"""simple docstring"""





from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule


def    lowercase_   (   _lowerCamelCase:	Tuple						,   _lowerCamelCase:	List[str]						,   _lowerCamelCase:	str=1E-12      ) ->      Dict:



							'''simple docstring'''

							__lowerCamelCase					:		Optional[int]		   =     jnp.divide(emb_a.T						,   jnp.clip(jnp.linalg.norm(_lowerCamelCase						,   axis=1      )						,   a_min=_lowerCamelCase      )      ).T
							__lowerCamelCase					:		Dict		   =     jnp.divide(emb_a.T						,   jnp.clip(jnp.linalg.norm(_lowerCamelCase						,   axis=1      )						,   a_min=_lowerCamelCase      )      ).T
							return jnp.matmul(_lowerCamelCase						,   norm_emb_a.T      )
class  _snake_case     (					nn.Module		):
						snake_case__			  =			42
						snake_case__			  =			jnp.floataa






						def       lowerCamelCase__     (       self      :			List[Any]    ):
													__lowerCamelCase					:		str		   =     FlaxCLIPVisionModule(self.config.vision_config    )
													__lowerCamelCase					:		List[Any]		   =     nn.Dense(self.config.projection_dim							,					use_bias=UpperCAmelCase							,					dtype=self.dtype    )

													__lowerCamelCase					:		Dict		   =     self.param("concept_embeds"							,					jax.nn.initializers.ones							,					(17, self.config.projection_dim)    )
													__lowerCamelCase					:		Optional[int]		   =     self.param(
													    "special_care_embeds"							,					jax.nn.initializers.ones							,					(3, self.config.projection_dim)    )

													__lowerCamelCase					:		Dict		   =     self.param("concept_embeds_weights"							,					jax.nn.initializers.ones							,					(17,)    )
													__lowerCamelCase					:		str		   =     self.param("special_care_embeds_weights"							,					jax.nn.initializers.ones							,					(3,)    )







						def __call__(       self      :			List[str]							,					UpperCAmelCase      :			Union[str, Any]    ):
													__lowerCamelCase					:		List[str]		   =     self.vision_model(UpperCAmelCase    )[1]
													__lowerCamelCase					:		int		   =     self.visual_projection(UpperCAmelCase    )

													__lowerCamelCase					:		Optional[int]		   =     jax_cosine_distance(UpperCAmelCase							,					self.special_care_embeds    )
													__lowerCamelCase					:		Tuple		   =     jax_cosine_distance(UpperCAmelCase							,					self.concept_embeds    )

													# increase this value to create a stronger `nfsw` filter
													# at the cost of increasing the possibility of filtering benign image inputs
													__lowerCamelCase					:		List[Any]		   =     0.0

													__lowerCamelCase					:		Dict		   =     special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
													__lowerCamelCase					:		str		   =     jnp.round(UpperCAmelCase							,					3    )
													__lowerCamelCase					:		Union[str, Any]		   =     jnp.any(special_scores > 0							,					axis=1							,					keepdims=UpperCAmelCase    )
													# Use a lower threshold if an image has any special care concept
													__lowerCamelCase					:		List[str]		   =     is_special_care * 0.0_1

													__lowerCamelCase					:		int		   =     cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
													__lowerCamelCase					:		List[Any]		   =     jnp.round(UpperCAmelCase							,					3    )
													__lowerCamelCase					:		Dict		   =     jnp.any(concept_scores > 0							,					axis=1    )

													return has_nsfw_concepts

class  _snake_case     (					a__		):
						snake_case__			  =			CLIPConfig
						snake_case__			  =			"clip_input"
						snake_case__			  =			FlaxStableDiffusionSafetyCheckerModule






						def __init__(       self      :			Dict							,					UpperCAmelCase      :			CLIPConfig							,					UpperCAmelCase      :			Optional[Tuple] = None							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			jnp.dtype = jnp.floataa							,					UpperCAmelCase      :			bool = True							,					**UpperCAmelCase      :			str							,					):
													if input_shape is None:
																				__lowerCamelCase					:		List[Any]		   =     (1, 224, 224, 3)
													__lowerCamelCase					:		Optional[Any]		   =     self.module_class(config=UpperCAmelCase							,					dtype=UpperCAmelCase							,					**UpperCAmelCase    )
													super().__init__(UpperCAmelCase							,					UpperCAmelCase							,					input_shape=UpperCAmelCase							,					seed=UpperCAmelCase							,					dtype=UpperCAmelCase							,					_do_init=_do_init    )






						def       lowerCamelCase__     (       self      :			List[Any]							,					UpperCAmelCase      :			jax.random.KeyArray							,					UpperCAmelCase      :			Tuple							,					UpperCAmelCase      :			FrozenDict = None    ):
													# init input tensor
													__lowerCamelCase					:		str		   =     jax.random.normal(UpperCAmelCase							,					UpperCAmelCase    )

													__lowerCamelCase   ,  __lowerCamelCase					:		int		   =     jax.random.split(UpperCAmelCase    )
													__lowerCamelCase					:		List[Any]		   =     {"params": params_rng, "dropout": dropout_rng}

													__lowerCamelCase					:		Optional[Any]		   =     self.module.init(UpperCAmelCase							,					UpperCAmelCase    )["params"]

													return random_params







						def __call__(       self      :			Any							,					UpperCAmelCase      :			str							,					UpperCAmelCase      :			dict = None							,					):
													__lowerCamelCase					:		Optional[int]		   =     jnp.transpose(UpperCAmelCase							,					(0, 2, 3, 1)    )

													return self.module.apply(
													    {"params": params or self.params}							,					jnp.array(UpperCAmelCase							,					dtype=jnp.floataa    )							,					rngs={}							,					)
"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__A  =				logging.get_logger('''transformers.models.speecht5''')


def    lowercase_   (   _lowerCamelCase:	Any						,   _lowerCamelCase:	List[Any]						,   _lowerCamelCase:	Tuple      ) ->      Union[str, Any]:



							'''simple docstring'''

							hf_model.apply_weight_norm()

							__lowerCamelCase					:		Union[str, Any]		   =     checkpoint["input_conv.weight_g"]
							__lowerCamelCase					:		int		   =     checkpoint["input_conv.weight_v"]
							__lowerCamelCase					:		Dict		   =     checkpoint["input_conv.bias"]

							for i in range(len(config.upsample_rates      )      ):
														__lowerCamelCase					:		Dict		   =     checkpoint[F"""upsamples.{i}.1.weight_g"""]
														__lowerCamelCase					:		Optional[int]		   =     checkpoint[F"""upsamples.{i}.1.weight_v"""]
														__lowerCamelCase					:		Any		   =     checkpoint[F"""upsamples.{i}.1.bias"""]

							for i in range(len(config.upsample_rates      ) * len(config.resblock_kernel_sizes      )      ):
														for j in range(len(config.resblock_dilation_sizes      )      ):
																					__lowerCamelCase					:		List[str]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_g"""]
																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.weight_v"""]
																					__lowerCamelCase					:		Union[str, Any]		   =     checkpoint[F"""blocks.{i}.convs1.{j}.1.bias"""]

																					__lowerCamelCase					:		str		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_g"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.weight_v"""]
																					__lowerCamelCase					:		int		   =     checkpoint[F"""blocks.{i}.convs2.{j}.1.bias"""]

							__lowerCamelCase					:		Any		   =     checkpoint["output_conv.1.weight_g"]
							__lowerCamelCase					:		Tuple		   =     checkpoint["output_conv.1.weight_v"]
							__lowerCamelCase					:		int		   =     checkpoint["output_conv.1.bias"]

							hf_model.remove_weight_norm()


@torch.no_grad()
def    lowercase_   (   _lowerCamelCase:	Optional[Any]						,   _lowerCamelCase:	Dict						,   _lowerCamelCase:	Optional[int]						,   _lowerCamelCase:	Any=None						,   _lowerCamelCase:	Optional[int]=None						,   ) ->      Optional[int]:



							'''simple docstring'''

							if config_path is not None:
														__lowerCamelCase					:		Dict		   =     SpeechTaHifiGanConfig.from_pretrained(_lowerCamelCase      )
							else:
														__lowerCamelCase					:		Tuple		   =     SpeechTaHifiGanConfig()

							__lowerCamelCase					:		List[Any]		   =     SpeechTaHifiGan(_lowerCamelCase      )

							__lowerCamelCase					:		int		   =     torch.load(_lowerCamelCase      )
							load_weights(orig_checkpoint["model"]["generator"]						,   _lowerCamelCase						,   _lowerCamelCase      )

							__lowerCamelCase					:		Dict		   =     np.load(_lowerCamelCase      )
							__lowerCamelCase					:		List[str]		   =     stats[0].reshape(-1      )
							__lowerCamelCase					:		Optional[int]		   =     stats[1].reshape(-1      )
							__lowerCamelCase					:		int		   =     torch.from_numpy(_lowerCamelCase      ).float()
							__lowerCamelCase					:		List[str]		   =     torch.from_numpy(_lowerCamelCase      ).float()

							model.save_pretrained(_lowerCamelCase      )

							if repo_id:
														print("Pushing to the hub..."      )
														model.push_to_hub(_lowerCamelCase      )


if __name__ == "__main__":
				__A  =				argparse.ArgumentParser()
				parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
				parser.add_argument('''--stats_path''', required=True, default=None, type=str, help='''Path to stats.npy file''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
				)

				__A  =				parser.parse_args()
				convert_hifigan_checkpoint(
				    args.checkpoint_path,
				    args.stats_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.push_to_hub,
				)