



import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      FalconForCausalLM,
      FalconForQuestionAnswering,
      FalconForSequenceClassification,
      FalconForTokenClassification,
      FalconModel,
  )







class 		snake_case						:



   '''simple docstring'''






   def __init__(					self							:			List[str]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[Any]=3				,		lowerCAmelCase							:			List[Any]=7				,		lowerCAmelCase							:			List[str]=True				,		lowerCAmelCase							:			Tuple=True				,		lowerCAmelCase							:			Union[str, Any]=False				,		lowerCAmelCase							:			Optional[Any]=True				,		lowerCAmelCase							:			Dict=99				,		lowerCAmelCase							:			List[str]=32				,		lowerCAmelCase							:			Optional[Any]=5				,		lowerCAmelCase							:			List[str]=4				,		lowerCAmelCase							:			Optional[int]=37				,		lowerCAmelCase							:			Optional[int]="gelu"				,		lowerCAmelCase							:			int=0.1				,		lowerCAmelCase							:			Optional[Any]=0.1				,		lowerCAmelCase							:			Union[str, Any]=512				,		lowerCAmelCase							:			str=16				,		lowerCAmelCase							:			Optional[int]=2				,		lowerCAmelCase							:			Optional[int]=0.02				,		lowerCAmelCase							:			Dict=3				,		lowerCAmelCase							:			Any=4				,		lowerCAmelCase							:			List[str]=None				,		)				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					parent
    _snake_case    :  str					=					batch_size
    _snake_case    :  str					=					seq_length
    _snake_case    :  str					=					is_training
    _snake_case    :  int					=					use_input_mask
    _snake_case    :  List[str]					=					use_token_type_ids
    _snake_case    :  Optional[int]					=					use_labels
    _snake_case    :  Dict					=					vocab_size
    _snake_case    :  Dict					=					hidden_size
    _snake_case    :  str					=					num_hidden_layers
    _snake_case    :  Dict					=					num_attention_heads
    _snake_case    :  str					=					intermediate_size
    _snake_case    :  Dict					=					hidden_act
    _snake_case    :  Dict					=					hidden_dropout_prob
    _snake_case    :  str					=					attention_probs_dropout_prob
    _snake_case    :  Union[str, Any]					=					max_position_embeddings
    _snake_case    :  Optional[Any]					=					type_vocab_size
    _snake_case    :  Tuple					=					type_sequence_label_size
    _snake_case    :  Tuple					=					initializer_range
    _snake_case    :  Tuple					=					num_labels
    _snake_case    :  Union[str, Any]					=					num_choices
    _snake_case    :  Any					=					scope

   def      UpperCamelCase_  (					self							:			str)				->				List[str]:

    """simple docstring"""







    _snake_case    :  List[str]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size)

    _snake_case    :  Optional[int]					=					None
    if self.use_input_mask:
     _snake_case    :  int					=					random_attention_mask([self.batch_size, self.seq_length])

    _snake_case    :  Union[str, Any]					=					None

    _snake_case    :  int					=					None
    _snake_case    :  Tuple					=					None
    _snake_case    :  Any					=					None
    if self.use_labels:
     _snake_case    :  Tuple					=					ids_tensor([self.batch_size]				,		self.type_sequence_label_size)
     _snake_case    :  List[Any]					=					ids_tensor([self.batch_size, self.seq_length]				,		self.num_labels)
     _snake_case    :  Any					=					ids_tensor([self.batch_size]				,		self.num_choices)

    _snake_case    :  List[str]					=					self.get_config()

    return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

   def      UpperCamelCase_  (					self							:			List[str])				->				str:

    """simple docstring"""







    return FalconConfig(
        vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=lowerCAmelCase				,		initializer_range=self.initializer_range				,		pad_token_id=1				,		new_decoder_architecture=lowerCAmelCase				,		)

   def      UpperCamelCase_  (					self							:			Optional[Any]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			Optional[Any])				->				Tuple:

    """simple docstring"""







