
import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

_lowerCamelCase        ={
    "post_extract_proj": "feature_projection.projection",
    "models.0.layer_norm": "feature_projection.layer_norm",
    "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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_lowerCamelCase        =[
    "lm_head",
]





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
	for attribute in key.split("""."""							):
		lowerCamelCase		:     Tuple          =				getattr(_lowerCAmelCase,     _lowerCAmelCase							)

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

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





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
	lowerCamelCase		:     Any          =				[]
	lowerCamelCase		:     Tuple          =				fairseq_model.state_dict()

	if not is_headless:
		lowerCamelCase		:     Optional[int]          =				hf_model.dataavec_audio.feature_extractor
		lowerCamelCase		:     Optional[int]          =				hf_model.dataavec_audio.encoder.pos_conv_embed

	else:
		lowerCamelCase		:     Optional[Any]          =				hf_model.feature_extractor
		lowerCamelCase		:     List[str]          =				hf_model.encoder.pos_conv_embed

	for name, value in fairseq_dict.items():
		lowerCamelCase		:     Dict          =				False
		if "conv_layers" in name:
			load_conv_layer(
			    _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     )
			lowerCamelCase		:     str          =				True
		elif "pos_conv" in name:
			load_pos_conv_layer(
			    _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     )
			lowerCamelCase		:     Union[str, Any]          =				True
		else:
			for key, mapped_key in MAPPING.items():
				if not is_headless:
					lowerCamelCase		:     Union[str, Any]          =				'''data2vec_audio.''' + 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]:
					lowerCamelCase		:     Optional[int]          =				True
					if "*" in mapped_key:
						lowerCamelCase		:     str          =				name.split(_lowerCAmelCase							)[0].split("""."""							)[-2]
						lowerCamelCase		:     List[str]          =				mapped_key.replace("""*""",     _lowerCAmelCase							)
					if "weight_g" in name:
						lowerCamelCase		:     Tuple          =				'''weight_g'''
					elif "weight_v" in name:
						lowerCamelCase		:     List[Any]          =				'''weight_v'''
					elif "bias" in name:
						lowerCamelCase		:     List[str]          =				'''bias'''
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowerCamelCase		:     Union[str, Any]          =				'''weight'''
					else:
						lowerCamelCase		:     List[Any]          =				None
					set_recursively(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase							)
				continue
		if not is_used:
			unused_weights.append(_lowerCAmelCase							)

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





def    _a (						lowerCamelCase,     lowerCamelCase							):
	lowerCamelCase		:     Tuple          =				path.split("""."""							)
	return reduce(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
	lowerCamelCase		:     str          =				access_by_string(_lowerCAmelCase,     _lowerCAmelCase							)
	lowerCamelCase		:     Any          =				hf_weight.data

	if fsq_value.shape != hf_value.shape:
		raise ValueError(F'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''							)
	lowerCamelCase		:     Optional[int]          =				fsq_value
	logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.'''							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
	lowerCamelCase		:     Any          =				full_name.split("""conv_layers."""							)[-1]
	lowerCamelCase		:     int          =				name.split("""."""							)
	lowerCamelCase		:     Dict          =				int(items[0]							)
	lowerCamelCase		:     Tuple          =				int(items[1]							)

	lowerCamelCase		:     List[Any]          =				name.split("""."""							)[-1]
	if type_id == 0:
		lowerCamelCase		:     List[str]          =				'''conv'''
	elif type_id == 2:
		lowerCamelCase		:     int          =				'''layer_norm'''
	else:
		unused_weights.append(_lowerCAmelCase							)
		return

	set_weights(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     F'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''							)





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
	lowerCamelCase		:     str          =				full_name.split("""pos_conv."""							)[-1]
	lowerCamelCase		:     List[str]          =				name.split("""."""							)
	lowerCamelCase		:     List[str]          =				int(items[0]							)
	lowerCamelCase		:     Dict          =				int(items[1]							)

	lowerCamelCase		:     str          =				name.split("""."""							)[-1]
	if type_id != 0:
		unused_weights.append(_lowerCAmelCase							)
		return
	else:
		lowerCamelCase		:     Tuple          =				'''conv'''

	set_weights(_lowerCAmelCase,     _lowerCAmelCase,     _lowerCAmelCase,     F'''layers.{layer_id}.{layer_type}.{weight_type}'''							)





