

import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY


@is_pipeline_test
class 				SCREAMING_SNAKE_CASE__ (unittest.TestCase					):
				__lowerCamelCase						:	List[str]														=					MODEL_FOR_MASKED_LM_MAPPING
				__lowerCamelCase						:	Optional[int]														=					TF_MODEL_FOR_MASKED_LM_MAPPING





				def 				snake_case_    (				self):
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									if is_torch_available():
														import torch

														torch.cuda.empty_cache()





				@require_tf
				def 				snake_case_    (				self):
									lowercase__   :       Union[str, Any]				=	pipeline(task='fill-mask'						,       model='sshleifer/tiny-distilroberta-base'						,       top_k=2						,       framework='tf')
									lowercase__   :       Union[str, Any]				=	unmasker('My name is <mask>')
									self.assertEqual(
									    nested_simplify(a						,       decimals=6)						,       [
									        {'sequence': 'My name is grouped', 'score': 2.1e-05, 'token': 3_8015, 'token_str': ' grouped'},
									        {'sequence': 'My name is accuser', 'score': 2.1e-05, 'token': 2_5506, 'token_str': ' accuser'},
									    ]						,       )

									lowercase__   :       int				=	unmasker('The largest city in France is <mask>')
									self.assertEqual(
									    nested_simplify(a						,       decimals=6)						,       [
									        {
									            'sequence': 'The largest city in France is grouped',
									            'score': 2.1e-05,
									            'token': 3_8015,
									            'token_str': ' grouped',
									        },
									        {
									            'sequence': 'The largest city in France is accuser',
									            'score': 2.1e-05,
									            'token': 2_5506,
									            'token_str': ' accuser',
									        },
									    ]						,       )

									lowercase__   :       Dict				=	unmasker('My name is <mask>'						,       targets=[' Patrick', ' Clara', ' Teven']						,       top_k=3)
									self.assertEqual(
									    nested_simplify(a						,       decimals=6)						,       [
									        {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 1_3606, 'token_str': ' Clara'},
									        {'sequence': 'My name is Patrick', 'score': 2e-05, 'token': 3499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 1.9e-05, 'token': 2941, 'token_str': ' Te'},
									    ]						,       )





				@require_torch
				def 				snake_case_    (				self):
									lowercase__   :       List[Any]				=	pipeline(task='fill-mask'						,       model='sshleifer/tiny-distilroberta-base'						,       top_k=2						,       framework='pt')

									lowercase__   :       int				=	unmasker('My name is <mask>')
									self.assertEqual(
									    nested_simplify(a						,       decimals=6)						,       [
									        {'sequence': 'My name is Maul', 'score': 2.2e-05, 'token': 3_5676, 'token_str': ' Maul'},
									        {'sequence': 'My name isELS', 'score': 2.2e-05, 'token': 1_6416, 'token_str': 'ELS'},
									    ]						,       )

									lowercase__   :       Union[str, Any]				=	unmasker('The largest city in France is <mask>')
									self.assertEqual(
									    nested_simplify(a						,       decimals=6)						,       [
									        {
									            'sequence': 'The largest city in France is Maul',
									            'score': 2.2e-05,
									            'token': 3_5676,
									            'token_str': ' Maul',
									        },
									        {'sequence': 'The largest city in France isELS', 'score': 2.2e-05, 'token': 1_6416, 'token_str': 'ELS'},
									    ]						,       )

									lowercase__   :       Optional[int]				=	unmasker('My name is <mask>'						,       targets=[' Patrick', ' Clara', ' Teven']						,       top_k=3)
									self.assertEqual(
									    nested_simplify(a						,       decimals=6)						,       [
									        {'sequence': 'My name is Patrick', 'score': 2.1e-05, 'token': 3499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Te', 'score': 2e-05, 'token': 2941, 'token_str': ' Te'},
									        {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 1_3606, 'token_str': ' Clara'},
									    ]						,       )

