





'''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
  __SCREAMING_SNAKE_CASE	:List[str]	    =							'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_				(   __lowercase				: Optional[Any]     ,	__lowercase				: Any     ,	__lowercase				: List[str]=None     ,	__lowercase				: List[str]=None     ,	__lowercase				: List[str]=None     ,	__lowercase				: Optional[Any]=None     ,	__lowercase				: Optional[int]=None     ,	__lowercase				: Optional[Any]=None     ,	)						->			List[Any]:


     '''simple docstring'''



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




 def __init__(   self			:       List[Any]      ,     snake_case_			:       Union[str, Any]      ,     snake_case_			:       List[str]=1_3      ,     snake_case_			:       Optional[Any]=7      ,     snake_case_			:       Tuple=True      ,     snake_case_			:       Optional[Any]=False      ,     snake_case_			:       Optional[int]=9_9      ,     snake_case_			:       Any=1_6      ,     snake_case_			:       Any=2      ,     snake_case_			:       Union[str, Any]=4      ,     snake_case_			:       Any=4      ,     snake_case_			:       List[str]="gelu"      ,     snake_case_			:       Optional[int]=0.1      ,     snake_case_			:       Any=0.1      ,     snake_case_			:       Tuple=3_2      ,     snake_case_			:       Optional[int]=2      ,     snake_case_			:       int=1      ,     snake_case_			:       str=0      ,     snake_case_			:       Union[str, Any]=0.0_2      ,     ):
      _UpperCAmelCase					=  parent
      _UpperCAmelCase					=  batch_size
      _UpperCAmelCase					=  seq_length
      _UpperCAmelCase					=  is_training
      _UpperCAmelCase					=  use_labels
      _UpperCAmelCase					=  vocab_size
      _UpperCAmelCase					=  hidden_size
      _UpperCAmelCase					=  num_hidden_layers
      _UpperCAmelCase					=  num_attention_heads
      _UpperCAmelCase					=  intermediate_size
      _UpperCAmelCase					=  hidden_act
      _UpperCAmelCase					=  hidden_dropout_prob
      _UpperCAmelCase					=  attention_probs_dropout_prob
      _UpperCAmelCase					=  max_position_embeddings
      _UpperCAmelCase					=  eos_token_id
      _UpperCAmelCase					=  pad_token_id
      _UpperCAmelCase					=  bos_token_id
      _UpperCAmelCase					=  initializer_range




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

      _UpperCAmelCase					=  shift_tokens_right(snake_case_      ,     1      ,     2       )

      _UpperCAmelCase					=  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=snake_case_      ,     )
      _UpperCAmelCase					=  prepare_blenderbot_inputs_dict(snake_case_      ,     snake_case_      ,     snake_case_       )
      return config, inputs_dict




 def 	lowercase      (   self			:       Dict       ):
      _UpperCAmelCase				,    _UpperCAmelCase					=  self.prepare_config_and_inputs()
      return config, inputs_dict




 def 	lowercase      (   self			:       Any      ,     snake_case_			:       Optional[Any]      ,     snake_case_			:       Optional[int]      ,     snake_case_			:       Optional[Any]       ):
      _UpperCAmelCase					=  2_0
      _UpperCAmelCase					=  model_class_name(snake_case_       )

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

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

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

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

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

      _UpperCAmelCase					=  model.decode(snake_case_      ,     snake_case_       )

      _UpperCAmelCase					=  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			:       Tuple      ,     snake_case_			:       Any      ,     snake_case_			:       Optional[int]      ,     snake_case_			:       Dict       ):
      _UpperCAmelCase					=  2_0
      _UpperCAmelCase					=  model_class_name(snake_case_       )

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

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

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

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

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

      _UpperCAmelCase					=  model.decode(snake_case_      ,     snake_case_      ,     decoder_attention_mask=snake_case_       )

      _UpperCAmelCase					=  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     ):
 _lowerCamelCase    :					str       			=				99




 def 	lowercase      (   self			:       List[Any]       ):
      _UpperCAmelCase					=  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      ,     )

