

'''simple docstring'''
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 			__lowercase		:







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=1_4     ,			_lowerCamelCase=7     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			_lowerCamelCase=False     ,			_lowerCamelCase=True     ,			_lowerCamelCase=9_9     ,			_lowerCamelCase=3_2     ,			_lowerCamelCase=4     ,			_lowerCamelCase=4     ,			_lowerCamelCase=4     ,			_lowerCamelCase=3_7     ,			_lowerCamelCase="gelu"     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=0.1     ,			_lowerCamelCase=5_1_2     ,			_lowerCamelCase=0.0_2     ,			):
         __UpperCamelCase	:	List[str]		    =  parent
         __UpperCamelCase	:	str		    =  batch_size
         __UpperCamelCase	:	Optional[Any]		    =  seq_length
         __UpperCamelCase	:	str		    =  is_training
         __UpperCamelCase	:	str		    =  use_input_mask
         __UpperCamelCase	:	Union[str, Any]		    =  use_token_type_ids
         __UpperCamelCase	:	str		    =  use_labels
         __UpperCamelCase	:	Tuple		    =  vocab_size
         __UpperCamelCase	:	Any		    =  hidden_size
         __UpperCamelCase	:	Dict		    =  rotary_dim
         __UpperCamelCase	:	List[Any]		    =  num_hidden_layers
         __UpperCamelCase	:	Dict		    =  num_attention_heads
         __UpperCamelCase	:	Tuple		    =  intermediate_size
         __UpperCamelCase	:	Tuple		    =  hidden_act
         __UpperCamelCase	:	Optional[Any]		    =  hidden_dropout_prob
         __UpperCamelCase	:	Tuple		    =  attention_probs_dropout_prob
         __UpperCamelCase	:	Optional[int]		    =  max_position_embeddings
         __UpperCamelCase	:	List[Any]		    =  initializer_range
         __UpperCamelCase	:	Dict		    =  None
         __UpperCamelCase	:	Dict		    =  vocab_size - 1
         __UpperCamelCase	:	Tuple		    =  vocab_size - 1
         __UpperCamelCase	:	Optional[int]		    =  vocab_size - 1







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  ids_tensor([self.batch_size, self.seq_length]     ,			self.vocab_size    )

         __UpperCamelCase	:	Dict		    =  None
         if self.use_input_mask:
             __UpperCamelCase	:	int		    =  random_attention_mask([self.batch_size, self.seq_length]    )

         __UpperCamelCase	:	Union[str, Any]		    =  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=_lowerCamelCase     ,			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 lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.prepare_config_and_inputs()
         __UpperCamelCase	:	Optional[int]		    =  config_and_inputs
         __UpperCamelCase	:	Tuple		    =  {'input_ids': input_ids, 'attention_mask': attention_mask}
         return config, inputs_dict







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Any		    =  2_0
         __UpperCamelCase	:	str		    =  model_class_name(_lowerCamelCase    )

         __UpperCamelCase	:	Dict		    =  model.init_cache(input_ids.shape[0]     ,			_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  jnp.ones((input_ids.shape[0], max_decoder_length)     ,			dtype='i4'    )

         __UpperCamelCase	:	Tuple		    =  jnp.broadcast_to(
             jnp.arange(input_ids.shape[-1] - 1    )[None, :]     ,			(input_ids.shape[0], input_ids.shape[-1] - 1)    )
         __UpperCamelCase	:	List[str]		    =  model(
             input_ids[:, :-1]     ,			attention_mask=_lowerCamelCase     ,			past_key_values=_lowerCamelCase     ,			position_ids=_lowerCamelCase     ,			)

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

         __UpperCamelCase	:	Dict		    =  model(_lowerCamelCase    )

         __UpperCamelCase	:	str		    =  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 lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Any		    =  2_0
         __UpperCamelCase	:	List[Any]		    =  model_class_name(_lowerCamelCase    )

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

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

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

         __UpperCamelCase	:	List[str]		    =  model(_lowerCamelCase     ,			attention_mask=_lowerCamelCase    )

         __UpperCamelCase	:	List[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}"""    )






@require_flax
class 			__lowercase		(						_lowerCamelCase			,							_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






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







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Any		    =  FlaxGPTJModelTester(self    )







     def lowerCAmelCase     (		self    ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase	:	Any		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward(_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase	:	Optional[Any]		    =  self.model_tester.prepare_config_and_inputs()
             self.model_tester.check_use_cache_forward_with_attn_mask(
                 _lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase    )







