


import unittest

from transformers import (
    MODEL_FOR_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
    TextGenerationPipeline,
    logging,
    pipeline,
)
from transformers.testing_utils import (
    CaptureLogger,
    is_pipeline_test,
    require_accelerate,
    require_tf,
    require_torch,
    require_torch_gpu,
    require_torch_or_tf,
)

from .test_pipelines_common import ANY

@is_pipeline_test
@require_torch_or_tf
class 							lowercase  (						unittest.TestCase			):
     lowerCamelCase			:			Any      = MODEL_FOR_CAUSAL_LM_MAPPING
     lowerCamelCase			:			Optional[Any]      = TF_MODEL_FOR_CAUSAL_LM_MAPPING







     @require_torch
     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		pipeline(task='''text-generation'''   ,   model='''sshleifer/tiny-ctrl'''   ,   framework='''pt'''      )
           # Using `do_sample=False` to force deterministic output
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		text_generator('''This is a test'''   ,   do_sample=_lowercase      )
           self.assertEqual(
               _lowercase   ,   [
                   {
                       '''generated_text''': (
                           '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                           ''' oscope. FiliFili@@'''
                       )
                   }
               ]   ,   )

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		text_generator(['''This is a test''', '''This is a second test''']      )
           self.assertEqual(
               _lowercase   ,   [
                   [
                       {
                           '''generated_text''': (
                               '''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
                               ''' oscope. FiliFili@@'''
                           )
                       }
                   ],
                   [
                       {
                           '''generated_text''': (
                               '''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
                               ''' oscope. oscope. FiliFili@@'''
                           )
                       }
                   ],
               ]   ,   )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		text_generator('''This is a test'''   ,   do_sample=_lowercase   ,   num_return_sequences=2   ,   return_tensors=_lowercase      )
           self.assertEqual(
               _lowercase   ,   [
                   {'''generated_token_ids''': ANY(_lowercase      )},
                   {'''generated_token_ids''': ANY(_lowercase      )},
               ]   ,   )
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		text_generator.model.config.eos_token_id
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		'''<pad>'''
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		text_generator(
               ['''This is a test''', '''This is a second test''']   ,   do_sample=_lowercase   ,   num_return_sequences=2   ,   batch_size=2   ,   return_tensors=_lowercase   ,   )
           self.assertEqual(
               _lowercase   ,   [
                   [
                       {'''generated_token_ids''': ANY(_lowercase      )},
                       {'''generated_token_ids''': ANY(_lowercase      )},
                   ],
                   [
                       {'''generated_token_ids''': ANY(_lowercase      )},
                       {'''generated_token_ids''': ANY(_lowercase      )},
                   ],
               ]   ,   )







     @require_tf
     def 							lowercase__       (						self :						str      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		pipeline(task='''text-generation'''   ,   model='''sshleifer/tiny-ctrl'''   ,   framework='''tf'''      )

           # Using `do_sample=False` to force deterministic output
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		text_generator('''This is a test'''   ,   do_sample=_lowercase      )
           self.assertEqual(
               _lowercase   ,   [
                   {
                       '''generated_text''': (
                           '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                           ''' please,'''
                       )
                   }
               ]   ,   )

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		text_generator(['''This is a test''', '''This is a second test''']   ,   do_sample=_lowercase      )
           self.assertEqual(
               _lowercase   ,   [
                   [
                       {
                           '''generated_text''': (
                               '''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
                               ''' please,'''
                           )
                       }
                   ],
                   [
                       {
                           '''generated_text''': (
                               '''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
                               ''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
                           )
                       }
                   ],
               ]   ,   )







     def 							lowercase__       (						self :						Dict   ,   _lowercase :						int   ,   _lowercase :						Tuple   ,   _lowercase :						int      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		TextGenerationPipeline(model=_lowercase   ,   tokenizer=_lowercase      )
           return text_generator, ["This is a test", "Another test"]







     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		'''Hello I believe in'''
           SCREAMING_SNAKE_CASE__       :  int						     =		pipeline('''text-generation'''   ,   model='''hf-internal-testing/tiny-random-gpt2'''      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		text_generator(_lowercase      )
           self.assertEqual(
               _lowercase   ,   [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}]   ,   )

           SCREAMING_SNAKE_CASE__       :  str						     =		text_generator(_lowercase   ,   stop_sequence=''' fe'''      )
           self.assertEqual(_lowercase   ,   [{'''generated_text''': '''Hello I believe in fe'''}]      )







     def 							lowercase__       (						self :						Union[str, Any]   ,   _lowercase :						Optional[int]   ,   _lowercase :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		text_generator.model
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		text_generator.tokenizer

