"""simple docstring"""


import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__  		=						logging.get_logger(__name__)






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      WavaVecaForSequenceClassification.from_pretrained(lowercase   ,config=lowercase		)
  _UpperCAmelCase						 =      downstream_dict["""projector.weight"""]
  _UpperCAmelCase						 =      downstream_dict["""projector.bias"""]
  _UpperCAmelCase						 =      downstream_dict["""model.post_net.linear.weight"""]
  _UpperCAmelCase						 =      downstream_dict["""model.post_net.linear.bias"""]
  return model






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      WavaVecaForAudioFrameClassification.from_pretrained(lowercase   ,config=lowercase		)
  _UpperCAmelCase						 =      downstream_dict["""model.linear.weight"""]
  _UpperCAmelCase						 =      downstream_dict["""model.linear.bias"""]
  return model






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      WavaVecaForXVector.from_pretrained(lowercase   ,config=lowercase		)
  _UpperCAmelCase						 =      downstream_dict["""connector.weight"""]
  _UpperCAmelCase						 =      downstream_dict["""connector.bias"""]
  for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    _UpperCAmelCase						 =      downstream_dict[
        f'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
    ]
    _UpperCAmelCase						 =      downstream_dict[f'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

  _UpperCAmelCase						 =      downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
  _UpperCAmelCase						 =      downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
  _UpperCAmelCase						 =      downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
  _UpperCAmelCase						 =      downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
  _UpperCAmelCase						 =      downstream_dict["""objective.W"""]
  return model






@torch.no_grad()
def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      torch.load(lowercase   ,map_location="""cpu"""		)

  _UpperCAmelCase						 =      checkpoint["""Downstream"""]

  _UpperCAmelCase						 =      WavaVecaConfig.from_pretrained(lowercase		)
  _UpperCAmelCase						 =      WavaVecaFeatureExtractor.from_pretrained(
      lowercase   ,return_attention_mask=lowercase   ,do_normalize=lowercase		)

  _UpperCAmelCase						 =      hf_config.architectures[0]
  if arch.endswith("""ForSequenceClassification"""		):
    _UpperCAmelCase						 =      convert_classification(lowercase   ,lowercase   ,lowercase		)
  elif arch.endswith("""ForAudioFrameClassification"""		):
    _UpperCAmelCase						 =      convert_diarization(lowercase   ,lowercase   ,lowercase		)
  elif arch.endswith("""ForXVector"""		):
    _UpperCAmelCase						 =      convert_xvector(lowercase   ,lowercase   ,lowercase		)
  else:
    raise NotImplementedError(f'''S3PRL weights conversion is not supported for {arch}'''		)

  if hf_config.use_weighted_layer_sum:
    _UpperCAmelCase						 =      checkpoint["""Featurizer"""]["""weights"""]

  hf_feature_extractor.save_pretrained(lowercase		)
  hf_model.save_pretrained(lowercase		)


if __name__ == "__main__":
      UpperCAmelCase__  		=						argparse.ArgumentParser()
      parser.add_argument(
          """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
      )
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
      parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
      parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
      UpperCAmelCase__  		=						parser.parse_args()
      convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

"""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        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
    _snake_case     :  Optional[int]     							=							StableUnCLIPPipeline
    _snake_case     :  List[str]     							=							TEXT_TO_IMAGE_PARAMS
    _snake_case     :  Tuple     							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case     :  List[Any]     							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case     :  str     							=							TEXT_TO_IMAGE_IMAGE_PARAMS

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


    def      lowerCAmelCase_  (					self	:  Dict					):
      _UpperCAmelCase						 =      32
      _UpperCAmelCase						 =      embedder_hidden_size

      # prior components

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

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				projection_dim=__lowerCAmelCase   ,				intermediate_size=37   ,				layer_norm_eps=1e-0_5   ,				num_attention_heads=4   ,				num_hidden_layers=5   ,				pad_token_id=1   ,				vocab_size=1000   ,				)					)

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

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDPMScheduler(
          variance_type="""fixed_small_log"""   ,				prediction_type="""sample"""   ,				num_train_timesteps=1000   ,				clip_sample=__lowerCAmelCase   ,				clip_sample_range=5.0   ,				beta_schedule="""squaredcos_cap_v2"""   ,				)

      # regular denoising components

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase					)
      _UpperCAmelCase						 =      DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""					)

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

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0   ,				eos_token_id=2   ,				hidden_size=__lowerCAmelCase   ,				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=1000   ,				)					)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      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=__lowerCAmelCase   ,				layers_per_block=1   ,				upcast_attention=__lowerCAmelCase   ,				use_linear_projection=__lowerCAmelCase   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      DDIMScheduler(
          beta_schedule="""scaled_linear"""   ,				beta_start=0.00_085   ,				beta_end=0.012   ,				prediction_type="""v_prediction"""   ,				set_alpha_to_one=__lowerCAmelCase   ,				steps_offset=1   ,				)

      torch.manual_seed(0					)
      _UpperCAmelCase						 =      AutoencoderKL()

      _UpperCAmelCase						 =      {
          # 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	:  List[Any]   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  int=0					):
      if str(__lowerCAmelCase					).startswith("""mps"""					):
        _UpperCAmelCase						 =      torch.manual_seed(__lowerCAmelCase					)
      else:
        _UpperCAmelCase						 =      torch.Generator(device=__lowerCAmelCase					).manual_seed(__lowerCAmelCase					)
      _UpperCAmelCase						 =      {
          """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	:  List[str]					):
      _UpperCAmelCase						 =      torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  Any					):
      _UpperCAmelCase						 =      torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase					)



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


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


    def      lowerCAmelCase_  (					self	:  List[str]					):
      _UpperCAmelCase						 =      load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""					)

      _UpperCAmelCase						 =      StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l"""   ,				torch_dtype=torch.floataa					)
      pipe.to(__lowerCAmelCase					)
      pipe.set_progress_bar_config(disable=__lowerCAmelCase					)
      # 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()

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

      _UpperCAmelCase						 =      output.images[0]

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

      assert_mean_pixel_difference(__lowerCAmelCase   ,				__lowerCAmelCase					)


    def      lowerCAmelCase_  (					self	:  List[str]					):
      torch.cuda.empty_cache()
      torch.cuda.reset_max_memory_allocated()
      torch.cuda.reset_peak_memory_stats()

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

      _UpperCAmelCase						 =      pipe(
          """anime turtle"""   ,				prior_num_inference_steps=2   ,				num_inference_steps=2   ,				output_type="""np"""   ,				)

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