

"""simple docstring"""




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
      __UpperCAmelCase =				'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   lowerCAmelCase						(					__UpperCamelCase							,    __UpperCamelCase							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    __UpperCamelCase=None							,    ):
 '''simple docstring'''





 if attention_mask is None:
  UpperCAmelCase__					:  Tuple					   =					np.where(input_ids != config.pad_token_id							,    1							,    0      )
 if decoder_attention_mask is None:
  UpperCAmelCase__					:  Any					   =					np.where(decoder_input_ids != config.pad_token_id							,    1							,    0      )
 if head_mask is None:
  UpperCAmelCase__					:  Dict					   =					np.ones((config.encoder_layers, config.encoder_attention_heads)      )
 if decoder_head_mask is None:
  UpperCAmelCase__					:  Any					   =					np.ones((config.decoder_layers, config.decoder_attention_heads)      )
 if cross_attn_head_mask is None:
  UpperCAmelCase__					:  int					   =					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      __lowercase			:

  def __init__(  self		:			str					,A		:			Any					,A		:			Dict=13					,A		:			str=7					,A		:			Tuple=True					,A		:			Any=False					,A		:			Dict=99					,A		:			Union[str, Any]=16					,A		:			str=2					,A		:			Optional[Any]=4					,A		:			Optional[int]=4					,A		:			Union[str, Any]="gelu"					,A		:			Optional[Any]=0.1					,A		:			List[Any]=0.1					,A		:			Union[str, Any]=32					,A		:			Dict=2					,A		:			Tuple=1					,A		:			Dict=0					,A		:			str=0.0_2					,):






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

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






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

   UpperCAmelCase__					:  Optional[Any]					   =					shift_tokens_right(A					,1					,2    )

   UpperCAmelCase__					:  List[Any]					   =					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=A					,)
   UpperCAmelCase__					:  int					   =					prepare_blenderbot_inputs_dict(A					,A					,A    )
   return config, inputs_dict

  def    __lowercase       (  self		:			Optional[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  List[Any]					   =					self.prepare_config_and_inputs()
   return config, inputs_dict

  def    __lowercase       (  self		:			List[Any]					,A		:			Dict					,A		:			List[Any]					,A		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					20
   UpperCAmelCase__					:  List[str]					   =					model_class_name(A    )

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

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					(
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

   UpperCAmelCase__					:  Dict					   =					model.init_cache(decoder_input_ids.shape[0]					,A					,A    )
   UpperCAmelCase__					:  List[str]					   =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,dtype="""i4"""    )

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

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

   UpperCAmelCase__					:  Optional[int]					   =					model.decode(A					,A    )

   UpperCAmelCase__					:  Dict					   =					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    __lowercase       (  self		:			Optional[int]					,A		:			Dict					,A		:			Optional[Any]					,A		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					20
   UpperCAmelCase__					:  Tuple					   =					model_class_name(A    )

   UpperCAmelCase__					:  str					   =					model.encode(inputs_dict["""input_ids"""]    )

   UpperCAmelCase__   ,      UpperCAmelCase__					:  Dict					   =					(
       inputs_dict["""decoder_input_ids"""],
       inputs_dict["""decoder_attention_mask"""],
   )

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

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

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

   UpperCAmelCase__					:  str					   =					model.decode(A					,A					,decoder_attention_mask=A    )

   UpperCAmelCase__					:  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}"    )







@require_flax
class      __lowercase			(    unittest.TestCase					):
  snake_case_				       =       9_9

  def    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Tuple					   =					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					,)

   UpperCAmelCase__					:  List[Any]					   =					input_ids.shape[0]
   UpperCAmelCase__					:  List[str]					   =					BlenderbotConfig(
       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    __lowercase       (  self		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[Any]					   =					self._get_config_and_data()
   UpperCAmelCase__					:  Tuple					   =					FlaxBlenderbotForConditionalGeneration(A    )
   UpperCAmelCase__					:  List[Any]					   =					lm_model(input_ids=A    )
   UpperCAmelCase__					:  List[Any]					   =					(batch_size, input_ids.shape[1], config.vocab_size)
   self.assertEqual(outputs["""logits"""].shape					,A    )

