
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 		lowercase_    (     A__				,	A__	)							->							Optional[int]:
    """simple docstring"""

    for tf_name, hf_name in patterns:
        snake_case				  =      k.replace(A__				,	A__	)
    return k



def 		lowercase_    (     A__				,	A__	)							->							BigBirdPegasusForConditionalGeneration:
    """simple docstring"""

    snake_case				  =      BigBirdPegasusConfig(**A__	)
    snake_case				  =      BigBirdPegasusForConditionalGeneration(A__	)
    snake_case				  =      torch_model.state_dict()
    snake_case				  =      {}

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

    snake_case				  =      mapping["model.embed_positions.weight"]
    snake_case				  =      mapping.pop("model.embed_positions.weight"	)
    snake_case  ,							snake_case				  =      torch_model.load_state_dict(A__				,	strict=A__	)
    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 		lowercase_    (     A__	)							->							Dict:
    """simple docstring"""

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



def 		lowercase_    (     A__				,	A__				,	A__	)							->							List[Any]:
    """simple docstring"""

    snake_case				  =      get_tf_weights_as_numpy(A__	)
    snake_case				  =      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)



import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin







@require_jieba
class   lowerCamelCase	(						A_							,				unittest.TestCase						):
     UpperCAmelCase__       :							Any     =	CpmAntTokenizer
     UpperCAmelCase__       :							Optional[Any]     =	False





     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Dict:
         super().setUp()

         snake_case				  =      [
             "<d>",
             "</d>",
             "<s>",
             "</s>",
             "</_>",
             "<unk>",
             "<pad>",
             "</n>",
             "我",
             "是",
             "C",
             "P",
             "M",
             "A",
             "n",
             "t",
         ]
         snake_case				  =      os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES["vocab_file"]     )
         with open(self.vocab_file       ,					"w"       ,					encoding="utf-8"     ) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]     )     )





     @tooslow
     def 	UpperCAmelCase(self   :					Optional[Any]     )		->		Optional[int]:
         snake_case				  =      CpmAntTokenizer.from_pretrained("openbmb/cpm-ant-10b"     )
         snake_case				  =      "今天天气真好！"
         snake_case				  =      ["今天", "天气", "真", "好", "！"]
         snake_case				  =      tokenizer.tokenize(_A     )
         self.assertListEqual(_A       ,					_A     )
         snake_case				  =      "今天天气真好！"
         snake_case				  =      [tokenizer.bos_token] + tokens

         snake_case				  =      [6, 9_8_0_2, 1_4_9_6_2, 2_0_8_2, 8_3_1, 2_4_4]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_A     )       ,					_A     )

         snake_case				  =      tokenizer.decode(_A     )
         self.assertEqual(_A       ,					_A     )

