




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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
    lowerCamelCase_    		=  '''platform'''

    import jax
    import jax.numpy as jnp

    from transformers import BlenderbotTokenizer
    from transformers.models.blenderbot.modeling_flax_blenderbot import (
        FlaxBlenderbotForConditionalGeneration,
        FlaxBlenderbotModel,
        shift_tokens_right,
    )



def       UpperCAmelCase_       (      __UpperCamelCase,							__UpperCamelCase,							__UpperCamelCase=None,							__UpperCamelCase=None,							__UpperCamelCase=None,							__UpperCamelCase=None,							__UpperCamelCase=None,							__UpperCamelCase=None,							):

    if attention_mask is None:
        SCREAMING_SNAKE_CASE__        =np.where(input_ids != config.pad_token_id,							1,							0      )
    if decoder_attention_mask is None:
        SCREAMING_SNAKE_CASE__        =np.where(decoder_input_ids != config.pad_token_id,							1,							0      )
    if head_mask is None:
        SCREAMING_SNAKE_CASE__        =np.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        SCREAMING_SNAKE_CASE__        =np.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        SCREAMING_SNAKE_CASE__        =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  __a   :


     """simple docstring"""


     def __init__(      self     :					Tuple							,_UpperCamelCase     :					Optional[Any]							,_UpperCamelCase     :					Optional[Any]=1_3							,_UpperCamelCase     :					Dict=7							,_UpperCamelCase     :					Dict=True							,_UpperCamelCase     :					str=False							,_UpperCamelCase     :					Optional[Any]=9_9							,_UpperCamelCase     :					List[Any]=1_6							,_UpperCamelCase     :					List[Any]=2							,_UpperCamelCase     :					List[str]=4							,_UpperCamelCase     :					Optional[Any]=4							,_UpperCamelCase     :					Optional[Any]="gelu"							,_UpperCamelCase     :					Optional[int]=0.1							,_UpperCamelCase     :					List[Any]=0.1							,_UpperCamelCase     :					Tuple=3_2							,_UpperCamelCase     :					Optional[int]=2							,_UpperCamelCase     :					Union[str, Any]=1							,_UpperCamelCase     :					List[str]=0							,_UpperCamelCase     :					Optional[int]=0.02							,)						->  List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =parent
         SCREAMING_SNAKE_CASE__        =batch_size
         SCREAMING_SNAKE_CASE__        =seq_length
         SCREAMING_SNAKE_CASE__        =is_training
         SCREAMING_SNAKE_CASE__        =use_labels
         SCREAMING_SNAKE_CASE__        =vocab_size
         SCREAMING_SNAKE_CASE__        =hidden_size
         SCREAMING_SNAKE_CASE__        =num_hidden_layers
         SCREAMING_SNAKE_CASE__        =num_attention_heads
         SCREAMING_SNAKE_CASE__        =intermediate_size
         SCREAMING_SNAKE_CASE__        =hidden_act
         SCREAMING_SNAKE_CASE__        =hidden_dropout_prob
         SCREAMING_SNAKE_CASE__        =attention_probs_dropout_prob
         SCREAMING_SNAKE_CASE__        =max_position_embeddings
         SCREAMING_SNAKE_CASE__        =eos_token_id
         SCREAMING_SNAKE_CASE__        =pad_token_id
         SCREAMING_SNAKE_CASE__        =bos_token_id
         SCREAMING_SNAKE_CASE__        =initializer_range




     def     __A							(      self     :					Dict		)						->  List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,self.vocab_size		)							,3							,self.vocab_size		)
         SCREAMING_SNAKE_CASE__        =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,dtype=np.intaa		))							,-1		)

         SCREAMING_SNAKE_CASE__        =shift_tokens_right(__lowerCAmelCase							,1							,2		)

         SCREAMING_SNAKE_CASE__        =BlenderbotConfig(
             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=__lowerCAmelCase							,)
         SCREAMING_SNAKE_CASE__        =prepare_blenderbot_inputs_dict(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase		)
         return config, inputs_dict




     def     __A							(      self     :					Optional[int]		)						->  List[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =self.prepare_config_and_inputs()
         return config, inputs_dict




     def     __A							(      self     :					str							,_UpperCamelCase     :					Dict							,_UpperCamelCase     :					Tuple							,_UpperCamelCase     :					Any		)						->  Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =2_0
         SCREAMING_SNAKE_CASE__        =model_class_name(__lowerCAmelCase		)

         SCREAMING_SNAKE_CASE__        =model.encode(inputs_dict["""input_ids"""]		)

         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =(
             inputs_dict["""decoder_input_ids"""],
             inputs_dict["""decoder_attention_mask"""],
         )

         SCREAMING_SNAKE_CASE__        =model.init_cache(decoder_input_ids.shape[0]							,__lowerCAmelCase							,__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)							,dtype="""i4"""		)

         SCREAMING_SNAKE_CASE__        =jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)
         SCREAMING_SNAKE_CASE__        =model.decode(
             decoder_input_ids[:, :-1]							,__lowerCAmelCase							,decoder_attention_mask=__lowerCAmelCase							,past_key_values=__lowerCAmelCase							,decoder_position_ids=__lowerCAmelCase							,)

         SCREAMING_SNAKE_CASE__        =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype="""i4"""		)
         SCREAMING_SNAKE_CASE__        =model.decode(
             decoder_input_ids[:, -1:]							,__lowerCAmelCase							,decoder_attention_mask=__lowerCAmelCase							,past_key_values=outputs_cache.past_key_values							,decoder_position_ids=__lowerCAmelCase							,)

         SCREAMING_SNAKE_CASE__        =model.decode(__lowerCAmelCase							,__lowerCAmelCase		)

         SCREAMING_SNAKE_CASE__        =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     __A							(      self     :					List[Any]							,_UpperCamelCase     :					int							,_UpperCamelCase     :					Any							,_UpperCamelCase     :					Optional[Any]		)						->  Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =2_0
         SCREAMING_SNAKE_CASE__        =model_class_name(__lowerCAmelCase		)

         SCREAMING_SNAKE_CASE__        =model.encode(inputs_dict["""input_ids"""]		)

         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =(
             inputs_dict["""decoder_input_ids"""],
             inputs_dict["""decoder_attention_mask"""],
         )

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

         SCREAMING_SNAKE_CASE__        =model.init_cache(decoder_input_ids.shape[0]							,__lowerCAmelCase							,__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]							,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)							,)

         SCREAMING_SNAKE_CASE__        =model.decode(
             decoder_input_ids[:, :-1]							,__lowerCAmelCase							,decoder_attention_mask=__lowerCAmelCase							,past_key_values=__lowerCAmelCase							,decoder_position_ids=__lowerCAmelCase							,)
         SCREAMING_SNAKE_CASE__        =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]							,dtype="""i4"""		)
         SCREAMING_SNAKE_CASE__        =model.decode(
             decoder_input_ids[:, -1:]							,__lowerCAmelCase							,past_key_values=outputs_cache.past_key_values							,decoder_attention_mask=__lowerCAmelCase							,decoder_position_ids=__lowerCAmelCase							,)

         SCREAMING_SNAKE_CASE__        =model.decode(__lowerCAmelCase							,__lowerCAmelCase							,decoder_attention_mask=__lowerCAmelCase		)

         SCREAMING_SNAKE_CASE__        =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   (					unittest.TestCase	):


     """simple docstring"""


     _A							:       Tuple							   =    99




     def     __A							(      self     :					List[Any]		)						->  Union[str, Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =np.array(
             [
                 [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
                 [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
                 [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
                 [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
                 [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
                 [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
                 [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
                 [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
                 [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
                 [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
                 [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
                 [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
                 [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
             ]							,dtype=np.intaa							,)

         SCREAMING_SNAKE_CASE__        =input_ids.shape[0]
         SCREAMING_SNAKE_CASE__        =BlenderbotConfig(
             vocab_size=self.vocab_size							,d_model=2_4							,encoder_layers=2							,decoder_layers=2							,encoder_attention_heads=2							,decoder_attention_heads=2							,encoder_ffn_dim=3_2							,decoder_ffn_dim=3_2							,max_position_embeddings=4_8							,eos_token_id=2							,pad_token_id=1							,bos_token_id=0							,)
         return config, input_ids, batch_size




     def     __A							(      self     :					Dict		)						->  Optional[Any]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =self._get_config_and_data()
         SCREAMING_SNAKE_CASE__        =FlaxBlenderbotForConditionalGeneration(__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =lm_model(input_ids=__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape							,__lowerCAmelCase		)




     def     __A							(      self     :					Dict		)						->  List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =BlenderbotConfig(
             vocab_size=self.vocab_size							,d_model=1_4							,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=4_8							,)
         SCREAMING_SNAKE_CASE__        =FlaxBlenderbotForConditionalGeneration(__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]							,dtype=np.intaa		)
         SCREAMING_SNAKE_CASE__        =np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]							,dtype=np.intaa		)
         SCREAMING_SNAKE_CASE__        =lm_model(input_ids=__lowerCAmelCase							,decoder_input_ids=__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =(*summary.shape, config.vocab_size)
         self.assertEqual(outputs["""logits"""].shape							,__lowerCAmelCase		)




     def     __A							(      self     :					Any		)						->  Optional[int]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]							,dtype=np.intaa		)
         SCREAMING_SNAKE_CASE__        =shift_tokens_right(__lowerCAmelCase							,1							,2		)
         SCREAMING_SNAKE_CASE__        =np.equal(__lowerCAmelCase							,1		).astype(np.floataa		).sum()
         SCREAMING_SNAKE_CASE__        =np.equal(__lowerCAmelCase							,1		).astype(np.floataa		).sum()
         self.assertEqual(shifted.shape							,input_ids.shape		)
         self.assertEqual(__lowerCAmelCase							,n_pad_before - 1		)
         self.assertTrue(np.equal(shifted[:, 0]							,2		).all()		)






@require_flax
class  __a   (					__lowerCamelCase				,	unittest.TestCase				,	__lowerCamelCase	):


