





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      :       Optional[Any]									=  {
    """/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 A_	(		A__				)  ->		Any:
       # 1. in HF T5, we have block.{x}.layer.{y}. which corresponds to layer.{x} in
       # the original model
       a__						:	Dict =				list(s_dict.keys()				)
       for key in keys:
              a__						:	Dict =				R'.*/layers_(\d+)'
              a__						:	Tuple =				key
              if re.match(A__		,      A__				):
                     a__						:	Dict =				re.sub(R'layers_(\d+)'		,      R'block/\1/layer'		,      A__				)

              a__						:	Tuple =				R'(encoder|decoder)\/'

              if re.match(A__		,      A__				):
                     a__						:	List[str] =				re.match(A__		,      A__				).groups()
                     if groups[0] == "encoder":
                            a__						:	Any =				re.sub(R'/mlp/'		,      R'/1/mlp/'		,      A__				)
                            a__						:	str =				re.sub(R'/pre_mlp_layer_norm/'		,      R'/1/layer_norm/'		,      A__				)

                     elif groups[0] == "decoder":
                            a__						:	Any =				re.sub(R'/mlp/'		,      R'/2/mlp/'		,      A__				)
                            a__						:	str =				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__						:	Union[str, Any] =				new_key.replace(A__		,      A__				)

              print(F'{key} -> {new_key}'				)
              a__						:	str =				s_dict.pop(A__				)

       if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
              a__						:	Optional[int] =				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__						:	List[Any] =				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__						:	Tuple =				s_dict[key].shape[0]
                     a__						:	Optional[Any] =				s_dict[key]
                     for idx in range(A__				):
                            a__						:	List[str] =				expert_weihts[idx]
                            print(F'{key} -> {key.replace("expert/"		,      "nested fstring"				)}'				)

                     s_dict.pop(A__				)

       return s_dict


lowercase      :       Any									=  {
    """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 A_	(		A__		,      A__				)  ->		int:
       # Convert a google style config to the hugging face fromat
       import regex as re

       with open(A__		,      'r'				) as f:
              a__						:	Union[str, Any] =				f.read()

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

       a__						:	List[str] =				re.findall(R'(.*activations) = \(\'(.*)\',\)'		,      A__				)[0]
       a__						:	str =				str(activation[1]				)

       a__						:	List[str] =				num_experts
       a__						:	Optional[Any] =				SwitchTransformersConfig(**A__				)
       return config



def A_	(		A__		,      A__		,      A__=None		,      A__="./"		,      A__=8				)  ->		Tuple:
       # Initialise PyTorch model

       print(F'Loading flax weights from : {flax_checkpoint_path}'				)
       a__						:	Union[str, Any] =				checkpoints.load_tax_checkpoint(A__				)

       if gin_file is not None:
              a__						:	Union[str, Any] =				convert_gin_to_config(A__		,      A__				)
       else:
              a__						:	Union[str, Any] =				SwitchTransformersConfig.from_pretrained(A__				)

       a__						:	int =				SwitchTransformersForConditionalGeneration(A__				)

       a__						:	Any =				flax_params['target']
       a__						:	Any =				flatten_dict(A__		,      sep='/'				)
       a__						:	List[str] =				rename_keys(A__				)
       a__						:	Optional[Any] =				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      :       int									=  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      :       Optional[Any]									=  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,
    )









import argparse
from collections import defaultdict

import yaml


lowercase      :       int									=  """docs/source/en/_toctree.yml"""



def A_	(		A__				)  ->		Tuple:
       a__						:	int =				defaultdict(A__				)
       a__						:	List[str] =				[]
       a__						:	Any =				[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
              else:
                     new_doc_list.append(A__				)

       a__						:	int =				new_doc_list
       a__						:	List[str] =				[key for key, value in counts.items() if value > 1]

       a__						:	Tuple =				[]
       for duplicate_key in duplicates:
              a__						:	int =				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
              if len(A__				) > 1:
                     raise ValueError(
                         F'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'				)
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
       a__						:	Any =				sorted(A__		,      key=lambda A__				: s["title"].lower()				)

       # "overview" gets special treatment and is always first
       if len(A__				) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

       overview_doc.extend(A__				)

       # Sort
       return overview_doc



def A_	(		A__=False				)  ->		str:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	int =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Tuple =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	Any =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[str] =				0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       a__						:	Tuple =				api_doc[scheduler_idx]['sections']
       a__						:	Optional[int] =				clean_doc_toc(A__				)

       a__						:	int =				False
       if new_scheduler_doc != scheduler_doc:
              a__						:	int =				True
              if overwrite:
                     a__						:	int =				new_scheduler_doc

       if diff:
              if overwrite:
                     a__						:	Optional[Any] =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)



def A_	(		A__=False				)  ->		Tuple:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	Optional[Any] =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Optional[int] =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	int =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[Any] =				0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       a__						:	Optional[int] =				False
       a__						:	Union[str, Any] =				api_doc[pipeline_idx]['sections']
       a__						:	List[str] =				[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     a__						:	Any =				pipeline_doc['section']
                     a__						:	Dict =				clean_doc_toc(A__				)
                     if overwrite:
                            a__						:	Optional[Any] =				new_sub_pipeline_doc
              new_pipeline_docs.append(A__				)

       # sort overall pipeline doc
       a__						:	Optional[Any] =				clean_doc_toc(A__				)

       if new_pipeline_docs != pipeline_docs:
              a__						:	List[Any] =				True
              if overwrite:
                     a__						:	Union[str, Any] =				new_pipeline_docs

       if diff:
              if overwrite:
                     a__						:	Dict =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


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

    check_scheduler_doc(args.fix_and_overwrite)
    check_pipeline_doc(args.fix_and_overwrite)


