






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, 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.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
 import torch


class     _a							:






  """simple docstring"""





  def __init__(  self		,		_UpperCAmelCase		,		_UpperCAmelCase=14		,		_UpperCAmelCase=7		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=False		,		_UpperCAmelCase=True		,		_UpperCAmelCase=99		,		_UpperCAmelCase=32		,		_UpperCAmelCase=4		,		_UpperCAmelCase=4		,		_UpperCAmelCase=4		,		_UpperCAmelCase=37		,		_UpperCAmelCase="gelu"		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=0.1		,		_UpperCAmelCase=512		,		_UpperCAmelCase=0.0_2		,		)      ->			Dict:
     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_						=  rotary_dim
     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  _UpperCAmelCase  (  self			)      ->			Tuple:
     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_						=  GPTJConfig(
         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		,		rotary_dim=self.rotary_dim		,		)

     return (config, input_ids, input_mask)




  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Tuple:
     UpperCamelCase_						=  20
     UpperCamelCase_						=  model_class_name(_UpperCAmelCase			)

     UpperCamelCase_						=  model.init_cache(input_ids.shape[0]		,		_UpperCAmelCase			)
     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=_UpperCAmelCase		,		past_key_values=_UpperCAmelCase		,		position_ids=_UpperCAmelCase		,		)

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

     UpperCamelCase_						=  model(_UpperCAmelCase			)

     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  _UpperCAmelCase  (  self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)      ->			Optional[int]:
     UpperCamelCase_						=  20
     UpperCamelCase_						=  model_class_name(_UpperCAmelCase			)

     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]		,		_UpperCAmelCase			)
     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=_UpperCAmelCase		,		past_key_values=_UpperCAmelCase		,		position_ids=_UpperCAmelCase		,		)
     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=_UpperCAmelCase		,		position_ids=_UpperCAmelCase		,		)

     UpperCamelCase_						=  model(_UpperCAmelCase		,		attention_mask=_UpperCAmelCase			)

     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     _a							(	UpperCAmelCase__    ,							UpperCAmelCase__    ,							unittest.TestCase     ):






  """simple docstring"""





  A_						       = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
  A_						       = (FlaxGPTJForCausalLM,) if is_flax_available() else ()




  def  _UpperCAmelCase  (  self			)      ->			str:
     UpperCamelCase_						=  FlaxGPTJModelTester(self			)




  def  _UpperCAmelCase  (  self			)      ->			Any:
     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(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  def  _UpperCAmelCase  (  self			)      ->			List[Any]:
     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(
            _UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase			)




  @tooslow
  def  _UpperCAmelCase  (  self			)      ->			Optional[Any]:
     UpperCamelCase_						=  GPTaTokenizer.from_pretrained('gpt2'		,		pad_token='<|endoftext|>'		,		padding_side='left'			)
     UpperCamelCase_						=  tokenizer(['Hello this is a long string', 'Hey']		,		return_tensors='np'		,		padding=_UpperCAmelCase		,		truncation=_UpperCAmelCase			)

     UpperCamelCase_						=  FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'			)
     UpperCamelCase_						=  False
     UpperCamelCase_						=  model.config.eos_token_id

     UpperCamelCase_						=  jax.jit(model.generate			)

     UpperCamelCase_						=  jit_generate(
         inputs['input_ids']		,		attention_mask=inputs['attention_mask']		,		pad_token_id=tokenizer.pad_token_id			).sequences

     UpperCamelCase_						=  tokenizer.batch_decode(_UpperCAmelCase		,		skip_special_tokens=_UpperCAmelCase			)

     UpperCamelCase_						=  [
         'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
         'Hey, I\'m a little late to the party. I\'m going to',
     ]

     self.assertListEqual(_UpperCAmelCase		,		_UpperCAmelCase			)




  @is_pt_flax_cross_test
  def  _UpperCAmelCase  (  self			)      ->			Union[str, Any]:
     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(_UpperCAmelCase		,		_UpperCAmelCase			)
           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(_UpperCAmelCase		,		_UpperCAmelCase			)

           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(_UpperCAmelCase			):
              UpperCamelCase_						=  0
              UpperCamelCase_						=  1
              UpperCamelCase_						=  0
              UpperCamelCase_						=  1
           UpperCamelCase_						=  pt_model_class(_UpperCAmelCase			).eval()
           UpperCamelCase_						=  model_class(_UpperCAmelCase		,		dtype=jnp.floataa			)

           UpperCamelCase_						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,		_UpperCAmelCase			)
           UpperCamelCase_						=  fx_state

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

           UpperCamelCase_						=  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			)
              UpperCamelCase_						=  model_class.from_pretrained(_UpperCAmelCase		,		from_pt=_UpperCAmelCase			)

           UpperCamelCase_						=  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			)      ->			Tuple:
     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(_UpperCAmelCase		,		_UpperCAmelCase			)
           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(_UpperCAmelCase		,		_UpperCAmelCase			)

           UpperCamelCase_						=  pt_model_class(_UpperCAmelCase			).eval()
           UpperCamelCase_						=  model_class(_UpperCAmelCase		,		dtype=jnp.floataa			)

           UpperCamelCase_						=  load_flax_weights_in_pytorch_model(_UpperCAmelCase		,		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(_UpperCAmelCase			):
              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(**_UpperCAmelCase			).to_tuple()

           UpperCamelCase_						=  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			)
              UpperCamelCase_						=  pt_model_class.from_pretrained(_UpperCAmelCase		,		from_flax=_UpperCAmelCase			)

           with torch.no_grad():
              UpperCamelCase_						=  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			)




  @tooslow
  def  _UpperCAmelCase  (  self			)      ->			Optional[int]:
     for model_class_name in self.all_model_classes:
        UpperCamelCase_						=  model_class_name.from_pretrained('EleutherAI/gpt-j-6B'			)
        UpperCamelCase_						=  model(np.ones((1, 1)			)			)
        self.assertIsNotNone(_UpperCAmelCase			)













import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  AlbertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  AlbertForPreTraining(__lowercase)

   # Load weights from tf checkpoint
   load_tf_weights_in_albert(__lowercase     ,   __lowercase     ,   __lowercase)

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						Tuple													=   argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
 )
 parser.add_argument(
     """--albert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained ALBERT model. \n"""
         """This specifies the model architecture."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 snake_case__   :						str													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)





