



'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

lowerCAmelCase	:		Dict				=   (
    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
)

lowerCAmelCase	:		Dict				=   (
    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
)

lowerCAmelCase	:		int				=   [
    '''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   __lowerCAmelCase					(							lowerCamelCase   :						List[str]				,		lowerCamelCase   :						Union[str, Any]					):






	'''simple docstring'''




	for tf_name, hf_name in patterns:
		__lowerCAmelCase												=  k.replace(lowerCamelCase				,		lowerCamelCase					)
	return k



def   __lowerCAmelCase					(							lowerCamelCase   :						dict				,		lowerCamelCase   :						dict					):






	'''simple docstring'''




	__lowerCAmelCase												=  BigBirdPegasusConfig(**lowerCamelCase					)
	__lowerCAmelCase												=  BigBirdPegasusForConditionalGeneration(lowerCamelCase					)
	__lowerCAmelCase												=  torch_model.state_dict()
	__lowerCAmelCase												=  {}

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

	__lowerCAmelCase												=  mapping["model.embed_positions.weight"]
	__lowerCAmelCase												=  mapping.pop("model.embed_positions.weight"					)
	__lowerCAmelCase							,       __lowerCAmelCase												=  torch_model.load_state_dict(lowerCamelCase				,		strict=lowerCamelCase					)
	__lowerCAmelCase												=  [
	    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   __lowerCAmelCase					(							lowerCamelCase   :						Tuple					):






	'''simple docstring'''




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



def   __lowerCAmelCase					(							lowerCamelCase   :						str				,		lowerCamelCase   :						str				,		lowerCamelCase   :						dict					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_tf_weights_as_numpy(lowerCamelCase					)
	__lowerCAmelCase												=  convert_bigbird_pegasus(lowerCamelCase				,		lowerCamelCase					)
	torch_model.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
	lowerCAmelCase	:		str				=   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.''')
	lowerCAmelCase	:		str				=   parser.parse_args()
	lowerCAmelCase	:		Tuple				=   {}
	convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)




'''simple docstring'''



import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union

import numpy as np



def   __lowerCAmelCase					(							lowerCamelCase   :						bytes				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''{sampling_rate}'''
	__lowerCAmelCase												=  "1"
	__lowerCAmelCase												=  "f32le"
	__lowerCAmelCase												=  [
	    "ffmpeg",
	    "-i",
	    "pipe:0",
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]

	try:
		with subprocess.Popen(lowerCamelCase				,		stdin=subprocess.PIPE				,		stdout=subprocess.PIPE					) as ffmpeg_process:
			__lowerCAmelCase												=  ffmpeg_process.communicate(lowerCamelCase					)
	except FileNotFoundError as error:
		raise ValueError("ffmpeg was not found but is required to load audio files from filename"					) from error
	__lowerCAmelCase												=  output_stream[0]
	__lowerCAmelCase												=  np.frombuffer(lowerCamelCase				,		np.floataa					)
	if audio.shape[0] == 0:
		raise ValueError("Malformed soundfile"					)
	return audio



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						float				,		lowerCamelCase   :						str = "f32le"				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  f'''{sampling_rate}'''
	__lowerCAmelCase												=  "1"
	if format_for_conversion == "s16le":
		__lowerCAmelCase												=  2
	elif format_for_conversion == "f32le":
		__lowerCAmelCase												=  4
	else:
		raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

	__lowerCAmelCase												=  platform.system()
	if system == "Linux":
		__lowerCAmelCase												=  "alsa"
		__lowerCAmelCase												=  "default"
	elif system == "Darwin":
		__lowerCAmelCase												=  "avfoundation"
		__lowerCAmelCase												=  ":0"
	elif system == "Windows":
		__lowerCAmelCase												=  "dshow"
		__lowerCAmelCase												=  "default"

	__lowerCAmelCase												=  [
	    "ffmpeg",
	    "-f",
	    format_,
	    "-i",
	    input_,
	    "-ac",
	    ac,
	    "-ar",
	    ar,
	    "-f",
	    format_for_conversion,
	    "-fflags",
	    "nobuffer",
	    "-hide_banner",
	    "-loglevel",
	    "quiet",
	    "pipe:1",
	]
	__lowerCAmelCase												=  int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
	__lowerCAmelCase												=  _ffmpeg_stream(lowerCamelCase				,		lowerCamelCase					)
	for item in iterator:
		yield item



def   __lowerCAmelCase					(							lowerCamelCase   :						int				,		lowerCamelCase   :						float				,		lowerCamelCase   :						Optional[int] = None				,		lowerCamelCase   :						Optional[Union[Tuple[float, float], float]] = None				,		lowerCamelCase   :						str = "f32le"				,		):






	'''simple docstring'''




	if stream_chunk_s is not None:
		__lowerCAmelCase												=  stream_chunk_s
	else:
		__lowerCAmelCase												=  chunk_length_s

	__lowerCAmelCase												=  ffmpeg_microphone(lowerCamelCase				,		lowerCamelCase				,		format_for_conversion=lowerCamelCase					)
	if format_for_conversion == "s16le":
		__lowerCAmelCase												=  np.intaa
		__lowerCAmelCase												=  2
	elif format_for_conversion == "f32le":
		__lowerCAmelCase												=  np.floataa
		__lowerCAmelCase												=  4
	else:
		raise ValueError(f'''Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`'''					)

	if stride_length_s is None:
		__lowerCAmelCase												=  chunk_length_s / 6
	__lowerCAmelCase												=  int(round(sampling_rate * chunk_length_s					)					) * size_of_sample
	if isinstance(lowerCamelCase				,		(int, float)					):
		__lowerCAmelCase												=  [stride_length_s, stride_length_s]

	__lowerCAmelCase												=  int(round(sampling_rate * stride_length_s[0]					)					) * size_of_sample
	__lowerCAmelCase												=  int(round(sampling_rate * stride_length_s[1]					)					) * size_of_sample
	__lowerCAmelCase												=  datetime.datetime.now()
	__lowerCAmelCase												=  datetime.timedelta(seconds=lowerCamelCase					)
	for item in chunk_bytes_iter(lowerCamelCase				,		lowerCamelCase				,		stride=(stride_left, stride_right)				,		stream=lowerCamelCase					):
		# Put everything back in numpy scale
		__lowerCAmelCase												=  np.frombuffer(item["raw"]				,		dtype=lowerCamelCase					)
		__lowerCAmelCase												=  (
		    item["stride"][0] // size_of_sample,
		    item["stride"][1] // size_of_sample,
		)
		__lowerCAmelCase												=  sampling_rate
		audio_time += delta
		if datetime.datetime.now() > audio_time + 10 * delta:
			# We're late !! SKIP
			continue
		yield item



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[Any]				,		lowerCamelCase   :						int				,		lowerCamelCase   :						Tuple[int, int]				,		lowerCamelCase   :						bool = False					):






	'''simple docstring'''




	__lowerCAmelCase												=  B""
	__lowerCAmelCase							,       __lowerCAmelCase												=  stride
	if stride_left + stride_right >= chunk_len:
		raise ValueError(
		    f'''Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}'''					)
	__lowerCAmelCase												=  0
	for raw in iterator:
		acc += raw
		if stream and len(lowerCamelCase					) < chunk_len:
			__lowerCAmelCase												=  (_stride_left, 0)
			yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
		else:
			while len(lowerCamelCase					) >= chunk_len:
				# We are flushing the accumulator
				__lowerCAmelCase												=  (_stride_left, stride_right)
				__lowerCAmelCase												=  {"raw": acc[:chunk_len], "stride": stride}
				if stream:
					__lowerCAmelCase												=  False
				yield item
				__lowerCAmelCase												=  stride_left
				__lowerCAmelCase												=  acc[chunk_len - stride_left - stride_right :]
    # Last chunk
	if len(lowerCamelCase					) > stride_left:
		__lowerCAmelCase												=  {"raw": acc, "stride": (_stride_left, 0)}
		if stream:
			__lowerCAmelCase												=  False
		yield item




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[int]				,		lowerCamelCase   :						int					):






	'''simple docstring'''




	__lowerCAmelCase												=  2**24  # 16Mo
	try:
		with subprocess.Popen(lowerCamelCase				,		stdout=subprocess.PIPE				,		bufsize=lowerCamelCase					) as ffmpeg_process:
			while True:
				__lowerCAmelCase												=  ffmpeg_process.stdout.read(lowerCamelCase					)
				if raw == b"":
					break
				yield raw
	except FileNotFoundError as error:
		raise ValueError("ffmpeg was not found but is required to stream audio files from filename"					) from error