"""simple docstring"""






import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase    :       List[str]					     =logging.get_logger(__name__)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :List[Any]					,		lowerCAmelCase__     :Tuple		) ->    Any:
      '''simple docstring'''


      lowercase														=	os.path.abspath(__lowerCamelCase		)
      logger.info(f'Converting TensorFlow checkpoint from {tf_path}'		)
      # Load weights from TF model
      lowercase														=	tf.train.list_variables(__lowerCamelCase		)
      lowercase														=	[]
      lowercase														=	[]
      lowercase														=	[]
      for full_name, shape in init_vars:
            # logger.info(f"Loading TF weight {name} with shape {shape}")
            lowercase														=	full_name.split("""/"""		)
            if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
                  logger.info(f'Skipping non-model layer {full_name}'		)
                  continue
            if "optimizer" in full_name:
                  logger.info(f'Skipping optimization layer {full_name}'		)
                  continue
            if name[0] == "model":
                  # ignore initial 'model'
                  lowercase														=	name[1:]
            # figure out how many levels deep the name is
            lowercase														=	0
            for _name in name:
                  if _name.startswith("""layer_with_weights"""		):
                        depth += 1
                  else:
                        break
            layer_depth.append(__lowerCamelCase		)
            # read data
            lowercase														=	tf.train.load_variable(__lowerCamelCase					,		__lowerCamelCase		)
            names.append("""/""".join(__lowerCamelCase		)		)
            arrays.append(__lowerCamelCase		)
      logger.info(f'Read a total of {len(__lowerCamelCase		):,} layers'		)

      # Sanity check
      if len(set(__lowerCamelCase		)		) != 1:
            raise ValueError(f'Found layer names with different depths (layer depth {list(set(__lowerCamelCase		)		)})'		)
      lowercase														=	list(set(__lowerCamelCase		)		)[0]
      if layer_depth != 1:
            raise ValueError(
                """The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"""
                """ heads."""		)

      # convert layers
      logger.info("""Converting weights..."""		)
      for full_name, array in zip(__lowerCamelCase					,		__lowerCamelCase		):
            lowercase														=	full_name.split("""/"""		)
            lowercase														=	model
            lowercase														=	[]
            for i, m_name in enumerate(__lowerCamelCase		):
                  if m_name == ".ATTRIBUTES":
                        # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                        break
                  if m_name.startswith("""layer_with_weights"""		):
                        lowercase														=	int(m_name.split("""-"""		)[-1]		)
                        if layer_num <= 2:
                              # embedding layers
                              # layer_num 0: word_embeddings
                              # layer_num 1: position_embeddings
                              # layer_num 2: token_type_embeddings
                              continue
                        elif layer_num == 3:
                              # embedding LayerNorm
                              trace.extend(["""embeddings""", """LayerNorm"""]		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""embeddings"""		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""LayerNorm"""		)
                        elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                              # encoder layers
                              trace.extend(["""encoder""", """layer""", str(layer_num - 4		)]		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""encoder"""		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""layer"""		)
                              lowercase														=	pointer[layer_num - 4]
                        elif layer_num == config.num_hidden_layers + 4:
                              # pooler layer
                              trace.extend(["""pooler""", """dense"""]		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""pooler"""		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""dense"""		)
                  elif m_name == "embeddings":
                        trace.append("""embeddings"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""embeddings"""		)
                        if layer_num == 0:
                              trace.append("""word_embeddings"""		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""word_embeddings"""		)
                        elif layer_num == 1:
                              trace.append("""position_embeddings"""		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""position_embeddings"""		)
                        elif layer_num == 2:
                              trace.append("""token_type_embeddings"""		)
                              lowercase														=	getattr(__lowerCamelCase					,		"""token_type_embeddings"""		)
                        else:
                              raise ValueError(f'Unknown embedding layer with name {full_name}'		)
                        trace.append("""weight"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""weight"""		)
                  elif m_name == "_attention_layer":
                        # self-attention layer
                        trace.extend(["""attention""", """self"""]		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""attention"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""self"""		)
                  elif m_name == "_attention_layer_norm":
                        # output attention norm
                        trace.extend(["""attention""", """output""", """LayerNorm"""]		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""attention"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""output"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""LayerNorm"""		)
                  elif m_name == "_attention_output_dense":
                        # output attention dense
                        trace.extend(["""attention""", """output""", """dense"""]		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""attention"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""output"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""dense"""		)
                  elif m_name == "_output_dense":
                        # output dense
                        trace.extend(["""output""", """dense"""]		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""output"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""dense"""		)
                  elif m_name == "_output_layer_norm":
                        # output dense
                        trace.extend(["""output""", """LayerNorm"""]		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""output"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""LayerNorm"""		)
                  elif m_name == "_key_dense":
                        # attention key
                        trace.append("""key"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""key"""		)
                  elif m_name == "_query_dense":
                        # attention query
                        trace.append("""query"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""query"""		)
                  elif m_name == "_value_dense":
                        # attention value
                        trace.append("""value"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""value"""		)
                  elif m_name == "_intermediate_dense":
                        # attention intermediate dense
                        trace.extend(["""intermediate""", """dense"""]		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""intermediate"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""dense"""		)
                  elif m_name == "_output_layer_norm":
                        # output layer norm
                        trace.append("""output"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""output"""		)
                  # weights & biases
                  elif m_name in ["bias", "beta"]:
                        trace.append("""bias"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""bias"""		)
                  elif m_name in ["kernel", "gamma"]:
                        trace.append("""weight"""		)
                        lowercase														=	getattr(__lowerCamelCase					,		"""weight"""		)
                  else:
                        logger.warning(f'Ignored {m_name}'		)
        # for certain layers reshape is necessary
            lowercase														=	'''.'''.join(__lowerCamelCase		)
            if re.match(R"""(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"""					,		__lowerCamelCase		) or re.match(
                R"""(\S+)\.attention\.output\.dense\.weight"""					,		__lowerCamelCase		):
                  lowercase														=	array.reshape(pointer.data.shape		)
            if "kernel" in full_name:
                  lowercase														=	array.transpose()
            if pointer.shape == array.shape:
                  lowercase														=	torch.from_numpy(__lowerCamelCase		)
            else:
                  raise ValueError(
                      f'Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'
                      f' {array.shape}'		)
            logger.info(f'Successfully set variable {full_name} to PyTorch layer {trace}'		)
      return model