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase=None,     lowerCamelCase=None,     lowerCamelCase=True							):
	if config_path is not None:
		lowerCamelCase		:     Dict          =				DataaVecAudioConfig.from_pretrained(_lowerCAmelCase							)
	else:
		lowerCamelCase		:     Any          =				DataaVecAudioConfig()

	if not is_finetuned:
		# Modify final_proj layer name
		lowerCamelCase		:     Dict          =				DataaVecAudioModel(_lowerCAmelCase							)
		lowerCamelCase		:     Dict          =				os.path.dirname(_lowerCAmelCase							)

		lowerCamelCase		:     str          =				torch.load(_lowerCAmelCase							)
		lowerCamelCase		:     Optional[Any]          =				state_dict['''model'''].pop("""final_proj.0.weight"""							)
		lowerCamelCase		:     Tuple          =				state_dict['''model'''].pop("""final_proj.0.bias"""							)
		lowerCamelCase		:     Optional[Any]          =				os.path.join(_lowerCAmelCase,     """converted.pt"""							)
		torch.save(_lowerCAmelCase,     _lowerCAmelCase							)
	else:
		lowerCamelCase		:     Any          =				DataaVecAudioForCTC(_lowerCAmelCase							)
		lowerCamelCase		:     Union[str, Any]          =				checkpoint_path

	def load_dataavec(lowerCamelCase							):
		lowerCamelCase		:     int          =				fairseq.checkpoint_utils.load_model_ensemble_and_task([path]							)
		return model[0].eval()

	lowerCamelCase		:     Union[str, Any]          =				load_dataavec(_lowerCAmelCase							)

	recursively_load_weights(_lowerCAmelCase,     _lowerCAmelCase,     not is_finetuned							)

	lowerCamelCase		:     Dict          =				WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""							)

	lowerCamelCase		:     Optional[Any]          =				load_dataset("""patrickvonplaten/librispeech_asr_dummy""",     """clean""",     split="""validation"""							)
	lowerCamelCase		:     Dict          =				[x['''array'''] for x in ds[:4]['''audio''']]

	lowerCamelCase		:     int          =				processor(_lowerCAmelCase,     return_tensors="""pt""",     padding=_lowerCAmelCase							)

	lowerCamelCase		:     Any          =				inputs.input_values
	lowerCamelCase		:     Dict          =				inputs.attention_mask
	#    input_values = inputs.input_values[:, :-1]
	#    attention_mask = inputs.attention_mask[:, :-1]

	hf_wavavec.eval()
	model.eval()
	if is_finetuned:
		lowerCamelCase		:     Optional[Any]          =				model(source=_lowerCAmelCase,     padding_mask=(1 - attention_mask),     mask=_lowerCAmelCase,     features_only=_lowerCAmelCase							)[
		    '''encoder_out'''
		].transpose(0,     1							)
		lowerCamelCase		:     str          =				hf_wavavec(_lowerCAmelCase,     attention_mask=_lowerCAmelCase							)['''logits''']

		lowerCamelCase		:     Union[str, Any]          =				torch.argmax(_lowerCAmelCase,     dim=-1							)
		lowerCamelCase		:     List[str]          =				processor.batch_decode(_lowerCAmelCase							)

		print(F'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''							)
	else:
		lowerCamelCase		:     Union[str, Any]          =				model(source=_lowerCAmelCase,     padding_mask=(1 - attention_mask),     mask=_lowerCAmelCase,     features_only=_lowerCAmelCase							)[
		    '''layer_results'''
		][-1][0].transpose(0,     1							)
		lowerCamelCase		:     List[str]          =				hf_wavavec(_lowerCAmelCase,     attention_mask=_lowerCAmelCase							)['''last_hidden_state''']

	print(our_output.shape,     their_output.shape							)
	lowerCamelCase		:     Optional[int]          =				torch.max(torch.abs(our_output - their_output							)							).item()
	print(F'''max_absolute_diff = {max_absolute_diff}'''							)  # ~ 1e-7
	lowerCamelCase		:     Optional[Any]          =				torch.allclose(_lowerCAmelCase,     _lowerCAmelCase,     atol=1e-3							)
	print("""Do both models output the same tensors?""",     """🔥""" if success else """💩"""							)
	if not success:
		raise Exception("""Something went wRoNg"""							)

	hf_wavavec.save_pretrained(_lowerCAmelCase							)

	if is_finetuned:
		processor.save_pretrained(_lowerCAmelCase							)
	else:
		processor.feature_extractor.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
						_lowerCamelCase        =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"""
						)
						_lowerCamelCase        =parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)






import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY





@is_pipeline_test
class 							A__		(     unittest.TestCase):
      _UpperCAmelCase    :   Tuple   	=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      _UpperCAmelCase    :   Optional[Any]   	=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__   ,  __magic_name__				):
       if isinstance(model.config   ,  __magic_name__				):
        lowerCamelCase      ,   lowerCamelCase		:     Union[str, Any]          =				list(tokenizer.lang_code_to_id.keys()				)[:2]
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang=__magic_name__   ,  tgt_lang=__magic_name__				)
       else:
        lowerCamelCase		:     Tuple          =				TranslationPipeline(model=__magic_name__   ,  tokenizer=__magic_name__				)
       return translator, ["Some string", "Some other text"]
      def 						UpperCamelCase__							(				self   ,  __magic_name__   ,  __magic_name__				):
       lowerCamelCase		:     Optional[Any]          =				translator("""Some string"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Dict          =				translator(["""Some string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}]				)

       lowerCamelCase		:     Any          =				translator(["""Some string""", """other string"""]				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": ANY(__magic_name__				)}, {"""translation_text""": ANY(__magic_name__				)}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Any          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     str          =				pipeline("""translation_en_to_ro"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Tuple          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"""
                       """ Beide Beide"""
                   )
               }
           ]   ,  )
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     int          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""pt"""				)
       lowerCamelCase		:     List[Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )






      @require_tf
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Union[str, Any]          =				pipeline("""translation_en_to_de"""   ,  model="""patrickvonplaten/t5-tiny-random"""   ,  framework="""tf"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test string"""   ,  max_length=2_0				)
       self.assertEqual(
           __magic_name__   ,  [
               {
                   """translation_text""": (
                       """monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"""
                       """ urine urine urine urine urine urine urine"""
                   )
               }
           ]   ,  )





class 							A__		(     unittest.TestCase):
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       # We don't provide a default for this pair
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation_cn_to_ar"""				)

       # but we do for this one
       lowerCamelCase		:     Optional[Any]          =				pipeline(task="""translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)
      @require_torch
      @slow
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Optional[int]          =				MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)
       lowerCamelCase		:     List[Any]          =				MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt"""				)

       lowerCamelCase		:     Any          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(__magic_name__				):
        translator("""This is a test"""				)

       lowerCamelCase		:     Optional[int]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)

       lowerCamelCase		:     Optional[Any]          =				translator("""This is a test"""   ,  src_lang="""en_XX"""   ,  tgt_lang="""hi_IN"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """यह एक परीक्षण है"""}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       lowerCamelCase		:     str          =				pipeline(task="""translation"""   ,  model=__magic_name__   ,  tokenizer=__magic_name__   ,  src_lang="""en_XX"""   ,  tgt_lang="""ar_AR"""				)
       lowerCamelCase		:     Union[str, Any]          =				translator("""This is a test"""				)
       self.assertEqual(__magic_name__   ,  [{"""translation_text""": """هذا إختبار"""}]				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     List[str]          =				"""patrickvonplaten/t5-tiny-random"""
       lowerCamelCase		:     Tuple          =				pipeline(task="""translation_cn_to_ar"""   ,  model=__magic_name__				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """cn"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """ar"""				)
      @require_torch
      def 						UpperCamelCase__							(				self				):
       lowerCamelCase		:     Dict          =				"""patrickvonplaten/t5-tiny-random"""
       with pytest.warns(__magic_name__   ,  match=r""".*translation_en_to_de.*"""				):
        lowerCamelCase		:     Optional[int]          =				pipeline(task="""translation"""   ,  model=__magic_name__				)
       self.assertEqual(translator.task   ,  """translation_en_to_de"""				)
       self.assertEqual(translator._preprocess_params["""src_lang"""]   ,  """en"""				)
       self.assertEqual(translator._preprocess_params["""tgt_lang"""]   ,  """de"""				)






      @require_torch
      def 						UpperCamelCase__							(				self				):
       with self.assertRaises(__magic_name__				):
        pipeline(task="""translation"""				)