     """simple docstring"""


     _A							:       int							   =    True
     _A							:       Any							   =    (
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     _A							:       Dict							   =    (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




     def     __A							(      self     :					List[str]		)						->  List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        =FlaxBlenderbotModelTester(self		)




     def     __A							(      self     :					Union[str, Any]		)						->  Tuple:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase		)




     def     __A							(      self     :					Optional[int]		)						->  Any:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =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(__lowerCAmelCase							,__lowerCAmelCase							,__lowerCAmelCase		)




     def     __A							(      self     :					Union[str, Any]		)						->  List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__		):
                 SCREAMING_SNAKE_CASE__        =self._prepare_for_class(__lowerCAmelCase							,__lowerCAmelCase		)
                 SCREAMING_SNAKE_CASE__        =model_class(__lowerCAmelCase		)

                 @jax.jit
                 def encode_jitted(_UpperCamelCase     :					List[Any]							,_UpperCamelCase     :					Union[str, Any]=None							,**_UpperCamelCase     :					int		):
                     return model.encode(input_ids=__lowerCAmelCase							,attention_mask=__lowerCAmelCase		)

                 with self.subTest("""JIT Enabled"""		):
                     SCREAMING_SNAKE_CASE__        =encode_jitted(**__lowerCAmelCase		).to_tuple()

                 with self.subTest("""JIT Disabled"""		):
                     with jax.disable_jit():
                         SCREAMING_SNAKE_CASE__        =encode_jitted(**__lowerCAmelCase		).to_tuple()

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




     def     __A							(      self     :					List[str]		)						->  List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__     ,						SCREAMING_SNAKE_CASE__        =self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__		):
                 SCREAMING_SNAKE_CASE__        =model_class(__lowerCAmelCase		)
                 SCREAMING_SNAKE_CASE__        =model.encode(inputs_dict["""input_ids"""]							,inputs_dict["""attention_mask"""]		)

                 SCREAMING_SNAKE_CASE__        ={
                     """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(_UpperCamelCase     :					str							,_UpperCamelCase     :					Tuple							,_UpperCamelCase     :					List[str]		):
                     return model.decode(
                         decoder_input_ids=__lowerCAmelCase							,decoder_attention_mask=__lowerCAmelCase							,encoder_outputs=__lowerCAmelCase							,)

                 with self.subTest("""JIT Enabled"""		):
                     SCREAMING_SNAKE_CASE__        =decode_jitted(**__lowerCAmelCase		).to_tuple()

                 with self.subTest("""JIT Disabled"""		):
                     with jax.disable_jit():
                         SCREAMING_SNAKE_CASE__        =decode_jitted(**__lowerCAmelCase		).to_tuple()

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




     @slow
     def     __A							(      self     :					List[Any]		)						->  Dict:


         '''simple docstring'''





         for model_class_name in self.all_model_classes:
             SCREAMING_SNAKE_CASE__        =model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""		)
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             SCREAMING_SNAKE_CASE__        =np.ones((1, 1)		) * model.config.eos_token_id
             SCREAMING_SNAKE_CASE__        =model(__lowerCAmelCase		)
             self.assertIsNotNone(__lowerCAmelCase		)




     @unittest.skipUnless(jax_device != """cpu"""							,"""3B test too slow on CPU."""		)
     @slow
     def     __A							(      self     :					Optional[int]		)						->  List[str]:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__        ={"""num_beams""": 1, """early_stopping""": True, """min_length""": 1_5, """max_length""": 2_5}
         SCREAMING_SNAKE_CASE__        ={"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

         SCREAMING_SNAKE_CASE__        =FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""							,from_pt=__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        =BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""		)

         SCREAMING_SNAKE_CASE__        =["""Sam"""]
         SCREAMING_SNAKE_CASE__        =tokenizer(__lowerCAmelCase							,return_tensors="""jax"""		)

         SCREAMING_SNAKE_CASE__        =model.generate(**__lowerCAmelCase							,**__lowerCAmelCase		)
         SCREAMING_SNAKE_CASE__        ="""Sam is a great name. It means \"sun\" in Gaelic."""

         SCREAMING_SNAKE_CASE__        =tokenizer.batch_decode(__lowerCAmelCase							,**__lowerCAmelCase		)
         assert generated_txt[0].strip() == tgt_text













import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   __lowerCamelCase   (				__a  :Optional[int]       )						->							str:






		"""simple docstring"""

		A__     =  {}
		A__     =  tokenizer(example["""content"""]       ,	truncation=__a       )["""input_ids"""]
		A__     =  len(example["""content"""]       ) / len(output["""input_ids"""]       )
		return output


A       :   Union[str, Any]					= HfArgumentParser(PretokenizationArguments)
A       :   str					= parser.parse_args()
if args.num_workers is None:
					A       :   Any					= multiprocessing.cpu_count()
A       :   int					= AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

A       :   Optional[Any]					= time.time()
A       :   int					= 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       :   Tuple					= time.time()
ds.push_to_hub(args.tokenized_data_repo)






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



