






"""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
   _UpperCamelCase    :   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       snake_case  (A_				:Union[str, Any]     ,      A_				:Dict     ,      A_				:str=None     ,      A_				:Union[str, Any]=None     ,      A_				:List[Any]=None     ,      A_				:Dict=None     ,      A_				:Any=None     ,      A_				:int=None     ,      ):







  '''simple docstring'''

  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					:      Optional[int]    =       np.where(decoder_input_ids != config.pad_token_id     ,      1     ,      0				)
  if head_mask is None:
    a					:      Optional[int]    =       np.ones((config.encoder_layers, config.encoder_attention_heads)				)
  if decoder_head_mask is None:
    a					:      Dict    =       np.ones((config.decoder_layers, config.decoder_attention_heads)				)
  if cross_attn_head_mask is None:
    a					:      List[Any]    =       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 						snake_case  :





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


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1]    ,		self.vocab_size      )    ,		3    ,		self.vocab_size      )
      a					:      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					:      Dict    =       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					:      List[str]    =       prepare_blenderbot_inputs_dict(A    ,		A    ,		A      )
      return config, inputs_dict





    def     lowerCamelCase__				(							self     : Union[str, Any]      ):


      '''simple docstring'''




      a,					a					:      Tuple    =       self.prepare_config_and_inputs()
      return config, inputs_dict





    def     lowerCamelCase__				(							self     : Any    ,		A     : Optional[int]    ,		A     : str    ,		A     : List[Any]      ):


      '''simple docstring'''




      a					:      int    =       2_0
      a					:      Optional[Any]    =       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					:      int    =       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					:      Optional[Any]    =       jnp.broadcast_to(
          jnp.arange(decoder_input_ids.shape[-1] - 1      )[None, :]    ,		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,		)
      a					:      int    =       model.decode(
          decoder_input_ids[:, :-1]    ,		A    ,		decoder_attention_mask=A    ,		past_key_values=A    ,		decoder_position_ids=A    ,		)

      a					:      Union[str, 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					:      List[str]    =       model.decode(A    ,		A      )

      a					:      List[str]    =       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     : Tuple    ,		A     : Dict    ,		A     : List[str]    ,		A     : Any      ):


      '''simple docstring'''




      a					:      Any    =       2_0
      a					:      Optional[Any]    =       model_class_name(A      )

      a					:      str    =       model.encode(inputs_dict['input_ids']      )

      a,					a					:      Union[str, Any]    =       (
          inputs_dict['decoder_input_ids'],
          inputs_dict['decoder_attention_mask'],
      )

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

      a					:      List[str]    =       model.init_cache(decoder_input_ids.shape[0]    ,		A    ,		A      )
      a					:      Union[str, Any]    =       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					:      int    =       model.decode(
          decoder_input_ids[:, -1:]    ,		A    ,		past_key_values=outputs_cache.past_key_values    ,		decoder_attention_mask=A    ,		decoder_position_ids=A    ,		)

      a					:      List[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 						snake_case  (  unittest.TestCase	):
    __magic_name__   					=					99





    def     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a					:      str    =       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    ,		)

      a					:      str    =       input_ids.shape[0]
      a					:      Optional[Any]    =       BlenderbotSmallConfig(
          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     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      a,					a,					a					:      Optional[int]    =       self._get_config_and_data()
      a					:      Dict    =       FlaxBlenderbotSmallForConditionalGeneration(A      )
      a					:      Union[str, Any]    =       lm_model(input_ids=A      )
      a					:      int    =       (batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs['logits'].shape    ,		A      )





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      Tuple    =       BlenderbotSmallConfig(
          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    ,		)
      a					:      Optional[Any]    =       FlaxBlenderbotSmallForConditionalGeneration(A      )
      a					:      str    =       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      )
      a					:      Optional[Any]    =       np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]    ,		dtype=np.intaa      )
      a					:      Dict    =       lm_model(input_ids=A    ,		decoder_input_ids=A      )
      a					:      Union[str, Any]    =       (*summary.shape, config.vocab_size)
      self.assertEqual(outputs['logits'].shape    ,		A      )





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Any    =       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      )
      a					:      List[Any]    =       shift_tokens_right(A    ,		1    ,		2      )
      a					:      Union[str, Any]    =       np.equal(A    ,		1      ).astype(np.floataa      ).sum()
      a					:      Dict    =       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 						snake_case  (  UpperCAmelCase       ,			unittest.TestCase       ,			UpperCAmelCase	):
    __magic_name__   					=					True
    __magic_name__   					=					(
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    __magic_name__   					=					(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Tuple    =       FlaxBlenderbotSmallModelTester(self      )





    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a,					a					:      Optional[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     lowerCamelCase__				(							self     : List[Any]      ):


      '''simple docstring'''




      a,					a					:      List[Any]    =       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     lowerCamelCase__				(							self     : Dict      ):


      '''simple docstring'''




      a,					a					:      Dict    =       self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
        with self.subTest(model_class.__name__      ):
          a					:      Optional[Any]    =       self._prepare_for_class(A    ,		A      )
          a					:      Tuple    =       model_class(A      )

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

          with self.subTest('JIT Enabled'      ):
            a					:      str    =       encode_jitted(**A      ).to_tuple()

          with self.subTest('JIT Disabled'      ):
            with jax.disable_jit():
              a					:      Tuple    =       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     lowerCamelCase__				(							self     : Optional[int]      ):


      '''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					:      Optional[Any]    =       model_class(A      )
          a					:      int    =       model.encode(inputs_dict['input_ids']    ,		inputs_dict['attention_mask']      )

          a					:      List[str]    =       {
              '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(A     : List[Any]    ,		A     : int    ,		A     : int      ):
            return model.decode(
                decoder_input_ids=A    ,		decoder_attention_mask=A    ,		encoder_outputs=A    ,		)

          with self.subTest('JIT Enabled'      ):
            a					:      Tuple    =       decode_jitted(**A      ).to_tuple()

          with self.subTest('JIT Disabled'      ):
            with jax.disable_jit():
              a					:      Any    =       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     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      for model_class_name in self.all_model_classes:
        a					:      List[Any]    =       model_class_name.from_pretrained('facebook/blenderbot_small-90M'      )
        # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
        a					:      List[Any]    =       np.ones((1, 1)      ) * model.config.eos_token_id
        a					:      Optional[int]    =       model(A      )
        self.assertIsNotNone(A      )












"""simple docstring"""


def       snake_case  (A_				:str     ,      A_				:bool = False				):







  '''simple docstring'''

  if not isinstance(A_     ,      A_				):
    a					:      Union[str, Any]    =       f'''Expected string as input, found {type(A_				)}'''
    raise ValueError(A_				)
  if not isinstance(A_     ,      A_				):
    a					:      Optional[int]    =       f'''Expected boolean as use_pascal parameter, found {type(A_				)}'''
    raise ValueError(A_				)

  a					:      Tuple    =       input_str.split('_'				)

  a					:      Dict    =       0 if use_pascal else 1

  a					:      int    =       words[start_index:]

  a					:      int    =       [word[0].upper() + word[1:] for word in words_to_capitalize]

  a					:      List[str]    =       '' if use_pascal else words[0]

  return "".join([initial_word, *capitalized_words]				)


if __name__ == "__main__":
   from doctest import testmod

   testmod()




