






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
	import os

	# The slow tests are often failing with OOM error on GPU
	# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
	# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
	UpperCAmelCase_ :						Tuple      =      '''platform'''

	import jax
	import jax.numpy as jnp

	from transformers import BlenderbotTokenizer
	from transformers.models.blenderbot.modeling_flax_blenderbot import (
	    FlaxBlenderbotForConditionalGeneration,
	    FlaxBlenderbotModel,
	    shift_tokens_right,
	)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  int	,			__magic_name__					:  List[str]=None	,			__magic_name__					:  List[str]=None	,			__magic_name__					:  int=None	,			__magic_name__					:  Optional[int]=None	,			__magic_name__					:  int=None	,			__magic_name__					:  int=None	,			)		->			Optional[Any]:






	"""simple docstring"""



	if attention_mask is None:
		UpperCamelCase     :List[str]          =	np.where(input_ids != config.pad_token_id	,			1	,			0					)
	if decoder_attention_mask is None:
		UpperCamelCase     :Union[str, Any]          =	np.where(decoder_input_ids != config.pad_token_id	,			1	,			0					)
	if head_mask is None:
		UpperCamelCase     :int          =	np.ones((config.encoder_layers, config.encoder_attention_heads)					)
	if decoder_head_mask is None:
		UpperCamelCase     :Union[str, Any]          =	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
	if cross_attn_head_mask is None:
		UpperCamelCase     :List[Any]          =	np.ones((config.decoder_layers, config.decoder_attention_heads)					)
	return {
	    "input_ids": input_ids,
	    "decoder_input_ids": decoder_input_ids,
	    "attention_mask": attention_mask,
	    "decoder_attention_mask": attention_mask,
	}

class     _SCREAMING_SNAKE_CASE       :





	def __init__(		self				:					str ,  __lowerCamelCase				:					Dict ,  __lowerCamelCase				:					Any=13 ,  __lowerCamelCase				:					str=7 ,  __lowerCamelCase				:					Optional[int]=True ,  __lowerCamelCase				:					Optional[int]=False ,  __lowerCamelCase				:					int=99 ,  __lowerCamelCase				:					List[Any]=16 ,  __lowerCamelCase				:					List[str]=2 ,  __lowerCamelCase				:					Optional[Any]=4 ,  __lowerCamelCase				:					Dict=4 ,  __lowerCamelCase				:					int="gelu" ,  __lowerCamelCase				:					Dict=0.1 ,  __lowerCamelCase				:					Optional[Any]=0.1 ,  __lowerCamelCase				:					Optional[int]=32 ,  __lowerCamelCase				:					List[Any]=2 ,  __lowerCamelCase				:					Dict=1 ,  __lowerCamelCase				:					List[Any]=0 ,  __lowerCamelCase				:					int=0.02 ,  ):
		UpperCamelCase     :List[str]          =	parent
		UpperCamelCase     :Any          =	batch_size
		UpperCamelCase     :Dict          =	seq_length
		UpperCamelCase     :Union[str, Any]          =	is_training
		UpperCamelCase     :Union[str, Any]          =	use_labels
		UpperCamelCase     :int          =	vocab_size
		UpperCamelCase     :Optional[Any]          =	hidden_size
		UpperCamelCase     :str          =	num_hidden_layers
		UpperCamelCase     :Tuple          =	num_attention_heads
		UpperCamelCase     :List[Any]          =	intermediate_size
		UpperCamelCase     :List[str]          =	hidden_act
		UpperCamelCase     :List[Any]          =	hidden_dropout_prob
		UpperCamelCase     :Optional[Any]          =	attention_probs_dropout_prob
		UpperCamelCase     :Tuple          =	max_position_embeddings
		UpperCamelCase     :Optional[int]          =	eos_token_id
		UpperCamelCase     :Any          =	pad_token_id
		UpperCamelCase     :Dict          =	bos_token_id
		UpperCamelCase     :Tuple          =	initializer_range





