






"""simple docstring"""



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

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


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      A_    =							'''platform'''

      import jax
      import jax.numpy as jnp

      from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
          FlaxBlenderbotSmallForConditionalGeneration,
          FlaxBlenderbotSmallModel,
          shift_tokens_right,
      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Tuple,						UpperCAmelCase__       :					List[Any],						UpperCAmelCase__       :					Optional[Any]=None,						UpperCAmelCase__       :					List[str]=None,						UpperCAmelCase__       :					Optional[int]=None,						UpperCAmelCase__       :					Dict=None,						UpperCAmelCase__       :					Dict=None,						UpperCAmelCase__       :					Union[str, Any]=None,						)		->Optional[Any]:
      if attention_mask is None:
            A__						:  Optional[int]		  =  np.where(input_ids != config.pad_token_id,						1,						0      )
      if decoder_attention_mask is None:
            A__						:  List[Any]		  =  np.where(decoder_input_ids != config.pad_token_id,						1,						0      )
      if head_mask is None:
            A__						:  Dict		  =  np.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            A__						:  Union[str, Any]		  =  np.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            A__						:  Tuple		  =  np.ones((config.decoder_layers, config.decoder_attention_heads)      )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }






class 							__SCREAMING_SNAKE_CASE						:


       def __init__(			self      :						Optional[int]			,    snake_case      :						Dict			,    snake_case      :						int=13			,    snake_case      :						Any=7			,    snake_case      :						Optional[Any]=True			,    snake_case      :						Dict=False			,    snake_case      :						Optional[Any]=99			,    snake_case      :						Any=16			,    snake_case      :						List[Any]=2			,    snake_case      :						int=4			,    snake_case      :						Tuple=4			,    snake_case      :						int="gelu"			,    snake_case      :						int=0.1			,    snake_case      :						Optional[int]=0.1			,    snake_case      :						List[Any]=32			,    snake_case      :						List[str]=2			,    snake_case      :						List[str]=1			,    snake_case      :						Union[str, Any]=0			,    snake_case      :						Any=0.02			,    ):
             '''simple docstring'''





             A__						:  List[Any]		  =  parent
             A__						:  Optional[int]		  =  batch_size
             A__						:  Any		  =  seq_length
             A__						:  Optional[int]		  =  is_training
             A__						:  int		  =  use_labels
             A__						:  Optional[int]		  =  vocab_size
             A__						:  Tuple		  =  hidden_size
             A__						:  Union[str, Any]		  =  num_hidden_layers
             A__						:  List[str]		  =  num_attention_heads
             A__						:  Any		  =  intermediate_size
             A__						:  Dict		  =  hidden_act
             A__						:  Any		  =  hidden_dropout_prob
             A__						:  Any		  =  attention_probs_dropout_prob
             A__						:  Any		  =  max_position_embeddings
             A__						:  List[str]		  =  eos_token_id
             A__						:  Union[str, Any]		  =  pad_token_id
             A__						:  List[Any]		  =  bos_token_id
             A__						:  Any		  =  initializer_range


       def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
             '''simple docstring'''





             A__						:  List[str]		  =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]			,    self.vocab_size							)			,    3			,    self.vocab_size							)
             A__						:  Optional[int]		  =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)			,    dtype=np.intaa							))			,    -1							)

             A__						:  int		  =  shift_tokens_right(_A			,    1			,    2							)

             A__						:  Optional[int]		  =  BlenderbotSmallConfig(
                 vocab_size=self.vocab_size			,    d_model=self.hidden_size			,    encoder_layers=self.num_hidden_layers			,    decoder_layers=self.num_hidden_layers			,    encoder_attention_heads=self.num_attention_heads			,    decoder_attention_heads=self.num_attention_heads			,    encoder_ffn_dim=self.intermediate_size			,    decoder_ffn_dim=self.intermediate_size			,    dropout=self.hidden_dropout_prob			,    attention_dropout=self.attention_probs_dropout_prob			,    max_position_embeddings=self.max_position_embeddings			,    eos_token_id=self.eos_token_id			,    bos_token_id=self.bos_token_id			,    pad_token_id=self.pad_token_id			,    initializer_range=self.initializer_range			,    use_cache=_A			,    )
             A__						:  Optional[int]		  =  prepare_blenderbot_inputs_dict(_A			,    _A			,    _A							)
             return config, inputs_dict


       def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
             '''simple docstring'''





             A__  ,  A__						:  Dict		  =  self.prepare_config_and_inputs()
             return config, inputs_dict


       def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						Optional[Any]			,    snake_case      :						str			,    snake_case      :						Tuple							):
             '''simple docstring'''





             A__						:  Union[str, Any]		  =  20
             A__						:  Dict		  =  model_class_name(_A							)

             A__						:  Any		  =  model.encode(inputs_dict["""input_ids"""]							)

             A__  ,  A__						:  Tuple		  =  (
                 inputs_dict["""decoder_input_ids"""],
                 inputs_dict["""decoder_attention_mask"""],
             )

             A__						:  List[Any]		  =  model.init_cache(decoder_input_ids.shape[0]			,    _A			,    _A							)
             A__						:  str		  =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)			,    dtype="""i4"""							)

             A__						:  int		  =  jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )
             A__						:  str		  =  model.decode(
                 decoder_input_ids[:, :-1]			,    _A			,    decoder_attention_mask=_A			,    past_key_values=_A			,    decoder_position_ids=_A			,    )

             A__						:  Optional[Any]		  =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""							)
             A__						:  List[str]		  =  model.decode(
                 decoder_input_ids[:, -1:]			,    _A			,    decoder_attention_mask=_A			,    past_key_values=outputs_cache.past_key_values			,    decoder_position_ids=_A			,    )

             A__						:  Union[str, Any]		  =  model.decode(_A			,    _A							)

             A__						:  Union[str, 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      :						int			,    snake_case      :						List[str]			,    snake_case      :						Optional[int]			,    snake_case      :						Optional[Any]							):
             '''simple docstring'''





             A__						:  Tuple		  =  20
             A__						:  Union[str, Any]		  =  model_class_name(_A							)

             A__						:  List[Any]		  =  model.encode(inputs_dict["""input_ids"""]							)

             A__  ,  A__						:  str		  =  (
                 inputs_dict["""decoder_input_ids"""],
                 inputs_dict["""decoder_attention_mask"""],
             )

             A__						:  List[str]		  =  jnp.concatenate(
                 [
                     decoder_attention_mask,
                     jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
                 ]			,    axis=-1			,    )

             A__						:  Any		  =  model.init_cache(decoder_input_ids.shape[0]			,    _A			,    _A							)
             A__						:  List[str]		  =  jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]			,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)			,    )

             A__						:  Tuple		  =  model.decode(
                 decoder_input_ids[:, :-1]			,    _A			,    decoder_attention_mask=_A			,    past_key_values=_A			,    decoder_position_ids=_A			,    )
             A__						:  Optional[Any]		  =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]			,    dtype="""i4"""							)
             A__						:  List[str]		  =  model.decode(
                 decoder_input_ids[:, -1:]			,    _A			,    past_key_values=outputs_cache.past_key_values			,    decoder_attention_mask=_A			,    decoder_position_ids=_A			,    )

             A__						:  Optional[Any]		  =  model.decode(_A			,    _A			,    decoder_attention_mask=_A							)

             A__						:  Union[str, 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 							__SCREAMING_SNAKE_CASE						(				unittest.TestCase		):
       snake_case_     =							99


       def 						_UpperCamelCase       (			self      :						int							):
             '''simple docstring'''