    _snake_case    :  Union[str, Any]					=					FalconModel(config=lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  Dict					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase)
    _snake_case    :  Any					=					model(lowerCAmelCase)
    self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size))

   def      UpperCamelCase_  (					self							:			Optional[int]				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Union[str, Any]				,		lowerCAmelCase							:			Optional[Any]				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			str				,		lowerCAmelCase							:			Dict				,		)				->				Tuple:

    """simple docstring"""







    _snake_case    :  Dict					=					True
    _snake_case    :  Optional[Any]					=					FalconModel(lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  Dict					=					model(
        lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		encoder_hidden_states=lowerCAmelCase				,		encoder_attention_mask=lowerCAmelCase				,		)
    _snake_case    :  str					=					model(
        lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		encoder_hidden_states=lowerCAmelCase				,		)
    _snake_case    :  Optional[Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase)
    self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.seq_length, self.hidden_size))

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Dict				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Dict				,		)				->				int:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					FalconForCausalLM(config=lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  List[str]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		labels=lowerCAmelCase)
    self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.seq_length, self.vocab_size))

   def      UpperCamelCase_  (					self							:			str				,		lowerCAmelCase							:			Tuple				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			List[Any]				,		lowerCAmelCase							:			Any				,		lowerCAmelCase							:			List[str]				,		lowerCAmelCase							:			Optional[int]				,		lowerCAmelCase							:			int				,		lowerCAmelCase							:			Optional[Any]				,		)				->				List[Any]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					True
    _snake_case    :  Union[str, Any]					=					True
    _snake_case    :  Optional[int]					=					FalconForCausalLM(config=lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()

    # first forward pass
    _snake_case    :  Union[str, Any]					=					model(
        lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		encoder_hidden_states=lowerCAmelCase				,		encoder_attention_mask=lowerCAmelCase				,		use_cache=lowerCAmelCase				,		)
    _snake_case    :  str					=					outputs.past_key_values

    # create hypothetical multiple next token and extent to next_input_ids
    _snake_case    :  Any					=					ids_tensor((self.batch_size, 3)				,		config.vocab_size)
    _snake_case    :  Tuple					=					ids_tensor((self.batch_size, 3)				,		vocab_size=2)

    # append to next input_ids and
    _snake_case    :  Union[str, Any]					=					torch.cat([input_ids, next_tokens]				,		dim=-1)
    _snake_case    :  int					=					torch.cat([input_mask, next_mask]				,		dim=-1)

    _snake_case    :  List[Any]					=					model(
        lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		encoder_hidden_states=lowerCAmelCase				,		encoder_attention_mask=lowerCAmelCase				,		output_hidden_states=lowerCAmelCase				,		)["""hidden_states"""][0]
    _snake_case    :  int					=					model(
        lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		encoder_hidden_states=lowerCAmelCase				,		encoder_attention_mask=lowerCAmelCase				,		past_key_values=lowerCAmelCase				,		output_hidden_states=lowerCAmelCase				,		)["""hidden_states"""][0]

    # select random slice
    _snake_case    :  Tuple					=					ids_tensor((1,)				,		output_from_past.shape[-1]).item()
    _snake_case    :  Union[str, Any]					=					output_from_no_past[:, -3:, random_slice_idx].detach()
    _snake_case    :  Dict					=					output_from_past[:, :, random_slice_idx].detach()

    self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])

    # test that outputs are equal for slice
    self.parent.assertTrue(torch.allclose(lowerCAmelCase				,		lowerCAmelCase				,		atol=1E-3))


   def      UpperCamelCase_  (					self							:			Dict)				->				str:

    """simple docstring"""







    _snake_case    :  List[Any]					=					self.prepare_config_and_inputs()
    (
        (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    (
        _snake_case
    )	,    
    )    :  Any					=					config_and_inputs
    _snake_case    :  Optional[int]					=					{"""input_ids""": input_ids, """attention_mask""": input_mask}
    return config, inputs_dict







@require_torch
class 		snake_case						(	SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_					,unittest.TestCase  ):



