'''simple docstring'''




import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

_a   :	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
)

_a   :	str 							=   (
    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   :	List[str] 							=   [
    "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	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Any:





       """simple docstring"""


       for tf_name, hf_name in patterns:
              __UpperCAmelCase  :				Union[str, Any]       				=		k.replace(lowerCamelCase__							,    lowerCamelCase__						)
       return k

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       BigBirdPegasusForConditionalGeneration:





       """simple docstring"""


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

       # separating decoder weights
       __UpperCAmelCase  :				Optional[Any]       				=		{k: tf_weights[k] for k in tf_weights if k.startswith("pegasus/decoder"						)}
       __UpperCAmelCase  :				Any       				=		{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  :				Union[str, Any]       				=		[k.endswith(lowerCamelCase__						) for ending in KEYS_TO_IGNORE]
              if any(lowerCamelCase__						):
                     continue
              __UpperCAmelCase  :				List[str]       				=		DECODER_PATTERNS
              __UpperCAmelCase  :				Optional[Any]       				=		rename_state_dict_key(lowerCamelCase__							,    lowerCamelCase__						)
              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  :				List[str]       				=		v.T
              __UpperCAmelCase  :				int       				=		torch.from_numpy(lowerCamelCase__						)
              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  :				Optional[int]       				=		[k.endswith(lowerCamelCase__						) for ending in KEYS_TO_IGNORE]
              if any(lowerCamelCase__						):
                     continue
              __UpperCAmelCase  :				Optional[Any]       				=		REMAINING_PATTERNS
              __UpperCAmelCase  :				Any       				=		rename_state_dict_key(lowerCamelCase__							,    lowerCamelCase__						)
              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  :				Optional[Any]       				=		v.T
              __UpperCAmelCase  :				int       				=		torch.from_numpy(lowerCamelCase__						)
              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  :				List[str]       				=		mapping["model.embed_positions.weight"]
       __UpperCAmelCase  :				Optional[Any]       				=		mapping.pop("model.embed_positions.weight"						)
       __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		torch_model.load_state_dict(lowerCamelCase__							,    strict=lowerCamelCase__						)
       __UpperCAmelCase  :				str       				=		[
           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	(      lowerCamelCase__						)					->       Dict:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__						)					->       Any:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		get_tf_weights_as_numpy(lowerCamelCase__						)
       __UpperCAmelCase  :				Tuple       				=		convert_bigbird_pegasus(lowerCamelCase__							,    lowerCamelCase__						)
       torch_model.save_pretrained(lowerCamelCase__						)


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







'''simple docstring'''




from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

_a							,					_a							,					_a							,					_a   :	Any 							=   train_test_split(X, y)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


       return np.linalg.norm(np.array(lowerCamelCase__						) - np.array(lowerCamelCase__						)						)

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		zip(lowerCamelCase__							,    lowerCamelCase__						)
       # List of distances of all points from the point to be classified
       __UpperCAmelCase  :				int       				=		[]
       for data_point in data:
              __UpperCAmelCase  :				Optional[Any]       				=		euclidean_distance(data_point[0]							,    lowerCamelCase__						)
              distances.append((distance, data_point[1])						)
       # Choosing 'k' points with the least distances.
       __UpperCAmelCase  :				Union[str, Any]       				=		[i[1] for i in sorted(lowerCamelCase__						)[:k]]
       # Most commonly occurring class among them
       # is the class into which the point is classified
       __UpperCAmelCase  :				Dict       				=		Counter(lowerCamelCase__						).most_common(1						)[0][0]
       return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))






