
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowerCamelCase        ={
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "attention.self.query_proj": "encoder.encoder.layer.*.attention.self.query_proj",
    "attention.self.key_proj": "encoder.encoder.layer.*.attention.self.key_proj",
    "attention.self.value_proj": "encoder.encoder.layer.*.attention.self.value_proj",
    "attention.output.dense": "encoder.encoder.layer.*.attention.output.dense",
    "attention.output.LayerNorm": "encoder.encoder.layer.*.attention.output.LayerNorm",
    "intermediate.dense": "encoder.encoder.layer.*.intermediate.dense",
    "output.dense": "encoder.encoder.layer.*.output.dense",
    "output.LayerNorm": "encoder.encoder.layer.*.output.LayerNorm",
    "encoder.encoder.rel_embeddings": "encoder.encoder.rel_embeddings",
    "encoder.encoder.LayerNorm": "encoder.encoder.LayerNorm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}





def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):



	for attribute in key.split("""."""							):
		lowerCamelCase		:     int          =				getattr(UpperCamelCase_,     UpperCamelCase_							)

	if weight_type is not None:
		lowerCamelCase		:     Tuple          =				getattr(UpperCamelCase_,     UpperCamelCase_							).shape
	else:
		lowerCamelCase		:     str          =				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		:     str          =				value
	elif weight_type == "weight_v":
		lowerCamelCase		:     Optional[int]          =				value
	elif weight_type == "bias":
		lowerCamelCase		:     Any          =				value
	else:
		lowerCamelCase		:     Optional[int]          =				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		:     Union[str, Any]          =				[]
	lowerCamelCase		:     str          =				fairseq_model.state_dict()

	lowerCamelCase		:     Tuple          =				hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		lowerCamelCase		:     Union[str, Any]          =				False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_,     hf_model.config.feat_extract_norm == """group""",     )
			lowerCamelCase		:     Union[str, Any]          =				True
		else:
			for key, mapped_key in MAPPING.items():
				lowerCamelCase		:     Dict          =				"""sew_d.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

				if key in name or key.split("""w2v_model."""							)[-1] == name.split("""."""							)[0]:
					lowerCamelCase		:     Optional[Any]          =				True
					if "*" in mapped_key:
						lowerCamelCase		:     Any          =				name.split(UpperCamelCase_							)[0].split("""."""							)[-2]
						if not layer_index.isnumeric():
							continue
						lowerCamelCase		:     List[Any]          =				mapped_key.replace("""*""",     UpperCamelCase_							)
					if "weight_g" in name:
						lowerCamelCase		:     Union[str, Any]          =				"""weight_g"""
					elif "weight_v" in name:
						lowerCamelCase		:     Any          =				"""weight_v"""
					elif "weight" in name:
						lowerCamelCase		:     Any          =				"""weight"""
					elif "bias" in name:
						lowerCamelCase		:     str          =				"""bias"""
					else:
						lowerCamelCase		:     Dict          =				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    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase,     lowerCamelCase							):
	lowerCamelCase		:     int          =				full_name.split("""conv_layers."""							)[-1]
	lowerCamelCase		:     List[str]          =				name.split("""."""							)
	lowerCamelCase		:     Optional[int]          =				int(items[0]							)
	lowerCamelCase		:     List[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		:     str          =				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		:     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		:     List[Any]          =				value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
	else:
		unused_weights.append(UpperCamelCase_							)





def    _a (						lowerCamelCase,     lowerCamelCase							):
	lowerCamelCase		:     List[Any]          =				SEWDConfig()
	if is_finetuned:
		lowerCamelCase		:     Dict          =				model.wav_encoder.wav_model.cfg
	else:
		lowerCamelCase		:     Tuple          =				model.cfg

	lowerCamelCase		:     Tuple          =				fs_config.conv_bias
	lowerCamelCase		:     List[Any]          =				eval(fs_config.conv_feature_layers							)
	lowerCamelCase		:     Optional[int]          =				[x[0] for x in conv_layers]
	lowerCamelCase		:     List[str]          =				[x[1] for x in conv_layers]
	lowerCamelCase		:     str          =				[x[2] for x in conv_layers]
	lowerCamelCase		:     int          =				"""gelu"""
	lowerCamelCase		:     Any          =				"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
	lowerCamelCase		:     Any          =				0.0
	lowerCamelCase		:     Optional[Any]          =				fs_config.activation_fn.name
	lowerCamelCase		:     Optional[Any]          =				fs_config.encoder_embed_dim
	lowerCamelCase		:     Union[str, Any]          =				0.0_2
	lowerCamelCase		:     Optional[Any]          =				fs_config.encoder_ffn_embed_dim
	lowerCamelCase		:     List[str]          =				1e-5
	lowerCamelCase		:     Dict          =				fs_config.encoder_layerdrop
	lowerCamelCase		:     int          =				fs_config.encoder_attention_heads
	lowerCamelCase		:     Union[str, Any]          =				fs_config.conv_pos_groups
	lowerCamelCase		:     Dict          =				fs_config.conv_pos
	lowerCamelCase		:     Dict          =				len(UpperCamelCase_							)
	lowerCamelCase		:     Dict          =				fs_config.encoder_layers
	lowerCamelCase		:     int          =				fs_config.squeeze_factor
	# DeBERTa-specific parameters:
	lowerCamelCase		:     Union[str, Any]          =				fs_config.max_position_embeddings
	lowerCamelCase		:     str          =				fs_config.position_buckets
	lowerCamelCase		:     str          =				fs_config.share_att_key
	lowerCamelCase		:     List[str]          =				fs_config.relative_attention
	lowerCamelCase		:     Any          =				fs_config.position_biased_input
	lowerCamelCase		:     str          =				tuple(fs_config.pos_att_type.split("""|"""							)							)
	lowerCamelCase		:     Optional[int]          =				fs_config.norm_rel_ebd

