


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any      ):
      """simple docstring"""




      if model.config.model_type == "gpt2":
            return model.transformer.h[0].mlp.c_fc
      return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
    import torch
    import torch.nn as nn



    class 							_UpperCamelCase (						nn.Module						):

       '''simple docstring'''







       def __init__(  self  : List[str]       ,  _lowerCamelCase  : nn.Module       ,  _lowerCamelCase  : int							):






             '''simple docstring'''



             super().__init__()
             __lowerCamelCase     :      Optional[Any]       						=						module
             __lowerCamelCase     :      Union[str, Any]       						=						nn.Sequential(
                 nn.Linear(module.in_features       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)       ,  nn.Linear(_lowerCamelCase       ,  module.out_features       ,  bias=_lowerCamelCase							)       ,  )
             __lowerCamelCase     :      List[str]       						=						(2.0 / (5 * min(module.in_features       ,  module.out_features							))) ** 0.5
             nn.init.normal_(self.adapter[0].weight       ,  std=_lowerCamelCase							)
             nn.init.zeros_(self.adapter[1].weight							)
             self.adapter.to(module.weight.device							)


       def   _snake_case    (  self  : List[Any]       ,  _lowerCamelCase  : Any       ,  *_lowerCamelCase  : List[str]       ,  **_lowerCamelCase  : Union[str, Any]							):






             '''simple docstring'''



             return self.module(_lowerCamelCase       ,  *_lowerCamelCase       ,  **_lowerCamelCase							) + self.adapter(_lowerCamelCase							)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   a_				:							Tuple    = "bigscience/bloom-1b7"

   # Constant values
   a_				:							str    = 2.1_09_65_95_52_69_25_74

   a_				:							Optional[int]    = "Hello my name is"
   a_				:							int    = set()
   EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"						)
   EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"						)
   EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"						)
   a_				:							str    = 10

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						AutoTokenizer.from_pretrained(self.model_name							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         super().setUp()

         # Models and tokenizer
         __lowerCamelCase     :      Dict       						=						AutoModelForCausalLM.from_pretrained(
             self.model_name       ,  torch_dtype=torch.floataa       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Any       						=						AutoModelForCausalLM.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         del self.model_fpaa
         del self.model_abit

         gc.collect()
         torch.cuda.empty_cache()

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.model_abit.config

         self.assertTrue(hasattr(_lowerCamelCase       ,  """quantization_config"""							)							)

         __lowerCamelCase     :      int       						=						config.to_dict()
         __lowerCamelCase     :      List[str]       						=						config.to_diff_dict()

         __lowerCamelCase     :      Optional[int]       						=						config.to_json_string()

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         from bitsandbytes.nn import Paramsabit

         __lowerCamelCase     :      Dict       						=						self.model_fpaa.get_memory_footprint()
         __lowerCamelCase     :      Union[str, Any]       						=						self.model_abit.get_memory_footprint()

         self.assertAlmostEqual(mem_fpaa / mem_abit       ,  self.EXPECTED_RELATIVE_DIFFERENCE							)
         __lowerCamelCase     :      Optional[Any]       						=						get_some_linear_layer(self.model_abit							)
         self.assertTrue(linear.weight.__class__ == Paramsabit							)

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         from transformers import TaPreTrainedModel

         self.model_fpaa.get_memory_footprint()
         self.model_abit.get_memory_footprint()

         for name, module in self.model_abit.named_modules():
               if isinstance(_lowerCamelCase       ,  torch.nn.Linear							):
                     if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                           # 4-bit parameters are packed in uint8 variables
                           self.assertTrue(module.weight.dtype == torch.uinta							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)
         __lowerCamelCase     :      Any       						=						self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)

         self.assertIn(self.tokenizer.decode(output_sequences[0]       ,  skip_special_tokens=_lowerCamelCase							)       ,  self.EXPECTED_OUTPUTS							)

   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						BitsAndBytesConfig()
         __lowerCamelCase     :      Union[str, Any]       						=						True

         __lowerCamelCase     :      Dict       						=						AutoModelForCausalLM.from_pretrained(
             self.model_name       ,  quantization_config=_lowerCamelCase       ,  device_map="""auto"""							)

