



'''simple docstring'''





import collections
import os
import re
from pathlib import Path


lowercase_									=    "src/transformers"


# Matches is_xxx_available()
lowercase_									=    re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
lowercase_									=    re.compile(R"^_import_structure\s+=\s+\{([^\}]+)\}")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase_									=    re.compile(R"\s+\"\S*\":\s+\[([^\]]*)\]")
# Catches a line if not is_foo_available
lowercase_									=    re.compile(R"^\s*if\s+not\s+is\_[a-z_]*\_available\(\)")
# Catches a line _import_struct["bla"].append("foo")
lowercase_									=    re.compile(R"^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase_									=    re.compile(R"^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]")
# Catches a line with an object between quotes and a comma:     "MyModel",
lowercase_									=    re.compile(R"^\s+\"([^\"]+)\",")
# Catches a line with objects between brackets only:    ["foo", "bar"],
lowercase_									=    re.compile(R"^\s+\[([^\]]+)\]")
# Catches a line with from foo import bar, bla, boo
lowercase_									=    re.compile(R"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
# Catches a line with try:
lowercase_									=    re.compile(R"^\s*try:")
# Catches a line with else:
lowercase_									=    re.compile(R"^\s*else:")




def lowerCAmelCase    (__A):



     """simple docstring"""



     if _re_test_backend.search(__A) is None:
          return None
     _a           =    [b[0] for b in _re_backend.findall(__A)]
     backends.sort()
     return "_and_".join(__A)




def lowerCAmelCase    (__A):



     """simple docstring"""



     with open(__A    ,      '''r'''    ,      encoding='''utf-8'''    ,      newline='''\n''') as f:
          _a           =    f.readlines()

     _a           =    0
     while line_index < len(__A) and not lines[line_index].startswith('''_import_structure = {'''):
          line_index += 1

     # If this is a traditional init, just return.
     if line_index >= len(__A):
          return None

     # First grab the objects without a specific backend in _import_structure
     _a           =    []
     while not lines[line_index].startswith('''if TYPE_CHECKING''') and find_backend(lines[line_index]) is None:
          _a           =    lines[line_index]
          # If we have everything on a single line, let's deal with it.
          if _re_one_line_import_struct.search(__A):
               _a           =    _re_one_line_import_struct.search(__A).groups()[0]
               _a           =    re.findall(r'''\[([^\]]+)\]'''    ,      __A)
               for imp in imports:
                    objects.extend([obj[1:-1] for obj in imp.split(''', ''')])
               line_index += 1
               continue
          _a           =    _re_import_struct_key_value.search(__A)
          if single_line_import_search is not None:
               _a           =    [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''') if len(__A) > 0]
               objects.extend(__A)
          elif line.startswith(''' ''' * 8 + '''"'''):
               objects.append(line[9:-3])
          line_index += 1

     _a           =    {'''none''': objects}
     # Let's continue with backend-specific objects in _import_structure
     while not lines[line_index].startswith('''if TYPE_CHECKING'''):
          # If the line is an if not is_backend_available, we grab all objects associated.
          _a           =    find_backend(lines[line_index])
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]) is None:
               _a           =    None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]) is None:
                    line_index += 1

               line_index += 1

               _a           =    []
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]) <= 1 or lines[line_index].startswith(''' ''' * 4):
                    _a           =    lines[line_index]
                    if _re_import_struct_add_one.search(__A) is not None:
                         objects.append(_re_import_struct_add_one.search(__A).groups()[0])
                    elif _re_import_struct_add_many.search(__A) is not None:
                         _a           =    _re_import_struct_add_many.search(__A).groups()[0].split(''', ''')
                         _a           =    [obj[1:-1] for obj in imports if len(__A) > 0]
                         objects.extend(__A)
                    elif _re_between_brackets.search(__A) is not None:
                         _a           =    _re_between_brackets.search(__A).groups()[0].split(''', ''')
                         _a           =    [obj[1:-1] for obj in imports if len(__A) > 0]
                         objects.extend(__A)
                    elif _re_quote_object.search(__A) is not None:
                         objects.append(_re_quote_object.search(__A).groups()[0])
                    elif line.startswith(''' ''' * 8 + '''"'''):
                         objects.append(line[9:-3])
                    elif line.startswith(''' ''' * 12 + '''"'''):
                         objects.append(line[13:-3])
                    line_index += 1

               _a           =    objects
          else:
               line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
     _a           =    []
     while (
         line_index < len(__A)
         and find_backend(lines[line_index]) is None
         and not lines[line_index].startswith('''else''')
     ):
          _a           =    lines[line_index]
          _a           =    _re_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

     _a           =    {'''none''': objects}
     # Let's continue with backend-specific objects
     while line_index < len(__A):
          # If the line is an if is_backend_available, we grab all objects associated.
          _a           =    find_backend(lines[line_index])
          # Check if the backend declaration is inside a try block:
          if _re_try.search(lines[line_index - 1]) is None:
               _a           =    None

          if backend is not None:
               line_index += 1

               # Scroll until we hit the else block of try-except-else
               while _re_else.search(lines[line_index]) is None:
                    line_index += 1

               line_index += 1

               _a           =    []
               # Until we unindent, add backend objects to the list
               while len(lines[line_index]) <= 1 or lines[line_index].startswith(''' ''' * 8):
                    _a           =    lines[line_index]
                    _a           =    _re_import.search(__A)
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(''', '''))
                    elif line.startswith(''' ''' * 12):
                         objects.append(line[12:-2])
                    line_index += 1

               _a           =    objects
          else:
               line_index += 1

     return import_dict_objects, type_hint_objects




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     def find_duplicates(__A):
          return [k for k, v in collections.Counter(__A).items() if v > 1]

     if list(import_dict_objects.keys()) != list(type_hint_objects.keys()):
          return ["Both sides of the init do not have the same backends!"]

     _a           =    []
     for key in import_dict_objects.keys():
          _a           =    find_duplicates(import_dict_objects[key])
          if duplicate_imports:
               errors.append(F'''Duplicate _import_structure definitions for: {duplicate_imports}''')
          _a           =    find_duplicates(type_hint_objects[key])
          if duplicate_type_hints:
               errors.append(F'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''')

          if sorted(set(import_dict_objects[key])) != sorted(set(type_hint_objects[key])):
               _a           =    '''base imports''' if key == '''none''' else F'''{key} backend'''
               errors.append(F'''Differences for {name}:''')
               for a in type_hint_objects[key]:
                    if a not in import_dict_objects[key]:
                         errors.append(F'''  {a} in TYPE_HINT but not in _import_structure.''')
               for a in import_dict_objects[key]:
                    if a not in type_hint_objects[key]:
                         errors.append(F'''  {a} in _import_structure but not in TYPE_HINT.''')
     return errors




def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    []
     for root, _, files in os.walk(__A):
          if "__init__.py" in files:
               _a           =    os.path.join(__A    ,      '''__init__.py''')
               _a           =    parse_init(__A)
               if objects is not None:
                    _a           =    analyze_results(*__A)
                    if len(__A) > 0:
                         _a           =    F'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
                         failures.append('''\n'''.join(__A))
     if len(__A) > 0:
          raise ValueError('''\n\n'''.join(__A))




def lowerCAmelCase    ():



     """simple docstring"""



     _a           =    []
     for path, directories, files in os.walk(__A):
          for folder in directories:
               # Ignore private modules
               if folder.startswith('''_'''):
                    directories.remove(__A)
                    continue
               # Ignore leftovers from branches (empty folders apart from pycache)
               if len(list((Path(__A) / folder).glob('''*.py'''))) == 0:
                    continue
               _a           =    str((Path(__A) / folder).relative_to(__A))
               _a           =    short_path.replace(os.path.sep    ,      '''.''')
               submodules.append(__A)
          for fname in files:
               if fname == "__init__.py":
                    continue
               _a           =    str((Path(__A) / fname).relative_to(__A))
               _a           =    short_path.replace('''.py'''    ,      '''''').replace(os.path.sep    ,      '''.''')
               if len(submodule.split('''.''')) == 1:
                    submodules.append(__A)
     return submodules


lowercase_									=    [
    "convert_pytorch_checkpoint_to_tf2",
    "modeling_flax_pytorch_utils",
    "models.esm.openfold_utils",
]




def lowerCAmelCase    ():



     """simple docstring"""



     from transformers.utils import direct_transformers_import

     _a           =    direct_transformers_import(__A)

     _a           =    set(transformers._import_structure.keys())
     # This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
     # some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
     # (potentiall re-) add them.
     with open(os.path.join(__A    ,      '''__init__.py''')    ,      '''r''') as f:
          _a           =    f.read()
     import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]'''    ,      __A)))

