



"""simple docstring"""

import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowercase__					:						List[str]												=			'''src/transformers'''
lowercase__					:						List[Any]												=			'''docs/source/en'''
lowercase__					:						List[Any]												=			'''.'''




def __lowercase    (							_a	,    _a	,    _a					):
    with open(_a	,    '''r'''	,    encoding='''utf-8'''	,    newline='''\n'''					) as f:
        snake_case_				:  Optional[int]       =			f.readlines()
    # Find the start prompt.
    snake_case_				:  Tuple       =			0
    while not lines[start_index].startswith(_a					):
        start_index += 1
    start_index += 1

    snake_case_				:  Optional[Any]       =			start_index
    while not lines[end_index].startswith(_a					):
        end_index += 1
    end_index -= 1

    while len(lines[start_index]					) <= 1:
        start_index += 1
    while len(lines[end_index]					) <= 1:
        end_index -= 1
    end_index += 1
    return "".join(lines[start_index:end_index]					), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowercase__					:						Any												=			'''Model|Encoder|Decoder|ForConditionalGeneration'''
# Regexes that match TF/Flax/PT model names.
lowercase__					:						List[Any]												=			re.compile(r'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
lowercase__					:						Dict												=			re.compile(r'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowercase__					:						Dict												=			re.compile(r'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')


# This is to make sure the transformers module imported is the one in the repo.
lowercase__					:						int												=			direct_transformers_import(TRANSFORMERS_PATH)




def __lowercase    (							_a					):
    snake_case_				:  int       =			re.finditer('''.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'''	,    _a					)
    return [m.group(0					) for m in matches]




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Tuple       =			2 if text == '''✅''' or text == '''❌''' else len(_a					)
    snake_case_				:  Dict       =			(width - text_length) // 2
    snake_case_				:  List[str]       =			width - text_length - left_indent
    return " " * left_indent + text + " " * right_indent




def __lowercase    (							):
    snake_case_				:  Tuple       =			transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
    snake_case_				:  List[Any]       =			{
        name: config_maping_names[code]
        for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
        if code in config_maping_names
    }
    snake_case_				:  List[str]       =			{name: config.replace('''Config'''	,    ''''''					) for name, config in model_name_to_config.items()}

    # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
    snake_case_				:  str       =			collections.defaultdict(_a					)
    snake_case_				:  Optional[Any]       =			collections.defaultdict(_a					)
    snake_case_				:  Union[str, Any]       =			collections.defaultdict(_a					)
    snake_case_				:  Optional[int]       =			collections.defaultdict(_a					)
    snake_case_				:  Any       =			collections.defaultdict(_a					)

    # Let's lookup through all transformers object (once).
    for attr_name in dir(_a					):
        snake_case_				:  int       =			None
        if attr_name.endswith('''Tokenizer'''					):
            snake_case_				:  int       =			slow_tokenizers
            snake_case_				:  List[Any]       =			attr_name[:-9]
        elif attr_name.endswith('''TokenizerFast'''					):
            snake_case_				:  Optional[int]       =			fast_tokenizers
            snake_case_				:  Union[str, Any]       =			attr_name[:-13]
        elif _re_tf_models.match(_a					) is not None:
            snake_case_				:  int       =			tf_models
            snake_case_				:  List[str]       =			_re_tf_models.match(_a					).groups()[0]
        elif _re_flax_models.match(_a					) is not None:
            snake_case_				:  List[str]       =			flax_models
            snake_case_				:  List[str]       =			_re_flax_models.match(_a					).groups()[0]
        elif _re_pt_models.match(_a					) is not None:
            snake_case_				:  Optional[Any]       =			pt_models
            snake_case_				:  List[str]       =			_re_pt_models.match(_a					).groups()[0]

        if lookup_dict is not None:
            while len(_a					) > 0:
                if attr_name in model_name_to_prefix.values():
                    snake_case_				:  str       =			True
                    break
                # Try again after removing the last word in the name
                snake_case_				:  List[str]       =			''''''.join(camel_case_split(_a					)[:-1]					)

    # Let's build that table!
    snake_case_				:  Optional[Any]       =			list(model_name_to_config.keys()					)
    model_names.sort(key=str.lower					)
    snake_case_				:  List[Any]       =			['''Model''', '''Tokenizer slow''', '''Tokenizer fast''', '''PyTorch support''', '''TensorFlow support''', '''Flax Support''']
    # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
    snake_case_				:  List[Any]       =			[len(_a					) + 2 for c in columns]
    snake_case_				:  List[Any]       =			max([len(_a					) for name in model_names]					) + 2

    # Build the table per se
    snake_case_				:  Optional[int]       =			'''|''' + '''|'''.join([_center_text(_a	,    _a					) for c, w in zip(_a	,    _a					)]					) + '''|\n'''
    # Use ":-----:" format to center-aligned table cell texts
    table += "|" + "|".join([''':''' + '''-''' * (w - 2) + ''':''' for w in widths]					) + "|\n"

    snake_case_				:  Tuple       =			{True: '''✅''', False: '''❌'''}
    for name in model_names:
        snake_case_				:  List[Any]       =			model_name_to_prefix[name]
        snake_case_				:  Tuple       =			[
            name,
            check[slow_tokenizers[prefix]],
            check[fast_tokenizers[prefix]],
            check[pt_models[prefix]],
            check[tf_models[prefix]],
            check[flax_models[prefix]],
        ]
        table += "|" + "|".join([_center_text(_a	,    _a					) for l, w in zip(_a	,    _a					)]					) + "|\n"
    return table




def __lowercase    (							_a=False					):
    snake_case_,			snake_case_,			snake_case_,			snake_case_				:  Optional[int]       =			_find_text_in_file(
        filename=os.path.join(_a	,    '''index.md'''					)	,    start_prompt='''<!--This table is updated automatically from the auto modules'''	,    end_prompt='''<!-- End table-->'''	,    )
    snake_case_				:  Dict       =			get_model_table_from_auto_modules()

    if current_table != new_table:
        if overwrite:
            with open(os.path.join(_a	,    '''index.md'''					)	,    '''w'''	,    encoding='''utf-8'''	,    newline='''\n'''					) as f:
                f.writelines(lines[:start_index] + [new_table] + lines[end_index:]					)
        else:
            raise ValueError(
                '''The model table in the `index.md` has not been updated. Run `make fix-copies` 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__					:						Dict												=			parser.parse_args()

     check_model_table(args.fix_and_overwrite)










"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			AutoConfig.from_pretrained(_a					)
    snake_case_				:  Tuple       =			FlaxAutoModelForSeqaSeqLM.from_config(config=_a					)
    snake_case_				:  Union[str, Any]       =			checkpoints.load_tax_checkpoint(_a					)

    snake_case_				:  Optional[int]       =			'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

    if config.model_type == "t5":
        snake_case_				:  str       =			'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        snake_case_				:  List[str]       =			'''LocalSelfAttention'''
    elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  Dict       =			'''TransientGlobalSelfAttention'''
    else:
        raise ValueError(
            '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
            ''' attribute with a value from [\'local\', \'transient-global].'''					)

    # Encoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Any       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  Dict       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
        snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Tuple       =			tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

        # Layer Normalization
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

        if split_mlp_wi:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  int       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  str       =			tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[Any]       =			tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Optional[Any]       =			flax_model.params['''encoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  List[str]       =			tax_attention_key
        snake_case_				:  Optional[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  str       =			tax_attention_value

        snake_case_				:  Dict       =			tax_attention_layer_norm

        # Global input layer norm
        if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            snake_case_				:  Union[str, Any]       =			tax_global_layer_norm

        if split_mlp_wi:
            snake_case_				:  Any       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  Union[str, Any]       =			tax_mlp_wi

        snake_case_				:  List[Any]       =			tax_mlp_wo
        snake_case_				:  int       =			tax_mlp_layer_norm

        snake_case_				:  Any       =			flax_model_encoder_layer_block

    # Only for layer 0:
    snake_case_				:  Optional[int]       =			tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Any       =			tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        snake_case_				:  List[str]       =			tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        snake_case_				:  Tuple       =			tax_encoder_global_rel_embedding

    # Assigning
    snake_case_				:  Dict       =			tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
    snake_case_				:  Any       =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_layers					):
        snake_case_				:  Tuple       =			f"layers_{str(_a					)}"

        # Self-Attention
        snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  str       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
            '''scale'''
        ]

        # Encoder-Decoder-Attention
        snake_case_				:  Any       =			tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        snake_case_				:  Optional[Any]       =			tax_enc_dec_attention_module['''key''']['''kernel''']
        snake_case_				:  str       =			tax_enc_dec_attention_module['''out''']['''kernel''']
        snake_case_				:  Union[str, Any]       =			tax_enc_dec_attention_module['''query''']['''kernel''']
        snake_case_				:  List[str]       =			tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        snake_case_				:  Optional[int]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
            snake_case_				:  List[str]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
        else:
            snake_case_				:  Dict       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

        # Layer Normalization
        snake_case_				:  List[Any]       =			tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        snake_case_				:  Dict       =			flax_model.params['''decoder''']['''block'''][str(_a					)]['''layer''']
        snake_case_				:  int       =			tax_attention_key
        snake_case_				:  List[Any]       =			tax_attention_out
        snake_case_				:  Any       =			tax_attention_query
        snake_case_				:  Dict       =			tax_attention_value

        snake_case_				:  str       =			tax_pre_attention_layer_norm

        snake_case_				:  Any       =			tax_enc_dec_attention_key
        snake_case_				:  str       =			tax_enc_dec_attention_out
        snake_case_				:  int       =			tax_enc_dec_attention_query
        snake_case_				:  Any       =			tax_enc_dec_attention_value

        snake_case_				:  Optional[Any]       =			tax_cross_layer_norm

        if split_mlp_wi:
            snake_case_				:  Tuple       =			tax_mlp_wi_a
            snake_case_				:  List[Any]       =			tax_mlp_wi_a
        else:
            snake_case_				:  List[Any]       =			tax_mlp_wi

        snake_case_				:  Dict       =			tax_mlp_wo

        snake_case_				:  List[Any]       =			txa_mlp_layer_norm

        snake_case_				:  Optional[int]       =			flax_model_decoder_layer_block

    # Decoder Normalization
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    snake_case_				:  Tuple       =			txa_decoder_norm

    # Only for layer 0:
    snake_case_				:  str       =			tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
    snake_case_				:  Optional[Any]       =			tax_decoder_rel_embedding

    # Token Embeddings
    snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''token_embedder''']['''embedding''']
    snake_case_				:  Optional[int]       =			txa_token_embeddings

    # LM Head (only in v1.1 and LongT5 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        snake_case_				:  Union[str, Any]       =			tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

    flax_model.save_pretrained(_a					)
    print('''T5X Model was sucessfully converted!'''					)


if __name__ == "__main__":
     lowercase__					:						Tuple												=			argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path the T5X checkpoint.'''
     )
     parser.add_argument('''--config_name''', default=None, type=str, required=True, help='''Config name of LongT5/T5 model.''')
     parser.add_argument(
         '''--flax_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output FLAX model.'''
     )
     lowercase__					:						Dict												=			parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