     @tooslow
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	int		    =  GPTaTokenizer.from_pretrained('gpt2'     ,			pad_token='<|endoftext|>'     ,			padding_side='left'    )
         __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(['Hello this is a long string', 'Hey']     ,			return_tensors='np'     ,			padding=_lowerCamelCase     ,			truncation=_lowerCamelCase    )

         __UpperCamelCase	:	Optional[int]		    =  FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B'    )
         __UpperCamelCase	:	List[Any]		    =  False
         __UpperCamelCase	:	List[str]		    =  model.config.eos_token_id

         __UpperCamelCase	:	str		    =  jax.jit(model.generate    )

         __UpperCamelCase	:	List[Any]		    =  jit_generate(
             inputs['input_ids']     ,			attention_mask=inputs['attention_mask']     ,			pad_token_id=tokenizer.pad_token_id    ).sequences

         __UpperCamelCase	:	int		    =  tokenizer.batch_decode(_lowerCamelCase     ,			skip_special_tokens=_lowerCamelCase    )

         __UpperCamelCase	:	int		    =  [
             '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(_lowerCamelCase     ,			_lowerCamelCase    )







     @is_pt_flax_cross_test
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  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	:	Union[str, Any]		    =  self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase    )
                 __UpperCamelCase	:	Union[str, Any]		    =  {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

                 # load corresponding PyTorch class
                 __UpperCamelCase	:	List[str]		    =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
                 __UpperCamelCase	:	str		    =  getattr(_lowerCamelCase     ,			_lowerCamelCase    )

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

                 __UpperCamelCase	:	Any		    =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,			_lowerCamelCase    )
                 __UpperCamelCase	:	Dict		    =  fx_state

                 with torch.no_grad():
                     __UpperCamelCase	:	List[str]		    =  pt_model(**_lowerCamelCase    ).to_tuple()

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(_lowerCamelCase    )
                     __UpperCamelCase	:	List[Any]		    =  model_class.from_pretrained(_lowerCamelCase     ,			from_pt=_lowerCamelCase    )

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







     @is_pt_flax_cross_test
     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	List[Any]		    =  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	:	Tuple		    =  self._prepare_for_class(_lowerCamelCase     ,			_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

                 __UpperCamelCase	:	List[str]		    =  pt_model_class(_lowerCamelCase    ).eval()
                 __UpperCamelCase	:	str		    =  model_class(_lowerCamelCase     ,			dtype=jnp.floataa    )

                 __UpperCamelCase	:	List[str]		    =  load_flax_weights_in_pytorch_model(_lowerCamelCase     ,			fx_model.params    )
                 __UpperCamelCase	:	Tuple		    =  pt_inputs['input_ids'].shape
                 __UpperCamelCase	:	List[str]		    =  np.random.randint(0     ,			seq_length - 1     ,			size=(batch_size,)    )
                 for batch_idx, start_index in enumerate(_lowerCamelCase    ):
                     __UpperCamelCase	:	Dict		    =  0
                     __UpperCamelCase	:	Tuple		    =  1
                     __UpperCamelCase	:	Tuple		    =  0
                     __UpperCamelCase	:	Dict		    =  1

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

                 with torch.no_grad():
                     __UpperCamelCase	:	Optional[int]		    =  pt_model(**_lowerCamelCase    ).to_tuple()

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

                 with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(_lowerCamelCase    )
                     __UpperCamelCase	:	List[str]		    =  pt_model_class.from_pretrained(_lowerCamelCase     ,			from_flax=_lowerCamelCase    )

                 with torch.no_grad():
                     __UpperCamelCase	:	int		    =  pt_model_loaded(**_lowerCamelCase    ).to_tuple()

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







     @tooslow
     def lowerCAmelCase     (		self    ):
         for model_class_name in self.all_model_classes:
             __UpperCamelCase	:	Tuple		    =  model_class_name.from_pretrained('EleutherAI/gpt-j-6B'    )
             __UpperCamelCase	:	str		    =  model(np.ones((1, 1)    )    )
             self.assertIsNotNone(_lowerCamelCase    )




'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a=  {}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizer''']

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  a=  ['''NllbTokenizerFast''']


if TYPE_CHECKING:
  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb import NllbTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_nllb_fast import NllbTokenizerFast

else:
  import sys

  a=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