   '''simple docstring'''






   snake_case_					: Union[str, Any]          =		(
       (
           FalconModel,
           FalconForCausalLM,
           FalconForSequenceClassification,
           FalconForTokenClassification,
           FalconForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   snake_case_					: Optional[int]          =		(FalconForCausalLM,) if is_torch_available() else ()
   snake_case_					: Optional[int]          =		(
       {
           """feature-extraction""": FalconModel,
           """text-classification""": FalconForSequenceClassification,
           """text-generation""": FalconForCausalLM,
           """question-answering""": FalconForQuestionAnswering,
           """token-classification""": FalconForTokenClassification,
           """zero-shot""": FalconForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )
   snake_case_					: List[Any]          =		False
   snake_case_					: List[Any]          =		False

   def      UpperCamelCase_  (					self							:			Tuple)				->				str:

    """simple docstring"""







    _snake_case    :  Dict					=					FalconModelTester(self)
    _snake_case    :  str					=					ConfigTester(self				,		config_class=lowerCAmelCase				,		hidden_size=37)

   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[int]:

    """simple docstring"""







    self.config_tester.run_common_tests()

   def      UpperCamelCase_  (					self							:			Any)				->				Optional[int]:

    """simple docstring"""







    _snake_case    :  Optional[int]					=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			int)				->				Tuple:

    """simple docstring"""







    _snake_case	,    *_snake_case    :  str					=					self.model_tester.prepare_config_and_inputs()
    for alibi in [True, False]:
     _snake_case    :  Union[str, Any]					=					alibi
     self.model_tester.create_and_check_model(lowerCAmelCase				,		*lowerCAmelCase)

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Optional[Any]:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Union[str, Any]					=					self.model_tester.prepare_config_and_inputs_for_common()
    _snake_case    :  str					=					3
    _snake_case    :  Any					=					input_dict["""input_ids"""]
    _snake_case    :  List[str]					=					input_ids.ne(1).to(lowerCAmelCase)
    _snake_case    :  List[str]					=					ids_tensor([self.model_tester.batch_size]				,		self.model_tester.type_sequence_label_size)
    _snake_case    :  Optional[Any]					=					FalconForSequenceClassification(lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  int					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		labels=lowerCAmelCase)
    self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels))

   def      UpperCamelCase_  (					self							:			int)				->				Optional[int]:

    """simple docstring"""







    _snake_case	,    _snake_case    :  int					=					self.model_tester.prepare_config_and_inputs_for_common()
    _snake_case    :  int					=					3
    _snake_case    :  str					=					"""single_label_classification"""
    _snake_case    :  Optional[int]					=					input_dict["""input_ids"""]
    _snake_case    :  List[Any]					=					input_ids.ne(1).to(lowerCAmelCase)
    _snake_case    :  Tuple					=					ids_tensor([self.model_tester.batch_size]				,		self.model_tester.type_sequence_label_size)
    _snake_case    :  Dict					=					FalconForSequenceClassification(lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  List[str]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		labels=lowerCAmelCase)
    self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels))

   def      UpperCamelCase_  (					self							:			Union[str, Any])				->				Any:

    """simple docstring"""







    _snake_case	,    _snake_case    :  List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()
    _snake_case    :  str					=					input_dict["""input_ids"""]
    _snake_case    :  Tuple					=					FalconForCausalLM(lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  int					=					model(lowerCAmelCase				,		use_cache=lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					input_ids.shape[0]
    _snake_case    :  Any					=					model._convert_to_rw_cache(result.past_key_values)
    _snake_case    :  Optional[int]					=					model._convert_cache_to_standard_format(lowerCAmelCase				,		lowerCAmelCase)
    for layer in range(len(lowerCAmelCase)):
     for tensor_idx in range(2):
      self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3)
      self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4)
      self.assertTrue(
          torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]))

   def      UpperCamelCase_  (					self							:			int)				->				Tuple:

    """simple docstring"""