           SCREAMING_SNAKE_CASE__       :  Any						     =		text_generator('''This is a test'''      )
           self.assertEqual(_lowercase   ,   [{'''generated_text''': ANY(_lowercase      )}]      )
           self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''      )      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		text_generator('''This is a test'''   ,   return_full_text=_lowercase      )
           self.assertEqual(_lowercase   ,   [{'''generated_text''': ANY(_lowercase      )}]      )
           self.assertNotIn('''This is a test'''   ,   outputs[0]['''generated_text''']      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		pipeline(task='''text-generation'''   ,   model=_lowercase   ,   tokenizer=_lowercase   ,   return_full_text=_lowercase      )
           SCREAMING_SNAKE_CASE__       :  int						     =		text_generator('''This is a test'''      )
           self.assertEqual(_lowercase   ,   [{'''generated_text''': ANY(_lowercase      )}]      )
           self.assertNotIn('''This is a test'''   ,   outputs[0]['''generated_text''']      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		text_generator('''This is a test'''   ,   return_full_text=_lowercase      )
           self.assertEqual(_lowercase   ,   [{'''generated_text''': ANY(_lowercase      )}]      )
           self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test'''      )      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		text_generator(['''This is great !''', '''Something else''']   ,   num_return_sequences=2   ,   do_sample=_lowercase      )
           self.assertEqual(
               _lowercase   ,   [
                   [{'''generated_text''': ANY(_lowercase      )}, {'''generated_text''': ANY(_lowercase      )}],
                   [{'''generated_text''': ANY(_lowercase      )}, {'''generated_text''': ANY(_lowercase      )}],
               ]   ,   )

           if text_generator.tokenizer.pad_token is not None:
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		text_generator(
                     ['''This is great !''', '''Something else''']   ,   num_return_sequences=2   ,   batch_size=2   ,   do_sample=_lowercase      )
                 self.assertEqual(
                     _lowercase   ,   [
                         [{'''generated_text''': ANY(_lowercase      )}, {'''generated_text''': ANY(_lowercase      )}],
                         [{'''generated_text''': ANY(_lowercase      )}, {'''generated_text''': ANY(_lowercase      )}],
                     ]   ,   )

           with self.assertRaises(_lowercase      ):
                 SCREAMING_SNAKE_CASE__       :  List[Any]						     =		text_generator('''test'''   ,   return_full_text=_lowercase   ,   return_text=_lowercase      )
           with self.assertRaises(_lowercase      ):
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		text_generator('''test'''   ,   return_full_text=_lowercase   ,   return_tensors=_lowercase      )
           with self.assertRaises(_lowercase      ):
                 SCREAMING_SNAKE_CASE__       :  List[str]						     =		text_generator('''test'''   ,   return_text=_lowercase   ,   return_tensors=_lowercase      )

           # Empty prompt is slighly special
           # it requires BOS token to exist.
           # Special case for Pegasus which will always append EOS so will
           # work even without BOS.
           if (
               text_generator.tokenizer.bos_token_id is not None
               or "Pegasus" in tokenizer.__class__.__name__
               or "Git" in model.__class__.__name__
           ):
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		text_generator(''''''      )
                 self.assertEqual(_lowercase   ,   [{'''generated_text''': ANY(_lowercase      )}]      )
           else:
                 with self.assertRaises((ValueError, AssertionError)      ):
                       SCREAMING_SNAKE_CASE__       :  int						     =		text_generator(''''''      )

           if text_generator.framework == "tf":
                 # TF generation does not support max_new_tokens, and it's impossible
                 # to control long generation with only max_length without
                 # fancy calculation, dismissing tests for now.
                 return
           # We don't care about infinite range models.
           # They already work.
           # Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		['''RwkvForCausalLM''', '''XGLMForCausalLM''', '''GPTNeoXForCausalLM''']
           if (
               tokenizer.model_max_length < 1_00_00
               and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
           ):
                 # Handling of large generations
                 with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError)      ):
                       text_generator('''This is a test''' * 5_00   ,   max_new_tokens=20      )

                 SCREAMING_SNAKE_CASE__       :  Any						     =		text_generator('''This is a test''' * 5_00   ,   handle_long_generation='''hole'''   ,   max_new_tokens=20      )
                 # Hole strategy cannot work
                 with self.assertRaises(_lowercase      ):
                       text_generator(
                           '''This is a test''' * 5_00   ,   handle_long_generation='''hole'''   ,   max_new_tokens=tokenizer.model_max_length + 10   ,   )