         __lowerCamelCase     :      str       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)
         __lowerCamelCase     :      List[Any]       						=						model_abit_from_config.generate(
             input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)

         self.assertIn(self.tokenizer.decode(output_sequences[0]       ,  skip_special_tokens=_lowerCamelCase							)       ,  self.EXPECTED_OUTPUTS							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         with self.assertRaises(_lowerCamelCase							), tempfile.TemporaryDirectory() as tmpdirname:
               self.model_abit.save_pretrained(_lowerCamelCase							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						BitsAndBytesConfig()

         with self.assertRaises(_lowerCamelCase							):
               __lowerCamelCase     :      List[Any]       						=						AutoModelForCausalLM.from_pretrained(
                   self.model_name       ,  quantization_config=_lowerCamelCase       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""       ,  bnb_abit_quant_type="""nf4"""       ,  )

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         with self.assertRaises(_lowerCamelCase							):
               # Tries with `str`
               self.model_abit.to("""cpu"""							)

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `dtype``
               self.model_abit.to(torch.floataa							)

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `device`
               self.model_abit.to(torch.device("""cuda:0"""							)							)

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `device`
               self.model_abit.float()

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `device`
               self.model_abit.half()

         # Test if we did not break anything
         __lowerCamelCase     :      Union[str, Any]       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)

         __lowerCamelCase     :      str       						=						self.model_fpaa.to(torch.floataa							)
         __lowerCamelCase     :      str       						=						self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)

         # Check this does not throw an error
         __lowerCamelCase     :      List[Any]       						=						self.model_fpaa.to("""cpu"""							)

         # Check this does not throw an error
         __lowerCamelCase     :      str       						=						self.model_fpaa.half()

         # Check this does not throw an error
         __lowerCamelCase     :      Dict       						=						self.model_fpaa.float()


   def   _snake_case    (  self  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   @classmethod
   def   _snake_case    (  cls  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						"""t5-small"""
         __lowerCamelCase     :      List[str]       						=						"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
         __lowerCamelCase     :      List[str]       						=						AutoTokenizer.from_pretrained(cls.model_name							)
         __lowerCamelCase     :      Optional[Any]       						=						"""Translate in German: Hello, my dog is cute"""

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         gc.collect()
         torch.cuda.empty_cache()

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         from transformers import TaForConditionalGeneration

         __lowerCamelCase     :      List[str]       						=						TaForConditionalGeneration._keep_in_fpaa_modules
         __lowerCamelCase     :      int       						=						None

         # test with `t5-small`
         __lowerCamelCase     :      Optional[int]       						=						TaForConditionalGeneration.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Any       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Union[str, Any]       						=						model.generate(**_lowerCamelCase							)

         # test with `flan-t5-small`
         __lowerCamelCase     :      List[str]       						=						TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Optional[int]       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Union[str, Any]       						=						model.generate(**_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						modules


   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         __lowerCamelCase     :      Union[str, Any]       						=						TaForConditionalGeneration.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)

         # there was a bug with decoders - this test checks that it is fixed
         self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q       ,  bnb.nn.Linearabit							)							)

         __lowerCamelCase     :      Dict       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Optional[int]       						=						model.generate(**_lowerCamelCase							)

         # test with `flan-t5-small`
         __lowerCamelCase     :      Optional[Any]       						=						TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Dict       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Any       						=						model.generate(**_lowerCamelCase							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         super().setUp()
         # model_name
         __lowerCamelCase     :      int       						=						"""bigscience/bloom-560m"""
         __lowerCamelCase     :      Union[str, Any]       						=						"""t5-small"""

         # Different types of model

         __lowerCamelCase     :      Any       						=						AutoModel.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         # Sequence classification model
         __lowerCamelCase     :      Dict       						=						AutoModelForSequenceClassification.from_pretrained(
             self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         # CausalLM model
         __lowerCamelCase     :      Any       						=						AutoModelForCausalLM.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         # Seq2seq model
         __lowerCamelCase     :      Dict       						=						AutoModelForSeqaSeqLM.from_pretrained(
             self.seq_to_seq_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         del self.base_model
         del self.sequence_model
         del self.model_abit
         del self.seq_to_seq_model

         gc.collect()
         torch.cuda.empty_cache()


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         from bitsandbytes.nn import Paramsabit

         self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

         # Other heads should be nn.Parameter
         self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
         self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
         self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         super().setUp()

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         del self.pipe

         gc.collect()
         torch.cuda.empty_cache()


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						pipeline(
             """text-generation"""       ,  model=self.model_name       ,  model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}       ,  max_new_tokens=self.MAX_NEW_TOKENS       ,  )

         # Real second forward pass
         __lowerCamelCase     :      Union[str, Any]       						=						self.pipe(self.input_text							)
         self.assertIn(pipeline_output[0]["""generated_text"""]       ,  self.EXPECTED_OUTPUTS							)



@require_torch_multi_gpu
class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         super().setUp()


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						AutoModelForCausalLM.from_pretrained(
             self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""balanced"""							)

         # Check correct device map
         self.assertEqual(set(model_parallel.hf_device_map.values()							)       ,  {0, 1}							)

         # Check that inference pass works on the model
         __lowerCamelCase     :      Tuple       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)

         # Second real batch
         __lowerCamelCase     :      Tuple       						=						model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)
         self.assertIn(self.tokenizer.decode(output_parallel[0]       ,  skip_special_tokens=_lowerCamelCase							)       ,  self.EXPECTED_OUTPUTS							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						"""facebook/opt-350m"""
         super().setUp()


   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         if version.parse(importlib.metadata.version("""bitsandbytes"""							)							) < version.parse("""0.37.0"""							):
               return

         # Step 1: freeze all parameters
         __lowerCamelCase     :      Optional[int]       						=						AutoModelForCausalLM.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase							)

         self.assertEqual(set(model.hf_device_map.values()							)       ,  {torch.cuda.current_device()}							)

         for param in model.parameters():
               __lowerCamelCase     :      int       						=						False  # freeze the model - train adapters later
               if param.ndim == 1:
                     # cast the small parameters (e.g. layernorm) to fp32 for stability
                     __lowerCamelCase     :      str       						=						param.data.to(torch.floataa							)

        # Step 2: add adapters
         for _, module in model.named_modules():
               if "OPTAttention" in repr(type(_lowerCamelCase							)							):
                     __lowerCamelCase     :      List[Any]       						=						LoRALayer(module.q_proj       ,  rank=1_6							)
                     __lowerCamelCase     :      Dict       						=						LoRALayer(module.k_proj       ,  rank=1_6							)
                     __lowerCamelCase     :      List[Any]       						=						LoRALayer(module.v_proj       ,  rank=1_6							)

        # Step 3: dummy batch
         __lowerCamelCase     :      Tuple       						=						self.tokenizer("""Test batch """       ,  return_tensors="""pt"""							).to(0							)

         # Step 4: Check if the gradient is not None
         with torch.cuda.amp.autocast():
               __lowerCamelCase     :      List[Any]       						=						model.forward(**_lowerCamelCase							)
               out.logits.norm().backward()

         for module in model.modules():
               if isinstance(_lowerCamelCase       ,  _lowerCamelCase							):
                     self.assertTrue(module.adapter[1].weight.grad is not None							)
                     self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
               elif isinstance(_lowerCamelCase       ,  nn.Embedding							):
                     self.assertTrue(module.weight.grad is None							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							List[str]    = "gpt2-xl"
   a_				:							Dict    = 3.31_91_85_48_54_15_21_87







import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


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

__UpperCamelCase    :						List[str]     =							{
    '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',
    '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',
}
__UpperCamelCase    :						Dict     =							[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]





def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			Any      ):
      """simple docstring"""




      for attribute in key.split("""."""      ):
            __lowerCamelCase     :      Any       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      )

      if weight_type is not None:
            __lowerCamelCase     :      List[Any]       						=						getattr(UpperCAmelCase						,	UpperCAmelCase      ).shape
      else:
            __lowerCamelCase     :      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":
            __lowerCamelCase     :      List[Any]       						=						value
      elif weight_type == "weight_g":
            __lowerCamelCase     :      Tuple       						=						value
      elif weight_type == "weight_v":
            __lowerCamelCase     :      List[Any]       						=						value
      elif weight_type == "bias":
            __lowerCamelCase     :      Optional[int]       						=						value
      else:
            __lowerCamelCase     :      Dict       						=						value

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Tuple       						=						[]
      __lowerCamelCase     :      Union[str, Any]       						=						fairseq_model.state_dict()

      __lowerCamelCase     :      Optional[Any]       						=						hf_model.feature_extractor
      __lowerCamelCase     :      List[str]       						=						hf_model.adapter

      for name, value in fairseq_dict.items():
            __lowerCamelCase     :      List[str]       						=						False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	hf_model.config.feat_extract_norm == """group"""						,	)
                  __lowerCamelCase     :      Any       						=						True
            elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]      ):
                  load_adapter(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                  __lowerCamelCase     :      Dict       						=						True
            else:
                  for key, mapped_key in MAPPING.items():
                        if key in name or key.split("""w2v_model."""      )[-1] == name.split("""."""      )[0]:
                              __lowerCamelCase     :      Union[str, Any]       						=						True
                              if "*" in mapped_key:
                                    __lowerCamelCase     :      int       						=						name.split(UpperCAmelCase      )[0].split("""."""      )[-2]
                                    __lowerCamelCase     :      Dict       						=						mapped_key.replace("""*"""						,	UpperCAmelCase      )
                              if "weight_g" in name:
                                    __lowerCamelCase     :      int       						=						"""weight_g"""
                              elif "weight_v" in name:
                                    __lowerCamelCase     :      List[str]       						=						"""weight_v"""
                              elif "bias" in name:
                                    __lowerCamelCase     :      str       						=						"""bias"""
                              elif "weight" in name:
                                    __lowerCamelCase     :      List[str]       						=						"""weight"""
                              else:
                                    __lowerCamelCase     :      int       						=						None
                              set_recursively(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
                        continue
            if not is_used:
                  unused_weights.append(UpperCAmelCase      )

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





def   _UpperCAmelCase       (     UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[int]						,	UpperCAmelCase      :			Any						,	UpperCAmelCase      :			Union[str, Any]						,	UpperCAmelCase      :			Union[str, Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[str]       						=						full_name.split("""conv_layers."""      )[-1]
      __lowerCamelCase     :      Tuple       						=						name.split("""."""      )
      __lowerCamelCase     :      Tuple       						=						int(items[0]      )
      __lowerCamelCase     :      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."""
                  )
                  __lowerCamelCase     :      Dict       						=						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."""
                  )
                  __lowerCamelCase     :      List[str]       						=						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."
                  )
                  __lowerCamelCase     :      Any       						=						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."""
                  )
                  __lowerCamelCase     :      str       						=						value
                  logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      Union[str, Any]       						=						full_name.split("""adaptor."""      )[-1]
      __lowerCamelCase     :      Any       						=						name.split("""."""      )

      if items[1].isdigit():
            __lowerCamelCase     :      Dict       						=						int(items[1]      )
      else:
            __lowerCamelCase     :      List[str]       						=						None

      if "adaptor" not in full_name:
            if "proj_ln" in full_name:
                  # has to be layer norm
                  if "bias" in name:
                        assert (
                            value.shape == adapter.proj_layer_norm.bias.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                        __lowerCamelCase     :      str       						=						value
                        logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""      )
                  if "weight" in name:
                        assert (
                            value.shape == adapter.proj_layer_norm.weight.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                        __lowerCamelCase     :      str       						=						value
            else:
                  # has to be projection layer
                  if "bias" in name:
                        assert (
                            value.shape == adapter.proj.bias.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                        __lowerCamelCase     :      Optional[int]       						=						value
                        logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""      )
                  if "weight" in name:
                        assert (
                            value.shape == adapter.proj.weight.data.shape
                        ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                        __lowerCamelCase     :      List[str]       						=						value
                        logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""      )
      elif isinstance(UpperCAmelCase						,	UpperCAmelCase      ):
            if "bias" in name:
                  assert (
                      value.shape == adapter.layers[layer_id].conv.bias.data.shape
                  ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
                  __lowerCamelCase     :      int       						=						value
                  logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
            elif "weight" in name:
                  assert (
                      value.shape == adapter.layers[layer_id].conv.weight.data.shape
                  ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
                  __lowerCamelCase     :      Tuple       						=						value
                  logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""      )
      else:
            unused_weights.append(UpperCAmelCase      )





def   _UpperCAmelCase       (     UpperCAmelCase      :			List[Any]      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      Tuple       						=						emb.weight.shape
      __lowerCamelCase     :      Any       						=						nn.Linear(UpperCAmelCase						,	UpperCAmelCase						,	bias=UpperCAmelCase      )
      __lowerCamelCase     :      List[Any]       						=						emb.weight.data
      return lin_layer





@torch.no_grad()
def   _UpperCAmelCase       (     UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			List[str]						,	UpperCAmelCase      :			List[Any]						,	UpperCAmelCase      :			str						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Optional[Any]						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Optional[Any]						,	):
      """simple docstring"""




