}
						return components


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Optional[Any]      ,  UpperCamelCase_      :    Tuple=0):


						"""simple docstring"""




						if str(UpperCamelCase_).startswith("mps"):
									__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						else:
									__UpperCAmelCase	: Tuple		=					torch.Generator(device=UpperCamelCase_).manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "generator": generator,
						    "num_inference_steps": 2,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Tuple):


						"""simple docstring"""




						__UpperCAmelCase	: Any		=					"cpu"  # ensure determinism for the device-dependent torch.Generator

						__UpperCAmelCase	: Union[str, Any]		=					self.get_dummy_components()
						__UpperCAmelCase	: Union[str, Any]		=					LDMTextToImagePipeline(**UpperCamelCase_)
						pipe.to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: int		=					self.get_dummy_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: List[Any]		=					image[0, -3:, -3:, -1]

						assert image.shape == (1, 16, 16, 3)
						__UpperCAmelCase	: List[str]		=					np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])

						assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3






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


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()


			def       a_     (      self      :    List[str]      ,  UpperCamelCase_      :    str      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Dict=0):


						"""simple docstring"""




						__UpperCAmelCase	: Optional[Any]		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Optional[Any]		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: str		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 3,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Optional[int]):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Optional[int]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: Union[str, Any]		=					pipe(**UpperCamelCase_).images
						__UpperCAmelCase	: Dict		=					image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 256, 256, 3)
						__UpperCAmelCase	: List[Any]		=					np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
						__UpperCAmelCase	: List[Any]		=					np.abs(expected_slice - image_slice).max()
						assert max_diff < 1e-3






@nightly
@require_torch_gpu
class      a__				(	unittest.TestCase   ):


			def       a_     (      self      :    Optional[Any]):


						"""simple docstring"""




						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()


			def       a_     (      self      :    Tuple      ,  UpperCamelCase_      :    Tuple      ,  UpperCamelCase_      :    int=torch.floataa      ,  UpperCamelCase_      :    Optional[int]=0):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					torch.manual_seed(UpperCamelCase_)
						__UpperCAmelCase	: Tuple		=					np.random.RandomState(UpperCamelCase_).standard_normal((1, 4, 32, 32))
						__UpperCAmelCase	: Optional[Any]		=					torch.from_numpy(UpperCamelCase_).to(device=UpperCamelCase_      ,  dtype=UpperCamelCase_)
						__UpperCAmelCase	: Dict		=					{
						    "prompt": "A painting of a squirrel eating a burger",
						    "latents": latents,
						    "generator": generator,
						    "num_inference_steps": 50,
						    "guidance_scale": 6.0,
						    "output_type": "numpy",
						}
						return inputs


			def       a_     (      self      :    Dict):


						"""simple docstring"""




						__UpperCAmelCase	: str		=					LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(UpperCamelCase_)
						pipe.set_progress_bar_config(disable=UpperCamelCase_)

						__UpperCAmelCase	: Union[str, Any]		=					self.get_inputs(UpperCamelCase_)
						__UpperCAmelCase	: List[str]		=					pipe(**UpperCamelCase_).images[0]

						__UpperCAmelCase	: int		=					load_numpy(
						    "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
						__UpperCAmelCase	: int		=					np.abs(expected_image - image).max()
						assert max_diff < 1e-3