     @require_torch
     @require_accelerate
     @require_torch_gpu
     def 							lowercase__       (						self :						str      ):
           import torch

           # Classic `model_kwargs`
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		pipeline(
               model='''hf-internal-testing/tiny-random-bloom'''   ,   model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa}   ,   )
           self.assertEqual(pipe.model.device   ,   torch.device(0      )      )
           self.assertEqual(pipe.model.lm_head.weight.dtype   ,   torch.bfloataa      )
           SCREAMING_SNAKE_CASE__       :  int						     =		pipe('''This is a test'''      )
           self.assertEqual(
               _lowercase   ,   [
                   {
                       '''generated_text''': (
                           '''This is a test test test test test test test test test test test test test test test test'''
                           ''' test'''
                       )
                   }
               ]   ,   )

           # Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
           SCREAMING_SNAKE_CASE__       :  int						     =		pipeline(model='''hf-internal-testing/tiny-random-bloom'''   ,   device_map='''auto'''   ,   torch_dtype=torch.bfloataa      )
           self.assertEqual(pipe.model.device   ,   torch.device(0      )      )
           self.assertEqual(pipe.model.lm_head.weight.dtype   ,   torch.bfloataa      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		pipe('''This is a test'''      )
           self.assertEqual(
               _lowercase   ,   [
                   {
                       '''generated_text''': (
                           '''This is a test test test test test test test test test test test test test test test test'''
                           ''' test'''
                       )
                   }
               ]   ,   )

           # torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		pipeline(model='''hf-internal-testing/tiny-random-bloom'''   ,   device_map='''auto'''      )
           self.assertEqual(pipe.model.device   ,   torch.device(0      )      )
           self.assertEqual(pipe.model.lm_head.weight.dtype   ,   torch.floataa      )
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pipe('''This is a test'''      )
           self.assertEqual(
               _lowercase   ,   [
                   {
                       '''generated_text''': (
                           '''This is a test test test test test test test test test test test test test test test test'''
                           ''' test'''
                       )
                   }
               ]   ,   )







     @require_torch
     @require_torch_gpu
     def 							lowercase__       (						self :						Optional[int]      ):
           import torch

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		pipeline(model='''hf-internal-testing/tiny-random-bloom'''   ,   device=0   ,   torch_dtype=torch.floataa      )
           pipe('''This is a test'''      )







     @require_torch
     @require_accelerate
     @require_torch_gpu
     def 							lowercase__       (						self :						Any      ):
           import torch

           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		pipeline(model='''hf-internal-testing/tiny-random-bloom'''   ,   device_map='''auto'''   ,   torch_dtype=torch.floataa      )
           pipe('''This is a test'''   ,   do_sample=_lowercase   ,   top_p=0.5      )







     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		'''Hello world'''
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		pipeline('''text-generation'''   ,   model='''hf-internal-testing/tiny-random-gpt2'''      )
           if text_generator.model.framework == "tf":
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		logging.get_logger('''transformers.generation.tf_utils'''      )
           else:
                 SCREAMING_SNAKE_CASE__       :  Any						     =		logging.get_logger('''transformers.generation.utils'''      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		'''Both `max_new_tokens`'''  # The beggining of the message to be checked in this test

           # Both are set by the user -> log warning
           with CaptureLogger(_lowercase      ) as cl:
                 SCREAMING_SNAKE_CASE__       :  Dict						     =		text_generator(_lowercase   ,   max_length=10   ,   max_new_tokens=1      )
           self.assertIn(_lowercase   ,   cl.out      )

           # The user only sets one -> no warning
           with CaptureLogger(_lowercase      ) as cl:
                 SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		text_generator(_lowercase   ,   max_new_tokens=1      )
           self.assertNotIn(_lowercase   ,   cl.out      )

           with CaptureLogger(_lowercase      ) as cl:
                 SCREAMING_SNAKE_CASE__       :  Tuple						     =		text_generator(_lowercase   ,   max_length=10      )
           self.assertNotIn(_lowercase   ,   cl.out      )








"""simple docstring"""





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()
_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""",
    """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""",
}
_a								=						[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Tuple:


					"""simple docstring"""





					for attribute in key.split('''.'''      ):
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      )

					if weight_type is not None:
										_UpperCamelCase									=    getattr(__snake_case,	__snake_case      ).shape
					else:
										_UpperCamelCase									=    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									=    value
					elif weight_type == "weight_g":
										_UpperCamelCase									=    value
					elif weight_type == "weight_v":
										_UpperCamelCase									=    value
					elif weight_type == "bias":
										_UpperCamelCase									=    value
					else:
										_UpperCamelCase									=    value

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



def  lowerCamelCase__						(     __snake_case,	__snake_case      )				->						List[str]:


					"""simple docstring"""





					_UpperCamelCase									=    []
					_UpperCamelCase									=    fairseq_model.state_dict()

					_UpperCamelCase									=    hf_model.feature_extractor
					_UpperCamelCase									=    hf_model.adapter

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

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



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						List[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''conv_layers.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )
					_UpperCamelCase									=    int(items[0]      )
					_UpperCamelCase									=    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									=    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									=    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									=    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									=    value
															logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case      )				->						Dict:


					"""simple docstring"""





					_UpperCamelCase									=    full_name.split('''adaptor.'''      )[-1]
					_UpperCamelCase									=    name.split('''.'''      )

					if items[1].isdigit():
										_UpperCamelCase									=    int(items[1]      )
					else:
										_UpperCamelCase									=    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.'''
																				_UpperCamelCase									=    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.'''
																				_UpperCamelCase									=    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.'''
																				_UpperCamelCase									=    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.'''
																				_UpperCamelCase									=    value
																				logger.info(F'''Adapter proj layer weight was initialized from {full_name}.'''      )
					elif isinstance(__snake_case,	__snake_case      ):
										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.'''
															_UpperCamelCase									=    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.'''
															_UpperCamelCase									=    value
															logger.info(F'''Adapter layer {layer_id} bias was initialized from {full_name}.'''      )
					else:
										unused_weights.append(__snake_case      )



def  lowerCamelCase__						(     __snake_case      )				->						str:


					"""simple docstring"""





					_UpperCamelCase					,    _UpperCamelCase									=    emb.weight.shape
					_UpperCamelCase									=    nn.Linear(__snake_case,	__snake_case,	bias=__snake_case      )
					_UpperCamelCase									=    emb.weight.data
					return lin_layer



@torch.no_grad()
def  lowerCamelCase__						(     __snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	__snake_case,	)				->						Optional[Any]:


					"""simple docstring"""





					_UpperCamelCase									=    WavaVecaConfig.from_pretrained(
					    __snake_case,	add_adapter=__snake_case,	adapter_stride=__snake_case,	adapter_kernel_size=__snake_case,	use_auth_token=__snake_case,	output_hidden_size=__snake_case,	)
					_UpperCamelCase									=    MBartConfig.from_pretrained(__snake_case      )

					# load model
					_UpperCamelCase					,    _UpperCamelCase					,    _UpperCamelCase									=    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,
					    },	)
					_UpperCamelCase									=    model[0].eval()

					# load feature extractor
					_UpperCamelCase									=    WavaVecaFeatureExtractor.from_pretrained(__snake_case,	use_auth_token=__snake_case      )

					# set weights for wav2vec2 encoder
					_UpperCamelCase									=    WavaVecaModel(__snake_case      )

					recursively_load_weights_wavaveca(model.encoder,	__snake_case      )

					# load decoder weights
					_UpperCamelCase									=    MBartForCausalLM(__snake_case      )
					_UpperCamelCase					,    _UpperCamelCase									=    hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict(),	strict=__snake_case      )
					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}'''      )

					_UpperCamelCase									=    SpeechEncoderDecoderModel(encoder=__snake_case,	decoder=__snake_case      )
					_UpperCamelCase									=    False

					_UpperCamelCase									=    MBartaaTokenizer(__snake_case      )
					tokenizer.save_pretrained(__snake_case      )

					_UpperCamelCase									=    hf_wavavec.config.to_dict()
					_UpperCamelCase									=    tokenizer.pad_token_id
					_UpperCamelCase									=    tokenizer.bos_token_id
					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    '''mbart50'''
					_UpperCamelCase									=    '''wav2vec2'''

					_UpperCamelCase									=    tokenizer.eos_token_id
					_UpperCamelCase									=    25_00_04
					_UpperCamelCase									=    tokenizer.eos_token_id

					_UpperCamelCase									=    SpeechEncoderDecoderConfig.from_dict(__snake_case      )

					hf_wavavec.save_pretrained(__snake_case      )
					feature_extractor.save_pretrained(__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_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=25_0004, type=int, help="""`decoder_start_token_id` of model config""")

				_a								=						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,
				)






