

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






  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					ids_tensor([self.batch_size, self.seq_length]   ,self.vocab_size     )

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

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






  def    SCREAMING_SNAKE_CASE ( self  :	List[str]   ,lowercase__  :	Optional[int]   ,lowercase__  :	int   ,lowercase__  :	List[str]   ,lowercase__  :	str     ):
   __lowercase				    =					2_0
   __lowercase				    =					model_class_name(__lowercase     )

   __lowercase				    =					model.init_cache(input_ids.shape[0]   ,__lowercase     )
   __lowercase				    =					jnp.ones((input_ids.shape[0], max_decoder_length)   ,dtype='''i4'''     )

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

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

   __lowercase				    =					model(__lowercase     )

   __lowercase				    =					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    SCREAMING_SNAKE_CASE ( self  :	Dict   ,lowercase__  :	Dict   ,lowercase__  :	List[Any]   ,lowercase__  :	List[Any]   ,lowercase__  :	List[str]     ):
   __lowercase				    =					2_0
   __lowercase				    =					model_class_name(__lowercase     )

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

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

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

   __lowercase				    =					model(__lowercase   ,attention_mask=__lowercase     )

   __lowercase				    =					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_      (snake_case__							,			snake_case__							,			unittest.TestCase ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE       :    Dict							     =     (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
  SCREAMING_SNAKE_CASE       :    int							     =     (FlaxGPTJForCausalLM,) if is_flax_available() else ()






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[Any]     ):
   __lowercase				    =					FlaxGPTJModelTester(self     )






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   for model_class_name in self.all_model_classes:
    __lowercase				    =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward(__lowercase   ,__lowercase   ,__lowercase   ,__lowercase     )






  def    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   for model_class_name in self.all_model_classes:
    __lowercase				    =					self.model_tester.prepare_config_and_inputs()
    self.model_tester.check_use_cache_forward_with_attn_mask(
        __lowercase   ,__lowercase   ,__lowercase   ,__lowercase     )






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

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

   __lowercase				    =					jax.jit(model.generate     )

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

   __lowercase				    =					tokenizer.batch_decode(__lowercase   ,skip_special_tokens=__lowercase     )

   __lowercase				    =					[
       '''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(__lowercase   ,__lowercase     )






  @is_pt_flax_cross_test
  def    SCREAMING_SNAKE_CASE ( self  :	Tuple     ):
   __lowercase				    =					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
     __lowercase				    =					self._prepare_for_class(__lowercase   ,__lowercase     )
     __lowercase				    =					{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

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

     __lowercase				    =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,__lowercase     )
     __lowercase				    =					fx_state

     with torch.no_grad():
      __lowercase				    =					pt_model(**__lowercase     ).to_tuple()

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

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

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






  @is_pt_flax_cross_test
  def    SCREAMING_SNAKE_CASE ( self  :	int     ):
   __lowercase				    =					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
     __lowercase				    =					self._prepare_for_class(__lowercase   ,__lowercase     )
     __lowercase				    =					{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

     __lowercase				    =					pt_model_class(__lowercase     ).eval()
     __lowercase				    =					model_class(__lowercase   ,dtype=jnp.floataa     )

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

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

     with torch.no_grad():
      __lowercase				    =					pt_model(**__lowercase     ).to_tuple()

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

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

     with torch.no_grad():
      __lowercase				    =					pt_model_loaded(**__lowercase     ).to_tuple()

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






  @tooslow
  def    SCREAMING_SNAKE_CASE ( self  :	List[str]     ):
   for model_class_name in self.all_model_classes:
    __lowercase				    =					model_class_name.from_pretrained('''EleutherAI/gpt-j-6B'''     )
    __lowercase				    =					model(np.ones((1, 1)     )     )
    self.assertIsNotNone(__lowercase     )








"""simple docstring"""





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_camembert import CamembertTokenizer
else:
							_UpperCamelCase					      =   None


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase					      =   {
    'vocab_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/tokenizer.json',
    },
}

_UpperCamelCase					      =   {
    'camembert-base': 512,
}

_UpperCamelCase					      =   '▁'
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							List[str]									=		VOCAB_FILES_NAMES
				__snake_case :							Optional[int]									=		PRETRAINED_VOCAB_FILES_MAP
				__snake_case :							Dict									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__snake_case :							List[str]									=		["""input_ids""", """attention_mask"""]
				__snake_case :							List[Any]									=		CamembertTokenizer




				def __init__(  self	:List[Any]    ,     __lowercase	:Optional[int]=None    ,     __lowercase	:str=None    ,     __lowercase	:Optional[Any]="<s>"    ,     __lowercase	:List[str]="</s>"    ,     __lowercase	:Tuple="</s>"    ,     __lowercase	:int="<s>"    ,     __lowercase	:Union[str, Any]="<unk>"    ,     __lowercase	:Optional[int]="<pad>"    ,     __lowercase	:Union[str, Any]="<mask>"    ,     __lowercase	:Tuple=["<s>NOTUSED", "</s>NOTUSED"]    ,     **__lowercase	:List[str]    ,     ):
											# Mask token behave like a normal word, i.e. include the space before it
											__lowerCamelCase   :   Optional[int]       =AddedToken(__lowercase    ,     lstrip=__lowercase    ,     rstrip=__lowercase      ) if isinstance(__lowercase    ,     __lowercase      ) else mask_token

											super().__init__(
											    __lowercase    ,     tokenizer_file=__lowercase    ,     bos_token=__lowercase    ,     eos_token=__lowercase    ,     sep_token=__lowercase    ,     cls_token=__lowercase    ,     unk_token=__lowercase    ,     pad_token=__lowercase    ,     mask_token=__lowercase    ,     additional_special_tokens=__lowercase    ,     **__lowercase    ,     )

											__lowerCamelCase   :   Any       =vocab_file
											__lowerCamelCase   :   Union[str, Any]       =False if not self.vocab_file else True




				def        __lowercase	(  self	:List[str]    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):

											if token_ids_a is None:
																		return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											__lowerCamelCase   :   Tuple       =[self.cls_token_id]
											__lowerCamelCase   :   str       =[self.sep_token_id]
											return cls + token_ids_a + sep + sep + token_ids_a + sep




				def        __lowercase	(  self	:Tuple    ,     __lowercase	:List[int]    ,     __lowercase	:Optional[List[int]] = None      ):
											__lowerCamelCase   :   Any       =[self.sep_token_id]
											__lowerCamelCase   :   str       =[self.cls_token_id]

											if token_ids_a is None:
																		return len(cls + token_ids_a + sep      ) * [0]
											return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]




				def        __lowercase	(  self	:List[Any]    ,     __lowercase	:str    ,     __lowercase	:Optional[str] = None      ):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''      )

											if not os.path.isdir(__lowercase      ):
																		logger.error(f'Vocabulary path ({save_directory}) should be a directory'      )
																		return
											__lowerCamelCase   :   List[str]       =os.path.join(
											    __lowercase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )

											if os.path.abspath(self.vocab_file      ) != os.path.abspath(__lowercase      ):
																		copyfile(self.vocab_file    ,     __lowercase      )

											return (out_vocab_file,)