             A__						:  Any		  =  np.array(
                 [
                     [71, 82, 18, 33, 46, 91, 2],
                     [68, 34, 26, 58, 30, 82, 2],
                     [5, 97, 17, 39, 94, 40, 2],
                     [76, 83, 94, 25, 70, 78, 2],
                     [87, 59, 41, 35, 48, 66, 2],
                     [55, 13, 16, 58, 5, 2, 1],  # note padding
                     [64, 27, 31, 51, 12, 75, 2],
                     [52, 64, 86, 17, 83, 39, 2],
                     [48, 61, 9, 24, 71, 82, 2],
                     [26, 1, 60, 48, 22, 13, 2],
                     [21, 5, 62, 28, 14, 76, 2],
                     [45, 98, 37, 86, 59, 48, 2],
                     [70, 70, 50, 9, 28, 0, 2],
                 ]			,    dtype=np.intaa			,    )

             A__						:  Optional[Any]		  =  input_ids.shape[0]
             A__						:  List[str]		  =  BlenderbotSmallConfig(
                 vocab_size=self.vocab_size			,    d_model=24			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=32			,    decoder_ffn_dim=32			,    max_position_embeddings=48			,    eos_token_id=2			,    pad_token_id=1			,    bos_token_id=0			,    )
             return config, input_ids, batch_size


       def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
             '''simple docstring'''





             A__  ,  A__  ,  A__						:  str		  =  self._get_config_and_data()
             A__						:  Any		  =  FlaxBlenderbotSmallForConditionalGeneration(_A							)
             A__						:  List[Any]		  =  lm_model(input_ids=_A							)
             A__						:  List[Any]		  =  (batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["""logits"""].shape			,    _A							)


       def 						_UpperCamelCase       (			self      :						Union[str, Any]							):
             '''simple docstring'''





             A__						:  Optional[int]		  =  BlenderbotSmallConfig(
                 vocab_size=self.vocab_size			,    d_model=14			,    encoder_layers=2			,    decoder_layers=2			,    encoder_attention_heads=2			,    decoder_attention_heads=2			,    encoder_ffn_dim=8			,    decoder_ffn_dim=8			,    max_position_embeddings=48			,    )
             A__						:  Tuple		  =  FlaxBlenderbotSmallForConditionalGeneration(_A							)
             A__						:  str		  =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]			,    dtype=np.intaa							)
             A__						:  Tuple		  =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]			,    dtype=np.intaa							)
             A__						:  List[str]		  =  lm_model(input_ids=_A			,    decoder_input_ids=_A							)
             A__						:  str		  =  (*summary.shape, config.vocab_size)
             self.assertEqual(outputs["""logits"""].shape			,    _A							)


       def 						_UpperCamelCase       (			self      :						Optional[Any]							):
             '''simple docstring'''





             A__						:  Any		  =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]			,    dtype=np.intaa							)
             A__						:  Optional[Any]		  =  shift_tokens_right(_A			,    1			,    2							)
             A__						:  List[str]		  =  np.equal(_A			,    1							).astype(np.floataa							).sum()
             A__						:  str		  =  np.equal(_A			,    1							).astype(np.floataa							).sum()
             self.assertEqual(shifted.shape			,    input_ids.shape							)
             self.assertEqual(_A			,    n_pad_before - 1							)
             self.assertTrue(np.equal(shifted[:, 0]			,    2							).all()							)






@require_flax
class 							__SCREAMING_SNAKE_CASE						(				__lowercase       ,		unittest.TestCase       ,		__lowercase		):
       snake_case_     =							True
       snake_case_     =							(
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       snake_case_     =							(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


       def 						_UpperCamelCase       (			self      :						Optional[int]							):
             '''simple docstring'''





             A__						:  Union[str, Any]		  =  FlaxBlenderbotSmallModelTester(self							)


       def 						_UpperCamelCase       (			self      :						List[str]							):
             '''simple docstring'''





             A__  ,  A__						:  Any		  =  self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward(_A			,    _A			,    _A							)


       def 						_UpperCamelCase       (			self      :						Dict							):
             '''simple docstring'''





             A__  ,  A__						:  List[str]		  =  self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward_with_attn_mask(_A			,    _A			,    _A							)


       def 						_UpperCamelCase       (			self      :						Tuple							):
             '''simple docstring'''





             A__  ,  A__						:  int		  =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__							):
                         A__						:  str		  =  self._prepare_for_class(_A			,    _A							)
                         A__						:  Union[str, Any]		  =  model_class(_A							)

                         @jax.jit
                         def encode_jitted(snake_case      :						Optional[int]			,    snake_case      :						Any=None			,    **snake_case      :						str							):
                               return model.encode(input_ids=_A			,    attention_mask=_A							)

                         with self.subTest("""JIT Enabled"""							):
                               A__						:  Any		  =  encode_jitted(**_A							).to_tuple()

                         with self.subTest("""JIT Disabled"""							):
                               with jax.disable_jit():
                                     A__						:  Union[str, Any]		  =  encode_jitted(**_A							).to_tuple()

                         self.assertEqual(len(_A							)			,    len(_A							)							)
                         for jitted_output, output in zip(_A			,    _A							):
                               self.assertEqual(jitted_output.shape			,    output.shape							)


       def 						_UpperCamelCase       (			self      :						Tuple							):
             '''simple docstring'''





             A__  ,  A__						:  str		  =  self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__							):
                         A__						:  Dict		  =  model_class(_A							)
                         A__						:  Union[str, Any]		  =  model.encode(inputs_dict["""input_ids"""]			,    inputs_dict["""attention_mask"""]							)

                         A__						:  int		  =  {
                             """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                             """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                             """encoder_outputs""": encoder_outputs,
                         }

                         @jax.jit
                         def decode_jitted(snake_case      :						Optional[Any]			,    snake_case      :						Dict			,    snake_case      :						List[str]							):
                               return model.decode(
                                   decoder_input_ids=_A			,    decoder_attention_mask=_A			,    encoder_outputs=_A			,    )

                         with self.subTest("""JIT Enabled"""							):
                               A__						:  Union[str, Any]		  =  decode_jitted(**_A							).to_tuple()

                         with self.subTest("""JIT Disabled"""							):
                               with jax.disable_jit():
                                     A__						:  int		  =  decode_jitted(**_A							).to_tuple()

                         self.assertEqual(len(_A							)			,    len(_A							)							)
                         for jitted_output, output in zip(_A			,    _A							):
                               self.assertEqual(jitted_output.shape			,    output.shape							)


       @slow
       def 						_UpperCamelCase       (			self      :						Any							):
             '''simple docstring'''





             for model_class_name in self.all_model_classes:
                   A__						:  str		  =  model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""							)
                   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                   A__						:  Dict		  =  np.ones((1, 1)							) * model.config.eos_token_id
                   A__						:  List[Any]		  =  model(_A							)
                   self.assertIsNotNone(_A							)














"""simple docstring"""



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser




def      _lowerCAmelCase		(							UpperCAmelCase__       :					List[str]      )		->str:
						A__						:  Tuple		  =  {}
						A__						:  Union[str, Any]		  =  tokenizer(example["""content"""],						truncation=UpperCAmelCase__      )["""input_ids"""]
						A__						:  Any		  =  len(example["""content"""]      ) / len(output["""input_ids"""]      )
						return output


A_    =							HfArgumentParser(PretokenizationArguments)
A_    =							parser.parse_args()
if args.num_workers is None:
						A_    =							multiprocessing.cpu_count()
A_    =							AutoTokenizer.from_pretrained(args.tokenizer_dir)

A_    =							time.time()
A_    =							load_dataset(args.dataset_name, split='''train''')
print(F'Dataset loaded in {time.time()-t_start:.2f}s')

A_    =							time.time()
A_    =							ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(F'Dataset tokenized in {time.time()-t_start:.2f}s')

A_    =							time.time()
ds.push_to_hub(args.tokenized_data_repo)

print(F'Data pushed to the hub in {time.time()-t_start:.2f}s')