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Any					,		lowerCAmelCase__     :Tuple					,		lowerCAmelCase__     :int		) ->    Union[str, Any]:
      '''simple docstring'''


      logger.info(f'Loading model based on config from {config_path}...'		)
      lowercase														=	BertConfig.from_json_file(__lowerCamelCase		)
      lowercase														=	BertModel(__lowerCamelCase		)

      # Load weights from checkpoint
      logger.info(f'Loading weights from checkpoint {tf_checkpoint_path}...'		)
      load_tfa_weights_in_bert(__lowerCamelCase					,		__lowerCamelCase					,		__lowerCamelCase		)

      # Save pytorch-model
      logger.info(f'Saving PyTorch model to {pytorch_dump_path}...'		)
      torch.save(model.state_dict()					,		__lowerCamelCase		)


if __name__ == "__main__":
       __lowerCAmelCase    :       str					     =argparse.ArgumentParser()
       parser.add_argument(
           """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
       )
       parser.add_argument(
           """--bert_config_file""",
           type=str,
           required=True,
           help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
       )
       parser.add_argument(
           """--pytorch_dump_path""",
           type=str,
           required=True,
           help="""Path to the output PyTorch model (must include filename).""",
       )
       __lowerCAmelCase    :       Optional[Any]					     =parser.parse_args()
       convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


_lowercase : int      						=						logging.get_logger(__name__)  # pylint: disable=invalid-name

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

								if audio_length_in_s is None:
											lowerCamelCase__		:		int			=self.unet.config.sample_size / self.unet.config.sample_rate

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		Any			=2 ** len(self.unet.up_blocks     )
								if sample_size < 3 * down_scale_factor:
											raise ValueError(
											    F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
											    F''' {3 * down_scale_factor / self.unet.config.sample_rate}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
											) * down_scale_factor
											logger.info(
											    F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
											    F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
											    ''' process.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		Union[str, Any]			=(batch_size, self.unet.config.in_channels, sample_size)
								if isinstance(lowerCamelCase,  lowerCamelCase     ) and len(lowerCamelCase     ) != batch_size:
											raise ValueError(
											    F'''You have passed a list of generators of length {len(lowerCamelCase     )}, but requested an effective batch'''
											    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

											# 2. compute previous image: x_t -> t_t-1
											lowerCamelCase__		:		Dict			=self.scheduler.step(lowerCamelCase,  lowerCamelCase,  lowerCamelCase     ).prev_sample

								lowerCamelCase__		:		Optional[int]			=audio.clamp(-1,  1     ).float().cpu().numpy()

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