      _UpperCAmelCase					=  input_ids.shape[0]
      _UpperCAmelCase					=  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 	lowercase      (   self			:       List[Any]       ):
      _UpperCAmelCase				,    _UpperCAmelCase				,    _UpperCAmelCase					=  self._get_config_and_data()
      _UpperCAmelCase					=  FlaxBlenderbotForConditionalGeneration(snake_case_       )
      _UpperCAmelCase					=  lm_model(input_ids=snake_case_       )
      _UpperCAmelCase					=  (batch_size, input_ids.shape[1], config.vocab_size)
      self.assertEqual(outputs["logits"].shape      ,     snake_case_       )




 def 	lowercase      (   self			:       List[str]       ):
      _UpperCAmelCase					=  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      ,     )
      _UpperCAmelCase					=  FlaxBlenderbotForConditionalGeneration(snake_case_       )
      _UpperCAmelCase					=  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       )
      _UpperCAmelCase					=  np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]      ,     dtype=np.intaa       )
      _UpperCAmelCase					=  lm_model(input_ids=snake_case_      ,     decoder_input_ids=snake_case_       )
      _UpperCAmelCase					=  (*summary.shape, config.vocab_size)
      self.assertEqual(outputs["logits"].shape      ,     snake_case_       )




 def 	lowercase      (   self			:       Tuple       ):
      _UpperCAmelCase					=  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       )
      _UpperCAmelCase					=  shift_tokens_right(snake_case_      ,     1      ,     2       )
      _UpperCAmelCase					=  np.equal(snake_case_      ,     1       ).astype(np.floataa       ).sum()
      _UpperCAmelCase					=  np.equal(snake_case_      ,     1       ).astype(np.floataa       ).sum()
      self.assertEqual(shifted.shape      ,     input_ids.shape       )
      self.assertEqual(snake_case_      ,     n_pad_before - 1       )
      self.assertTrue(np.equal(shifted[:, 0]      ,     2       ).all()       )







@require_flax
class     A_      (    _lowercase				,		unittest.TestCase				,		_lowercase     ):
 _lowerCamelCase    :					Optional[int]       			=				True
 _lowerCamelCase    :					int       			=				(
     (
         FlaxBlenderbotModel,
         FlaxBlenderbotForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 _lowerCamelCase    :					List[Any]       			=				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




 def 	lowercase      (   self			:       Any       ):
      _UpperCAmelCase					=  FlaxBlenderbotModelTester(self       )




 def 	lowercase      (   self			:       Dict       ):
      _UpperCAmelCase				,    _UpperCAmelCase					=  self.model_tester.prepare_config_and_inputs()
      for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(snake_case_      ,     snake_case_      ,     snake_case_       )




 def 	lowercase      (   self			:       Union[str, Any]       ):
      _UpperCAmelCase				,    _UpperCAmelCase					=  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(snake_case_      ,     snake_case_      ,     snake_case_       )




 def 	lowercase      (   self			:       Tuple       ):
      _UpperCAmelCase				,    _UpperCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
                _UpperCAmelCase					=  self._prepare_for_class(snake_case_      ,     snake_case_       )
                _UpperCAmelCase					=  model_class(snake_case_       )

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

                with self.subTest("JIT Enabled"       ):
                     _UpperCAmelCase					=  encode_jitted(**snake_case_       ).to_tuple()

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

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




 def 	lowercase      (   self			:       Dict       ):
      _UpperCAmelCase				,    _UpperCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase					=  {
                    "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(snake_case_			:       int      ,     snake_case_			:       List[str]      ,     snake_case_			:       List[Any]       ):
                     return model.decode(
                         decoder_input_ids=snake_case_      ,     decoder_attention_mask=snake_case_      ,     encoder_outputs=snake_case_      ,     )

                with self.subTest("JIT Enabled"       ):
                     _UpperCAmelCase					=  decode_jitted(**snake_case_       ).to_tuple()

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

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




 @slow
 def 	lowercase      (   self			:       Dict       ):
      for model_class_name in self.all_model_classes:
           _UpperCAmelCase					=  model_class_name.from_pretrained("facebook/blenderbot-400M-distill"       )
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           _UpperCAmelCase					=  np.ones((1, 1)       ) * model.config.eos_token_id
           _UpperCAmelCase					=  model(snake_case_       )
           self.assertIsNotNone(snake_case_       )




 @unittest.skipUnless(jax_device != "cpu"      ,     "3B test too slow on CPU."       )
 @slow
 def 	lowercase      (   self			:       List[Any]       ):
      _UpperCAmelCase					=  {"num_beams": 1, "early_stopping": True, "min_length": 1_5, "max_length": 2_5}
      _UpperCAmelCase					=  {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

      _UpperCAmelCase					=  ["Sam"]
      _UpperCAmelCase					=  tokenizer(snake_case_      ,     return_tensors="jax"       )

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

      _UpperCAmelCase					=  tokenizer.batch_decode(snake_case_      ,     **snake_case_       )
      assert generated_txt[0].strip() == tgt_text









a_      :      str    =     6_55_21






def     __a					(		__UpperCAmelCase       ):
 a__												= 1
 a__												= 0
 for plain_chr in plain_text:
  a__												= (a + ord(__UpperCAmelCase       )) % MOD_ADLER
  a__												= (b + a) % MOD_ADLER
 return (b << 16) | a