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     :Dict          =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,  self.vocab_size	) ,  3 ,  self.vocab_size	)
		UpperCamelCase     :Any          =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,  dtype=np.intaa	)) ,  -1	)

		UpperCamelCase     :Union[str, Any]          =	shift_tokens_right(__lowerCamelCase ,  1 ,  2	)

		UpperCamelCase     :Tuple          =	BlenderbotConfig(
		    vocab_size=self.vocab_size ,  d_model=self.hidden_size ,  encoder_layers=self.num_hidden_layers ,  decoder_layers=self.num_hidden_layers ,  encoder_attention_heads=self.num_attention_heads ,  decoder_attention_heads=self.num_attention_heads ,  encoder_ffn_dim=self.intermediate_size ,  decoder_ffn_dim=self.intermediate_size ,  dropout=self.hidden_dropout_prob ,  attention_dropout=self.attention_probs_dropout_prob ,  max_position_embeddings=self.max_position_embeddings ,  eos_token_id=self.eos_token_id ,  bos_token_id=self.bos_token_id ,  pad_token_id=self.pad_token_id ,  initializer_range=self.initializer_range ,  use_cache=__lowerCamelCase ,  )
		UpperCamelCase     :List[Any]          =	prepare_blenderbot_inputs_dict(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)
		return config, inputs_dict





	def 						_A (		self				:					Dict	):
		UpperCamelCase     ,      UpperCamelCase     :int          =	self.prepare_config_and_inputs()
		return config, inputs_dict





	def 						_A (		self				:					Dict ,  __lowerCamelCase				:					List[str] ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Union[str, Any]	):
		UpperCamelCase     :Optional[Any]          =	20
		UpperCamelCase     :List[Any]          =	model_class_name(__lowerCamelCase	)

		UpperCamelCase     :List[str]          =	model.encode(inputs_dict["""input_ids"""]	)

		UpperCamelCase     ,      UpperCamelCase     :List[str]          =	(
		    inputs_dict["""decoder_input_ids"""],
		    inputs_dict["""decoder_attention_mask"""],
		)

		UpperCamelCase     :Optional[Any]          =	model.init_cache(decoder_input_ids.shape[0] ,  __lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length) ,  dtype="""i4"""	)

		UpperCamelCase     :int          =	jnp.broadcast_to(
		    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )
		UpperCamelCase     :Optional[Any]          =	model.decode(
		    decoder_input_ids[:, :-1] ,  __lowerCamelCase ,  decoder_attention_mask=__lowerCamelCase ,  past_key_values=__lowerCamelCase ,  decoder_position_ids=__lowerCamelCase ,  )

		UpperCamelCase     :Tuple          =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype="""i4"""	)
		UpperCamelCase     :Optional[int]          =	model.decode(
		    decoder_input_ids[:, -1:] ,  __lowerCamelCase ,  decoder_attention_mask=__lowerCamelCase ,  past_key_values=outputs_cache.past_key_values ,  decoder_position_ids=__lowerCamelCase ,  )

		UpperCamelCase     :Tuple          =	model.decode(__lowerCamelCase ,  __lowerCamelCase	)

		UpperCamelCase     :Dict          =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
		self.parent.assertTrue(diff < 1E-3 ,  msg=F"""Max diff is {diff}"""	)





	def 						_A (		self				:					Tuple ,  __lowerCamelCase				:					str ,  __lowerCamelCase				:					Optional[int] ,  __lowerCamelCase				:					Optional[Any]	):
		UpperCamelCase     :str          =	20
		UpperCamelCase     :str          =	model_class_name(__lowerCamelCase	)

		UpperCamelCase     :int          =	model.encode(inputs_dict["""input_ids"""]	)

		UpperCamelCase     ,      UpperCamelCase     :Optional[int]          =	(
		    inputs_dict["""decoder_input_ids"""],
		    inputs_dict["""decoder_attention_mask"""],
		)

		UpperCamelCase     :int          =	jnp.concatenate(
		    [
		        decoder_attention_mask,
		        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
		    ] ,  axis=-1 ,  )

		UpperCamelCase     :str          =	model.init_cache(decoder_input_ids.shape[0] ,  __lowerCamelCase ,  __lowerCamelCase	)
		UpperCamelCase     :Optional[int]          =	jnp.broadcast_to(
		    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :] ,  (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) ,  )

		UpperCamelCase     :int          =	model.decode(
		    decoder_input_ids[:, :-1] ,  __lowerCamelCase ,  decoder_attention_mask=__lowerCamelCase ,  past_key_values=__lowerCamelCase ,  decoder_position_ids=__lowerCamelCase ,  )
		UpperCamelCase     :Union[str, Any]          =	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] ,  dtype="""i4"""	)
		UpperCamelCase     :Union[str, Any]          =	model.decode(
		    decoder_input_ids[:, -1:] ,  __lowerCamelCase ,  past_key_values=outputs_cache.past_key_values ,  decoder_attention_mask=__lowerCamelCase ,  decoder_position_ids=__lowerCamelCase ,  )

		UpperCamelCase     :Tuple          =	model.decode(__lowerCamelCase ,  __lowerCamelCase ,  decoder_attention_mask=__lowerCamelCase	)

		UpperCamelCase     :Optional[int]          =	np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
		self.parent.assertTrue(diff < 1E-3 ,  msg=F"""Max diff is {diff}"""	)

@require_flax
class     _SCREAMING_SNAKE_CASE       (			unittest.TestCase      ):
	snake_case__							:     Optional[Any]     	=  9_9





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     :int          =	np.array(
		    [
		        [71, 82, 18, 33, 46, 91, 2],
		        [68, 34, 26, 58, 30, 82, 2],
		        [5, 97, 17, 39, 94, 40, 2],
		        [76, 83, 94, 25, 70, 78, 2],
		        [87, 59, 41, 35, 48, 66, 2],
		        [55, 13, 16, 58, 5, 2, 1],  # note padding
		        [64, 27, 31, 51, 12, 75, 2],
		        [52, 64, 86, 17, 83, 39, 2],
		        [48, 61, 9, 24, 71, 82, 2],
		        [26, 1, 60, 48, 22, 13, 2],
		        [21, 5, 62, 28, 14, 76, 2],
		        [45, 98, 37, 86, 59, 48, 2],
		        [70, 70, 50, 9, 28, 0, 2],
		    ] ,  dtype=np.intaa ,  )

		UpperCamelCase     :Dict          =	input_ids.shape[0]
		UpperCamelCase     :List[str]          =	BlenderbotConfig(
		    vocab_size=self.vocab_size ,  d_model=24 ,  encoder_layers=2 ,  decoder_layers=2 ,  encoder_attention_heads=2 ,  decoder_attention_heads=2 ,  encoder_ffn_dim=32 ,  decoder_ffn_dim=32 ,  max_position_embeddings=48 ,  eos_token_id=2 ,  pad_token_id=1 ,  bos_token_id=0 ,  )
		return config, input_ids, batch_size





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     ,      UpperCamelCase     ,      UpperCamelCase     :Dict          =	self._get_config_and_data()
		UpperCamelCase     :List[Any]          =	FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
		UpperCamelCase     :List[Any]          =	lm_model(input_ids=__lowerCamelCase	)
		UpperCamelCase     :str          =	(batch_size, input_ids.shape[1], config.vocab_size)
		self.assertEqual(outputs["""logits"""].shape ,  __lowerCamelCase	)





	def 						_A (		self				:					Dict	):
		UpperCamelCase     :List[str]          =	BlenderbotConfig(
		    vocab_size=self.vocab_size ,  d_model=14 ,  encoder_layers=2 ,  decoder_layers=2 ,  encoder_attention_heads=2 ,  decoder_attention_heads=2 ,  encoder_ffn_dim=8 ,  decoder_ffn_dim=8 ,  max_position_embeddings=48 ,  )
		UpperCamelCase     :List[Any]          =	FlaxBlenderbotForConditionalGeneration(__lowerCamelCase	)
		UpperCamelCase     :List[str]          =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,  dtype=np.intaa	)
		UpperCamelCase     :Optional[int]          =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,  dtype=np.intaa	)
		UpperCamelCase     :List[Any]          =	lm_model(input_ids=__lowerCamelCase ,  decoder_input_ids=__lowerCamelCase	)
		UpperCamelCase     :Tuple          =	(*summary.shape, config.vocab_size)
		self.assertEqual(outputs["""logits"""].shape ,  __lowerCamelCase	)





	def 						_A (		self				:					List[Any]	):
		UpperCamelCase     :int          =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,  dtype=np.intaa	)
		UpperCamelCase     :Optional[int]          =	shift_tokens_right(__lowerCamelCase ,  1 ,  2	)
		UpperCamelCase     :Dict          =	np.equal(__lowerCamelCase ,  1	).astype(np.floataa	).sum()
		UpperCamelCase     :Dict          =	np.equal(__lowerCamelCase ,  1	).astype(np.floataa	).sum()
		self.assertEqual(shifted.shape ,  input_ids.shape	)
		self.assertEqual(__lowerCamelCase ,  n_pad_before - 1	)
		self.assertTrue(np.equal(shifted[:, 0] ,  2	).all()	)

@require_flax
class     _SCREAMING_SNAKE_CASE       (			_a		,   unittest.TestCase		,   _a      ):
	snake_case__							:     Optional[int]     	=  True
	snake_case__							:     Optional[int]     	=  (
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	snake_case__							:     Union[str, Any]     	=  (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





	def 						_A (		self				:					Optional[Any]	):
		UpperCamelCase     :List[str]          =	FlaxBlenderbotModelTester(self	)





	def 						_A (		self				:					Any	):
		UpperCamelCase     ,      UpperCamelCase     :List[str]          =	self.model_tester.prepare_config_and_inputs()
		for model_class in self.all_model_classes:
			self.model_tester.check_use_cache_forward(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					Tuple	):
		UpperCamelCase     ,      UpperCamelCase     :Any          =	self.model_tester.prepare_config_and_inputs()
		for model_class in self.all_model_classes:
			self.model_tester.check_use_cache_forward_with_attn_mask(__lowerCamelCase ,  __lowerCamelCase ,  __lowerCamelCase	)





	def 						_A (		self				:					List[str]	):
		UpperCamelCase     ,      UpperCamelCase     :List[Any]          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__	):
				UpperCamelCase     :Any          =	self._prepare_for_class(__lowerCamelCase ,  __lowerCamelCase	)
				UpperCamelCase     :List[str]          =	model_class(__lowerCamelCase	)

				@jax.jit
				def encode_jitted(__lowerCamelCase				:					Any ,  __lowerCamelCase				:					str=None ,  **__lowerCamelCase				:					Any	):
					return model.encode(input_ids=__lowerCamelCase ,  attention_mask=__lowerCamelCase	)

				with self.subTest("""JIT Enabled"""	):
					UpperCamelCase     :Optional[Any]          =	encode_jitted(**__lowerCamelCase	).to_tuple()

				with self.subTest("""JIT Disabled"""	):
					with jax.disable_jit():
						UpperCamelCase     :str          =	encode_jitted(**__lowerCamelCase	).to_tuple()

				self.assertEqual(len(__lowerCamelCase	) ,  len(__lowerCamelCase	)	)
				for jitted_output, output in zip(__lowerCamelCase ,  __lowerCamelCase	):
					self.assertEqual(jitted_output.shape ,  output.shape	)





	def 						_A (		self				:					Optional[int]	):
		UpperCamelCase     ,      UpperCamelCase     :Any          =	self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__	):
				UpperCamelCase     :Tuple          =	model_class(__lowerCamelCase	)
				UpperCamelCase     :str          =	model.encode(inputs_dict["""input_ids"""] ,  inputs_dict["""attention_mask"""]	)

				UpperCamelCase     :Optional[int]          =	{
				    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
				    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
				    """encoder_outputs""": encoder_outputs,
				}

				@jax.jit
				def decode_jitted(__lowerCamelCase				:					Any ,  __lowerCamelCase				:					List[Any] ,  __lowerCamelCase				:					Tuple	):
					return model.decode(
					    decoder_input_ids=__lowerCamelCase ,  decoder_attention_mask=__lowerCamelCase ,  encoder_outputs=__lowerCamelCase ,  )

				with self.subTest("""JIT Enabled"""	):
					UpperCamelCase     :int          =	decode_jitted(**__lowerCamelCase	).to_tuple()

				with self.subTest("""JIT Disabled"""	):
					with jax.disable_jit():
						UpperCamelCase     :str          =	decode_jitted(**__lowerCamelCase	).to_tuple()

				self.assertEqual(len(__lowerCamelCase	) ,  len(__lowerCamelCase	)	)
				for jitted_output, output in zip(__lowerCamelCase ,  __lowerCamelCase	):
					self.assertEqual(jitted_output.shape ,  output.shape	)





	@slow
	def 						_A (		self				:					str	):
		for model_class_name in self.all_model_classes:
			UpperCamelCase     :List[Any]          =	model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""	)
			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
			UpperCamelCase     :List[str]          =	np.ones((1, 1)	) * model.config.eos_token_id
			UpperCamelCase     :Tuple          =	model(__lowerCamelCase	)
			self.assertIsNotNone(__lowerCamelCase	)





	@unittest.skipUnless(jax_device != """cpu""" ,  """3B test too slow on CPU."""	)
	@slow
	def 						_A (		self				:					Any	):
		UpperCamelCase     :Any          =	{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
		UpperCamelCase     :List[Any]          =	{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

		UpperCamelCase     :Any          =	FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" ,  from_pt=__lowerCamelCase	)
		UpperCamelCase     :Optional[Any]          =	BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""	)

		UpperCamelCase     :Any          =	["""Sam"""]
		UpperCamelCase     :Union[str, Any]          =	tokenizer(__lowerCamelCase ,  return_tensors="""jax"""	)

		UpperCamelCase     :Union[str, Any]          =	model.generate(**__lowerCamelCase ,  **__lowerCamelCase	)
		UpperCamelCase     :List[Any]          =	"""Sam is a great name. It means \"sun\" in Gaelic."""

		UpperCamelCase     :List[Any]          =	tokenizer.batch_decode(__lowerCamelCase ,  **__lowerCamelCase	)
		assert generated_txt[0].strip() == tgt_text








import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


logging.set_verbosity_info()
UpperCAmelCase_ :						Optional[Any]      =      logging.get_logger(__name__)

UpperCAmelCase_ :						Optional[Any]      =      {
    '''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.grep_linear''': '''encoder.layers.*.attention.gru_rel_pos_linear''',
    '''self_attn.relative_attention_bias''': '''encoder.layers.*.attention.rel_attn_embed''',
    '''self_attn.grep_a''': '''encoder.layers.*.attention.gru_rel_pos_const''',
    '''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''',
    '''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''': '''ctc_proj''',
    '''mask_emb''': '''masked_spec_embed''',
}
UpperCAmelCase_ :						int      =      [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Dict	,			__magic_name__					:  Dict	,			__magic_name__					:  Union[str, Any]	,			__magic_name__					:  Tuple	,			__magic_name__					:  Optional[int]					)		->			Dict:






	"""simple docstring"""



	for attribute in key.split("""."""					):
		UpperCamelCase     :Dict          =	getattr(__magic_name__	,			__magic_name__					)

	if weight_type is not None:
		UpperCamelCase     :Optional[int]          =	getattr(__magic_name__	,			__magic_name__					).shape
	else:
		UpperCamelCase     :Optional[int]          =	hf_pointer.shape

	assert hf_shape == value.shape, (
	    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":
		UpperCamelCase     :str          =	value
	elif weight_type == "weight_g":
		UpperCamelCase     :int          =	value
	elif weight_type == "weight_v":
		UpperCamelCase     :int          =	value
	elif weight_type == "bias":
		UpperCamelCase     :List[Any]          =	value
	else:
		UpperCamelCase     :Any          =	value

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Optional[Any]	,			__magic_name__					:  List[str]					)		->			Optional[Any]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[]
	UpperCamelCase     :Dict          =	fairseq_model.state_dict()

	UpperCamelCase     :int          =	hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		UpperCamelCase     :str          =	False
		if "conv_layers" in name:
			load_conv_layer(
			    __magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			hf_model.config.feat_extract_norm == """group"""	,			)
			UpperCamelCase     :Union[str, Any]          =	True
		else:
			for key, mapped_key in MAPPING.items():
				if key in name or key.split("""w2v_model."""					)[-1] == name.split("""."""					)[0]:
					UpperCamelCase     :Optional[int]          =	True
					if "*" in mapped_key:
						UpperCamelCase     :List[Any]          =	name.split(__magic_name__					)[0].split("""."""					)[-2]
						UpperCamelCase     :int          =	mapped_key.replace("""*"""	,			__magic_name__					)
					if "weight_g" in name:
						UpperCamelCase     :List[Any]          =	"""weight_g"""
					elif "weight_v" in name:
						UpperCamelCase     :List[Any]          =	"""weight_v"""
					elif "bias" in name and "relative_attention_bias" not in name:
						UpperCamelCase     :Any          =	"""bias"""
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						UpperCamelCase     :List[str]          =	"""weight"""
					else:
						UpperCamelCase     :Optional[int]          =	None

					set_recursively(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					)
				continue
		if not is_used:
			unused_weights.append(__magic_name__					)

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

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  Any	,			__magic_name__					:  str	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  List[str]					)		->			Dict:






	"""simple docstring"""



	UpperCamelCase     :Dict          =	full_name.split("""conv_layers."""					)[-1]
	UpperCamelCase     :int          =	name.split("""."""					)
	UpperCamelCase     :str          =	int(items[0]					)
	UpperCamelCase     :str          =	int(items[1]					)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
			)
			UpperCamelCase     :Tuple          =	value
			logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""					)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
			)
			UpperCamelCase     :Dict          =	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:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
			    " found."
			)
			UpperCamelCase     :Tuple          =	value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    f"""{full_name} has size {value.shape}, but"""
			    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
			)
			UpperCamelCase     :Union[str, Any]          =	value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(__magic_name__					)

@torch.no_grad()
def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  List[str]	,			__magic_name__					:  List[Any]	,			__magic_name__					:  str=None					)		->			int:






	"""simple docstring"""



	UpperCamelCase     :List[Any]          =	torch.load(__magic_name__					)
	UpperCamelCase     :List[Any]          =	WavLMConfigOrig(checkpoint["""cfg"""]					)
	UpperCamelCase     :int          =	WavLMOrig(__magic_name__					)
	model.load_state_dict(checkpoint["""model"""]					)
	model.eval()

	if config_path is not None:
		UpperCamelCase     :List[Any]          =	WavLMConfig.from_pretrained(__magic_name__					)
	else:
		UpperCamelCase     :Any          =	WavLMConfig()

	UpperCamelCase     :Dict          =	WavLMModel(__magic_name__					)

	recursively_load_weights(__magic_name__	,			__magic_name__					)

	hf_wavlm.save_pretrained(__magic_name__					)


if __name__ == "__main__":
	UpperCAmelCase_ :						Union[str, Any]      =      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('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
	UpperCAmelCase_ :						Optional[int]      =      parser.parse_args()
	convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
