'''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  _UpperCamelCase (     _a	:	Any				,     _a	:	Optional[int]			):




    """simple docstring"""



    for tf_name, hf_name in patterns:
        __UpperCamelCase	:	Optional[Any]		    =  k.replace(UpperCAmelCase__				,     UpperCAmelCase__			)
    return k







def  _UpperCamelCase (     _a	:	dict				,     _a	:	dict			):




    """simple docstring"""



    __UpperCamelCase	:	Union[str, Any]		    =  BigBirdPegasusConfig(**UpperCAmelCase__			)
    __UpperCamelCase	:	List[str]		    =  BigBirdPegasusForConditionalGeneration(UpperCAmelCase__			)
    __UpperCamelCase	:	List[Any]		    =  torch_model.state_dict()
    __UpperCamelCase	:	Dict		    =  {}

    # separating decoder weights
    __UpperCamelCase	:	Optional[int]		    =  {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'			)}
    __UpperCamelCase	:	List[str]		    =  {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'			):
        __UpperCamelCase	:	Dict		    =  [k.endswith(UpperCAmelCase__			) for ending in KEYS_TO_IGNORE]
        if any(UpperCAmelCase__			):
            continue
        __UpperCamelCase	:	Any		    =  DECODER_PATTERNS
        __UpperCamelCase	:	Tuple		    =  rename_state_dict_key(UpperCAmelCase__				,     UpperCAmelCase__			)
        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']			):
            __UpperCamelCase	:	Any		    =  v.T
        __UpperCamelCase	:	Optional[Any]		    =  torch.from_numpy(UpperCAmelCase__			)
        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'			):
        __UpperCamelCase	:	Any		    =  [k.endswith(UpperCAmelCase__			) for ending in KEYS_TO_IGNORE]
        if any(UpperCAmelCase__			):
            continue
        __UpperCamelCase	:	str		    =  REMAINING_PATTERNS
        __UpperCamelCase	:	Optional[int]		    =  rename_state_dict_key(UpperCAmelCase__				,     UpperCAmelCase__			)
        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']			):
            __UpperCamelCase	:	Any		    =  v.T
        __UpperCamelCase	:	List[str]		    =  torch.from_numpy(UpperCAmelCase__			)
        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}"""

    __UpperCamelCase	:	int		    =  mapping["""model.embed_positions.weight"""]
    __UpperCamelCase	:	List[str]		    =  mapping.pop('model.embed_positions.weight'			)
    __UpperCamelCase	:	List[Any]		    =  torch_model.load_state_dict(UpperCAmelCase__				,     strict=UpperCAmelCase__			)
    __UpperCamelCase	:	Optional[Any]		    =  [
        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  _UpperCamelCase (     _a	:	Any			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  tf.train.list_variables(UpperCAmelCase__			)
    __UpperCamelCase	:	Optional[int]		    =  {}
    __UpperCamelCase	:	int		    =  ["""global_step"""]
    for name, shape in tqdm(UpperCAmelCase__				,     desc='converting tf checkpoint to dict'			):
        __UpperCamelCase	:	Optional[int]		    =  any(pat in name for pat in ignore_name			)
        if skip_key:
            continue
        __UpperCamelCase	:	List[str]		    =  tf.train.load_variable(UpperCAmelCase__				,     UpperCAmelCase__			)
        __UpperCamelCase	:	Union[str, Any]		    =  array
    return tf_weights







def  _UpperCamelCase (     _a	:	str				,     _a	:	str				,     _a	:	dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  get_tf_weights_as_numpy(UpperCAmelCase__			)
    __UpperCamelCase	:	Any		    =  convert_bigbird_pegasus(UpperCAmelCase__				,     UpperCAmelCase__			)
    torch_model.save_pretrained(UpperCAmelCase__			)


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'''
from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url







@pytest.mark.parametrize('repo_id'				,     ['canonical_dataset_name', 'org-name/dataset-name']			)
@pytest.mark.parametrize('path'				,     ['filename.csv', 'filename with blanks.csv']			)
@pytest.mark.parametrize('revision'				,     [None, 'v2']			)
def  _UpperCamelCase (     _a	:	Any				,     _a	:	int				,     _a	:	Dict			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  hf_hub_url(repo_id=_a				,     path=_a				,     revision=_a			)
    assert url == f"""https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(_a			)}"""



