




'''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_						:      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      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Tuple ,						_UpperCamelCase:   Tuple=None ,						_UpperCamelCase:   List[str]=None ,						_UpperCamelCase:   Any=None ,						_UpperCamelCase:   Union[str, Any]=None ,						_UpperCamelCase:   List[Any]=None ,						_UpperCamelCase:   Any=None ,						)						->			Dict:

     """simple docstring"""




     if attention_mask is None:
          _snake_case							=				np.where(input_ids != config.pad_token_id ,						1 ,						0   )
     if decoder_attention_mask is None:
          _snake_case							=				np.where(decoder_input_ids != config.pad_token_id ,						1 ,						0   )
     if head_mask is None:
          _snake_case							=				np.ones((config.encoder_layers, config.encoder_attention_heads)   )
     if decoder_head_mask is None:
          _snake_case							=				np.ones((config.decoder_layers, config.decoder_attention_heads)   )
     if cross_attn_head_mask is None:
          _snake_case							=				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 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=13 ,_SCREAMING_SNAKE_CASE=7 ,_SCREAMING_SNAKE_CASE=True ,_SCREAMING_SNAKE_CASE=False ,_SCREAMING_SNAKE_CASE=99 ,_SCREAMING_SNAKE_CASE=16 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE=4 ,_SCREAMING_SNAKE_CASE="gelu" ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=0.1 ,_SCREAMING_SNAKE_CASE=32 ,_SCREAMING_SNAKE_CASE=2 ,_SCREAMING_SNAKE_CASE=1 ,_SCREAMING_SNAKE_CASE=0 ,_SCREAMING_SNAKE_CASE=0.0_2 ,)							->      List[str]:
          _snake_case							=				parent
          _snake_case							=				batch_size
          _snake_case							=				seq_length
          _snake_case							=				is_training
          _snake_case							=				use_labels
          _snake_case							=				vocab_size
          _snake_case							=				hidden_size
          _snake_case							=				num_hidden_layers
          _snake_case							=				num_attention_heads
          _snake_case							=				intermediate_size
          _snake_case							=				hidden_act
          _snake_case							=				hidden_dropout_prob
          _snake_case							=				attention_probs_dropout_prob
          _snake_case							=				max_position_embeddings
          _snake_case							=				eos_token_id
          _snake_case							=				pad_token_id
          _snake_case							=				bos_token_id
          _snake_case							=				initializer_range






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				np.clip(ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size   ) ,3 ,self.vocab_size   )
          _snake_case							=				np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) ,dtype=np.intaa   )) ,-1   )

          _snake_case							=				shift_tokens_right(_SCREAMING_SNAKE_CASE ,1 ,2   )

          _snake_case							=				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=_SCREAMING_SNAKE_CASE ,)
          _snake_case							=				prepare_blenderbot_inputs_dict(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
          return config, inputs_dict






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							, _snake_case							=				self.prepare_config_and_inputs()
          return config, inputs_dict






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          _snake_case							=				20
          _snake_case							=				model_class_name(_SCREAMING_SNAKE_CASE   )

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

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

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

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

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

          _snake_case							=				model.decode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				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 ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )							->      List[Any]:
          _snake_case							=				20
          _snake_case							=				model_class_name(_SCREAMING_SNAKE_CASE   )

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

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

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

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

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

          _snake_case							=				model.decode(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,decoder_attention_mask=_SCREAMING_SNAKE_CASE   )

          _snake_case							=				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							):
     SCREAMING_SNAKE_CASE_							:   str       =						99






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							=				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 ,)

          _snake_case							=				input_ids.shape[0]
          _snake_case							=				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   )							->      List[Any]:
          _snake_case							, _snake_case							, _snake_case							=				self._get_config_and_data()
          _snake_case							=				FlaxBlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				lm_model(input_ids=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs["logits"].shape ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							=				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 ,)
          _snake_case							=				FlaxBlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE   )
          _snake_case							=				np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] ,dtype=np.intaa   )
          _snake_case							=				np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] ,dtype=np.intaa   )
          _snake_case							=				lm_model(input_ids=_SCREAMING_SNAKE_CASE ,decoder_input_ids=_SCREAMING_SNAKE_CASE   )
          _snake_case							=				(*summary.shape, config.vocab_size)
          self.assertEqual(outputs["logits"].shape ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      Dict:
          _snake_case							=				np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] ,dtype=np.intaa   )
          _snake_case							=				shift_tokens_right(_SCREAMING_SNAKE_CASE ,1 ,2   )
          _snake_case							=				np.equal(_SCREAMING_SNAKE_CASE ,1   ).astype(np.floataa   ).sum()
          _snake_case							=				np.equal(_SCREAMING_SNAKE_CASE ,1   ).astype(np.floataa   ).sum()
          self.assertEqual(shifted.shape ,input_ids.shape   )
          self.assertEqual(_SCREAMING_SNAKE_CASE ,n_pad_before - 1   )
          self.assertTrue(np.equal(shifted[:, 0] ,2   ).all()   )







@require_flax
class        _a    (						__lowerCAmelCase , unittest.TestCase , __lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						True
     SCREAMING_SNAKE_CASE_							:   Any       =						(
         (
             FlaxBlenderbotModel,
             FlaxBlenderbotForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()






     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				FlaxBlenderbotModelTester(self   )






     def     _lowercase     (			self   )							->      str:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
               self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[str]:
          _snake_case							, _snake_case							=				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(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self   )							->      List[Any]:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               with self.subTest(model_class.__name__   ):
                    _snake_case							=				self._prepare_for_class(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				model_class(_SCREAMING_SNAKE_CASE   )

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

                    with self.subTest("JIT Enabled"   ):
                         _snake_case							=				encode_jitted(**_SCREAMING_SNAKE_CASE   ).to_tuple()

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

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






     def     _lowercase     (			self   )							->      Tuple:
          _snake_case							, _snake_case							=				self.model_tester.prepare_config_and_inputs_for_common()

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

                    _snake_case							=				{
                        "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(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ):
                         return model.decode(
                             decoder_input_ids=_SCREAMING_SNAKE_CASE ,decoder_attention_mask=_SCREAMING_SNAKE_CASE ,encoder_outputs=_SCREAMING_SNAKE_CASE ,)

                    with self.subTest("JIT Enabled"   ):
                         _snake_case							=				decode_jitted(**_SCREAMING_SNAKE_CASE   ).to_tuple()

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

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






     @slow
     def     _lowercase     (			self   )							->      List[Any]:
          for model_class_name in self.all_model_classes:
               _snake_case							=				model_class_name.from_pretrained("facebook/blenderbot-400M-distill"   )
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _snake_case							=				np.ones((1, 1)   ) * model.config.eos_token_id
               _snake_case							=				model(_SCREAMING_SNAKE_CASE   )
               self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )






     @unittest.skipUnless(jax_device != "cpu" ,"3B test too slow on CPU."   )
     @slow
     def     _lowercase     (			self   )							->      Union[str, Any]:
          _snake_case							=				{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
          _snake_case							=				{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

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

          _snake_case							=				["Sam"]
          _snake_case							=				tokenizer(_SCREAMING_SNAKE_CASE ,return_tensors="jax"   )

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

          _snake_case							=				tokenizer.batch_decode(_SCREAMING_SNAKE_CASE ,**_SCREAMING_SNAKE_CASE   )
          assert generated_txt[0].strip() == tgt_text








'''simple docstring'''





def      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if number > 0:
          raise ValueError("input must be a negative integer"   )
     _snake_case							=				len(bin(_UpperCamelCase   )[3:]   )
     _snake_case							=				bin(abs(_UpperCamelCase   ) - (1 << binary_number_length)   )[3:]
     _snake_case							=				(
         (
             "1"
             + "0" * (binary_number_length - len(_UpperCamelCase   ))
             + twos_complement_number
         )
         if number < 0
         else "0"
     )
     return "0b" + twos_complement_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()


