


'''simple docstring'''







import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
  import torch
class    lowercase_	:


    def __init__(	self    ,   a    ,   a=14    ,   a=7    ,   a=True    ,   a=True    ,   a=False    ,   a=True    ,   a=99    ,   a=32    ,   a=5    ,   a=4    ,   a=37    ,   a="gelu"    ,   a=0.1    ,   a=0.1    ,   a=5_12    ,   a=0.02    ,   ):
          UpperCamelCase__      =							parent
          UpperCamelCase__      =							batch_size
          UpperCamelCase__      =							seq_length
          UpperCamelCase__      =							is_training
          UpperCamelCase__      =							use_input_mask
          UpperCamelCase__      =							use_token_type_ids
          UpperCamelCase__      =							use_labels
          UpperCamelCase__      =							vocab_size
          UpperCamelCase__      =							hidden_size
          UpperCamelCase__      =							num_hidden_layers
          UpperCamelCase__      =							num_attention_heads
          UpperCamelCase__      =							intermediate_size
          UpperCamelCase__      =							hidden_act
          UpperCamelCase__      =							hidden_dropout_prob
          UpperCamelCase__      =							attention_probs_dropout_prob
          UpperCamelCase__      =							max_position_embeddings
          UpperCamelCase__      =							initializer_range
          UpperCamelCase__      =							None
          UpperCamelCase__      =							vocab_size - 1
          UpperCamelCase__      =							vocab_size - 1
          UpperCamelCase__      =							vocab_size - 1


    def   __a				(	self       ):
          UpperCamelCase__      =							ids_tensor([self.batch_size, self.seq_length]    ,   self.vocab_size       )

          UpperCamelCase__      =							None
          if self.use_input_mask:
                UpperCamelCase__      =							random_attention_mask([self.batch_size, self.seq_length]       )

          UpperCamelCase__      =							GPTaConfig(
              vocab_size=self.vocab_size    ,   n_embd=self.hidden_size    ,   n_layer=self.num_hidden_layers    ,   n_head=self.num_attention_heads    ,   n_positions=self.max_position_embeddings    ,   use_cache=a    ,   bos_token_id=self.bos_token_id    ,   eos_token_id=self.eos_token_id    ,   pad_token_id=self.pad_token_id    ,   )

          return (config, input_ids, input_mask)


    def   __a				(	self       ):
          UpperCamelCase__      =							self.prepare_config_and_inputs()
          UpperCamelCase__   ,      UpperCamelCase__   ,      UpperCamelCase__      =							config_and_inputs
          UpperCamelCase__      =							{"input_ids": input_ids, "attention_mask": attention_mask}
          return config, inputs_dict


    def   __a				(	self       ):
          UpperCamelCase__   ,      UpperCamelCase__   ,      UpperCamelCase__      =							self.prepare_config_and_inputs()

          UpperCamelCase__      =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
          UpperCamelCase__      =							ids_tensor([self.batch_size, self.seq_length]    ,   vocab_size=2       )

          return (
              config,
              input_ids,
              attention_mask,
              encoder_hidden_states,
              encoder_attention_mask,
          )


    def   __a				(	self    ,   a    ,   a    ,   a    ,   a       ):
          UpperCamelCase__      =							20
          UpperCamelCase__      =							model_class_name(a       )

          UpperCamelCase__      =							model.init_cache(input_ids.shape[0]    ,   a       )
          UpperCamelCase__      =							jnp.ones((input_ids.shape[0], max_decoder_length)    ,   dtype="i4"       )

          UpperCamelCase__      =							jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1       )[None, :]    ,   (input_ids.shape[0], input_ids.shape[-1] - 1)       )
          UpperCamelCase__      =							model(
              input_ids[:, :-1]    ,   attention_mask=a    ,   past_key_values=a    ,   position_ids=a    ,   )

          UpperCamelCase__      =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,   dtype="i4"       )
          UpperCamelCase__      =							model(
              input_ids[:, -1:]    ,   attention_mask=a    ,   past_key_values=outputs_cache.past_key_values    ,   position_ids=a    ,   )

          UpperCamelCase__      =							model(a       )

          UpperCamelCase__      =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
          self.parent.assertTrue(diff < 1e-3    ,   msg=f'''Max diff is {diff}'''       )


    def   __a				(	self    ,   a    ,   a    ,   a    ,   a       ):
          UpperCamelCase__      =							20
          UpperCamelCase__      =							model_class_name(a       )

          UpperCamelCase__      =							jnp.concatenate(
              [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]    ,   axis=-1    ,   )

          UpperCamelCase__      =							model.init_cache(input_ids.shape[0]    ,   a       )
          UpperCamelCase__      =							jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1       )[None, :]    ,   (input_ids.shape[0], input_ids.shape[-1] - 1)       )

          UpperCamelCase__      =							model(
              input_ids[:, :-1]    ,   attention_mask=a    ,   past_key_values=a    ,   position_ids=a    ,   )
          UpperCamelCase__      =							jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,   dtype="i4"       )
          UpperCamelCase__      =							model(
              input_ids[:, -1:]    ,   past_key_values=outputs_cache.past_key_values    ,   attention_mask=a    ,   position_ids=a    ,   )

          UpperCamelCase__      =							model(a    ,   attention_mask=a       )

          UpperCamelCase__      =							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
          self.parent.assertTrue(diff < 1e-3    ,   msg=f'''Max diff is {diff}'''       )

@require_flax
class    lowercase_	(       a__    ,		a__    ,		unittest.TestCase  ):
    __UpperCAmelCase       =      (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    __UpperCAmelCase       =      (FlaxGPTaLMHeadModel,) if is_flax_available() else ()


    def   __a				(	self       ):
          UpperCamelCase__      =							FlaxGPTaModelTester(self       )


    def   __a				(	self       ):
          for model_class_name in self.all_model_classes:
                UpperCamelCase__   ,      UpperCamelCase__   ,      UpperCamelCase__      =							self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward(a    ,   a    ,   a    ,   a       )


    def   __a				(	self       ):
          for model_class_name in self.all_model_classes:
                UpperCamelCase__   ,      UpperCamelCase__   ,      UpperCamelCase__      =							self.model_tester.prepare_config_and_inputs()
                self.model_tester.check_use_cache_forward_with_attn_mask(
                    a    ,   a    ,   a    ,   a       )


    @slow
    def   __a				(	self       ):
          UpperCamelCase__      =							GPTaTokenizer.from_pretrained("gpt2"    ,   pad_token="</s>"    ,   padding_side="left"       )
          UpperCamelCase__      =							tokenizer(["Hello this is a long string", "Hey"]    ,   return_tensors="np"    ,   padding=a    ,   truncation=a       )

          UpperCamelCase__      =							FlaxGPTaLMHeadModel.from_pretrained("gpt2"       )
          UpperCamelCase__      =							False
          UpperCamelCase__      =							model.config.eos_token_id

          UpperCamelCase__      =							jax.jit(model.generate       )

          UpperCamelCase__      =							jit_generate(inputs["input_ids"]    ,   attention_mask=inputs["attention_mask"]       ).sequences

          UpperCamelCase__      =							tokenizer.batch_decode(a    ,   skip_special_tokens=a       )

          UpperCamelCase__      =							[
              "Hello this is a long string of words. I'm going to start with the first one.\n",
              "Hey, I'm not sure if I'm going to be able to do",
          ]

          self.assertListEqual(a    ,   a       )


    @is_pt_flax_cross_test
    def   __a				(	self       ):
          UpperCamelCase__   ,      UpperCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__       ):
                      # prepare inputs
                      UpperCamelCase__      =							self._prepare_for_class(a    ,   a       )
                      UpperCamelCase__      =							{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      UpperCamelCase__      =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      UpperCamelCase__      =							getattr(a    ,   a       )

                      UpperCamelCase__   ,      UpperCamelCase__      =							pt_inputs["input_ids"].shape
                      UpperCamelCase__      =							np.random.randint(0    ,   seq_length - 1    ,   size=(batch_size,)       )
                      for batch_idx, start_index in enumerate(a       ):
                            UpperCamelCase__      =							0
                            UpperCamelCase__      =							1
                            UpperCamelCase__      =							0
                            UpperCamelCase__      =							1
                      UpperCamelCase__      =							pt_model_class(a       ).eval()
                      UpperCamelCase__      =							model_class(a    ,   dtype=jnp.floataa       )

                      UpperCamelCase__      =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,   a       )
                      UpperCamelCase__      =							fx_state

                      with torch.no_grad():
                            UpperCamelCase__      =							pt_model(**a       ).to_tuple()

                      UpperCamelCase__      =							fx_model(**a       ).to_tuple()
                      self.assertEqual(len(a       )    ,   len(a       )    ,   "Output lengths differ between Flax and PyTorch"       )
                      for fx_output, pt_output in zip(a    ,   a       ):
                            self.assert_almost_equals(fx_output[:, -1]    ,   pt_output[:, -1].numpy()    ,   4e-2       )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                            pt_model.save_pretrained(a       )
                            UpperCamelCase__      =							model_class.from_pretrained(a    ,   from_pt=a       )

                      UpperCamelCase__      =							fx_model_loaded(**a       ).to_tuple()
                      self.assertEqual(
                          len(a       )    ,   len(a       )    ,   "Output lengths differ between Flax and PyTorch"       )
                      for fx_output_loaded, pt_output in zip(a    ,   a       ):
                            self.assert_almost_equals(fx_output_loaded[:, -1]    ,   pt_output[:, -1].numpy()    ,   4e-2       )


    @is_pt_flax_cross_test
    def   __a				(	self       ):
          UpperCamelCase__   ,      UpperCamelCase__      =							self.model_tester.prepare_config_and_inputs_for_common()
          for model_class in self.all_model_classes:
                with self.subTest(model_class.__name__       ):
                      # prepare inputs
                      UpperCamelCase__      =							self._prepare_for_class(a    ,   a       )
                      UpperCamelCase__      =							{k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