									lowercase__   :       List[Any]				=	unmasker('My name is <mask> <mask>'						,       top_k=2)

									self.assertEqual(
									    nested_simplify(a						,       decimals=6)						,       [
									        [
									            {
									                'score': 2.2e-05,
									                'token': 3_5676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is Maul<mask></s>',
									            },
									            {'score': 2.2e-05, 'token': 1_6416, 'token_str': 'ELS', 'sequence': '<s>My name isELS<mask></s>'},
									        ],
									        [
									            {
									                'score': 2.2e-05,
									                'token': 3_5676,
									                'token_str': ' Maul',
									                'sequence': '<s>My name is<mask> Maul</s>',
									            },
									            {'score': 2.2e-05, 'token': 1_6416, 'token_str': 'ELS', 'sequence': '<s>My name is<mask>ELS</s>'},
									        ],
									    ]						,       )





				@require_torch_gpu
				def 				snake_case_    (				self):
									lowercase__   :       int				=	pipeline('fill-mask'						,       model='hf-internal-testing/tiny-random-distilbert'						,       device=0						,       framework='pt')

									# convert model to fp16
									pipe.model.half()

									lowercase__   :       Optional[int]				=	pipe('Paris is the [MASK] of France.')
									# We actually don't care about the result, we just want to make sure
									# it works, meaning the float16 tensor got casted back to float32
									# for postprocessing.
									self.assertIsInstance(a						,       a)





				@slow
				@require_torch
				def 				snake_case_    (				self):
									lowercase__   :       Tuple				=	pipeline(task='fill-mask'						,       model='distilroberta-base'						,       top_k=2						,       framework='pt')
									self.run_large_test(a)





				@slow
				@require_tf
				def 				snake_case_    (				self):
									lowercase__   :       Optional[Any]				=	pipeline(task='fill-mask'						,       model='distilroberta-base'						,       top_k=2						,       framework='tf')
									self.run_large_test(a)





				def 				snake_case_    (				self						,       a):
									lowercase__   :       Any				=	unmasker('My name is <mask>')
									self.assertEqual(
									    nested_simplify(a)						,       [
									        {'sequence': 'My name is John', 'score': 0.008, 'token': 610, 'token_str': ' John'},
									        {'sequence': 'My name is Chris', 'score': 0.007, 'token': 1573, 'token_str': ' Chris'},
									    ]						,       )
									lowercase__   :       Any				=	unmasker('The largest city in France is <mask>')
									self.assertEqual(
									    nested_simplify(a)						,       [
									        {
									            'sequence': 'The largest city in France is Paris',
									            'score': 0.251,
									            'token': 2201,
									            'token_str': ' Paris',
									        },
									        {
									            'sequence': 'The largest city in France is Lyon',
									            'score': 0.214,
									            'token': 1_2790,
									            'token_str': ' Lyon',
									        },
									    ]						,       )

									lowercase__   :       Optional[Any]				=	unmasker('My name is <mask>'						,       targets=[' Patrick', ' Clara', ' Teven']						,       top_k=3)
									self.assertEqual(
									    nested_simplify(a)						,       [
									        {'sequence': 'My name is Patrick', 'score': 0.005, 'token': 3499, 'token_str': ' Patrick'},
									        {'sequence': 'My name is Clara', 'score': 0.000, 'token': 1_3606, 'token_str': ' Clara'},
									        {'sequence': 'My name is Te', 'score': 0.000, 'token': 2941, 'token_str': ' Te'},
									    ]						,       )





				@require_torch
				def 				snake_case_    (				self):
									lowercase__   :       Union[str, Any]				=	pipeline(task='fill-mask'						,       model='sshleifer/tiny-distilroberta-base'						,       framework='pt')
									lowercase__   :       List[Any]				=	None
									lowercase__   :       Optional[int]				=	None
									self.run_pipeline_test(a						,       [])





				@require_tf
				def 				snake_case_    (				self):
									lowercase__   :       int				=	pipeline(task='fill-mask'						,       model='sshleifer/tiny-distilroberta-base'						,       framework='tf')
									lowercase__   :       Any				=	None
									lowercase__   :       Optional[Any]				=	None
									self.run_pipeline_test(a						,       [])





				def 				snake_case_    (				self						,       a						,       a						,       a):
									if tokenizer is None or tokenizer.mask_token_id is None:
														self.skipTest('The provided tokenizer has no mask token, (probably reformer or wav2vec2)')

									lowercase__   :       Union[str, Any]				=	FillMaskPipeline(model=a						,       tokenizer=a)
									lowercase__   :       Optional[Any]				=	[
									    f"""This is another {tokenizer.mask_token} test""",
									]
									return fill_masker, examples





				def 				snake_case_    (				self						,       a						,       a):
									lowercase__   :       Any				=	fill_masker.tokenizer
									lowercase__   :       Optional[Any]				=	fill_masker.model

									lowercase__   :       Optional[int]				=	fill_masker(
									    f"""This is a {tokenizer.mask_token}"""						,       )
									self.assertEqual(
									    a						,       [
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									    ]						,       )

									lowercase__   :       Union[str, Any]				=	fill_masker([f"""This is a {tokenizer.mask_token}"""])
									self.assertEqual(
									    a						,       [
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									    ]						,       )

									lowercase__   :       Optional[Any]				=	fill_masker([f"""This is a {tokenizer.mask_token}""", f"""Another {tokenizer.mask_token} great test."""])
									self.assertEqual(
									    a						,       [
									        [
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        ],
									        [
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        ],
									    ]						,       )

									with self.assertRaises(a):
														fill_masker([None])
									# No mask_token is not supported
									with self.assertRaises(a):
														fill_masker('This is')

									self.run_test_top_k(a						,       a)
									self.run_test_targets(a						,       a)
									self.run_test_top_k_targets(a						,       a)
									self.fill_mask_with_duplicate_targets_and_top_k(a						,       a)
									self.fill_mask_with_multiple_masks(a						,       a)





				def 				snake_case_    (				self						,       a						,       a):
									lowercase__   :       List[str]				=	tokenizer.get_vocab()
									lowercase__   :       Dict				=	sorted(vocab.keys())[:2]
									# Pipeline argument
									lowercase__   :       str				=	FillMaskPipeline(model=a						,       tokenizer=a						,       targets=a)
									lowercase__   :       Tuple				=	fill_masker(f"""This is a {tokenizer.mask_token}""")
									self.assertEqual(
									    a						,       [
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									    ]						,       )
									lowercase__   :       Optional[int]				=	{vocab[el] for el in targets}
									self.assertEqual({el['token'] for el in outputs}						,       a)
									lowercase__   :       Dict				=	[tokenizer.decode([x]) for x in target_ids]
									self.assertEqual({el['token_str'] for el in outputs}						,       set(a))

									# Call argument
									lowercase__   :       Dict				=	FillMaskPipeline(model=a						,       tokenizer=a)
									lowercase__   :       List[str]				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       targets=a)
									self.assertEqual(
									    a						,       [
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									    ]						,       )
									lowercase__   :       List[Any]				=	{vocab[el] for el in targets}
									self.assertEqual({el['token'] for el in outputs}						,       a)
									lowercase__   :       int				=	[tokenizer.decode([x]) for x in target_ids]
									self.assertEqual({el['token_str'] for el in outputs}						,       set(a))

									# Score equivalence
									lowercase__   :       Tuple				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       targets=a)
									lowercase__   :       Optional[int]				=	[top_mask['token_str'] for top_mask in outputs]
									lowercase__   :       Dict				=	[top_mask['score'] for top_mask in outputs]

									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(a) == set(a):
														lowercase__   :       Dict				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       targets=a)
														lowercase__   :       Optional[int]				=	[top_mask['score'] for top_mask in unmasked_targets]
														self.assertEqual(nested_simplify(a)						,       nested_simplify(a))

									# Raises with invalid
									with self.assertRaises(a):
														lowercase__   :       List[Any]				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       targets=[])
									# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
									if "" not in tokenizer.get_vocab():
														with self.assertRaises(a):
																			lowercase__   :       List[str]				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       targets=[''])
														with self.assertRaises(a):
																			lowercase__   :       str				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       targets='')





				def 				snake_case_    (				self						,       a						,       a):
									lowercase__   :       Union[str, Any]				=	FillMaskPipeline(model=a						,       tokenizer=a						,       top_k=2)
									lowercase__   :       Tuple				=	fill_masker(f"""This is a {tokenizer.mask_token}""")
									self.assertEqual(
									    a						,       [
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									    ]						,       )

									lowercase__   :       Optional[int]				=	FillMaskPipeline(model=a						,       tokenizer=a)
									lowercase__   :       str				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       top_k=2)
									self.assertEqual(
									    a						,       [
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									    ]						,       )
									self.assertEqual(nested_simplify(a)						,       nested_simplify(a))





				def 				snake_case_    (				self						,       a						,       a):
									lowercase__   :       str				=	tokenizer.get_vocab()
									lowercase__   :       Optional[int]				=	FillMaskPipeline(model=a						,       tokenizer=a)

									# top_k=2, ntargets=3
									lowercase__   :       str				=	sorted(vocab.keys())[:3]
									lowercase__   :       List[str]				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       top_k=2						,       targets=a)

									# If we use the most probably targets, and filter differently, we should still
									# have the same results
									lowercase__   :       Optional[Any]				=	[el['token_str'] for el in sorted(a						,       key=lambda a: x["score"]						,       reverse=a)]
									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(a).issubset(a):
														lowercase__   :       Any				=	fill_masker(f"""This is a {tokenizer.mask_token}"""						,       top_k=3						,       targets=a)
														# They should yield exactly the same result
														self.assertEqual(nested_simplify(a)						,       nested_simplify(a))





				def 				snake_case_    (				self						,       a						,       a):
									lowercase__   :       List[Any]				=	FillMaskPipeline(model=a						,       tokenizer=a)
									lowercase__   :       str				=	tokenizer.get_vocab()
									# String duplicates + id duplicates
									lowercase__   :       List[str]				=	sorted(vocab.keys())[:3]
									lowercase__   :       Optional[int]				=	[targets[0], targets[1], targets[0], targets[2], targets[1]]
									lowercase__   :       Union[str, Any]				=	fill_masker(f"""My name is {tokenizer.mask_token}"""						,       targets=a						,       top_k=10)

									# The target list contains duplicates, so we can't output more
									# than them
									self.assertEqual(len(a)						,       3)





				def 				snake_case_    (				self						,       a						,       a):
									lowercase__   :       str				=	FillMaskPipeline(model=a						,       tokenizer=a)

									lowercase__   :       int				=	fill_masker(
									    f"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""						,       top_k=2)
									self.assertEqual(
									    a						,       [
									        [
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        ],
									        [
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        ],
									        [
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									            {'sequence': ANY(a), 'score': ANY(a), 'token': ANY(a), 'token_str': ANY(a)},
									        ],
									    ]						,       )




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
snake_case_        =			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Dict      ):



     '''simple docstring'''





     for attribute in key.split('.'      ):
          lowercase__   :       Optional[Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     if weight_type is not None:
          lowercase__   :       Union[str, Any]				=	getattr(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      ).shape
     else:
          lowercase__   :       Optional[int]				=	hf_pointer.shape

     if hf_shape != value.shape:
          raise ValueError(
              f"""Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be"""
              f""" {value.shape} for {full_name}"""      )

     if weight_type == "weight":
          lowercase__   :       Optional[int]				=	value
     elif weight_type == "weight_g":
          lowercase__   :       Union[str, Any]				=	value
     elif weight_type == "weight_v":
          lowercase__   :       Tuple				=	value
     elif weight_type == "bias":
          lowercase__   :       Any				=	value
     else:
          lowercase__   :       Union[str, Any]				=	value

     logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   Any      ):



