

import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
_lowerCamelCase      :					Optional[int]					    =      '''src/diffusers'''

# Matches is_xxx_available()
_lowerCamelCase      :					int					    =      re.compile(R'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
_lowerCamelCase      :					int					    =      re.compile(R'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


_lowerCamelCase      :					Optional[Any]					    =      '''
{0} = None
'''

_lowerCamelCase      :					Tuple					    =      '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


_lowerCamelCase      :					Tuple					    =      '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''





def      A__					(			__A  :						Tuple			)					->Optional[Any]:
      __A    =_re_backend.findall(__A			)
      if len(__A			) == 0:
            return None

      return "_and_".join(__A			)





def      A__					(			)					->int:
      with open(os.path.join(__A     ,				'''__init__.py'''			)     ,				'''r'''     ,				encoding='''utf-8'''     ,				newline='''\n'''			) as f:
            __A    =f.readlines()

      # Get to the point we do the actual imports for type checking
      __A    =0
      __A    ={}
      # Go through the end of the file
      while line_index < len(__A			):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            __A    =find_backend(lines[line_index]			)
            if backend is not None:
                  while not lines[line_index].startswith('''else:'''			):
                        line_index += 1
                  line_index += 1
                  __A    =[]
                  # Until we unindent, add backend objects to the list
                  while line_index < len(__A			) and len(lines[line_index]			) > 1:
                        __A    =lines[line_index]
                        __A    =_re_single_line_import.search(__A			)
                        if single_line_import_search is not None:
                              objects.extend(single_line_import_search.groups()[0].split(''', '''			)			)
                        elif line.startswith(''' ''' * 8			):
                              objects.append(line[8:-2]			)
                        line_index += 1

                  if len(__A			) > 0:
                        __A    =objects
            else:
                  line_index += 1

      return backend_specific_objects





def      A__					(			__A  :						Any     ,				__A  :						int			)					->Any:
      if name.isupper():
            return DUMMY_CONSTANT.format(__A			)
      elif name.islower():
            return DUMMY_FUNCTION.format(__A     ,				__A			)
      else:
            return DUMMY_CLASS.format(__A     ,				__A			)





def      A__					(			__A  :						Dict=None			)					->Optional[int]:
      if backend_specific_objects is None:
            __A    =read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      __A    ={}

      for backend, objects in backend_specific_objects.items():
            __A    ='''[''' + ''', '''.join(F'''"{b}"''' for b in backend.split('''_and_'''			)			) + ''']'''
            __A    ='''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(__A     ,				__A			) for o in objects]			)
            __A    =dummy_file

      return dummy_files





def      A__					(			__A  :						Tuple=False			)					->Tuple:
      __A    =create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      __A    ={'''torch''': '''pt'''}

      # Locate actual dummy modules and read their content.
      __A    =os.path.join(__A     ,				'''utils'''			)
      __A    ={
          backend: os.path.join(__A     ,				F'''dummy_{short_names.get(__A     ,				__A			)}_objects.py'''			)
          for backend in dummy_files.keys()
      }

      __A    ={}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(__A			):
                  with open(__A     ,				'''r'''     ,				encoding='''utf-8'''     ,				newline='''\n'''			) as f:
                        __A    =f.read()
            else:
                  __A    =''''''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            F'''Updating diffusers.utils.dummy_{short_names.get(__A     ,				__A			)}_objects.py as the main '''
                            '''__init__ has new objects.'''			)
                        with open(dummy_file_paths[backend]     ,				'''w'''     ,				encoding='''utf-8'''     ,				newline='''\n'''			) as f:
                              f.write(dummy_files[backend]			)
                  else:
                        raise ValueError(
                            '''The main __init__ has objects that are not present in '''
                            F'''diffusers.utils.dummy_{short_names.get(__A     ,				__A			)}_objects.py. Run `make fix-copies` '''
                            '''to fix this.'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					int					    =      argparse.ArgumentParser()
   parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
   _lowerCamelCase      :					Union[str, Any]					    =      parser.parse_args()

   check_dummies(args.fix_and_overwrite)





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      A__					(			__A  :						List[str]     ,				__A  :						Optional[Any]     ,				__A  :						Union[str, Any]     ,				__A  :						int="attention"			)					->str:
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      __A    =params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v





def      A__					(			__A  :						Optional[int]     ,				__A  :						List[str]     ,				__A  :						Any     ,				__A  :						Tuple=False			)					->Any:
      if split_mlp_wi:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
            __A    =(wi_a, wi_a)
      else:
            __A    =params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

      __A    =params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
      return wi, wo





def      A__					(			__A  :						int     ,				__A  :						Any     ,				__A  :						Any     ,				__A  :						Optional[Any]			)					->str:
      return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']





def      A__					(			__A  :						dict     ,				*, __A  :						int     ,				__A  :						bool			)					->Optional[Any]:
      __A    =traverse_util.flatten_dict(variables['''target''']			)
      __A    ={'''/'''.join(__A			): v for k, v in old.items()}

      # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
      __A    ='''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:'''     ,				__A			)

      __A    =collections.OrderedDict()

      # Shared embeddings.
      __A    =old['''token_embedder/embedding''']

      # Encoder.
      for i in range(__A			):
            # Block i, layer 0 (Self Attention).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_attention_layer_norm'''			)
            __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''attention'''			)
            __A    =layer_norm
            __A    =k.T
            __A    =o.T
            __A    =q.T
            __A    =v.T

            # Block i, layer 1 (MLP).
            __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''encoder'''     ,				'''pre_mlp_layer_norm'''			)
            __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''encoder'''     ,				__A			)
            __A    =layer_norm
            if split_mlp_wi:
                  __A    =wi[0].T
                  __A    =wi[1].T
            else:
                  __A    =wi.T
            __A    =wo.T

      __A    =old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      __A    =old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(__A			):
                  # Block i, layer 0 (Self Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_self_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''self_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 1 (Cross Attention).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_cross_attention_layer_norm'''			)
                  __A    ,		__A    ,		__A    ,		__A    =tax_attention_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''encoder_decoder_attention'''			)
                  __A    =layer_norm
                  __A    =k.T
                  __A    =o.T
                  __A    =q.T
                  __A    =v.T

                  # Block i, layer 2 (MLP).
                  __A    =tax_layer_norm_lookup(__A     ,				__A     ,				'''decoder'''     ,				'''pre_mlp_layer_norm'''			)
                  __A    ,		__A    =tax_mlp_lookup(__A     ,				__A     ,				'''decoder'''     ,				__A			)
                  __A    =layer_norm
                  if split_mlp_wi:
                        __A    =wi[0].T
                        __A    =wi[1].T
                  else:
                        __A    =wi.T
                  __A    =wo.T

            __A    =old['''decoder/decoder_norm/scale''']
            __A    =old[
                '''decoder/relpos_bias/rel_embedding'''
            ].T

            # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
            if "decoder/logits_dense/kernel" in old:
                  __A    =old['''decoder/logits_dense/kernel'''].T

      return new





def      A__					(			__A  :						Union[str, Any]     ,				__A  :						bool			)					->Any:
      __A    =collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

      # Add what is missing.
      if "encoder.embed_tokens.weight" not in state_dict:
            __A    =state_dict['''shared.weight''']

      if not is_encoder_only:
            if "decoder.embed_tokens.weight" not in state_dict:
                  __A    =state_dict['''shared.weight''']

            if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                  print('''Using shared word embeddings as lm_head.'''			)
                  __A    =state_dict['''shared.weight''']

      return state_dict





def      A__					(			__A  :						str     ,				__A  :						Optional[int]     ,				__A  :						int     ,				__A  :						Optional[Any]			)					->Tuple:
      __A    =checkpoints.load_tax_checkpoint(__A			)
      __A    =convert_tax_to_pytorch(__A     ,				num_layers=config.num_layers     ,				is_encoder_only=__A			)
      __A    =make_state_dict(__A     ,				__A			)
      model.load_state_dict(__A     ,				strict=__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						str     ,				__A  :						str     ,				__A  :						bool = False			)					->List[str]:
      __A    =TaConfig.from_json_file(__A			)
      print(F'''Building PyTorch model from configuration: {config}'''			)
      # Non-v1.1 checkpoints could also use T5Model, but this works for all.
      # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
      if is_encoder_only:
            __A    =TaEncoderModel(__A			)
      else:
            __A    =TaForConditionalGeneration(__A			)

      # Load weights from tf checkpoint
      load_tax_weights_in_ta(__A     ,				__A     ,				__A     ,				__A			)

      # Save pytorch-model
      print(F'''Save PyTorch model to {pytorch_dump_path}'''			)
      model.save_pretrained(__A			)

      # Verify that we can load the checkpoint.
      model.from_pretrained(__A			)
      print('''Done'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
   # Required parameters
   parser.add_argument(
       '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
   )
   parser.add_argument(
       '''--config_file''',
       default=None,
       type=str,
       required=True,
       help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   parser.add_argument(
       '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
   )
   _lowerCamelCase      :					int					    =      parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )


