

"""simple docstring"""





import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


_a							=							[
    # tf -> hf
    ('/', '.'),
    ('layer_', 'layers.'),
    ('kernel', 'weight'),
    ('beta', 'bias'),
    ('gamma', 'weight'),
    ('pegasus', 'model'),
]
_a							=							[
    ('.output.dense', '.fc2'),
    ('intermediate.LayerNorm', 'final_layer_norm'),
    ('intermediate.dense', 'fc1'),
]

_a							=							(
    INIT_COMMON
    + [
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.out_proj'),
        ('attention.self', 'self_attn'),
        ('attention.encdec.LayerNorm', 'encoder_attn_layer_norm'),
        ('attention.encdec_output.dense', 'encoder_attn.out_proj'),
        ('attention.encdec', 'encoder_attn'),
        ('key', 'k_proj'),
        ('value', 'v_proj'),
        ('query', 'q_proj'),
        ('decoder.LayerNorm', 'decoder.layernorm_embedding'),
    ]
    + END_COMMON
)

_a							=							(
    INIT_COMMON
    + [
        ('embeddings.word_embeddings', 'shared.weight'),
        ('embeddings.position_embeddings', 'embed_positions.weight'),
        ('attention.self.LayerNorm', 'self_attn_layer_norm'),
        ('attention.output.dense', 'self_attn.output'),
        ('attention.self', 'self_attn.self'),
        ('encoder.LayerNorm', 'encoder.layernorm_embedding'),
    ]
    + END_COMMON
)

_a							=							[
    'encdec/key/bias',
    'encdec/query/bias',
    'encdec/value/bias',
    'self/key/bias',
    'self/query/bias',
    'self/value/bias',
    'encdec_output/dense/bias',
    'attention/output/dense/bias',
]




def   _A  (  UpperCamelCase_					:	int,      UpperCamelCase_					:	List[str])     ->						int:
   '''simple docstring'''


   for tf_name, hf_name in patterns:
      __lowercase								=						k.replace(a_,      a_)
   return k




def   _A  (  UpperCamelCase_					:	str,      UpperCamelCase_					:	Union[str, Any])     ->						BigBirdPegasusForConditionalGeneration:
   '''simple docstring'''


   __lowercase								=						BigBirdPegasusConfig(**a_)
   __lowercase								=						BigBirdPegasusForConditionalGeneration(a_)
   __lowercase								=						torch_model.state_dict()
   __lowercase								=						{}

   # separating decoder weights
   __lowercase								=						{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder")}
   __lowercase								=						{k: tf_weights[k] for k in tf_weights if not k.startswith("pegasus/decoder")}

   for k, v in tqdm(decoder_weights.items(),      "tf -> hf conversion"):
      __lowercase								=						[k.endswith(a_) for ending in KEYS_TO_IGNORE]
      if any(a_):
         continue
      __lowercase								=						DECODER_PATTERNS
      __lowercase								=						rename_state_dict_key(a_,      a_)
      if new_k not in state_dict:
         raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""")
      if any(True if i in k else False for i in ["dense", "query", "key", "value"]):
         __lowercase								=						v.T
      __lowercase								=						torch.from_numpy(a_)
      assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

   for k, v in tqdm(remaining_weights.items(),      "tf -> hf conversion"):
      __lowercase								=						[k.endswith(a_) for ending in KEYS_TO_IGNORE]
      if any(a_):
         continue
      __lowercase								=						REMAINING_PATTERNS
      __lowercase								=						rename_state_dict_key(a_,      a_)
      if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
         raise ValueError(F"""could not find new key {new_k} in state dict. (converted from {k})""")
      if any(True if i in k else False for i in ["dense", "query", "key", "value"]):
         __lowercase								=						v.T
      __lowercase								=						torch.from_numpy(a_)
      if k != "pegasus/embeddings/position_embeddings":
         assert v.shape == state_dict[new_k].shape, F"""{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}"""

   __lowercase								=						mapping["model.embed_positions.weight"]
   __lowercase								=						mapping.pop("model.embed_positions.weight")
   __lowercase		,__lowercase								=						torch_model.load_state_dict(a_,      strict=a_)
   __lowercase								=						[
       k
       for k in missing
       if k
       not in [
           "final_logits_bias",
           "model.encoder.embed_tokens.weight",
           "model.decoder.embed_tokens.weight",
           "lm_head.weight",
       ]
   ]
   assert unexpected_missing == [], F"""no matches found for the following torch keys {unexpected_missing}"""
   assert extra == [], F"""no matches found for the following tf keys {extra}"""
   return torch_model




def   _A  (  UpperCamelCase_					:	List[str])     ->						Dict:
   '''simple docstring'''


   __lowercase								=						tf.train.list_variables(a_)
   __lowercase								=						{}
   __lowercase								=						["global_step"]
   for name, shape in tqdm(a_,      desc="converting tf checkpoint to dict"):
      __lowercase								=						any(pat in name for pat in ignore_name)
      if skip_key:
         continue
      __lowercase								=						tf.train.load_variable(a_,      a_)
      __lowercase								=						array
   return tf_weights




def   _A  (  UpperCamelCase_					:	Dict,      UpperCamelCase_					:	str,      UpperCamelCase_					:	Optional[int])     ->						Dict:
   '''simple docstring'''


   __lowercase								=						get_tf_weights_as_numpy(a_)
   __lowercase								=						convert_bigbird_pegasus(a_,      a_)
   torch_model.save_pretrained(a_)


if __name__ == "__main__":
   _a							=							argparse.ArgumentParser()
   parser.add_argument('--tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
   parser.add_argument('--save_dir', default=None, type=str, help='Path to the output PyTorch model.')
   _a							=							parser.parse_args()
   _a							=							{}
   convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)







"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer


_a							=							logging.get_logger(__name__)

_a							=							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_a							=							[
    'small',
    'small-base',
    'medium',
    'medium-base',
    'intermediate',
    'intermediate-base',
    'large',
    'large-base',
    'xlarge',
    'xlarge-base',
]

_a							=							{
    'vocab_file': {
        'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt',
        'funnel-transformer/small-base': 'https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt',
        'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt',
        'funnel-transformer/medium-base': (
            'https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt'
        ),
        'funnel-transformer/intermediate': (
            'https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt'
        ),
        'funnel-transformer/intermediate-base': (
            'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt'
        ),
        'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt',
        'funnel-transformer/large-base': 'https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt',
        'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt',
        'funnel-transformer/xlarge-base': (
            'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'funnel-transformer/small': 'https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json',
        'funnel-transformer/small-base': (
            'https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json'
        ),
        'funnel-transformer/medium': 'https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json',
        'funnel-transformer/medium-base': (
            'https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json'
        ),
        'funnel-transformer/intermediate': (
            'https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json'
        ),
        'funnel-transformer/intermediate-base': (
            'https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json'
        ),
        'funnel-transformer/large': 'https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json',
        'funnel-transformer/large-base': (
            'https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json'
        ),
        'funnel-transformer/xlarge': 'https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json',
        'funnel-transformer/xlarge-base': (
            'https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json'
        ),
    },
}
_a							=							{F"funnel-transformer/{name}": 5_12 for name in _model_names}
_a							=							{F"funnel-transformer/{name}": {'do_lower_case': True} for name in _model_names}




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[Any]       		=     VOCAB_FILES_NAMES
 __UpperCAmelCase     : Tuple       		=     PRETRAINED_VOCAB_FILES_MAP
 __UpperCAmelCase     : Union[str, Any]       		=     PRETRAINED_INIT_CONFIGURATION
 __UpperCAmelCase     : Optional[Any]       		=     FunnelTokenizer
 __UpperCAmelCase     : List[str]       		=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __UpperCAmelCase     : int       		=     2


 def __init__(      self							:      Dict, UpperCAmelCase__							:      Dict=None, UpperCAmelCase__							:      Optional[int]=None, UpperCAmelCase__							:      Dict=True, UpperCAmelCase__							:      List[str]="<unk>", UpperCAmelCase__							:      Optional[Any]="<sep>", UpperCAmelCase__							:      Optional[Any]="<pad>", UpperCAmelCase__							:      Union[str, Any]="<cls>", UpperCAmelCase__							:      str="<mask>", UpperCAmelCase__							:      Optional[Any]="<s>", UpperCAmelCase__							:      Tuple="</s>", UpperCAmelCase__							:      Union[str, Any]=True, UpperCAmelCase__							:      Optional[int]=True, UpperCAmelCase__							:      Optional[Any]=None, UpperCAmelCase__							:      Dict="##", **UpperCAmelCase__							:      List[str], ):
    super().__init__(
        UpperCAmelCase__, tokenizer_file=UpperCAmelCase__, do_lower_case=UpperCAmelCase__, unk_token=UpperCAmelCase__, sep_token=UpperCAmelCase__, pad_token=UpperCAmelCase__, cls_token=UpperCAmelCase__, mask_token=UpperCAmelCase__, bos_token=UpperCAmelCase__, eos_token=UpperCAmelCase__, clean_text=UpperCAmelCase__, tokenize_chinese_chars=UpperCAmelCase__, strip_accents=UpperCAmelCase__, wordpieces_prefix=UpperCAmelCase__, **UpperCAmelCase__, )

    __lowercase								=						json.loads(self.backend_tokenizer.normalizer.__getstate__()     )
    if (
        normalizer_state.get("lowercase", UpperCAmelCase__     ) != do_lower_case
        or normalizer_state.get("strip_accents", UpperCAmelCase__     ) != strip_accents
        or normalizer_state.get("handle_chinese_chars", UpperCAmelCase__     ) != tokenize_chinese_chars
    ):
       __lowercase								=						getattr(UpperCAmelCase__, normalizer_state.pop("type"     )     )
       __lowercase								=						do_lower_case
       __lowercase								=						strip_accents
       __lowercase								=						tokenize_chinese_chars
       __lowercase								=						normalizer_class(**UpperCAmelCase__     )

    __lowercase								=						do_lower_case


 def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Any=None     ):
    __lowercase								=						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

    return output


 def  _lowercase			(      self							:      str, UpperCAmelCase__							:      List[int], UpperCAmelCase__							:      Optional[List[int]] = None     ):
    __lowercase								=						[self.sep_token_id]
    __lowercase								=						[self.cls_token_id]
    if token_ids_a is None:
       return len(cls     ) * [self.cls_token_type_id] + len(token_ids_a + sep     ) * [0]
    return len(cls     ) * [self.cls_token_type_id] + len(token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



 def  _lowercase			(      self							:      Optional[Any], UpperCAmelCase__							:      str, UpperCAmelCase__							:      Optional[str] = None     ):
    __lowercase								=						self._tokenizer.model.save(UpperCAmelCase__, name=UpperCAmelCase__     )
    return tuple(UpperCAmelCase__     )




