
"""simple docstring"""





import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.generation import DisjunctiveConstraint






@require_torch
class     _UpperCAmelCase(    unittest.TestCase	):



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






							'''simple docstring'''
							_UpperCamelCase							 =  [[1, 2, 4], [1, 2, 3, 4]]
							_UpperCamelCase							 =  DisjunctiveConstraint(__a)
							self.assertTrue(isinstance(dc.token_ids				,		__a))

							with self.assertRaises(__a):
												DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]))

							with self.assertRaises(__a):
												DisjunctiveConstraint([torch.LongTensor([1, 2, 4]), torch.LongTensor([1, 2, 3, 4, 5])])



		def 						UpperCAmelCase							(					self)       ->       str:






							'''simple docstring'''
							_UpperCamelCase							 =  [[1, 2], [1, 2, 3, 4]]

							with self.assertRaises(__a):
												DisjunctiveConstraint(__a)  # fails here



		def 						UpperCAmelCase							(					self)       ->       Any:






							'''simple docstring'''
							_UpperCamelCase							 =  [[1, 2, 3], [1, 2, 4]]

							_UpperCamelCase							 =  DisjunctiveConstraint(__a)

							_UpperCamelCase							 =  dc.update(1)
							_UpperCamelCase							 =  stepped is True and completed is False and reset is False
							self.assertTrue(__a)
							self.assertTrue(not dc.completed)
							self.assertTrue(dc.current_seq == [1])

							_UpperCamelCase							 =  dc.update(2)
							_UpperCamelCase							 =  stepped is True and completed is False and reset is False
							self.assertTrue(__a)
							self.assertTrue(not dc.completed)
							self.assertTrue(dc.current_seq == [1, 2])

							_UpperCamelCase							 =  dc.update(3)
							_UpperCamelCase							 =  stepped is True and completed is True and reset is False
							self.assertTrue(__a)
							self.assertTrue(dc.completed)  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 3])




		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

							_UpperCamelCase							 =  DisjunctiveConstraint(__a)

							_UpperCamelCase							 =  dc.update(1)
							self.assertTrue(not dc.completed)
							self.assertTrue(dc.current_seq == [1])

							_UpperCamelCase							 =  dc.update(2)
							self.assertTrue(not dc.completed)
							self.assertTrue(dc.current_seq == [1, 2])

							_UpperCamelCase							 =  dc.update(4)
							self.assertTrue(not dc.completed)
							self.assertTrue(dc.current_seq == [1, 2, 4])

							_UpperCamelCase							 =  dc.update(5)
							self.assertTrue(dc.completed)  # Completed!
							self.assertTrue(dc.current_seq == [1, 2, 4, 5])

							dc.reset()

							_UpperCamelCase							 =  dc.update(1)
							self.assertTrue(not dc.completed)
							self.assertTrue(dc.remaining() == 3)
							self.assertTrue(dc.current_seq == [1])

							_UpperCamelCase							 =  dc.update(2)
							self.assertTrue(not dc.completed)
							self.assertTrue(dc.remaining() == 2)
							self.assertTrue(dc.current_seq == [1, 2])

							_UpperCamelCase							 =  dc.update(5)
							self.assertTrue(dc.completed)  # Completed!
							self.assertTrue(dc.remaining() == 0)
							self.assertTrue(dc.current_seq == [1, 2, 5])













import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    logging,
)


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

UpperCAmelCase_     							= {
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
UpperCAmelCase_     							= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			int		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			str		,   __UpperCAmelCase:			Any      )      ->	List[str]:
    for attribute in key.split('''.'''      ):
        if is_finetuned:
            if attribute in ["quantizer", "project_q", "project_hid"]:
                # those layers are only relevant for pretraining and should be dropped
                return

            if attribute == "ctc_proj":
                # we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
                UpperCamelCase__							:				Tuple          =      '''lm_head'''