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		lowerCamelCase		:     Dict          =				model.cfg
		lowerCamelCase		:     List[str]          =				fs_config.final_dropout
		lowerCamelCase		:     Optional[int]          =				fs_config.layerdrop
	lowerCamelCase		:     Tuple          =				fs_config.activation_dropout
	lowerCamelCase		:     Optional[Any]          =				fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	lowerCamelCase		:     List[str]          =				fs_config.attention_dropout
	lowerCamelCase		:     Union[str, Any]          =				fs_config.dropout_input
	lowerCamelCase		:     Optional[Any]          =				fs_config.dropout
	lowerCamelCase		:     Optional[int]          =				fs_config.mask_channel_length
	lowerCamelCase		:     Optional[Any]          =				fs_config.mask_channel_prob
	lowerCamelCase		:     Optional[Any]          =				fs_config.mask_length
	lowerCamelCase		:     List[str]          =				fs_config.mask_prob

	lowerCamelCase		:     Optional[Any]          =				"""Wav2Vec2FeatureExtractor"""
	lowerCamelCase		:     Dict          =				"""Wav2Vec2CTCTokenizer"""

	return config





@torch.no_grad()
def    _a (						lowerCamelCase,     lowerCamelCase,     lowerCamelCase=None,     lowerCamelCase=None,     lowerCamelCase=True							):



	if is_finetuned:
		lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     Dict          =				fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path],     arg_overrides={"""data""": """/""".join(dict_path.split("""/"""							)[:-1]							)}							)
	else:
		lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase		:     Tuple          =				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)

	if config_path is not None:
		lowerCamelCase		:     int          =				SEWDConfig.from_pretrained(UpperCamelCase_							)
	else:
		lowerCamelCase		:     Optional[int]          =				convert_config(model[0],     UpperCamelCase_							)
	lowerCamelCase		:     Any          =				model[0].eval()

	lowerCamelCase		:     Union[str, Any]          =				True if config.feat_extract_norm == """layer""" else False
	lowerCamelCase		:     str          =				WavaVecaFeatureExtractor(
	    feature_size=1,     sampling_rate=1_6000,     padding_value=0,     do_normalize=UpperCamelCase_,     return_attention_mask=UpperCamelCase_,     )

	if is_finetuned:
		if dict_path:
			lowerCamelCase		:     int          =				Dictionary.load(UpperCamelCase_							)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			lowerCamelCase		:     Dict          =				target_dict.pad_index
			lowerCamelCase		:     List[str]          =				target_dict.bos_index
			lowerCamelCase		:     Any          =				target_dict.pad_index
			lowerCamelCase		:     List[Any]          =				target_dict.bos_index
			lowerCamelCase		:     Optional[int]          =				target_dict.eos_index
			lowerCamelCase		:     Tuple          =				len(target_dict.symbols							)
			lowerCamelCase		:     Tuple          =				os.path.join(UpperCamelCase_,     """vocab.json"""							)
			if not os.path.isdir(UpperCamelCase_							):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCamelCase_							)							)
				return
			os.makedirs(UpperCamelCase_,     exist_ok=UpperCamelCase_							)
			with open(UpperCamelCase_,     """w""",     encoding="""utf-8"""							) as vocab_handle:
				json.dump(target_dict.indices,     UpperCamelCase_							)
			lowerCamelCase		:     int          =				WavaVecaCTCTokenizer(
			    UpperCamelCase_,     unk_token=target_dict.unk_word,     pad_token=target_dict.pad_word,     bos_token=target_dict.bos_word,     eos_token=target_dict.eos_word,     word_delimiter_token="""|""",     do_lower_case=UpperCamelCase_,     )
			lowerCamelCase		:     str          =				WavaVecaProcessor(feature_extractor=UpperCamelCase_,     tokenizer=UpperCamelCase_							)
			processor.save_pretrained(UpperCamelCase_							)

		lowerCamelCase		:     Tuple          =				SEWDForCTC(UpperCamelCase_							)
	else:
		lowerCamelCase		:     Dict          =				SEWDModel(UpperCamelCase_							)
		feature_extractor.save_pretrained(UpperCamelCase_							)

	recursively_load_weights(UpperCamelCase_,     UpperCamelCase_,     UpperCamelCase_							)

	hf_model.save_pretrained(UpperCamelCase_							)


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(
						    """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
						)
						_lowerCamelCase        =parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_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"""				)