                      # load corresponding PyTorch class
                      UpperCamelCase__      =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
                      UpperCamelCase__      =							getattr(a    ,   a       )

                      UpperCamelCase__      =							pt_model_class(a       ).eval()
                      UpperCamelCase__      =							model_class(a    ,   dtype=jnp.floataa       )

                      UpperCamelCase__      =							load_flax_weights_in_pytorch_model(a    ,   fx_model.params       )
                      UpperCamelCase__   ,      UpperCamelCase__      =							pt_inputs["input_ids"].shape
                      UpperCamelCase__      =							np.random.randint(0    ,   seq_length - 1    ,   size=(batch_size,)       )
                      for batch_idx, start_index in enumerate(a       ):
                            UpperCamelCase__      =							0
                            UpperCamelCase__      =							1
                            UpperCamelCase__      =							0
                            UpperCamelCase__      =							1

                      # make sure weights are tied in PyTorch
                      pt_model.tie_weights()

                      with torch.no_grad():
                            UpperCamelCase__      =							pt_model(**a       ).to_tuple()

                      UpperCamelCase__      =							fx_model(**a       ).to_tuple()
                      self.assertEqual(len(a       )    ,   len(a       )    ,   "Output lengths differ between Flax and PyTorch"       )
                      for fx_output, pt_output in zip(a    ,   a       ):
                            self.assert_almost_equals(fx_output[:, -1]    ,   pt_output[:, -1].numpy()    ,   4e-2       )

                      with tempfile.TemporaryDirectory() as tmpdirname:
                            fx_model.save_pretrained(a       )
                            UpperCamelCase__      =							pt_model_class.from_pretrained(a    ,   from_flax=a       )

                      with torch.no_grad():
                            UpperCamelCase__      =							pt_model_loaded(**a       ).to_tuple()

                      self.assertEqual(
                          len(a       )    ,   len(a       )    ,   "Output lengths differ between Flax and PyTorch"       )
                      for fx_output, pt_output in zip(a    ,   a       ):
                            self.assert_almost_equals(fx_output[:, -1]    ,   pt_output[:, -1].numpy()    ,   4e-2       )


    @slow
    def   __a				(	self       ):
          for model_class_name in self.all_model_classes:
                UpperCamelCase__      =							model_class_name.from_pretrained("gpt2"    ,   from_pt=a       )
                UpperCamelCase__      =							model(np.ones((1, 1)       )       )
                self.assertIsNotNone(a       )









'''simple docstring'''







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
a__    :   Optional[Any]								=			logging.get_logger(__name__)







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForSequenceClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["projector.weight"]
      UpperCamelCase__      =							downstream_dict["projector.bias"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.post_net.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[str]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForAudioFrameClassification.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["model.linear.weight"]
      UpperCamelCase__      =							downstream_dict["model.linear.bias"]
      return model







def _UpperCamelCase			(	__A ,     __A ,     __A       )				->						List[Any]:


      '''simple docstring'''



      UpperCamelCase__      =							WavLMForXVector.from_pretrained(__A ,     config=__A       )
      UpperCamelCase__      =							downstream_dict["connector.weight"]
      UpperCamelCase__      =							downstream_dict["connector.bias"]
      for i, kernel_size in enumerate(hf_config.tdnn_kernel       ):
            UpperCamelCase__      =							downstream_dict[
                F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
            ]
            UpperCamelCase__      =							downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
      UpperCamelCase__      =							downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
      UpperCamelCase__      =							downstream_dict["objective.W"]
      return model







@torch.no_grad()
def _UpperCamelCase			(	__A ,     __A ,     __A ,     __A       )				->						str:


      '''simple docstring'''



      UpperCamelCase__      =							torch.load(__A ,     map_location="cpu"       )

      UpperCamelCase__      =							checkpoint["Downstream"]

      UpperCamelCase__      =							WavLMConfig.from_pretrained(__A       )
      UpperCamelCase__      =							WavaVecaFeatureExtractor.from_pretrained(
          __A ,     return_attention_mask=__A ,     do_normalize=__A       )

      UpperCamelCase__      =							hf_config.architectures[0]
      if arch.endswith("ForSequenceClassification"       ):
            UpperCamelCase__      =							convert_classification(__A ,     __A ,     __A       )
      elif arch.endswith("ForAudioFrameClassification"       ):
            UpperCamelCase__      =							convert_diarization(__A ,     __A ,     __A       )
      elif arch.endswith("ForXVector"       ):
            UpperCamelCase__      =							convert_xvector(__A ,     __A ,     __A       )
      else:
            raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''       )

      if hf_config.use_weighted_layer_sum:
            UpperCamelCase__      =							checkpoint["Featurizer"]["weights"]

      hf_feature_extractor.save_pretrained(__A       )
      hf_model.save_pretrained(__A       )


if __name__ == "__main__":
  a__    :   Union[str, Any]								=			argparse.ArgumentParser()
  parser.add_argument(
      '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
  )
  parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
  parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
  parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
  a__    :   Optional[Any]								=			parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





