






'''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      :       Optional[Any]      			=      '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	(				SCREAMING_SNAKE_CASE__:	Optional[Any],					SCREAMING_SNAKE_CASE__:	Tuple,					SCREAMING_SNAKE_CASE__:	List[Any]=None,					SCREAMING_SNAKE_CASE__:	Any=None,					SCREAMING_SNAKE_CASE__:	Optional[Any]=None,					SCREAMING_SNAKE_CASE__:	str=None,					SCREAMING_SNAKE_CASE__:	Tuple=None,					SCREAMING_SNAKE_CASE__:	List[Any]=None,					)  ->			List[str]:

   """simple docstring"""



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




      def __init__(  self ,   lowerCamelCase ,   lowerCamelCase=13 ,   lowerCamelCase=7 ,   lowerCamelCase=True ,   lowerCamelCase=False ,   lowerCamelCase=99 ,   lowerCamelCase=16 ,   lowerCamelCase=2 ,   lowerCamelCase=4 ,   lowerCamelCase=4 ,   lowerCamelCase="gelu" ,   lowerCamelCase=0.1 ,   lowerCamelCase=0.1 ,   lowerCamelCase=32 ,   lowerCamelCase=2 ,   lowerCamelCase=1 ,   lowerCamelCase=0 ,   lowerCamelCase=0.02 ,   )							->Union[str, Any]:



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




      def  __UpperCamelCase							(  self      )							->List[Any]:



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

         __a				     =    shift_tokens_right(lowercase__ ,   1 ,   2      )

         __a				     =    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=lowercase__ ,   )
         __a				     =    prepare_blenderbot_inputs_dict(lowercase__ ,   lowercase__ ,   lowercase__      )
         return config, inputs_dict




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



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




      def  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    20
         __a				     =    model_class_name(lowercase__      )

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

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

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

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

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

         __a				     =    model.decode(lowercase__ ,   lowercase__      )

         __a				     =    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  __UpperCamelCase							(  self ,   lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      )							->Tuple:



         '''simple docstring'''
         __a				     =    20
         __a				     =    model_class_name(lowercase__      )

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

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

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

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

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

         __a				     =    model.decode(lowercase__ ,   lowercase__ ,   decoder_attention_mask=lowercase__      )

         __a				     =    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 							__SCREAMING_SNAKE_CASE       (							unittest.TestCase     ):
      __a  					=99




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    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 ,   )

         __a				     =    input_ids.shape[0]
         __a				     =    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  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    self._get_config_and_data()
         __a				     =    FlaxBlenderbotForConditionalGeneration(lowercase__      )
         __a				     =    lm_model(input_ids=lowercase__      )
         __a				     =    (batch_size, input_ids.shape[1], config.vocab_size)
         self.assertEqual(outputs['logits'].shape ,   lowercase__      )




      def  __UpperCamelCase							(  self      )							->int:



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






      def  __UpperCamelCase							(  self      )							->Any:



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







@require_flax
class 							__SCREAMING_SNAKE_CASE       (							a__      ,      unittest.TestCase      ,      a__     ):
      __a  					=True
      __a  					=(
          (
              FlaxBlenderbotModel,
              FlaxBlenderbotForConditionalGeneration,
          )
          if is_flax_available()
          else ()
      )
      __a  					=(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''
         __a				     =    FlaxBlenderbotModelTester(self      )




      def  __UpperCamelCase							(  self      )							->List[str]:



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




      def  __UpperCamelCase							(  self      )							->Optional[int]:



         '''simple docstring'''
         __a				     =    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(lowercase__ ,   lowercase__ ,   lowercase__      )




      def  __UpperCamelCase							(  self      )							->Optional[Any]:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__      ):
               __a				     =    self._prepare_for_class(lowercase__ ,   lowercase__      )
               __a				     =    model_class(lowercase__      )

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

               with self.subTest('JIT Enabled'      ):
                  __a				     =    encode_jitted(**lowercase__      ).to_tuple()

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

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




      def  __UpperCamelCase							(  self      )							->List[Any]:



         '''simple docstring'''
         __a				     =    self.model_tester.prepare_config_and_inputs_for_common()

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

               __a				     =    {
                   '''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(lowerCamelCase ,   lowerCamelCase ,   lowerCamelCase      ):
                  return model.decode(
                      decoder_input_ids=lowercase__ ,   decoder_attention_mask=lowercase__ ,   encoder_outputs=lowercase__ ,   )

               with self.subTest('JIT Enabled'      ):
                  __a				     =    decode_jitted(**lowercase__      ).to_tuple()

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

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




      @slow
      def  __UpperCamelCase							(  self      )							->Tuple:



         '''simple docstring'''





         for model_class_name in self.all_model_classes:
            __a				     =    model_class_name.from_pretrained('facebook/blenderbot-400M-distill'      )
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            __a				     =    np.ones((1, 1)      ) * model.config.eos_token_id
            __a				     =    model(lowercase__      )
            self.assertIsNotNone(lowercase__      )






      @unittest.skipUnless(jax_device != 'cpu' ,   '3B test too slow on CPU.'      )
      @slow
      def  __UpperCamelCase							(  self      )							->List[str]:



         '''simple docstring'''
         __a				     =    {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
         __a				     =    {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

         __a				     =    FlaxBlenderbotForConditionalGeneration.from_pretrained('facebook/blenderbot-3B' ,   from_pt=lowercase__      )
         __a				     =    BlenderbotTokenizer.from_pretrained('facebook/blenderbot-3B'      )

         __a				     =    ['''Sam''']
         __a				     =    tokenizer(lowercase__ ,   return_tensors='jax'      )

         __a				     =    model.generate(**lowercase__ ,   **lowercase__      )
         __a				     =    '''Sam is a great name. It means "sun" in Gaelic.'''

         __a				     =    tokenizer.batch_decode(lowercase__ ,   **lowercase__      )
         assert generated_txt[0].strip() == tgt_text







'''simple docstring'''



from maths.prime_factors import prime_factors



def    __UpperCAmelCase	(				SCREAMING_SNAKE_CASE__:	int    )  ->			int:

   """simple docstring"""
   if not isinstance(SCREAMING_SNAKE_CASE__,					SCREAMING_SNAKE_CASE__    ):
      __a				     =    f"""Input value of [number={number}] must be an integer"""
      raise TypeError(SCREAMING_SNAKE_CASE__    )
   if number < 1:
      raise ValueError('Input must be a positive integer'    )
   return -1 if len(prime_factors(SCREAMING_SNAKE_CASE__    )    ) % 2 else 1


if __name__ == "__main__":
 import doctest

 doctest.testmod()