  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  str					   =					BlenderbotConfig(
       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					,)
   UpperCAmelCase__					:  Tuple					   =					FlaxBlenderbotForConditionalGeneration(A    )
   UpperCAmelCase__					:  Tuple					   =					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]					,dtype=np.intaa    )
   UpperCAmelCase__					:  int					   =					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]					,dtype=np.intaa    )
   UpperCAmelCase__					:  Any					   =					lm_model(input_ids=A					,decoder_input_ids=A    )
   UpperCAmelCase__					:  List[Any]					   =					(*summary.shape, config.vocab_size)
   self.assertEqual(outputs["""logits"""].shape					,A    )




  def    __lowercase       (  self		:			List[Any]    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Union[str, Any]					   =					np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]					,dtype=np.intaa    )
   UpperCAmelCase__					:  str					   =					shift_tokens_right(A					,1					,2    )
   UpperCAmelCase__					:  List[Any]					   =					np.equal(A					,1    ).astype(np.floataa    ).sum()
   UpperCAmelCase__					:  Union[str, Any]					   =					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      __lowercase			(    __lowerCamelCase	,			unittest.TestCase	,			__lowerCamelCase					):
  snake_case_				       =       True
  snake_case_				       =       (
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  snake_case_				       =       (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Any					   =					FlaxBlenderbotModelTester(self    )

  def    __lowercase       (  self		:			int    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					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    __lowercase       (  self		:			Union[str, Any]    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  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    __lowercase       (  self		:			Any    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__    ):
     UpperCAmelCase__					:  Dict					   =					self._prepare_for_class(A					,A    )
     UpperCAmelCase__					:  str					   =					model_class(A    )

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

     with self.subTest("""JIT Enabled"""    ):
      UpperCAmelCase__					:  Union[str, Any]					   =					encode_jitted(**A    ).to_tuple()

     with self.subTest("""JIT Disabled"""    ):
      with jax.disable_jit():
       UpperCAmelCase__					:  Optional[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    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Tuple					   =					self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    with self.subTest(model_class.__name__    ):
     UpperCAmelCase__					:  Dict					   =					model_class(A    )
     UpperCAmelCase__					:  Optional[int]					   =					model.encode(inputs_dict["""input_ids"""]					,inputs_dict["""attention_mask"""]    )

     UpperCAmelCase__					:  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(A		:			Dict					,A		:			List[str]					,A		:			Any    ):
      return model.decode(
          decoder_input_ids=A					,decoder_attention_mask=A					,encoder_outputs=A					,)

     with self.subTest("""JIT Enabled"""    ):
      UpperCAmelCase__					:  int					   =					decode_jitted(**A    ).to_tuple()

     with self.subTest("""JIT Disabled"""    ):
      with jax.disable_jit():
       UpperCAmelCase__					:  str					   =					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    __lowercase       (  self		:			Optional[int]    ):






   '''simple docstring'''
   for model_class_name in self.all_model_classes:
    UpperCAmelCase__					:  List[str]					   =					model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""    )
    # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
    UpperCAmelCase__					:  int					   =					np.ones((1, 1)    ) * model.config.eos_token_id
    UpperCAmelCase__					:  str					   =					model(A    )
    self.assertIsNotNone(A    )




  @unittest.skipUnless(jax_device != """cpu"""					,"""3B test too slow on CPU."""    )
  @slow
  def    __lowercase       (  self		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  Optional[Any]					   =					{"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
   UpperCAmelCase__					:  Tuple					   =					{"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}

   UpperCAmelCase__					:  List[str]					   =					FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""					,from_pt=A    )
   UpperCAmelCase__					:  Dict					   =					BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""    )

   UpperCAmelCase__					:  Union[str, Any]					   =					["""Sam"""]
   UpperCAmelCase__					:  List[str]					   =					tokenizer(A					,return_tensors="""jax"""    )

   UpperCAmelCase__					:  Optional[Any]					   =					model.generate(**A					,**A    )
   UpperCAmelCase__					:  int					   =					"""Sam is a great name. It means \"sun\" in Gaelic."""

   UpperCAmelCase__					:  int					   =					tokenizer.batch_decode(A					,**A    )
   assert generated_txt[0].strip() == tgt_text








"""simple docstring"""




# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__UpperCAmelCase =				abspath(join(dirname(dirname(__file__)), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)
def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_addoption_shared

 pytest_addoption_shared(__UpperCamelCase      )




def   lowerCAmelCase						(					__UpperCamelCase      ):
 '''simple docstring'''





 from diffusers.utils.testing_utils import pytest_terminal_summary_main

 UpperCAmelCase__					:  Optional[Any]					   =					terminalreporter.config.getoption("""--make-reports"""      )
 if make_reports:
  pytest_terminal_summary_main(__UpperCamelCase							,    id=__UpperCamelCase      )





