

import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
			import jax.numpy as jnp

			from transformers.models.roformer.modeling_flax_roformer import (
			    FlaxRoFormerForMaskedLM,
			    FlaxRoFormerForMultipleChoice,
			    FlaxRoFormerForQuestionAnswering,
			    FlaxRoFormerForSequenceClassification,
			    FlaxRoFormerForTokenClassification,
			    FlaxRoFormerModel,
			)






class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			def __init__(	self							:       Union[str, Any]					,					a							:       Optional[int]					,					a							:       str=13					,					a							:       str=7					,					a							:       List[Any]=True					,					a							:       List[str]=True					,					a							:       int=True					,					a							:       Any=True					,					a							:       Tuple=99					,					a							:       int=32					,					a							:       Union[str, Any]=5					,					a							:       str=4					,					a							:       Optional[Any]=37					,					a							:       Optional[Any]="gelu"					,					a							:       Any=0.1					,					a							:       Optional[Any]=0.1					,					a							:       Any=512					,					a							:       int=16					,					a							:       Optional[int]=2					,					a							:       Optional[int]=0.02					,					a							:       str=4					,					)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				parent
				SCREAMING_SNAKE_CASE      :					List[str]    =				batch_size
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				seq_length
				SCREAMING_SNAKE_CASE      :					List[Any]    =				is_training
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				use_attention_mask
				SCREAMING_SNAKE_CASE      :					Any    =				use_token_type_ids
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				use_labels
				SCREAMING_SNAKE_CASE      :					List[Any]    =				vocab_size
				SCREAMING_SNAKE_CASE      :					int    =				hidden_size
				SCREAMING_SNAKE_CASE      :					int    =				num_hidden_layers
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				num_attention_heads
				SCREAMING_SNAKE_CASE      :					List[str]    =				intermediate_size
				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				hidden_act
				SCREAMING_SNAKE_CASE      :					List[Any]    =				hidden_dropout_prob
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				max_position_embeddings
				SCREAMING_SNAKE_CASE      :					int    =				type_vocab_size
				SCREAMING_SNAKE_CASE      :					List[Any]    =				type_sequence_label_size
				SCREAMING_SNAKE_CASE      :					List[str]    =				initializer_range
				SCREAMING_SNAKE_CASE      :					str    =				num_choices
			def 							__UpperCamelCase       (	self							:       Optional[Any]		)							->				List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					str    =				ids_tensor([self.batch_size, self.seq_length]					,					self.vocab_size		)

				SCREAMING_SNAKE_CASE      :					Union[str, Any]    =				None
				if self.use_attention_mask:
					SCREAMING_SNAKE_CASE      :					List[str]    =				random_attention_mask([self.batch_size, self.seq_length]		)

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				None
				if self.use_token_type_ids:
					SCREAMING_SNAKE_CASE      :					Optional[Any]    =				ids_tensor([self.batch_size, self.seq_length]					,					self.type_vocab_size		)

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				RoFormerConfig(
				    vocab_size=self.vocab_size					,					hidden_size=self.hidden_size					,					num_hidden_layers=self.num_hidden_layers					,					num_attention_heads=self.num_attention_heads					,					intermediate_size=self.intermediate_size					,					hidden_act=self.hidden_act					,					hidden_dropout_prob=self.hidden_dropout_prob					,					attention_probs_dropout_prob=self.attention_probs_dropout_prob					,					max_position_embeddings=self.max_position_embeddings					,					type_vocab_size=self.type_vocab_size					,					is_decoder=a					,					initializer_range=self.initializer_range					,					)

				return config, input_ids, token_type_ids, attention_mask






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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				self.prepare_config_and_inputs()
				SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE   ,SCREAMING_SNAKE_CASE      :					Optional[Any]    =				config_and_inputs
				SCREAMING_SNAKE_CASE      :					Dict    =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
				return config, inputs_dict






@require_flax
class _UpperCamelCase							(							__A							,		unittest.TestCase      ):
			'''simple docstring'''







			lowerCamelCase__							=True

			lowerCamelCase__							=(
			    (
			        FlaxRoFormerModel,
			        FlaxRoFormerForMaskedLM,
			        FlaxRoFormerForSequenceClassification,
			        FlaxRoFormerForTokenClassification,
			        FlaxRoFormerForMultipleChoice,
			        FlaxRoFormerForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)
			def 							__UpperCamelCase       (	self							:       Union[str, Any]		)							->				List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					List[str]    =				FlaxRoFormerModelTester(self		)






			@slow
			def 							__UpperCamelCase       (	self							:       int		)							->				Optional[Any]:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE      :					int    =				model_class_name.from_pretrained("junnyu/roformer_chinese_small"					,					from_pt=a		)
					SCREAMING_SNAKE_CASE      :					Any    =				model(np.ones((1, 1)		)		)
					self.assertIsNotNone(a		)






@require_flax
class _UpperCamelCase							(							unittest.TestCase      ):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase       (	self							:       Tuple		)							->				int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE      :					Optional[Any]    =				FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				jnp.array([[0, 1, 2, 3, 4, 5]]		)
				SCREAMING_SNAKE_CASE      :					Tuple    =				model(a		)[0]

				SCREAMING_SNAKE_CASE      :					Optional[int]    =				5_0000

				SCREAMING_SNAKE_CASE      :					Any    =				(1, 6, vocab_size)
				self.assertEqual(output.shape					,					a		)

				SCREAMING_SNAKE_CASE      :					Any    =				jnp.array(
				    [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]		)

				self.assertTrue(jnp.allclose(output[:, :3, :3]					,					a					,					atol=1e-4		)		)




'''simple docstring'''





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()
lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "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",
}
lowercase_		       =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



     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
                    _a      =  '''lm_head'''

          _a      =  getattr(__A  ,	__A)

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

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




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  []
     _a      =  fairseq_model.state_dict()

     _a      =  hf_model.unispeech.feature_extractor

     for name, value in fairseq_dict.items():
          _a      =  False
          if "conv_layers" in name:
               load_conv_layer(
                   __A  ,	__A  ,	__A  ,	__A  ,	hf_model.config.feat_extract_norm == '''group'''  ,	)
               _a      =  True
          else:
               for key, mapped_key in MAPPING.items():
                    _a      =  '''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]:
                         _a      =  True
                         if "*" in mapped_key:
                              _a      =  name.split(__A)[0].split('''.''')[-2]
                              _a      =  mapped_key.replace('''*'''  ,	__A)
                         if "weight_g" in name:
                              _a      =  '''weight_g'''
                         elif "weight_v" in name:
                              _a      =  '''weight_v'''
                         elif "bias" in name:
                              _a      =  '''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              _a      =  '''weight'''
                         else:
                              _a      =  None
                         set_recursively(__A  ,	__A  ,	__A  ,	__A  ,	__A  ,	__A)
                    continue
          if not is_used:
               unused_weights.append(__A)

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




def lowerCAmelCase  (__A  ,	__A  ,	__A  ,	__A  ,	__A):



     """simple docstring"""



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




@torch.no_grad()
def lowerCAmelCase  (__A  ,	__A  ,	__A=None  ,	__A=None  ,	__A=True):



     """simple docstring"""



     if config_path is not None:
          _a      =  UniSpeechConfig.from_pretrained(__A)
     else:
          _a      =  UniSpeechConfig()

     if is_finetuned:
          if dict_path:
               _a      =  Dictionary.load_from_json(__A)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _a      =  target_dict.pad_index
               _a      =  target_dict.bos_index
               _a      =  target_dict.eos_index
               _a      =  len(target_dict.symbols)
               _a      =  os.path.join(__A  ,	'''vocab.json''')
               if not os.path.isdir(__A):
                    logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__A))
                    return
               os.makedirs(__A  ,	exist_ok=__A)
               _a      =  target_dict.indices

               # fairseq has the <pad> and <s> switched
               _a      =  42
               _a      =  43
               with open(__A  ,	'''w'''  ,	encoding='''utf-8''') as vocab_handle:
                    json.dump(__A  ,	__A)
               _a      =  WavaVecaPhonemeCTCTokenizer(
                   __A  ,	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=__A  ,	)
               _a      =  True if config.feat_extract_norm == '''layer''' else False
               _a      =  WavaVecaFeatureExtractor(
                   feature_size=1  ,	sampling_rate=16_000  ,	padding_value=0  ,	do_normalize=__A  ,	return_attention_mask=__A  ,	)
               _a      =  WavaVecaProcessor(feature_extractor=__A  ,	tokenizer=__A)
               processor.save_pretrained(__A)

          _a      =  UniSpeechForCTC(__A)
     else:
          _a      =  UniSpeechForPreTraining(__A)

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

     _a      =  model[0].eval()

     recursively_load_weights(__A  ,	__A  ,	__A)

     hf_unispeech.save_pretrained(__A)


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