    _snake_case	,    _snake_case    :  Any					=					self.model_tester.prepare_config_and_inputs_for_common()
    _snake_case    :  Union[str, Any]					=					3
    _snake_case    :  Tuple					=					"""multi_label_classification"""
    _snake_case    :  int					=					input_dict["""input_ids"""]
    _snake_case    :  Tuple					=					input_ids.ne(1).to(lowerCAmelCase)
    _snake_case    :  int					=					ids_tensor(
        [self.model_tester.batch_size, config.num_labels]				,		self.model_tester.type_sequence_label_size).to(torch.float)
    _snake_case    :  Optional[int]					=					FalconForSequenceClassification(lowerCAmelCase)
    model.to(lowerCAmelCase)
    model.eval()
    _snake_case    :  Union[str, Any]					=					model(lowerCAmelCase				,		attention_mask=lowerCAmelCase				,		labels=lowerCAmelCase)
    self.assertEqual(result.logits.shape				,		(self.model_tester.batch_size, self.model_tester.num_labels))


   def      UpperCamelCase_  (					self							:			List[str])				->				int:

    """simple docstring"""







    for model_class in self.all_generative_model_classes:
     _snake_case	,    _snake_case    :  int					=					self.model_tester.prepare_config_and_inputs_for_common()

     # If it doesn't support cache, pass the test
     if not hasattr(lowerCAmelCase				,		"""use_cache"""):
      return

     _snake_case    :  Any					=					model_class(lowerCAmelCase).to(lowerCAmelCase)
     if "use_cache" not in inputs:
      _snake_case    :  Dict					=					True
     _snake_case    :  Tuple					=					model(**lowerCAmelCase)

     # If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
     if "past_key_values" not in outputs:
      return

     _snake_case    :  int					=					(
         getattr(lowerCAmelCase				,		"""decoder_layers"""				,		lowerCAmelCase)
         or getattr(lowerCAmelCase				,		"""num_decoder_layers"""				,		lowerCAmelCase)
         or config.num_hidden_layers
     )
     _snake_case    :  Optional[int]					=					getattr(lowerCAmelCase				,		"""num_kv_heads"""				,		config.num_attention_heads)
     _snake_case    :  Optional[int]					=					getattr(lowerCAmelCase				,		"""d_model"""				,		config.hidden_size)
     _snake_case    :  Dict					=					embed_dim // num_attention_heads

     _snake_case    :  List[str]					=					outputs["""past_key_values"""]
     self.assertEqual(len(lowerCAmelCase)				,		lowerCAmelCase)

     _snake_case	,    _snake_case    :  Any					=					inputs["""input_ids"""].shape
     for i in range(lowerCAmelCase):
      if config.new_decoder_architecture:
       _snake_case    :  Optional[int]					=					config.num_attention_heads
      elif config.multi_query:
       _snake_case    :  Dict					=					1
      self.assertEqual(len(past_kv[0])				,		2)  # K V for the decoder = 2
      self.assertEqual(
          past_kv[i][0].shape				,		(batch_size, num_attention_heads, seq_length, per_head_embed_dim))
      self.assertEqual(
          past_kv[i][1].shape				,		(batch_size, num_attention_heads, seq_length, per_head_embed_dim))







@require_torch
class 		snake_case						(	unittest.TestCase  ):



   '''simple docstring'''






   @slow
   def      UpperCamelCase_  (					self							:			Optional[int])				->				str:

    """simple docstring"""







    _snake_case    :  Optional[Any]					=					AutoTokenizer.from_pretrained("""Rocketknight1/falcon-rw-1b""")
    _snake_case    :  int					=					FalconForCausalLM.from_pretrained("""Rocketknight1/falcon-rw-1b""")
    model.eval()
    model.to(lowerCAmelCase)
    _snake_case    :  Union[str, Any]					=					tokenizer("""My favorite food is"""				,		return_tensors="""pt""").to(lowerCAmelCase)

    _snake_case    :  int					=					(
        """My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."""
    )

    _snake_case    :  List[str]					=					model.generate(**lowerCAmelCase				,		do_sample=lowerCAmelCase				,		max_new_tokens=19)
    _snake_case    :  Optional[int]					=					tokenizer.batch_decode(lowerCAmelCase)[0]

    self.assertEqual(lowerCAmelCase				,		lowerCAmelCase)

   @slow
   def      UpperCamelCase_  (					self							:			Optional[int])				->				Optional[int]:

    """simple docstring"""







    for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
     _snake_case    :  Optional[int]					=					AutoTokenizer.from_pretrained(lowerCAmelCase)
     _snake_case    :  Union[str, Any]					=					FalconForCausalLM.from_pretrained(lowerCAmelCase)
     model.eval()
     model.to(lowerCAmelCase)
     _snake_case    :  Any					=					tokenizer("""My favorite food is"""				,		return_tensors="""pt""").to(lowerCAmelCase)

     # We just test that these run without errors - the models are randomly initialized
     # and so the actual text outputs will be garbage
     model.generate(**lowerCAmelCase				,		do_sample=lowerCAmelCase				,		max_new_tokens=4)
     model.generate(**lowerCAmelCase				,		do_sample=lowerCAmelCase				,		max_new_tokens=4)
     model.generate(**lowerCAmelCase				,		num_beams=2				,		max_new_tokens=4)


   @slow
   def      UpperCamelCase_  (					self							:			List[str])				->				Optional[Any]:

    """simple docstring"""







    with torch.no_grad():
     for repo in [
         "Rocketknight1/falcon-rw-1b",
         "Rocketknight1/tiny-random-falcon-7b",
         "Rocketknight1/tiny-random-falcon-40b",
     ]:
      _snake_case    :  Optional[int]					=					AutoTokenizer.from_pretrained(lowerCAmelCase)
      _snake_case    :  Optional[int]					=					FalconForCausalLM.from_pretrained(lowerCAmelCase)
      model.eval()
      model.to(device=lowerCAmelCase)
      _snake_case    :  Any					=					tokenizer("""My favorite food is"""				,		return_tensors="""pt""").to(lowerCAmelCase)

      # Test results are the same with and without cache
      _snake_case    :  Dict					=					model.generate(**lowerCAmelCase				,		do_sample=lowerCAmelCase				,		max_new_tokens=20				,		use_cache=lowerCAmelCase)
      _snake_case    :  Dict					=					model.generate(**lowerCAmelCase				,		do_sample=lowerCAmelCase				,		max_new_tokens=20				,		use_cache=lowerCAmelCase)
      self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0)





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

a__    =	{
    """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""",
    """adapter_layer""": """encoder.layers.*.adapter_layer""",
    """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""",
    """mask_emb""": """masked_spec_embed""",
    """pooling_layer.linear""": """projector""",
    """pooling_layer.projection""": """classifier""",
}
a__    =	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	str				)  ->     Any:
 _snake_case    :  Union[str, Any]					=					{}
 with open(SCREAMING_SNAKE_CASE__				,       """r"""				) as file:
  for line_number, line in enumerate(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Union[str, Any]					=					line.strip()
   if line:
    _snake_case    :  List[str]					=					line.split()
    _snake_case    :  List[Any]					=					line_number
    _snake_case    :  int					=					words[0]
    _snake_case    :  str					=					value
 return result



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				,       SCREAMING_SNAKE_CASE__       :	Any				)  ->     Tuple:
 for attribute in key.split("""."""				):
  _snake_case    :  List[str]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)

 _snake_case    :  List[Any]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  Union[str, Any]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Any					=					"""param"""

 if weight_type is not None and weight_type != "param":
  _snake_case    :  int					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				).shape
 elif weight_type is not None and weight_type == "param":
  _snake_case    :  Dict					=					hf_pointer
  for attribute in hf_param_name.split("""."""				):
   _snake_case    :  Tuple					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  Any					=					shape_pointer.shape

  # let's reduce dimension
  _snake_case    :  str					=					value[0]
 else:
  _snake_case    :  List[str]					=					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":
  _snake_case    :  Dict					=					value
 elif weight_type == "weight_g":
  _snake_case    :  Union[str, Any]					=					value
 elif weight_type == "weight_v":
  _snake_case    :  str					=					value
 elif weight_type == "bias":
  _snake_case    :  Dict					=					value
 elif weight_type == "param":
  for attribute in hf_param_name.split("""."""				):
   _snake_case    :  Union[str, Any]					=					getattr(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  _snake_case    :  int					=					value
 else:
  _snake_case    :  Dict					=					value

