"""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()
_a      :		Optional[int]           =   logging.get_logger(__name__)





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Optional[Any]			,_lowerCamelCase						:       str			,_lowerCamelCase						:       List[str]	)       ->      int:
  _lowerCAmelCase						:       List[str]  			=     os.path.abspath(_lowerCamelCase	)
  logger.info(f"Converting TensorFlow checkpoint from {tf_path}"	)
  # Load weights from TF model
  _lowerCAmelCase						:       int  			=     tf.train.list_variables(_lowerCamelCase	)
  _lowerCAmelCase						:       List[Any]  			=     []
  _lowerCAmelCase						:       Tuple  			=     []
  _lowerCAmelCase						:       Optional[int]  			=     []
  for full_name, shape in init_vars:
    # logger.info(f"Loading TF weight {name} with shape {shape}")
    _lowerCAmelCase						:       Dict  			=     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'
      _lowerCAmelCase						:       Dict  			=     name[1:]
    # figure out how many levels deep the name is
    _lowerCAmelCase						:       Optional[Any]  			=     0
    for _name in name:
      if _name.startswith("""layer_with_weights"""	):
        depth += 1
      else:
        break
    layer_depth.append(_lowerCamelCase	)
    # read data
    _lowerCAmelCase						:       List[Any]  			=     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	)	)})"	)
  _lowerCAmelCase						:       Union[str, Any]  			=     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	):
    _lowerCAmelCase						:       Optional[Any]  			=     full_name.split("""/"""	)
    _lowerCAmelCase						:       str  			=     model
    _lowerCAmelCase						:       Optional[int]  			=     []
    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"""	):
        _lowerCAmelCase						:       Dict  			=     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"""]	)
          _lowerCAmelCase						:       int  			=     getattr(_lowerCamelCase			,"""embeddings"""	)
          _lowerCAmelCase						:       List[Any]  			=     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	)]	)
          _lowerCAmelCase						:       int  			=     getattr(_lowerCamelCase			,"""encoder"""	)
          _lowerCAmelCase						:       Optional[int]  			=     getattr(_lowerCamelCase			,"""layer"""	)
          _lowerCAmelCase						:       List[Any]  			=     pointer[layer_num - 4]
        elif layer_num == config.num_hidden_layers + 4:
          # pooler layer
          trace.extend(["""pooler""", """dense"""]	)
          _lowerCAmelCase						:       int  			=     getattr(_lowerCamelCase			,"""pooler"""	)
          _lowerCAmelCase						:       Optional[Any]  			=     getattr(_lowerCamelCase			,"""dense"""	)
      elif m_name == "embeddings":
        trace.append("""embeddings"""	)
        _lowerCAmelCase						:       Tuple  			=     getattr(_lowerCamelCase			,"""embeddings"""	)
        if layer_num == 0:
          trace.append("""word_embeddings"""	)
          _lowerCAmelCase						:       Optional[int]  			=     getattr(_lowerCamelCase			,"""word_embeddings"""	)
        elif layer_num == 1:
          trace.append("""position_embeddings"""	)
          _lowerCAmelCase						:       List[Any]  			=     getattr(_lowerCamelCase			,"""position_embeddings"""	)
        elif layer_num == 2:
          trace.append("""token_type_embeddings"""	)
          _lowerCAmelCase						:       str  			=     getattr(_lowerCamelCase			,"""token_type_embeddings"""	)
        else:
          raise ValueError(f"Unknown embedding layer with name {full_name}"	)
        trace.append("""weight"""	)
        _lowerCAmelCase						:       Dict  			=     getattr(_lowerCamelCase			,"""weight"""	)
      elif m_name == "_attention_layer":
        # self-attention layer
        trace.extend(["""attention""", """self"""]	)
        _lowerCAmelCase						:       Tuple  			=     getattr(_lowerCamelCase			,"""attention"""	)
        _lowerCAmelCase						:       str  			=     getattr(_lowerCamelCase			,"""self"""	)
      elif m_name == "_attention_layer_norm":
        # output attention norm
        trace.extend(["""attention""", """output""", """LayerNorm"""]	)
        _lowerCAmelCase						:       List[str]  			=     getattr(_lowerCamelCase			,"""attention"""	)
        _lowerCAmelCase						:       Tuple  			=     getattr(_lowerCamelCase			,"""output"""	)
        _lowerCAmelCase						:       Dict  			=     getattr(_lowerCamelCase			,"""LayerNorm"""	)
      elif m_name == "_attention_output_dense":
        # output attention dense
        trace.extend(["""attention""", """output""", """dense"""]	)
        _lowerCAmelCase						:       int  			=     getattr(_lowerCamelCase			,"""attention"""	)
        _lowerCAmelCase						:       int  			=     getattr(_lowerCamelCase			,"""output"""	)
        _lowerCAmelCase						:       List[Any]  			=     getattr(_lowerCamelCase			,"""dense"""	)
      elif m_name == "_output_dense":
        # output dense
        trace.extend(["""output""", """dense"""]	)
        _lowerCAmelCase						:       List[str]  			=     getattr(_lowerCamelCase			,"""output"""	)
        _lowerCAmelCase						:       Any  			=     getattr(_lowerCamelCase			,"""dense"""	)
      elif m_name == "_output_layer_norm":
        # output dense
        trace.extend(["""output""", """LayerNorm"""]	)
        _lowerCAmelCase						:       Tuple  			=     getattr(_lowerCamelCase			,"""output"""	)
        _lowerCAmelCase						:       List[str]  			=     getattr(_lowerCamelCase			,"""LayerNorm"""	)
      elif m_name == "_key_dense":
        # attention key
        trace.append("""key"""	)
        _lowerCAmelCase						:       str  			=     getattr(_lowerCamelCase			,"""key"""	)
      elif m_name == "_query_dense":
        # attention query
        trace.append("""query"""	)
        _lowerCAmelCase						:       Optional[int]  			=     getattr(_lowerCamelCase			,"""query"""	)
      elif m_name == "_value_dense":
        # attention value
        trace.append("""value"""	)
        _lowerCAmelCase						:       Optional[Any]  			=     getattr(_lowerCamelCase			,"""value"""	)
      elif m_name == "_intermediate_dense":
        # attention intermediate dense
        trace.extend(["""intermediate""", """dense"""]	)
        _lowerCAmelCase						:       str  			=     getattr(_lowerCamelCase			,"""intermediate"""	)
        _lowerCAmelCase						:       Optional[Any]  			=     getattr(_lowerCamelCase			,"""dense"""	)
      elif m_name == "_output_layer_norm":
        # output layer norm
        trace.append("""output"""	)
        _lowerCAmelCase						:       Any  			=     getattr(_lowerCamelCase			,"""output"""	)
      # weights & biases
      elif m_name in ["bias", "beta"]:
        trace.append("""bias"""	)
        _lowerCAmelCase						:       List[str]  			=     getattr(_lowerCamelCase			,"""bias"""	)
      elif m_name in ["kernel", "gamma"]:
        trace.append("""weight"""	)
        _lowerCAmelCase						:       str  			=     getattr(_lowerCamelCase			,"""weight"""	)
      else:
        logger.warning(f"Ignored {m_name}"	)
        # for certain layers reshape is necessary
    _lowerCAmelCase						:       Any  			=     """.""".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	):
      _lowerCAmelCase						:       str  			=     array.reshape(pointer.data.shape	)
    if "kernel" in full_name:
      _lowerCAmelCase						:       Optional[int]  			=     array.transpose()
    if pointer.shape == array.shape:
      _lowerCAmelCase						:       Optional[int]  			=     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 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       str			,_lowerCamelCase						:       Any	)       ->      Tuple:
  # Instantiate model
  logger.info(f"Loading model based on config from {config_path}..."	)
  _lowerCAmelCase						:       List[Any]  			=     BertConfig.from_json_file(_lowerCamelCase	)
  _lowerCAmelCase						:       Tuple  			=     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__":
     _a      :		Union[str, Any]           =   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).',
     )
     _a      :		List[str]           =   parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






'''simple docstring'''







import unittest

import numpy as np
import torch

from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()







class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

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


     '''simple docstring'''






     torch.manual_seed(0       )
     _a				:	int			=   UNetaDModel(
         block_out_channels=(32, 64)							,layers_per_block=2							,sample_size=32							,in_channels=3							,out_channels=3							,down_block_types=('DownBlock2D', 'AttnDownBlock2D')							,up_block_types=('AttnUpBlock2D', 'UpBlock2D')							,)
     return model


  def   __lowercase		(		self      :     Optional[Any]       ):


     '''simple docstring'''






     _a				:	str			=   self.dummy_uncond_unet
     _a				:	int			=   PNDMScheduler()

     _a				:	str			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )

     _a				:	Optional[int]			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'       ).images

     _a				:	List[str]			=   torch.manual_seed(0       )
     _a				:	Any			=   pndm(generator=_a							,num_inference_steps=20							,output_type='numpy'							,return_dict=_a       )[0]

     _a				:	List[Any]			=   image[0, -3:, -3:, -1]
     _a				:	Any			=   image_from_tuple[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	List[Any]			=   np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
     assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1E-2







@slow
@require_torch
class UpperCAmelCase__	( unittest.TestCase	):





  """simple docstring"""

  def   __lowercase		(		self      :     Tuple       ):


     '''simple docstring'''






     _a				:	List[str]			=   'google/ddpm-cifar10-32'

     _a				:	str			=   UNetaDModel.from_pretrained(_a       )
     _a				:	Union[str, Any]			=   PNDMScheduler()

     _a				:	Tuple			=   PNDMPipeline(unet=_a							,scheduler=_a       )
     pndm.to(_a       )
     pndm.set_progress_bar_config(disable=_a       )
     _a				:	str			=   torch.manual_seed(0       )
     _a				:	Optional[Any]			=   pndm(generator=_a							,output_type='numpy'       ).images

     _a				:	Dict			=   image[0, -3:, -3:, -1]

     assert image.shape == (1, 32, 32, 3)
     _a				:	Tuple			=   np.array([0.1564, 0.1_4645, 0.1406, 0.1_4715, 0.1_2425, 0.1_4045, 0.1_3115, 0.1_2175, 0.125]       )

     assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2