     '''simple docstring'''





     lowercase__   :       Optional[int]				=	[]
     lowercase__   :       Union[str, Any]				=	fairseq_model.state_dict()

     lowercase__   :       Optional[int]				=	hf_model.unispeech_sat.feature_extractor

     for name, value in fairseq_dict.items():
          lowercase__   :       Tuple				=	False
          if "conv_layers" in name:
               load_conv_layer(
                   SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					hf_model.config.feat_extract_norm == 'group'     ,					)
               lowercase__   :       Optional[int]				=	True
          else:
               for key, mapped_key in MAPPING.items():
                    lowercase__   :       List[str]				=	'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                    if key in name or key.split('w2v_model.'      )[-1] == name.split('.'      )[0]:
                         if "layer_norm_for_extract" in name and (".".join(name.split('.'      )[:-1]      ) != key):
                              # special case since naming is very similar
                              continue
                         lowercase__   :       Tuple				=	True
                         if "*" in mapped_key:
                              lowercase__   :       Any				=	name.split(SCREAMING_SNAKE_CASE_      )[0].split('.'      )[-2]
                              lowercase__   :       Optional[Any]				=	mapped_key.replace('*'     ,					SCREAMING_SNAKE_CASE_      )
                         if "weight_g" in name:
                              lowercase__   :       int				=	'weight_g'
                         elif "weight_v" in name:
                              lowercase__   :       Any				=	'weight_v'
                         elif "bias" in name:
                              lowercase__   :       str				=	'bias'
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              lowercase__   :       Union[str, Any]				=	'weight'
                         else:
                              lowercase__   :       Union[str, Any]				=	None
                         set_recursively(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
                    continue
          if not is_used:
               unused_weights.append(SCREAMING_SNAKE_CASE_      )

     logger.warning(f"""Unused weights: {unused_weights}"""      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]      ):



     '''simple docstring'''





     lowercase__   :       List[Any]				=	full_name.split('conv_layers.'      )[-1]
     lowercase__   :       Dict				=	name.split('.'      )
     lowercase__   :       List[str]				=	int(items[0]      )
     lowercase__   :       Any				=	int(items[1]      )

     if type_id == 0:
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""      )
               lowercase__   :       List[Any]				=	value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""      )
               lowercase__   :       Any				=	value
               logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""      )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
          if "bias" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""      )
               lowercase__   :       str				=	value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
          elif "weight" in name:
               if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                    raise ValueError(
                        f"""{full_name} has size {value.shape}, but"""
                        f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""      )
               lowercase__   :       int				=	value
               logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
     else:
          unused_weights.append(SCREAMING_SNAKE_CASE_      )

@torch.no_grad()
def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]=None     ,					SCREAMING_SNAKE_CASE_		:   List[Any]=None     ,					SCREAMING_SNAKE_CASE_		:   int=True      ):



     '''simple docstring'''





     if config_path is not None:
          lowercase__   :       Any				=	UniSpeechSatConfig.from_pretrained(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Dict				=	UniSpeechSatConfig()

     lowercase__   :       str				=	''

     if is_finetuned:
          lowercase__   :       Any				=	UniSpeechSatForCTC(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Optional[Any]				=	UniSpeechSatForPreTraining(SCREAMING_SNAKE_CASE_      )

     lowercase__				,			lowercase__				,			lowercase__   :       List[Any]				=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
         [checkpoint_path]     ,					arg_overrides={'data': '/'.join(dict_path.split('/'      )[:-1]      )}      )
     lowercase__   :       List[str]				=	model[0].eval()

     recursively_load_weights(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )

     hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE_      )


if __name__ == "__main__":
       snake_case_        =			argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
       parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       parser.add_argument(
           '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
       )
       snake_case_        =			parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )