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



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	Tuple				)  ->     str:
 _snake_case    :  List[str]					=					None
 for param_key in PARAM_MAPPING.keys():
  if full_name.endswith(SCREAMING_SNAKE_CASE__				):
   _snake_case    :  List[str]					=					PARAM_MAPPING[full_name.split("""."""				)[-1]]
   _snake_case    :  Optional[Any]					=					"""param"""

 if weight_type is not None and weight_type != "param":
  _snake_case    :  int					=					""".""".join([key, weight_type]				)
 elif weight_type is not None and weight_type == "param":
  _snake_case    :  List[str]					=					""".""".join([key, hf_param_name]				)
 else:
  _snake_case    :  Optional[int]					=					key

 _snake_case    :  Any					=					value if """lm_head""" in full_key else value[0]


a__    =	{
    """W_a""": """linear_1.weight""",
    """W_b""": """linear_2.weight""",
    """b_a""": """linear_1.bias""",
    """b_b""": """linear_2.bias""",
    """ln_W""": """norm.weight""",
    """ln_b""": """norm.bias""",
}



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	int				,       SCREAMING_SNAKE_CASE__       :	int=None				,       SCREAMING_SNAKE_CASE__       :	Any=None				)  ->     Optional[Any]:
 _snake_case    :  Any					=					False
 for key, mapped_key in MAPPING.items():
  _snake_case    :  List[str]					=					"""wav2vec2.""" + 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]:
   _snake_case    :  Optional[int]					=					True
   if "*" in mapped_key:
    _snake_case    :  List[str]					=					name.split(SCREAMING_SNAKE_CASE__				)[0].split("""."""				)[-2]
    _snake_case    :  List[Any]					=					mapped_key.replace("""*"""				,       SCREAMING_SNAKE_CASE__				)
   if "weight_g" in name:
    _snake_case    :  Tuple					=					"""weight_g"""
   elif "weight_v" in name:
    _snake_case    :  Dict					=					"""weight_v"""
   elif "bias" in name:
    _snake_case    :  Optional[Any]					=					"""bias"""
   elif "weight" in name:
    # TODO: don't match quantizer.weight_proj
    _snake_case    :  List[Any]					=					"""weight"""
   else:
    _snake_case    :  Dict					=					None
   if hf_dict is not None:
    rename_dict(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   else:
    set_recursively(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   return is_used
 return is_used



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Any				,       SCREAMING_SNAKE_CASE__       :	List[Any]				)  ->     Tuple:
 _snake_case    :  Optional[Any]					=					[]
 _snake_case    :  Tuple					=					fairseq_model.state_dict()

 _snake_case    :  List[str]					=					hf_model.wavaveca.feature_extractor

 for name, value in fairseq_dict.items():
  _snake_case    :  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"""				,       )
   _snake_case    :  List[str]					=					True
  else:
   _snake_case    :  Dict					=					load_wavaveca_layer(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE__				)

 logger.warning(F'''Unused weights: {unused_weights}'''				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				,       SCREAMING_SNAKE_CASE__       :	Dict				,       SCREAMING_SNAKE_CASE__       :	str				,       SCREAMING_SNAKE_CASE__       :	List[Any]				,       SCREAMING_SNAKE_CASE__       :	Dict				)  ->     str:
 _snake_case    :  List[str]					=					full_name.split("""conv_layers."""				)[-1]
 _snake_case    :  Optional[int]					=					name.split("""."""				)
 _snake_case    :  Optional[Any]					=					int(items[0]				)
 _snake_case    :  Tuple					=					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.'''				)
   _snake_case    :  List[str]					=					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.'''				)
   _snake_case    :  int					=					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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''				)
   _snake_case    :  Dict					=					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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''				)
   _snake_case    :  str					=					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       lowercase       (   SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]				,       SCREAMING_SNAKE_CASE__       :	List[str]=None				,       SCREAMING_SNAKE_CASE__       :	Optional[int]=None				,       SCREAMING_SNAKE_CASE__       :	int=True				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]=False				)  ->     Optional[Any]:
 if config_path is not None:
  _snake_case    :  List[Any]					=					WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  Union[str, Any]					=					WavaVecaConfig()

