



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 		a__			(		_UpperCamelCase			: Any ,_UpperCamelCase			: Any ,_UpperCamelCase			: List[str]=None ,_UpperCamelCase			: int=None ,_UpperCamelCase			: Dict=None ,_UpperCamelCase			: List[Any]=None ,_UpperCamelCase			: Tuple=None ,_UpperCamelCase			: int=None ,):
   if attention_mask is None:
      __lowerCamelCase													=    np.where(input_ids != config.pad_token_id ,1 ,0							)
   if decoder_attention_mask is None:
      __lowerCamelCase													=    np.where(decoder_input_ids != config.pad_token_id ,1 ,0							)
   if head_mask is None:
      __lowerCamelCase													=    np.ones((config.encoder_layers, config.encoder_attention_heads)							)
   if decoder_head_mask is None:
      __lowerCamelCase													=    np.ones((config.decoder_layers, config.decoder_attention_heads)							)
   if cross_attn_head_mask is None:
      __lowerCamelCase													=    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  __lowerCAmelCase     :




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=13	,				__UpperCAmelCase=7	,				__UpperCAmelCase=True	,				__UpperCAmelCase=False	,				__UpperCAmelCase=99	,				__UpperCAmelCase=16	,				__UpperCAmelCase=2	,				__UpperCAmelCase=4	,				__UpperCAmelCase=4	,				__UpperCAmelCase="gelu"	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=0.1	,				__UpperCAmelCase=32	,				__UpperCAmelCase=2	,				__UpperCAmelCase=1	,				__UpperCAmelCase=0	,				__UpperCAmelCase=0.02	,				):



    '''simple docstring'''
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    is_training
    __lowerCamelCase													=    use_labels
    __lowerCamelCase													=    vocab_size
    __lowerCamelCase													=    hidden_size
    __lowerCamelCase													=    num_hidden_layers
    __lowerCamelCase													=    num_attention_heads
    __lowerCamelCase													=    intermediate_size
    __lowerCamelCase													=    hidden_act
    __lowerCamelCase													=    hidden_dropout_prob
    __lowerCamelCase													=    attention_probs_dropout_prob
    __lowerCamelCase													=    max_position_embeddings
    __lowerCamelCase													=    eos_token_id
    __lowerCamelCase													=    pad_token_id
    __lowerCamelCase													=    bos_token_id
    __lowerCamelCase													=    initializer_range




 def        lowerCamelCase      (						self				):



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

    __lowerCamelCase													=    shift_tokens_right(__UpperCAmelCase	,				1	,				2				)

    __lowerCamelCase													=    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=__UpperCAmelCase	,				)
    __lowerCamelCase													=    prepare_blenderbot_inputs_dict(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    return config, inputs_dict




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.prepare_config_and_inputs()
    return config, inputs_dict




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    20
    __lowerCamelCase													=    model_class_name(__UpperCAmelCase				)

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

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

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

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

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

    __lowerCamelCase													=    model.decode(__UpperCAmelCase	,				__UpperCAmelCase				)

    __lowerCamelCase													=    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	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    20
    __lowerCamelCase													=    model_class_name(__UpperCAmelCase				)

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

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

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

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

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

    __lowerCamelCase													=    model.decode(__UpperCAmelCase	,				__UpperCAmelCase	,				decoder_attention_mask=__UpperCAmelCase				)

    __lowerCamelCase													=    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  __lowerCAmelCase     (	unittest.TestCase   ):
 lowerCAmelCase__       					=  9_9




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    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	,				)

    __lowerCamelCase													=    input_ids.shape[0]
    __lowerCamelCase													=    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        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    self._get_config_and_data()
    __lowerCamelCase													=    FlaxBlenderbotSmallForConditionalGeneration(__UpperCAmelCase				)
    __lowerCamelCase													=    lm_model(input_ids=__UpperCAmelCase				)
    __lowerCamelCase													=    (batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['''logits'''].shape	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]	,				dtype=np.intaa				)
    __lowerCamelCase													=    shift_tokens_right(__UpperCAmelCase	,				1	,				2				)
    __lowerCamelCase													=    np.equal(__UpperCAmelCase	,				1				).astype(np.floataa				).sum()
    __lowerCamelCase													=    np.equal(__UpperCAmelCase	,				1				).astype(np.floataa				).sum()
    self.assertEqual(shifted.shape	,				input_ids.shape				)
    self.assertEqual(__UpperCAmelCase	,				n_pad_before - 1				)
    self.assertTrue(np.equal(shifted[:, 0]	,				2				).all()				)



@require_flax
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase  ,	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  (
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 lowerCAmelCase__       					=  (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    FlaxBlenderbotSmallModelTester(self				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    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(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__				):
          __lowerCamelCase													=    self._prepare_for_class(__UpperCAmelCase	,				__UpperCAmelCase				)
          __lowerCamelCase													=    model_class(__UpperCAmelCase				)

          @jax.jit
          def encode_jitted(__UpperCAmelCase	,				__UpperCAmelCase=None	,				**__UpperCAmelCase				):
             return model.encode(input_ids=__UpperCAmelCase	,				attention_mask=__UpperCAmelCase				)

          with self.subTest('''JIT Enabled'''				):
             __lowerCamelCase													=    encode_jitted(**__UpperCAmelCase				).to_tuple()

          with self.subTest('''JIT Disabled'''				):
             with jax.disable_jit():
                __lowerCamelCase													=    encode_jitted(**__UpperCAmelCase				).to_tuple()

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




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase							,__lowerCamelCase													=    self.model_tester.prepare_config_and_inputs_for_common()

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

          __lowerCamelCase													=    {
              '''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	,				__UpperCAmelCase	,				__UpperCAmelCase				):
             return model.decode(
                 decoder_input_ids=__UpperCAmelCase	,				decoder_attention_mask=__UpperCAmelCase	,				encoder_outputs=__UpperCAmelCase	,				)

          with self.subTest('''JIT Enabled'''				):
             __lowerCamelCase													=    decode_jitted(**__UpperCAmelCase				).to_tuple()

          with self.subTest('''JIT Disabled'''				):
             with jax.disable_jit():
                __lowerCamelCase													=    decode_jitted(**__UpperCAmelCase				).to_tuple()

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




 @slow
 def        lowerCamelCase      (						self				):



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











import unittest

from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_rjieba
@require_tokenizers
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  RoFormerTokenizer
 lowerCAmelCase__       					=  RoFormerTokenizerFast
 lowerCAmelCase__       					=  True
 lowerCAmelCase__       					=  True




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    super().setUp()




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base'''	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    '''永和服装饰品有限公司,今天天气非常好'''
    __lowerCamelCase													=    '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
    return input_text, output_text




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)

    self.assertListEqual(__UpperCAmelCase	,				output_text.split()				)

    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.get_rust_tokenizer()
    __lowerCamelCase							,__lowerCamelCase													=    self.get_chinese_input_output_texts()
    __lowerCamelCase													=    tokenizer.tokenize(__UpperCAmelCase				)
    self.assertListEqual(__UpperCAmelCase	,				output_text.split()				)
    __lowerCamelCase													=    tokens + [tokenizer.unk_token]
    __lowerCamelCase													=    [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(__UpperCAmelCase				)	,				__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass






