



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__   :					List[str] 						=      "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 (    A__    ,	A__    ,	A__=None    ,	A__=None    ,	A__=None    ,	A__=None    ,	A__=None    ,	A__=None    ,	):
      if attention_mask is None:
            lowercase__  =	np.where(input_ids != config.pad_token_id    ,	1    ,	0						)
      if decoder_attention_mask is None:
            lowercase__  =	np.where(decoder_input_ids != config.pad_token_id    ,	1    ,	0						)
      if head_mask is None:
            lowercase__  =	np.ones((config.encoder_layers, config.encoder_attention_heads)						)
      if decoder_head_mask is None:
            lowercase__  =	np.ones((config.decoder_layers, config.decoder_attention_heads)						)
      if cross_attn_head_mask is None:
            lowercase__  =	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    UpperCAmelCase__:

   '''simple docstring'''




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




         """simple docstring"""

         lowercase__  =	parent
         lowercase__  =	batch_size
         lowercase__  =	seq_length
         lowercase__  =	is_training
         lowercase__  =	use_labels
         lowercase__  =	vocab_size
         lowercase__  =	hidden_size
         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__  =	eos_token_id
         lowercase__  =	pad_token_id
         lowercase__  =	bos_token_id
         lowercase__  =	initializer_range


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




         """simple docstring"""

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

         lowercase__  =	shift_tokens_right(lowerCAmelCase      ,					1      ,					2)

         lowercase__  =	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=lowerCAmelCase      ,					)
         lowercase__  =	prepare_blenderbot_inputs_dict(lowerCAmelCase      ,					lowerCAmelCase      ,					lowerCAmelCase)
         return config, inputs_dict


   def       UpperCAmelCase		(    self	:      Optional[Any])						->					str:




         """simple docstring"""

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


   def       UpperCAmelCase		(    self	:      List[str]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      List[Any]      ,					lowerCAmelCase	:      Any)						->					Dict:




         """simple docstring"""

         lowercase__  =	20
         lowercase__  =	model_class_name(lowerCAmelCase)

         lowercase__  =	model.encode(inputs_dict['input_ids'])

         lowercase__,						lowercase__  =	(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

         lowercase__  =	model.init_cache(decoder_input_ids.shape[0]      ,					lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,					dtype='i4')

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

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

         lowercase__  =	model.decode(lowerCAmelCase      ,					lowerCAmelCase)

         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       UpperCAmelCase		(    self	:      Any      ,					lowerCAmelCase	:      Optional[Any]      ,					lowerCAmelCase	:      List[str]      ,					lowerCAmelCase	:      Any)						->					Union[str, Any]:




         """simple docstring"""

         lowercase__  =	20
         lowercase__  =	model_class_name(lowerCAmelCase)

         lowercase__  =	model.encode(inputs_dict['input_ids'])

         lowercase__,						lowercase__  =	(
             inputs_dict['decoder_input_ids'],
             inputs_dict['decoder_attention_mask'],
         )

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

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

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

         lowercase__  =	model.decode(lowerCAmelCase      ,					lowerCAmelCase      ,					decoder_attention_mask=lowerCAmelCase)

         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    UpperCAmelCase__(				unittest.TestCase						):

   '''simple docstring'''




   A     :	Union[str, Any]												=     99


   def       UpperCAmelCase		(    self	:      Tuple)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	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      ,					)

         lowercase__  =	input_ids.shape[0]
         lowercase__  =	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	:      int)						->					List[str]:




         """simple docstring"""

         lowercase__,						lowercase__,						lowercase__  =	self._get_config_and_data()
         lowercase__  =	FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase)
         lowercase__  =	lm_model(input_ids=lowerCAmelCase)
         lowercase__  =	(batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['logits'].shape      ,					lowerCAmelCase)


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




         """simple docstring"""

         lowercase__  =	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      ,					)
         lowercase__  =	FlaxBlenderbotSmallForConditionalGeneration(lowerCAmelCase)
         lowercase__  =	np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]      ,					dtype=np.intaa)
         lowercase__  =	np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]      ,					dtype=np.intaa)
         lowercase__  =	lm_model(input_ids=lowerCAmelCase      ,					decoder_input_ids=lowerCAmelCase)
         lowercase__  =	(*summary.shape, config.vocab_size)
         self.assertEqual(outputs['logits'].shape      ,					lowerCAmelCase)



   def       UpperCAmelCase		(    self	:      Optional[int])						->					Optional[int]:




         """simple docstring"""

         lowercase__  =	np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]      ,					dtype=np.intaa)
         lowercase__  =	shift_tokens_right(lowerCAmelCase      ,					1      ,					2)
         lowercase__  =	np.equal(lowerCAmelCase      ,					1).astype(np.floataa).sum()
         lowercase__  =	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    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase  ,				lowerCamelCase						):

   '''simple docstring'''




   A     :	List[str]												=     True
   A     :	Dict												=     (
       (
           FlaxBlenderbotSmallModel,
           FlaxBlenderbotSmallForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   A     :	List[Any]												=     (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()


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




         """simple docstring"""

         lowercase__  =	FlaxBlenderbotSmallModelTester(self)


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




         """simple docstring"""

         lowercase__,						lowercase__  =	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       UpperCAmelCase		(    self	:      List[str])						->					List[str]:




         """simple docstring"""

         lowercase__,						lowercase__  =	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       UpperCAmelCase		(    self	:      Dict)						->					Dict:




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                     lowercase__  =	self._prepare_for_class(lowerCAmelCase      ,					lowerCAmelCase)
                     lowercase__  =	model_class(lowerCAmelCase)

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

                     with self.subTest('JIT Enabled'):
                           lowercase__  =	encode_jitted(**lowerCAmelCase).to_tuple()

                     with self.subTest('JIT Disabled'):
                           with jax.disable_jit():
                                 lowercase__  =	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       UpperCAmelCase		(    self	:      List[str])						->					Tuple:




         """simple docstring"""

         lowercase__,						lowercase__  =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__):
                     lowercase__  =	model_class(lowerCAmelCase)
                     lowercase__  =	model.encode(inputs_dict['input_ids']      ,					inputs_dict['attention_mask'])

                     lowercase__  =	{
                         '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(lowerCAmelCase	:      Tuple      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Tuple):
                           return model.decode(
                               decoder_input_ids=lowerCAmelCase      ,					decoder_attention_mask=lowerCAmelCase      ,					encoder_outputs=lowerCAmelCase      ,					)

                     with self.subTest('JIT Enabled'):
                           lowercase__  =	decode_jitted(**lowerCAmelCase).to_tuple()

                     with self.subTest('JIT Disabled'):
                           with jax.disable_jit():
                                 lowercase__  =	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       UpperCAmelCase		(    self	:      Union[str, Any])						->					Optional[Any]:




         """simple docstring"""

         for model_class_name in self.all_model_classes:
               lowercase__  =	model_class_name.from_pretrained('facebook/blenderbot_small-90M')
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               lowercase__  =	np.ones((1, 1)) * model.config.eos_token_id
               lowercase__  =	model(lowerCAmelCase)
               self.assertIsNotNone(lowerCAmelCase)





import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin



@require_jieba
class    UpperCAmelCase__(				lowerCamelCase  ,				unittest.TestCase						):

   '''simple docstring'''




   A     :	Optional[int]												=     CpmAntTokenizer
   A     :	Optional[int]												=     False


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




         """simple docstring"""

         super().setUp()

         lowercase__  =	[
             '<d>',
             '</d>',
             '<s>',
             '</s>',
             '</_>',
             '<unk>',
             '<pad>',
             '</n>',
             '我',
             '是',
             'C',
             'P',
             'M',
             'A',
             'n',
             't',
         ]
         lowercase__  =	os.path.join(self.tmpdirname      ,					VOCAB_FILES_NAMES['vocab_file'])
         with open(self.vocab_file      ,					'w'      ,					encoding='utf-8') as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))



   @tooslow
   def       UpperCAmelCase		(    self	:      str)						->					Optional[Any]:




         """simple docstring"""

         lowercase__  =	CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b')
         lowercase__  =	'今天天气真好！'
         lowercase__  =	['今天', '天气', '真', '好', '！']
         lowercase__  =	tokenizer.tokenize(lowerCAmelCase)
         self.assertListEqual(lowerCAmelCase      ,					lowerCAmelCase)
         lowercase__  =	'今天天气真好！'
         lowercase__  =	[tokenizer.bos_token] + tokens

         lowercase__  =	[6, 98_02, 1_49_62, 20_82, 8_31, 2_44]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase)      ,					lowerCAmelCase)

         lowercase__  =	tokenizer.decode(lowerCAmelCase)
         self.assertEqual(lowerCAmelCase      ,					lowerCAmelCase)