 if is_seq_class:
  _snake_case    :  Union[str, Any]					=					read_txt_into_dict(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  str					=					idalabel
  _snake_case    :  Union[str, Any]					=					WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE__				)
  _snake_case    :  List[str]					=					WavaVecaFeatureExtractor(
      feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
  feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__				)

 elif is_finetuned:
  if dict_path:
   _snake_case    :  Optional[Any]					=					Dictionary.load(SCREAMING_SNAKE_CASE__				)

   # important change bos & pad token id since CTC symbol is <pad> and
   # not <s> as in fairseq
   _snake_case    :  Tuple					=					target_dict.pad_index
   _snake_case    :  Optional[int]					=					target_dict.bos_index
   _snake_case    :  int					=					target_dict.eos_index
   _snake_case    :  Any					=					len(target_dict.symbols				)
   _snake_case    :  int					=					os.path.join(SCREAMING_SNAKE_CASE__				,       """vocab.json"""				)
   if not os.path.isdir(SCREAMING_SNAKE_CASE__				):
    logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__				)				)
    return
   os.makedirs(SCREAMING_SNAKE_CASE__				,       exist_ok=SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Optional[int]					=					target_dict.indices

   # fairseq has the <pad> and <s> switched
   _snake_case    :  Optional[int]					=					0
   _snake_case    :  Tuple					=					1
   with open(SCREAMING_SNAKE_CASE__				,       """w"""				,       encoding="""utf-8"""				) as vocab_handle:
    json.dump(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)
   _snake_case    :  Dict					=					WavaVecaCTCTokenizer(
       SCREAMING_SNAKE_CASE__				,       unk_token=target_dict.unk_word				,       pad_token=target_dict.pad_word				,       bos_token=target_dict.bos_word				,       eos_token=target_dict.eos_word				,       word_delimiter_token="""|"""				,       do_lower_case=SCREAMING_SNAKE_CASE__				,       )
   _snake_case    :  Any					=					True if config.feat_extract_norm == """layer""" else False
   _snake_case    :  Optional[int]					=					WavaVecaFeatureExtractor(
       feature_size=1				,       sampling_rate=16_000				,       padding_value=0				,       do_normalize=SCREAMING_SNAKE_CASE__				,       return_attention_mask=SCREAMING_SNAKE_CASE__				,       )
   _snake_case    :  List[Any]					=					WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__				,       tokenizer=SCREAMING_SNAKE_CASE__				)
   processor.save_pretrained(SCREAMING_SNAKE_CASE__				)

  _snake_case    :  Tuple					=					WavaVecaForCTC(SCREAMING_SNAKE_CASE__				)
 else:
  _snake_case    :  List[str]					=					WavaVecaForPreTraining(SCREAMING_SNAKE_CASE__				)

 if is_finetuned or is_seq_class:
  _snake_case	,    _snake_case	,    _snake_case    :  List[Any]					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
      [checkpoint_path]				,       arg_overrides={"""data""": """/""".join(dict_path.split("""/"""				)[:-1]				)}				)
 else:
  _snake_case    :  Optional[Any]					=					argparse.Namespace(task="""audio_pretraining"""				)
  _snake_case    :  Dict					=					fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE__				)

  _snake_case	,    _snake_case	,    _snake_case    :  str					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				,       task=SCREAMING_SNAKE_CASE__				)

 _snake_case    :  Optional[Any]					=					model[0].eval()

 recursively_load_weights(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       not is_finetuned				)

 hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  a__    =	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"""
  )
  parser.add_argument(
      """--is_seq_class""",
      action="""store_true""",
      help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
  )
  a__    =	parser.parse_args()

  a__    =	not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )
