

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 							A_			:


     def __init__(							self      :		str      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		Optional[int]=1_4      ,  UpperCAmelCase      :		Optional[int]=7      ,  UpperCAmelCase      :		Any=True      ,  UpperCAmelCase      :		List[str]=True      ,  UpperCAmelCase      :		Tuple=False      ,  UpperCAmelCase      :		Optional[int]=True      ,  UpperCAmelCase      :		str=9_9      ,  UpperCAmelCase      :		Optional[int]=3_2      ,  UpperCAmelCase      :		List[Any]=5      ,  UpperCAmelCase      :		Union[str, Any]=4      ,  UpperCAmelCase      :		Union[str, Any]=3_7      ,  UpperCAmelCase      :		Union[str, Any]="gelu"      ,  UpperCAmelCase      :		Tuple=0.1      ,  UpperCAmelCase      :		Union[str, Any]=0.1      ,  UpperCAmelCase      :		Tuple=5_1_2      ,  UpperCAmelCase      :		int=0.02      ,  )							->     List[Any]:
      __lowerCAmelCase:       int            =				parent
      __lowerCAmelCase:       Optional[int]            =				batch_size
      __lowerCAmelCase:       Optional[int]            =				seq_length
      __lowerCAmelCase:       Tuple            =				is_training
      __lowerCAmelCase:       Any            =				use_input_mask
      __lowerCAmelCase:       Any            =				use_token_type_ids
      __lowerCAmelCase:       int            =				use_labels
      __lowerCAmelCase:       Dict            =				vocab_size
      __lowerCAmelCase:       Union[str, Any]            =				hidden_size
      __lowerCAmelCase:       List[Any]            =				num_hidden_layers
      __lowerCAmelCase:       Dict            =				num_attention_heads
      __lowerCAmelCase:       Optional[Any]            =				intermediate_size
      __lowerCAmelCase:       int            =				hidden_act
      __lowerCAmelCase:       List[Any]            =				hidden_dropout_prob
      __lowerCAmelCase:       List[str]            =				attention_probs_dropout_prob
      __lowerCAmelCase:       str            =				max_position_embeddings
      __lowerCAmelCase:       Optional[int]            =				initializer_range
      __lowerCAmelCase:       Union[str, Any]            =				None
      __lowerCAmelCase:       List[Any]            =				vocab_size - 1
      __lowerCAmelCase:       Union[str, Any]            =				vocab_size - 1
      __lowerCAmelCase:       str            =				vocab_size - 1


     def   UpperCAmelCase  (							self      :		List[str] )							->     List[Any]:
      __lowerCAmelCase:       List[Any]            =				ids_tensor([self.batch_size, self.seq_length]      ,  self.vocab_size )

      __lowerCAmelCase:       List[Any]            =				None
      if self.use_input_mask:
       __lowerCAmelCase:       int            =				random_attention_mask([self.batch_size, self.seq_length] )

      __lowerCAmelCase:       Union[str, Any]            =				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=_UpperCAmelCase      ,  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   UpperCAmelCase  (							self      :		Dict )							->     List[str]:
      __lowerCAmelCase:       str            =				self.prepare_config_and_inputs()
      __lowerCAmelCase:       List[str]            =				config_and_inputs
      __lowerCAmelCase:       Optional[Any]            =				{"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict


     def   UpperCAmelCase  (							self      :		Dict )							->     Dict:
      __lowerCAmelCase:       Dict            =				self.prepare_config_and_inputs()

      __lowerCAmelCase:       int            =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
      __lowerCAmelCase:       Any            =				ids_tensor([self.batch_size, self.seq_length]      ,  vocab_size=2 )

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


     def   UpperCAmelCase  (							self      :		int      ,  UpperCAmelCase      :		Optional[Any]      ,  UpperCAmelCase      :		List[Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		str )							->     Optional[int]:
      __lowerCAmelCase:       int            =				2_0
      __lowerCAmelCase:       int            =				model_class_name(_UpperCAmelCase )