     _a           =    [
         module
         for module in get_transformers_submodules()
         if module not in IGNORE_SUBMODULES and module not in import_structure_keys
     ]

     if len(__A) > 0:
          _a           =    '''\n'''.join(F'''- {module}''' for module in module_not_registered)
          raise ValueError(
              '''The following submodules are not properly registed in the main init of Transformers:\n'''
              F'''{list_of_modules}\n'''
              '''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''')


if __name__ == "__main__":
  check_all_inits()
  check_submodules()








'''simple docstring'''





import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
lowercase_									=    {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}




def lowerCAmelCase    (__A):



     """simple docstring"""



     _a           =    list(s_dict.keys())
     for key in keys:
          _a           =    r'''.*/layers_(\d+)'''
          _a           =    key
          if re.match(__A    ,      __A):
               _a           =    re.sub(r'''layers_(\d+)'''    ,      r'''block/\1/layer'''    ,      __A)

          _a           =    r'''(encoder|decoder)\/'''

          if re.match(__A    ,      __A):
               _a           =    re.match(__A    ,      __A).groups()
               if groups[0] == "encoder":
                    _a           =    re.sub(r'''/mlp/'''    ,      r'''/1/mlp/'''    ,      __A)
                    _a           =    re.sub(r'''/pre_mlp_layer_norm/'''    ,      r'''/1/layer_norm/'''    ,      __A)

               elif groups[0] == "decoder":
                    _a           =    re.sub(r'''/mlp/'''    ,      r'''/2/mlp/'''    ,      __A)
                    _a           =    re.sub(r'''/pre_mlp_layer_norm/'''    ,      r'''/2/layer_norm/'''    ,      __A)

        # 2. Convert other classic mappings
          for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
               if old_key in new_key:
                    _a           =    new_key.replace(__A    ,      __A)

          print(F'''{key} -> {new_key}''')
          _a           =    s_dict.pop(__A)

     if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          _a           =    s_dict[
              '''encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
          ].T
     if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          _a           =    s_dict[
              '''decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight'''
          ].T

     # 3. Take extra care of the EXPERTS layer
     for key in list(s_dict.keys()):
          if "expert" in key:
               _a           =    s_dict[key].shape[0]
               _a           =    s_dict[key]
               for idx in range(__A):
                    _a           =    expert_weihts[idx]
                    print(F'''{key} -> {key.replace('expert/'    ,      'nested fstring')}''')

               s_dict.pop(__A)

     return s_dict


lowercase_									=    {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}




def lowerCAmelCase    (__A    ,      __A):



     """simple docstring"""



     import regex as re

     with open(__A    ,      '''r''') as f:
          _a           =    f.read()

     _a           =    re.findall(r'''(.*) = ([0-9.]*)'''    ,      __A)
     _a           =    {}
     for param, value in regex_match:
          if param in GIN_TO_CONFIG_MAPPING and value != "":
               _a           =    float(__A) if '''.''' in value else int(__A)

     _a           =    re.findall(r'''(.*activations) = \(\'(.*)\',\)'''    ,      __A)[0]
     _a           =    str(activation[1])

     _a           =    num_experts
     _a           =    SwitchTransformersConfig(**__A)
     return config




def lowerCAmelCase    (__A    ,      __A    ,      __A=None    ,      __A="./"    ,      __A=8):



     """simple docstring"""



     print(F'''Loading flax weights from : {flax_checkpoint_path}''')
     _a           =    checkpoints.load_tax_checkpoint(__A)

     if gin_file is not None:
          _a           =    convert_gin_to_config(__A    ,      __A)
     else:
          _a           =    SwitchTransformersConfig.from_pretrained(__A)

     _a           =    SwitchTransformersForConditionalGeneration(__A)

     _a           =    flax_params['''target''']
     _a           =    flatten_dict(__A    ,      sep='''/''')
     _a           =    rename_keys(__A)
     _a           =    unflatten_dict(__A    ,      sep='''/''')

     # Load the flax params in the PT model
     load_flax_weights_in_pytorch_model(__A    ,      __A)

     print(F'''Save PyTorch model to {pytorch_dump_path}''')
     pt_model.save_pretrained(__A)


if __name__ == "__main__":
  lowercase_									=    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
          " model architecture. If not provided, a `gin_file` has to be provided."
      ),
  )
  parser.add_argument(
      "--gin_file",
      default=None,
      type=str,
      required=False,
      help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
  )
  parser.add_argument(
      "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
  )
  parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
  lowercase_									=    parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )



