





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:			List[Any]		,   __UpperCAmelCase:			Optional[int]		,   __UpperCAmelCase:			Optional[int]=None		,   __UpperCAmelCase:			Tuple=None		,   __UpperCAmelCase:			List[Any]=None		,   __UpperCAmelCase:			List[Any]=None		,   __UpperCAmelCase:			Any=None		,   __UpperCAmelCase:			Optional[Any]=None		,   )      ->	List[str]:
    if attention_mask is None:
        UpperCamelCase__							:				Optional[Any]          =      np.where(input_ids != config.pad_token_id		,   1		,   0      )
    if decoder_attention_mask is None:
        UpperCamelCase__							:				str          =      np.where(decoder_input_ids != config.pad_token_id		,   1		,   0      )
    if head_mask is None:
        UpperCamelCase__							:				Optional[Any]          =      np.ones((config.encoder_layers, config.encoder_attention_heads)      )
    if decoder_head_mask is None:
        UpperCamelCase__							:				str          =      np.ones((config.decoder_layers, config.decoder_attention_heads)      )
    if cross_attn_head_mask is None:
        UpperCamelCase__							:				Union[str, 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     lowercase__       :

   '''simple docstring'''


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






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




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






       """simple docstring"""
       UpperCamelCase__							:				List[Any]          =      np.clip(ids_tensor([self.batch_size, self.seq_length - 1],		self.vocab_size							),		3,		self.vocab_size							)
       UpperCamelCase__							:				Tuple          =      np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),		dtype=np.intaa							)),		-1							)

       UpperCamelCase__							:				List[str]          =      shift_tokens_right(__magic_name__,		1,		2							)

       UpperCamelCase__							:				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=__magic_name__,		)
       UpperCamelCase__							:				Any          =      prepare_blenderbot_inputs_dict(__magic_name__,		__magic_name__,		__magic_name__							)
       return config, inputs_dict




   def       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      self.prepare_config_and_inputs()
       return config, inputs_dict




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Any:






       """simple docstring"""
       UpperCamelCase__							:				Tuple          =      20
       UpperCamelCase__							:				Tuple          =      model_class_name(__magic_name__							)

       UpperCamelCase__							:				Optional[int]          =      model.encode(inputs_dict['''input_ids''']							)

       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCamelCase__							:				Optional[Any]          =      model.init_cache(decoder_input_ids.shape[0],		__magic_name__,		__magic_name__							)
       UpperCamelCase__							:				Dict          =      jnp.ones((decoder_input_ids.shape[0], max_decoder_length),		dtype='''i4'''							)

       UpperCamelCase__							:				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__							:				Union[str, Any]          =      model.decode(
           decoder_input_ids[:, :-1],		__magic_name__,		decoder_attention_mask=__magic_name__,		past_key_values=__magic_name__,		decoder_position_ids=__magic_name__,		)

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

       UpperCamelCase__							:				List[str]          =      model.decode(__magic_name__,		__magic_name__							)

       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}"							)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      20
       UpperCamelCase__							:				List[str]          =      model_class_name(__magic_name__							)

       UpperCamelCase__							:				Any          =      model.encode(inputs_dict['''input_ids''']							)

       UpperCamelCase__   ,UpperCamelCase__							:				int          =      (
           inputs_dict['''decoder_input_ids'''],
           inputs_dict['''decoder_attention_mask'''],
       )

       UpperCamelCase__							:				Optional[int]          =      jnp.concatenate(
           [
               decoder_attention_mask,
               jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])							),
           ],		axis=-1,		)

       UpperCamelCase__							:				List[str]          =      model.init_cache(decoder_input_ids.shape[0],		__magic_name__,		__magic_name__							)
       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__							:				int          =      model.decode(
           decoder_input_ids[:, :-1],		__magic_name__,		decoder_attention_mask=__magic_name__,		past_key_values=__magic_name__,		decoder_position_ids=__magic_name__,		)
       UpperCamelCase__							:				str          =      jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],		dtype='''i4'''							)
       UpperCamelCase__							:				Union[str, Any]          =      model.decode(
           decoder_input_ids[:, -1:],		__magic_name__,		past_key_values=outputs_cache.past_key_values,		decoder_attention_mask=__magic_name__,		decoder_position_ids=__magic_name__,		)

       UpperCamelCase__							:				int          =      model.decode(__magic_name__,		__magic_name__,		decoder_attention_mask=__magic_name__							)

       UpperCamelCase__							:				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     lowercase__       (	unittest.TestCase				):

   '''simple docstring'''


   a						:							List[str]   		=		99




   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				int          =      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__							:				Tuple          =      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       UpperCamelCase__  (    self							) ->      Tuple:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Tuple          =      self._get_config_and_data()
       UpperCamelCase__							:				int          =      FlaxBlenderbotForConditionalGeneration(__magic_name__							)
       UpperCamelCase__							:				Dict          =      lm_model(input_ids=__magic_name__							)
       UpperCamelCase__							:				Dict          =      (batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape,		__magic_name__							)




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






       """simple docstring"""
       UpperCamelCase__							:				Any          =      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__							:				Any          =      FlaxBlenderbotForConditionalGeneration(__magic_name__							)
       UpperCamelCase__							:				str          =      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=__magic_name__,		decoder_input_ids=__magic_name__							)
       UpperCamelCase__							:				List[str]          =      (*summary.shape, config.vocab_size)
       self.assertEqual(outputs['''logits'''].shape,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      Union[str, Any]:






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






@require_flax
class     lowercase__       (	__lowerCamelCase			,				unittest.TestCase			,				__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[int]   		=		True
   a						:							Any   		=		(
       (
           FlaxBlenderbotModel,
           FlaxBlenderbotForConditionalGeneration,
       )
       if is_flax_available()
       else ()
   )
   a						:							str   		=		(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()




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






       """simple docstring"""
       UpperCamelCase__							:				str          =      FlaxBlenderbotModelTester(self							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[Any]          =      self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(__magic_name__,		__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      int:






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				List[str]          =      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(__magic_name__,		__magic_name__,		__magic_name__							)




   def       UpperCamelCase__  (    self							) ->      List[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__							:				Optional[Any]          =      self._prepare_for_class(__magic_name__,		__magic_name__							)
               UpperCamelCase__							:				List[Any]          =      model_class(__magic_name__							)

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

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:				List[Any]          =      encode_jitted(**__magic_name__							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:				Union[str, Any]          =      encode_jitted(**__magic_name__							).to_tuple()

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




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






       """simple docstring"""
       UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__							):
               UpperCamelCase__							:				int          =      model_class(__magic_name__							)
               UpperCamelCase__							:				List[Any]          =      model.encode(inputs_dict['''input_ids'''],		inputs_dict['''attention_mask''']							)

               UpperCamelCase__							:				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(__magic_name__,		__magic_name__,		__magic_name__							):
                   return model.decode(
                       decoder_input_ids=__magic_name__,		decoder_attention_mask=__magic_name__,		encoder_outputs=__magic_name__,		)

               with self.subTest('''JIT Enabled'''							):
                   UpperCamelCase__							:				Optional[int]          =      decode_jitted(**__magic_name__							).to_tuple()

               with self.subTest('''JIT Disabled'''							):
                   with jax.disable_jit():
                       UpperCamelCase__							:				Optional[Any]          =      decode_jitted(**__magic_name__							).to_tuple()

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




   @slow
   def       UpperCamelCase__  (    self							) ->      str:






       """simple docstring"""
       for model_class_name in self.all_model_classes:
           UpperCamelCase__							:				Optional[Any]          =      model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''							)
           # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
           UpperCamelCase__							:				Tuple          =      np.ones((1, 1)							) * model.config.eos_token_id
           UpperCamelCase__							:				Optional[Any]          =      model(__magic_name__							)
           self.assertIsNotNone(__magic_name__							)




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






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

       UpperCamelCase__							:				Dict          =      FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B''',		from_pt=__magic_name__							)
       UpperCamelCase__							:				str          =      BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''							)

       UpperCamelCase__							:				Optional[int]          =      ['''Sam''']
       UpperCamelCase__							:				Union[str, Any]          =      tokenizer(__magic_name__,		return_tensors='''jax'''							)

       UpperCamelCase__							:				Tuple          =      model.generate(**__magic_name__,		**__magic_name__							)
       UpperCamelCase__							:				Optional[Any]          =      '''Sam is a great name. It means "sun" in Gaelic.'''

       UpperCamelCase__							:				List[Any]          =      tokenizer.batch_decode(__magic_name__,		**__magic_name__							)
       assert generated_txt[0].strip() == tgt_text








def 		lowerCAmelCase_    (  __UpperCAmelCase:			int = 100_0000      )      ->	int:
    UpperCamelCase__							:				str          =      limit + 1
    UpperCamelCase__							:				List[str]          =      [0] * limit
    for first_term in range(1		,   __UpperCAmelCase      ):
        for n in range(__UpperCAmelCase		,   __UpperCAmelCase		,   __UpperCAmelCase      ):
            UpperCamelCase__							:				str          =      first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    UpperCamelCase__							:				Any          =      sum(1 for x in frequency[1:limit] if x == 10      )

    return count


if __name__ == "__main__":
  print(F'''{solution() = }''')