      __lowerCAmelCase:       Any            =				model.init_cache(input_ids.shape[0]      ,  _UpperCAmelCase )
      __lowerCAmelCase:       Optional[Any]            =				jnp.ones((input_ids.shape[0], max_decoder_length)      ,  dtype='i4' )

      __lowerCAmelCase:       List[Any]            =				jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
      __lowerCAmelCase:       Tuple            =				model(
          input_ids[:, :-1]      ,  attention_mask=_UpperCAmelCase      ,  past_key_values=_UpperCAmelCase      ,  position_ids=_UpperCAmelCase      ,  )

      __lowerCAmelCase:       Tuple            =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='i4' )
      __lowerCAmelCase:       Optional[int]            =				model(
          input_ids[:, -1:]      ,  attention_mask=_UpperCAmelCase      ,  past_key_values=outputs_cache.past_key_values      ,  position_ids=_UpperCAmelCase      ,  )

      __lowerCAmelCase:       Optional[int]            =				model(_UpperCAmelCase )

      __lowerCAmelCase:       Optional[Any]            =				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   UpperCAmelCase  (							self      :		Optional[int]      ,  UpperCAmelCase      :		Optional[int]      ,  UpperCAmelCase      :		Union[str, Any]      ,  UpperCAmelCase      :		List[str]      ,  UpperCAmelCase      :		Tuple )							->     int:
      __lowerCAmelCase:       List[str]            =				2_0
      __lowerCAmelCase:       Any            =				model_class_name(_UpperCAmelCase )

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

      __lowerCAmelCase:       List[Any]            =				model.init_cache(input_ids.shape[0]      ,  _UpperCAmelCase )
      __lowerCAmelCase:       List[Any]            =				jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1 )[None, :]      ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

      __lowerCAmelCase:       Any            =				model(
          input_ids[:, :-1]      ,  attention_mask=_UpperCAmelCase      ,  past_key_values=_UpperCAmelCase      ,  position_ids=_UpperCAmelCase      ,  )
      __lowerCAmelCase:       List[str]            =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]      ,  dtype='i4' )
      __lowerCAmelCase:       List[str]            =				model(
          input_ids[:, -1:]      ,  past_key_values=outputs_cache.past_key_values      ,  attention_mask=_UpperCAmelCase      ,  position_ids=_UpperCAmelCase      ,  )

      __lowerCAmelCase:       Optional[int]            =				model(_UpperCAmelCase      ,  attention_mask=_UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				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 							A_			(		SCREAMING_SNAKE_CASE__							,     SCREAMING_SNAKE_CASE__							,     unittest.TestCase ):
     _lowercase      :					Any       =     (FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
     _lowercase      :					List[str]       =     (FlaxGPTaLMHeadModel,) if is_flax_available() else ()


     def   UpperCAmelCase  (							self      :		Any )							->     List[str]:
      __lowerCAmelCase:       Optional[int]            =				FlaxGPTaModelTester(self )


     def   UpperCAmelCase  (							self      :		Dict )							->     Dict:
      for model_class_name in self.all_model_classes:
       __lowerCAmelCase:       Tuple            =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward(_UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase )


     def   UpperCAmelCase  (							self      :		Any )							->     List[Any]:
      for model_class_name in self.all_model_classes:
       __lowerCAmelCase:       Tuple            =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.check_use_cache_forward_with_attn_mask(
           _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase      ,  _UpperCAmelCase )


     @slow
     def   UpperCAmelCase  (							self      :		Any )							->     int:
      __lowerCAmelCase:       Optional[Any]            =				GPTaTokenizer.from_pretrained('gpt2'      ,  pad_token='</s>'      ,  padding_side='left' )
      __lowerCAmelCase:       List[str]            =				tokenizer(['Hello this is a long string', 'Hey']      ,  return_tensors='np'      ,  padding=_UpperCAmelCase      ,  truncation=_UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				FlaxGPTaLMHeadModel.from_pretrained('gpt2' )
      __lowerCAmelCase:       List[Any]            =				False
      __lowerCAmelCase:       Optional[int]            =				model.config.eos_token_id

