

import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
			import jax
			import jax.numpy as jnp
			from flax.jax_utils import replicate
			from flax.training.common_utils import shard






@slow
@require_flax
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def 							__UpperCamelCase       (	self							:       Tuple		)							->				int:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
			def 							__UpperCamelCase       (	self							:       str		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					str    =				FlaxStableDiffusionPipeline.from_pretrained(
				    "stabilityai/stable-diffusion-2"					,					revision="bf16"					,					dtype=jnp.bfloataa					,					)

				SCREAMING_SNAKE_CASE      :					int    =				"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				jax.device_count()
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				num_samples * [prompt]
				SCREAMING_SNAKE_CASE      :					Any    =				sd_pipe.prepare_inputs(a		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				replicate(a		)
				SCREAMING_SNAKE_CASE      :					Dict    =				shard(a		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				jax.random.PRNGKey(0		)
				SCREAMING_SNAKE_CASE      :					Dict    =				jax.random.split(a					,					jax.device_count()		)

				SCREAMING_SNAKE_CASE      :					str    =				sd_pipe(a					,					a					,					a					,					num_inference_steps=25					,					jit=a		)[0]
				assert images.shape == (jax.device_count(), 1, 768, 768, 3)

				SCREAMING_SNAKE_CASE      :					Tuple    =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
				SCREAMING_SNAKE_CASE      :					List[str]    =				images[0, 253:256, 253:256, -1]

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				jnp.asarray(jax.device_get(image_slice.flatten()		)		)
				SCREAMING_SNAKE_CASE      :					Any    =				jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]		)
				print(F"output_slice: {output_slice}"		)
				assert jnp.abs(output_slice - expected_slice		).max() < 1e-2






			def 							__UpperCamelCase       (	self							:       List[str]		)							->				Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					int    =				"stabilityai/stable-diffusion-2"
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Tuple    =				FlaxDPMSolverMultistepScheduler.from_pretrained(a					,					subfolder="scheduler"		)
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					str    =				FlaxStableDiffusionPipeline.from_pretrained(
				    a					,					scheduler=a					,					revision="bf16"					,					dtype=jnp.bfloataa					,					)
				SCREAMING_SNAKE_CASE      :					Tuple    =				scheduler_params

				SCREAMING_SNAKE_CASE      :					List[str]    =				"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				jax.device_count()
				SCREAMING_SNAKE_CASE      :					int    =				num_samples * [prompt]
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				sd_pipe.prepare_inputs(a		)

				SCREAMING_SNAKE_CASE      :					Tuple    =				replicate(a		)
				SCREAMING_SNAKE_CASE      :					Optional[int]    =				shard(a		)

				SCREAMING_SNAKE_CASE      :					Dict    =				jax.random.PRNGKey(0		)
				SCREAMING_SNAKE_CASE      :					str    =				jax.random.split(a					,					jax.device_count()		)

				SCREAMING_SNAKE_CASE      :					str    =				sd_pipe(a					,					a					,					a					,					num_inference_steps=25					,					jit=a		)[0]
				assert images.shape == (jax.device_count(), 1, 768, 768, 3)

				SCREAMING_SNAKE_CASE      :					Any    =				images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				images[0, 253:256, 253:256, -1]

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				jnp.asarray(jax.device_get(image_slice.flatten()		)		)
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]		)
				print(F"output_slice: {output_slice}"		)
				assert jnp.abs(output_slice - expected_slice		).max() < 1e-2
import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


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





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     Any	):
 UpperCamelCase_					:						List[Any] =					UniSpeechSatForSequenceClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Any =					downstream_dict['projector.weight']
 UpperCamelCase_					:						List[str] =					downstream_dict['projector.bias']
 UpperCamelCase_					:						Tuple =					downstream_dict['model.post_net.linear.weight']
 UpperCamelCase_					:						int =					downstream_dict['model.post_net.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     Union[str, Any]	):
 UpperCamelCase_					:						Any =					UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Tuple =					downstream_dict['model.linear.weight']
 UpperCamelCase_					:						Any =					downstream_dict['model.linear.bias']
 return model





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]	):
 UpperCamelCase_					:						str =					UniSpeechSatForXVector.from_pretrained(lowerCamelCase  ,  config=lowerCamelCase	)
 UpperCamelCase_					:						Optional[Any] =					downstream_dict['connector.weight']
 UpperCamelCase_					:						int =					downstream_dict['connector.bias']
 for i, kernel_size in enumerate(hf_config.tdnn_kernel	):
  UpperCamelCase_					:						Union[str, Any] =					downstream_dict[
      F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  ]
  UpperCamelCase_					:						Tuple =					downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

 UpperCamelCase_					:						int =					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
 UpperCamelCase_					:						Dict =					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
 UpperCamelCase_					:						Optional[int] =					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
 UpperCamelCase_					:						List[Any] =					downstream_dict['objective.W']
 return model





@torch.no_grad()
def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						int =					torch.load(lowerCamelCase  ,  map_location='cpu'	)

 UpperCamelCase_					:						Any =					checkpoint['Downstream']

 UpperCamelCase_					:						Dict =					UniSpeechSatConfig.from_pretrained(lowerCamelCase	)
 UpperCamelCase_					:						str =					WavaVecaFeatureExtractor.from_pretrained(
     lowerCamelCase  ,  return_attention_mask=lowerCamelCase  ,  do_normalize=lowerCamelCase	)

 UpperCamelCase_					:						int =					hf_config.architectures[0]
 if arch.endswith('ForSequenceClassification'	):
  UpperCamelCase_					:						Tuple =					convert_classification(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForAudioFrameClassification'	):
  UpperCamelCase_					:						List[str] =					convert_diarization(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 elif arch.endswith('ForXVector'	):
  UpperCamelCase_					:						Union[str, Any] =					convert_xvector(lowerCamelCase  ,  lowerCamelCase  ,  lowerCamelCase	)
 else:
  raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}"	)

 if hf_config.use_weighted_layer_sum:
  UpperCamelCase_					:						Tuple =					checkpoint['Featurizer']['weights']

 hf_feature_extractor.save_pretrained(lowerCamelCase	)
 hf_model.save_pretrained(lowerCamelCase	)


if __name__ == "__main__":
   a_    					=       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.')
   a_    					=       parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)



