

import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

UpperCamelCase__  :      int			=    (
    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
)

UpperCamelCase__  :      Any			=    (
    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
)

UpperCamelCase__  :      Optional[Any]			=    [
    '''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       (  _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    for tf_name, hf_name in patterns:
        SCREAMING_SNAKE_CASE_									=						k.replace(_lowercase ,   _lowercase       )
    return k


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple ,   _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						BigBirdPegasusConfig(**_lowercase       )
    SCREAMING_SNAKE_CASE_									=						BigBirdPegasusForConditionalGeneration(_lowercase       )
    SCREAMING_SNAKE_CASE_									=						torch_model.state_dict()
    SCREAMING_SNAKE_CASE_									=						{}

    # separating decoder weights
    SCREAMING_SNAKE_CASE_									=						{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'       )}
    SCREAMING_SNAKE_CASE_									=						{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'       ):
        SCREAMING_SNAKE_CASE_									=						[k.endswith(_lowercase       ) for ending in KEYS_TO_IGNORE]
        if any(_lowercase       ):
            continue
        SCREAMING_SNAKE_CASE_									=						DECODER_PATTERNS
        SCREAMING_SNAKE_CASE_									=						rename_state_dict_key(_lowercase ,   _lowercase       )
        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']       ):
            SCREAMING_SNAKE_CASE_									=						v.T
        SCREAMING_SNAKE_CASE_									=						torch.from_numpy(_lowercase       )
        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'       ):
        SCREAMING_SNAKE_CASE_									=						[k.endswith(_lowercase       ) for ending in KEYS_TO_IGNORE]
        if any(_lowercase       ):
            continue
        SCREAMING_SNAKE_CASE_									=						REMAINING_PATTERNS
        SCREAMING_SNAKE_CASE_									=						rename_state_dict_key(_lowercase ,   _lowercase       )
        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']       ):
            SCREAMING_SNAKE_CASE_									=						v.T
        SCREAMING_SNAKE_CASE_									=						torch.from_numpy(_lowercase       )
        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}"""

    SCREAMING_SNAKE_CASE_									=						mapping['model.embed_positions.weight']
    SCREAMING_SNAKE_CASE_									=						mapping.pop('model.embed_positions.weight'       )
    SCREAMING_SNAKE_CASE_									=						torch_model.load_state_dict(_lowercase ,   strict=_lowercase       )
    SCREAMING_SNAKE_CASE_									=						[
        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       (  _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


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


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Dict ,   _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						get_tf_weights_as_numpy(_lowercase       )
    SCREAMING_SNAKE_CASE_									=						convert_bigbird_pegasus(_lowercase ,   _lowercase       )
    torch_model.save_pretrained(_lowercase       )


if __name__ == "__main__":
       UpperCamelCase__  :      List[Any]			=    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.")
       UpperCamelCase__  :      int			=    parser.parse_args()
       UpperCamelCase__  :      int			=    {}
       convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		int=7       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						None
    if token is not None:
        SCREAMING_SNAKE_CASE_									=						{'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    # The id of a workflow (not of a workflow run)
    SCREAMING_SNAKE_CASE_									=						'636036'

    SCREAMING_SNAKE_CASE_									=						f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
    # On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
    url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

    SCREAMING_SNAKE_CASE_									=						requests.get(_SCREAMING_SNAKE_CASE ,   headers=_SCREAMING_SNAKE_CASE       ).json()

    return result["workflow_runs"]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						get_daily_ci_runs(_SCREAMING_SNAKE_CASE       )
    SCREAMING_SNAKE_CASE_									=						None
    for workflow_run in workflow_runs:
        if workflow_run["status"] == "completed":
            SCREAMING_SNAKE_CASE_									=						workflow_run['id']
            break

    return workflow_run_id


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						get_last_daily_ci_runs(_SCREAMING_SNAKE_CASE       )
    if workflow_run_id is not None:
        SCREAMING_SNAKE_CASE_									=						get_artifacts_links(worflow_run_id=_SCREAMING_SNAKE_CASE ,   token=_SCREAMING_SNAKE_CASE       )
        for artifact_name in artifact_names:
            if artifact_name in artifacts_links:
                SCREAMING_SNAKE_CASE_									=						artifacts_links[artifact_name]
                download_artifact(
                    artifact_name=_SCREAMING_SNAKE_CASE ,   artifact_url=_SCREAMING_SNAKE_CASE ,   output_dir=_SCREAMING_SNAKE_CASE ,   token=_SCREAMING_SNAKE_CASE       )





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[Any] ,   _SCREAMING_SNAKE_CASE					:		str ,   _SCREAMING_SNAKE_CASE					:		List[Any]       ):





    """simple docstring"""


    get_last_daily_ci_artifacts(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )

    SCREAMING_SNAKE_CASE_									=						{}
    for artifact_name in artifact_names:
        SCREAMING_SNAKE_CASE_									=						os.path.join(_SCREAMING_SNAKE_CASE ,   f"""{artifact_name}.zip"""       )
        if os.path.isfile(_SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						{}
            with zipfile.ZipFile(_SCREAMING_SNAKE_CASE       ) as z:
                for filename in z.namelist():
                    if not os.path.isdir(_SCREAMING_SNAKE_CASE       ):
                        # read the file
                        with z.open(_SCREAMING_SNAKE_CASE       ) as f:
                            SCREAMING_SNAKE_CASE_									=						f.read().decode('UTF-8'       )

    return results