      __lowerCAmelCase:       List[Any]            =				jax.jit(model.generate )

      __lowerCAmelCase:       Any            =				jit_generate(inputs['input_ids']      ,  attention_mask=inputs['attention_mask'] ).sequences

      __lowerCAmelCase:       Tuple            =				tokenizer.batch_decode(_UpperCAmelCase      ,  skip_special_tokens=_UpperCAmelCase )

      __lowerCAmelCase:       Dict            =				[
          "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(_UpperCAmelCase      ,  _UpperCAmelCase )


     @is_pt_flax_cross_test
     def   UpperCAmelCase  (							self      :		Optional[int] )							->     Tuple:
      __lowerCAmelCase:       int            =				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
        __lowerCAmelCase:       List[Any]            =				self._prepare_for_class(_UpperCAmelCase      ,  _UpperCAmelCase )
        __lowerCAmelCase:       Tuple            =				{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        __lowerCAmelCase:       Optional[Any]            =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
        __lowerCAmelCase:       Tuple            =				getattr(_UpperCAmelCase      ,  _UpperCAmelCase )

        __lowerCAmelCase:       Dict            =				pt_inputs["input_ids"].shape
        __lowerCAmelCase:       List[Any]            =				np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(_UpperCAmelCase ):
         __lowerCAmelCase:       List[Any]            =				0
         __lowerCAmelCase:       Optional[int]            =				1
         __lowerCAmelCase:       Tuple            =				0
         __lowerCAmelCase:       Any            =				1
        __lowerCAmelCase:       List[str]            =				pt_model_class(_UpperCAmelCase ).eval()
        __lowerCAmelCase:       Optional[Any]            =				model_class(_UpperCAmelCase      ,  dtype=jnp.floataa )

        __lowerCAmelCase:       List[str]            =				convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,  _UpperCAmelCase )
        __lowerCAmelCase:       int            =				fx_state

        with torch.no_grad():
         __lowerCAmelCase:       Any            =				pt_model(**_UpperCAmelCase ).to_tuple()

        __lowerCAmelCase:       str            =				fx_model(**_UpperCAmelCase ).to_tuple()
        self.assertEqual(len(_UpperCAmelCase )      ,  len(_UpperCAmelCase )      ,  'Output lengths differ between Flax and PyTorch' )
        for fx_output, pt_output in zip(_UpperCAmelCase      ,  _UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4E-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         pt_model.save_pretrained(_UpperCAmelCase )
         __lowerCAmelCase:       str            =				model_class.from_pretrained(_UpperCAmelCase      ,  from_pt=_UpperCAmelCase )

        __lowerCAmelCase:       Any            =				fx_model_loaded(**_UpperCAmelCase ).to_tuple()
        self.assertEqual(
            len(_UpperCAmelCase )      ,  len(_UpperCAmelCase )      ,  'Output lengths differ between Flax and PyTorch' )
        for fx_output_loaded, pt_output in zip(_UpperCAmelCase      ,  _UpperCAmelCase ):
         self.assert_almost_equals(fx_output_loaded[:, -1]      ,  pt_output[:, -1].numpy()      ,  4E-2 )


     @is_pt_flax_cross_test
     def   UpperCAmelCase  (							self      :		str )							->     int:
      __lowerCAmelCase:       Dict            =				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
        __lowerCAmelCase:       Optional[Any]            =				self._prepare_for_class(_UpperCAmelCase      ,  _UpperCAmelCase )
        __lowerCAmelCase:       List[str]            =				{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

        # load corresponding PyTorch class
        __lowerCAmelCase:       List[Any]            =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
        __lowerCAmelCase:       Optional[Any]            =				getattr(_UpperCAmelCase      ,  _UpperCAmelCase )

        __lowerCAmelCase:       Any            =				pt_model_class(_UpperCAmelCase ).eval()
        __lowerCAmelCase:       Optional[int]            =				model_class(_UpperCAmelCase      ,  dtype=jnp.floataa )

        __lowerCAmelCase:       List[str]            =				load_flax_weights_in_pytorch_model(_UpperCAmelCase      ,  fx_model.params )
        __lowerCAmelCase:       str            =				pt_inputs["input_ids"].shape
        __lowerCAmelCase:       int            =				np.random.randint(0      ,  seq_length - 1      ,  size=(batch_size,) )
        for batch_idx, start_index in enumerate(_UpperCAmelCase ):
         __lowerCAmelCase:       Union[str, Any]            =				0
         __lowerCAmelCase:       Tuple            =				1
         __lowerCAmelCase:       int            =				0
         __lowerCAmelCase:       Optional[int]            =				1

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

        with torch.no_grad():
         __lowerCAmelCase:       str            =				pt_model(**_UpperCAmelCase ).to_tuple()

        __lowerCAmelCase:       Dict            =				fx_model(**_UpperCAmelCase ).to_tuple()
        self.assertEqual(len(_UpperCAmelCase )      ,  len(_UpperCAmelCase )      ,  'Output lengths differ between Flax and PyTorch' )
        for fx_output, pt_output in zip(_UpperCAmelCase      ,  _UpperCAmelCase ):
         self.assert_almost_equals(fx_output[:, -1]      ,  pt_output[:, -1].numpy()      ,  4E-2 )

        with tempfile.TemporaryDirectory() as tmpdirname:
         fx_model.save_pretrained(_UpperCAmelCase )
         __lowerCAmelCase:       str            =				pt_model_class.from_pretrained(_UpperCAmelCase      ,  from_flax=_UpperCAmelCase )

        with torch.no_grad():
         __lowerCAmelCase:       Any            =				pt_model_loaded(**_UpperCAmelCase ).to_tuple()

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


     @slow
     def   UpperCAmelCase  (							self      :		Union[str, Any] )							->     List[str]:
      for model_class_name in self.all_model_classes:
       __lowerCAmelCase:       Dict            =				model_class_name.from_pretrained('gpt2'      ,  from_pt=_UpperCAmelCase )
       __lowerCAmelCase:       Union[str, Any]            =				model(np.ones((1, 1) ) )
       self.assertIsNotNone(_UpperCAmelCase )








import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


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

_a			=       ['''UtteranceLevel''']







@torch.no_grad()
def _a			(	SCREAMING_SNAKE_CASE		:      Union[str, Any]   , SCREAMING_SNAKE_CASE		:      Optional[int]   , SCREAMING_SNAKE_CASE		:      List[str]   , SCREAMING_SNAKE_CASE		:      str		)   ->   str:

 """simple docstring"""






 __lowerCAmelCase:       Union[str, Any]            =				torch.load(SCREAMING_SNAKE_CASE   , map_location='cpu'		)
 if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
  raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''		)

 __lowerCAmelCase:       str            =				checkpoint['Downstream']

 __lowerCAmelCase:       List[str]            =				HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Any            =				HubertForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE   , config=SCREAMING_SNAKE_CASE		)
 __lowerCAmelCase:       Optional[int]            =				WavaVecaFeatureExtractor.from_pretrained(
     SCREAMING_SNAKE_CASE   , return_attention_mask=SCREAMING_SNAKE_CASE   , do_normalize=SCREAMING_SNAKE_CASE		)

 if hf_congfig.use_weighted_layer_sum:
  __lowerCAmelCase:       Dict            =				checkpoint['Featurizer']['weights']

 __lowerCAmelCase:       str            =				downstream_dict['projector.weight']
 __lowerCAmelCase:       Any            =				downstream_dict['projector.bias']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.weight']
 __lowerCAmelCase:       str            =				downstream_dict['model.post_net.linear.bias']

 hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE		)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
  _a			=       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			=       parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





