
import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

SCREAMING_SNAKE_CASE							:List[Any]										=    (
    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
)

SCREAMING_SNAKE_CASE							:Union[str, 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
)

SCREAMING_SNAKE_CASE							:Union[str, 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      (     a_		,       a_						)		->     List[Any]:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_						)		->     BigBirdPegasusForConditionalGeneration:







				"""simple docstring"""



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

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

				__A             =			mapping["model.embed_positions.weight"]
				__A             =			mapping.pop("model.embed_positions.weight"						)
				__A      ,							__A             =			torch_model.load_state_dict(a_		,       strict=a_						)
				__A             =			[
				    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_						)		->     Dict:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_		,       a_						)		->     List[str]:







				"""simple docstring"""



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


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


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate





@dataclass(frozen=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="automatic-speech-recognition"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"audio": Audio()}  )
	snake_case_    =					Features({"transcription": Value("string"  )}  )
	snake_case_    =					"audio"
	snake_case_    =					"transcription"






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]			):
					if self.audio_column not in features:
									raise ValueError(f'''Column {self.audio_column} is not present in features.'''			)
					if not isinstance(features[self.audio_column]							,A			):
									raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.input_schema.copy()
					__A             =			features[self.audio_column]
					__A             =			input_schema
					return task_template







	@property
	def     UpperCamelCase_  (	self :			int			):
					return {self.audio_column: "audio", self.transcription_column: "transcription"}