      __lowerCamelCase     :      int       						=						WavaVecaConfig.from_pretrained(
          UpperCAmelCase						,	add_adapter=UpperCAmelCase						,	adapter_stride=UpperCAmelCase						,	adapter_kernel_size=UpperCAmelCase						,	use_auth_token=UpperCAmelCase						,	output_hidden_size=UpperCAmelCase						,	)
      __lowerCamelCase     :      Optional[int]       						=						MBartConfig.from_pretrained(UpperCAmelCase      )

      # load model
      __lowerCamelCase						, __lowerCamelCase						, __lowerCamelCase     :      Dict       						=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]						,	arg_overrides={
              """config_yaml""": config_yaml_path,
              """data""": """/""".join(dict_path.split("""/"""      )[:-1]      ),
              """w2v_path""": checkpoint_path,
              """load_pretrained_decoder_from""": None,
          }						,	)
      __lowerCamelCase     :      Union[str, Any]       						=						model[0].eval()

      # load feature extractor
      __lowerCamelCase     :      Any       						=						WavaVecaFeatureExtractor.from_pretrained(UpperCAmelCase						,	use_auth_token=UpperCAmelCase      )

      # set weights for wav2vec2 encoder
      __lowerCamelCase     :      Tuple       						=						WavaVecaModel(UpperCAmelCase      )

      recursively_load_weights_wavaveca(model.encoder						,	UpperCAmelCase      )

      # load decoder weights
      __lowerCamelCase     :      Dict       						=						MBartForCausalLM(UpperCAmelCase      )
      __lowerCamelCase						, __lowerCamelCase     :      int       						=						hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()						,	strict=UpperCAmelCase      )
      logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""      )
      logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""      )

      __lowerCamelCase     :      List[Any]       						=						SpeechEncoderDecoderModel(encoder=UpperCAmelCase						,	decoder=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						False

      __lowerCamelCase     :      List[str]       						=						MBartaaTokenizer(UpperCAmelCase      )
      tokenizer.save_pretrained(UpperCAmelCase      )

      __lowerCamelCase     :      int       						=						hf_wavavec.config.to_dict()
      __lowerCamelCase     :      str       						=						tokenizer.pad_token_id
      __lowerCamelCase     :      Optional[Any]       						=						tokenizer.bos_token_id
      __lowerCamelCase     :      Dict       						=						tokenizer.eos_token_id
      __lowerCamelCase     :      Tuple       						=						"""mbart50"""
      __lowerCamelCase     :      List[str]       						=						"""wav2vec2"""

      __lowerCamelCase     :      List[str]       						=						tokenizer.eos_token_id
      __lowerCamelCase     :      Optional[int]       						=						250_004
      __lowerCamelCase     :      Dict       						=						tokenizer.eos_token_id

      __lowerCamelCase     :      Optional[Any]       						=						SpeechEncoderDecoderConfig.from_dict(UpperCAmelCase      )

      hf_wavavec.save_pretrained(UpperCAmelCase      )
      feature_extractor.save_pretrained(UpperCAmelCase      )


if __name__ == "__main__":
    __UpperCamelCase    :						Dict     =							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_yaml_path', default=None, type=str, help='Path to yaml file of fine-tuned model')
    parser.add_argument(
        '--encoder_config_path',
        default='facebook/wav2vec2-xls-r-1b',
        type=str,
        help='Path to hf encoder wav2vec2 checkpoint config',
    )
    parser.add_argument(
        '--decoder_config_path',
        default='facebook/mbart-large-50-one-to-many-mmt',
        type=str,
        help='Path to hf decoder checkpoint config',
    )
    parser.add_argument('--add_adapter', default=True, type=bool, help='whethere to add model adapter layers')
    parser.add_argument('--adapter_stride', default=2, type=int, help='stride of adapter layers')
    parser.add_argument('--adapter_kernel_size', default=3, type=int, help='kernel size of adapter layers')
    parser.add_argument('--encoder_output_dim', default=1024, type=int, help='encoder output dim')
    parser.add_argument('--start_token_id', default=250004, type=int, help='`decoder_start_token_id` of model config')

    __UpperCamelCase    :						int     =							parser.parse_args()
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.dict_path,
        args.config_yaml_path,
        encoder_config_path=args.encoder_config_path,
        decoder_config_path=args.decoder_config_path,
        add_adapter=args.add_adapter,
        adapter_kernel_size=args.adapter_kernel_size,
        adapter_stride=args.adapter_stride,
        decoder_start_token_id=args.start_token_id,
        encoder_output_dim=args.encoder_output_dim,
    )



