



"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


__A   :							Optional[Any]										=   random.Random()







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						int=1.0					,     _SCREAMING_SNAKE_CASE   :						Dict=None					,     _SCREAMING_SNAKE_CASE   :						List[str]=None				):






				'''simple docstring'''



				if rng is None:
								_UpperCAmelCase					=    global_rng

				_UpperCAmelCase					=    []
				for batch_idx in range(shape[0]				):
								values.append([]				)
								for _ in range(shape[1]				):
												values[-1].append(rng.random() * scale				)

				return values
class 			_a (					unittest.TestCase):
			"""simple docstring"""



			def __init__(		self     :    int				,   __UpperCamelCase     :    int				,   __UpperCamelCase     :    Any=7				,   __UpperCamelCase     :    str=4_0_0				,   __UpperCamelCase     :    List[str]=2_0_0_0				,   __UpperCamelCase     :    Union[str, Any]=1				,   __UpperCamelCase     :    Optional[int]=0.0				,   __UpperCamelCase     :    List[Any]=1_6_0_0_0				,   __UpperCamelCase     :    Optional[int]=True				,   __UpperCamelCase     :    Optional[Any]=True				,   )->int:
							_UpperCAmelCase					=    parent
							_UpperCAmelCase					=    batch_size
							_UpperCAmelCase					=    min_seq_length
							_UpperCAmelCase					=    max_seq_length
							_UpperCAmelCase					=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							_UpperCAmelCase					=    feature_size
							_UpperCAmelCase					=    padding_value
							_UpperCAmelCase					=    sampling_rate
							_UpperCAmelCase					=    return_attention_mask
							_UpperCAmelCase					=    do_normalize



			def  lowercase__						(		self     :    Dict   )->str:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}



			def  lowercase__						(		self     :    str				,   __UpperCamelCase     :    Dict=False				,   __UpperCamelCase     :    Optional[Any]=False   )->Tuple:
							def _flatten(__UpperCamelCase     :    List[Any]   ):
											return list(itertools.chain(*__UpperCamelCase   )   )

							if equal_length:
											_UpperCAmelCase					=    floats_list((self.batch_size, self.max_seq_length)   )
							else:
											# make sure that inputs increase in size
											_UpperCAmelCase					=    [
											    _flatten(floats_list((x, self.feature_size)   )   )
											    for x in range(self.min_seq_length				,   self.max_seq_length				,   self.seq_length_diff   )
											]

							if numpify:
											_UpperCAmelCase					=    [np.asarray(__UpperCamelCase   ) for x in speech_inputs]

							return speech_inputs



class 			_a (					lowerCAmelCase			,							unittest.TestCase):
			"""simple docstring"""
			UpperCamelCase__			      =	WavaVecaFeatureExtractor



			def  lowercase__						(		self     :    str   )->Optional[Any]:
							_UpperCAmelCase					=    WavaVecaFeatureExtractionTester(self   )



			def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    str   )->Dict:
							self.assertTrue(np.all(np.mean(__UpperCamelCase				,   axis=0   ) < 1e-3   )   )
							self.assertTrue(np.all(np.abs(np.var(__UpperCamelCase				,   axis=0   ) - 1   ) < 1e-3   )   )



			def  lowercase__						(		self     :    Optional[Any]   )->int:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							_UpperCAmelCase					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							# create three inputs of length 800, 1000, and 1200
							_UpperCAmelCase					=    [floats_list((1, x)   )[0] for x in range(8_0_0				,   1_4_0_0				,   2_0_0   )]
							_UpperCAmelCase					=    [np.asarray(__UpperCamelCase   ) for speech_input in speech_inputs]

							# Test not batched input
							_UpperCAmelCase					=    feat_extract(speech_inputs[0]				,   return_tensors='''np'''   ).input_values
							_UpperCAmelCase					=    feat_extract(np_speech_inputs[0]				,   return_tensors='''np'''   ).input_values
							self.assertTrue(np.allclose(__UpperCamelCase				,   __UpperCamelCase				,   atol=1e-3   )   )

							# Test batched
							_UpperCAmelCase					=    feat_extract(__UpperCamelCase				,   return_tensors='''np'''   ).input_values
							_UpperCAmelCase					=    feat_extract(__UpperCamelCase				,   return_tensors='''np'''   ).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											self.assertTrue(np.allclose(__UpperCamelCase				,   __UpperCamelCase				,   atol=1e-3   )   )

							# Test 2-D numpy arrays are batched.
							_UpperCAmelCase					=    [floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
							_UpperCAmelCase					=    np.asarray(__UpperCamelCase   )
							_UpperCAmelCase					=    feat_extract(__UpperCamelCase				,   return_tensors='''np'''   ).input_values
							_UpperCAmelCase					=    feat_extract(__UpperCamelCase				,   return_tensors='''np'''   ).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											self.assertTrue(np.allclose(__UpperCamelCase				,   __UpperCamelCase				,   atol=1e-3   )   )



			def  lowercase__						(		self     :    Optional[Any]   )->Any:
							_UpperCAmelCase					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							_UpperCAmelCase					=    [floats_list((1, x)   )[0] for x in range(8_0_0				,   1_4_0_0				,   2_0_0   )]

							_UpperCAmelCase					=    ['''longest''', '''max_length''', '''do_not_pad''']
							_UpperCAmelCase					=    [None, 1_6_0_0, None]
							for max_length, padding in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    feat_extract(__UpperCamelCase				,   padding=__UpperCamelCase				,   max_length=__UpperCamelCase				,   return_tensors='''np'''   )
											_UpperCAmelCase					=    processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
											self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6   )
											self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
											self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6   )
											self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )



			def  lowercase__						(		self     :    Optional[int]   )->Tuple:
							_UpperCAmelCase					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							_UpperCAmelCase					=    range(8_0_0				,   1_4_0_0				,   2_0_0   )
							_UpperCAmelCase					=    [floats_list((1, x)   )[0] for x in lengths]

							_UpperCAmelCase					=    ['''longest''', '''max_length''', '''do_not_pad''']
							_UpperCAmelCase					=    [None, 1_6_0_0, None]

							for max_length, padding in zip(__UpperCamelCase				,   __UpperCamelCase   ):
											_UpperCAmelCase					=    feat_extract(__UpperCamelCase				,   max_length=__UpperCamelCase				,   padding=__UpperCamelCase   )
											_UpperCAmelCase					=    processed.input_values

											self._check_zero_mean_unit_variance(input_values[0][:8_0_0]   )
											self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0]   )
											self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0]   )



			def  lowercase__						(		self     :    Dict   )->str:
							_UpperCAmelCase					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							_UpperCAmelCase					=    [floats_list((1, x)   )[0] for x in range(8_0_0				,   1_4_0_0				,   2_0_0   )]
							_UpperCAmelCase					=    feat_extract(
							    __UpperCamelCase				,   truncation=__UpperCamelCase				,   max_length=1_0_0_0				,   padding='''max_length'''				,   return_tensors='''np'''   )
							_UpperCAmelCase					=    processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
							self._check_zero_mean_unit_variance(input_values[1]   )
							self._check_zero_mean_unit_variance(input_values[2]   )



			def  lowercase__						(		self     :    Optional[int]   )->str:
							_UpperCAmelCase					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							_UpperCAmelCase					=    [floats_list((1, x)   )[0] for x in range(8_0_0				,   1_4_0_0				,   2_0_0   )]
							_UpperCAmelCase					=    feat_extract(
							    __UpperCamelCase				,   truncation=__UpperCamelCase				,   max_length=1_0_0_0				,   padding='''longest'''				,   return_tensors='''np'''   )
							_UpperCAmelCase					=    processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
							self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
							self._check_zero_mean_unit_variance(input_values[2]   )

							# make sure that if max_length < longest -> then pad to max_length
							self.assertTrue(input_values.shape == (3, 1_0_0_0)   )

							_UpperCAmelCase					=    [floats_list((1, x)   )[0] for x in range(8_0_0				,   1_4_0_0				,   2_0_0   )]
							_UpperCAmelCase					=    feat_extract(
							    __UpperCamelCase				,   truncation=__UpperCamelCase				,   max_length=2_0_0_0				,   padding='''longest'''				,   return_tensors='''np'''   )
							_UpperCAmelCase					=    processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :8_0_0]   )
							self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0]   )
							self._check_zero_mean_unit_variance(input_values[2]   )

							# make sure that if max_length > longest -> then pad to longest
							self.assertTrue(input_values.shape == (3, 1_2_0_0)   )



			@require_torch
			def  lowercase__						(		self     :    Optional[int]   )->str:
							import torch

							_UpperCAmelCase					=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							_UpperCAmelCase					=    np.random.rand(1_0_0   ).astype(np.floataa   )
							_UpperCAmelCase					=    np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											_UpperCAmelCase					=    feature_extractor.pad([{'''input_values''': inputs}]				,   return_tensors='''np'''   )
											self.assertTrue(np_processed.input_values.dtype == np.floataa   )
											_UpperCAmelCase					=    feature_extractor.pad([{'''input_values''': inputs}]				,   return_tensors='''pt'''   )
											self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )



			@slow
			@require_torch
			def  lowercase__						(		self     :    int   )->Any:
							# this test makes sure that models that are using
							# group norm don't have their feature extractor return the
							# attention_mask
							for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
											_UpperCAmelCase					=    WavaVecaConfig.from_pretrained(__UpperCamelCase   )
											_UpperCAmelCase					=    WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase   )

											# only "layer" feature extraction norm should make use of
											# attention_mask
											self.assertEqual(feat_extract.return_attention_mask				,   config.feat_extract_norm == '''layer'''   )








"""simple docstring"""


import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


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

__A   :							Tuple										=   (
    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   :							Optional[int]										=   (
    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   :							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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[int]					,     _SCREAMING_SNAKE_CASE   :						List[Any]				):






				'''simple docstring'''



				for tf_name, hf_name in patterns:
								_UpperCAmelCase					=    k.replace(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				return k







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						dict					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    BigBirdPegasusConfig(**_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    BigBirdPegasusForConditionalGeneration(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    torch_model.state_dict()
				_UpperCAmelCase					=    {}

				# separating decoder weights
				_UpperCAmelCase					=    {k: tf_weights[k] for k in tf_weights if k.startswith('''pegasus/decoder'''				)}
				_UpperCAmelCase					=    {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					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    DECODER_PATTERNS
								_UpperCAmelCase					=    rename_state_dict_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								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					=    v.T
								_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_SNAKE_CASE				)
								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					=    [k.endswith(_SCREAMING_SNAKE_CASE				) for ending in KEYS_TO_IGNORE]
								if any(_SCREAMING_SNAKE_CASE				):
												continue
								_UpperCAmelCase					=    REMAINING_PATTERNS
								_UpperCAmelCase					=    rename_state_dict_key(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								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					=    v.T
								_UpperCAmelCase					=    torch.from_numpy(_SCREAMING_SNAKE_CASE				)
								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					=    mapping['''model.embed_positions.weight''']
				_UpperCAmelCase					=    mapping.pop('''model.embed_positions.weight'''				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    torch_model.load_state_dict(_SCREAMING_SNAKE_CASE					,     strict=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    [
				    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	(      _SCREAMING_SNAKE_CASE   :						Any				):






				'''simple docstring'''



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







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						dict				):






				'''simple docstring'''



				_UpperCAmelCase					=    get_tf_weights_as_numpy(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    convert_bigbird_pegasus(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
				torch_model.save_pretrained(_SCREAMING_SNAKE_CASE				)


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