        UpperCamelCase__							:				Optional[int]          =      getattr(__UpperCAmelCase		,   __UpperCAmelCase      )

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

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict		,   __UpperCAmelCase:			str      )      ->	List[Any]:
    UpperCamelCase__							:				Optional[Any]          =      []
    UpperCamelCase__							:				str          =      fairseq_model.state_dict()

    UpperCamelCase__							:				Optional[int]          =      hf_model.unispeech.feature_extractor

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

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




def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Any		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			List[Any]		,   __UpperCAmelCase:			Tuple      )      ->	Optional[int]:
    UpperCamelCase__							:				List[str]          =      full_name.split('''conv_layers.'''      )[-1]
    UpperCamelCase__							:				List[str]          =      name.split('''.'''      )
    UpperCamelCase__							:				str          =      int(items[0]      )
    UpperCamelCase__							:				Union[str, Any]          =      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__							:				int          =      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__							:				Tuple          =      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__							:				int          =      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__							:				List[str]          =      value
            logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."      )
    else:
        unused_weights.append(__UpperCAmelCase      )




@torch.no_grad()
def 		lowerCAmelCase_    (  __UpperCAmelCase:			Tuple		,   __UpperCAmelCase:			Optional[Any]		,   __UpperCAmelCase:			Dict=None		,   __UpperCAmelCase:			Optional[Any]=None		,   __UpperCAmelCase:			Optional[int]=True      )      ->	Union[str, Any]:
    if config_path is not None:
        UpperCamelCase__							:				str          =      UniSpeechConfig.from_pretrained(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				List[Any]          =      UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            UpperCamelCase__							:				str          =      Dictionary.load_from_json(__UpperCAmelCase      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCamelCase__							:				Any          =      target_dict.pad_index
            UpperCamelCase__							:				str          =      target_dict.bos_index
            UpperCamelCase__							:				Any          =      target_dict.eos_index
            UpperCamelCase__							:				Tuple          =      len(target_dict.symbols      )
            UpperCamelCase__							:				List[str]          =      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      )
            UpperCamelCase__							:				Dict          =      target_dict.indices

            # fairseq has the <pad> and <s> switched
            UpperCamelCase__							:				Optional[Any]          =      42
            UpperCamelCase__							:				List[str]          =      43
            with open(__UpperCAmelCase		,   '''w'''		,   encoding='''utf-8'''      ) as vocab_handle:
                json.dump(__UpperCAmelCase		,   __UpperCAmelCase      )
            UpperCamelCase__							:				Dict          =      WavaVecaPhonemeCTCTokenizer(
                __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		,   )
            UpperCamelCase__							:				List[Any]          =      True if config.feat_extract_norm == '''layer''' else False
            UpperCamelCase__							:				str          =      WavaVecaFeatureExtractor(
                feature_size=1		,   sampling_rate=1_6000		,   padding_value=0		,   do_normalize=__UpperCAmelCase		,   return_attention_mask=__UpperCAmelCase		,   )
            UpperCamelCase__							:				str          =      WavaVecaProcessor(feature_extractor=__UpperCAmelCase		,   tokenizer=__UpperCAmelCase      )
            processor.save_pretrained(__UpperCAmelCase      )

        UpperCamelCase__							:				Optional[int]          =      UniSpeechForCTC(__UpperCAmelCase      )
    else:
        UpperCamelCase__							:				Any          =      UniSpeechForPreTraining(__UpperCAmelCase      )

    if is_finetuned:
        UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				str          =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]		,   arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''      )[:-1]      ), '''w2v_path''': checkpoint_path}      )
    else:
        UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Any          =      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

    UpperCamelCase__							:				Tuple          =      model[0].eval()

    recursively_load_weights(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      )

    hf_unispeech.save_pretrained(__UpperCAmelCase      )


if __name__ == "__main__":
  UpperCAmelCase_     							= 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'
  )
  UpperCAmelCase_     							= parser.parse_args()
  convert_unispeech_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )

