





'''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    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      ):





      """simple docstring"""



      if attention_mask is None:
            _snake_case		:	Optional[Any]					     =     np.where(input_ids != config.pad_token_id			,      1			,      0	)
      if decoder_attention_mask is None:
            _snake_case		:	Union[str, Any]					     =     np.where(decoder_input_ids != config.pad_token_id			,      1			,      0	)
      if head_mask is None:
            _snake_case		:	Dict					     =     np.ones((config.encoder_layers, config.encoder_attention_heads)	)
      if decoder_head_mask is None:
            _snake_case		:	List[Any]					     =     np.ones((config.decoder_layers, config.decoder_attention_heads)	)
      if cross_attn_head_mask is None:
            _snake_case		:	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 lowerCamelCase						:







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






        """simple docstring"""





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







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






        """simple docstring"""





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

        _snake_case		:	Any					     =     shift_tokens_right(lowercase__     ,     1     ,     2		)

        _snake_case		:	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=lowercase__     ,     )
        _snake_case		:	List[Any]					     =     prepare_blenderbot_inputs_dict(lowercase__     ,     lowercase__     ,     lowercase__		)
        return config, inputs_dict







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






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Any					     =     self.prepare_config_and_inputs()
        return config, inputs_dict







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	Union[str, Any]					     =     20
        _snake_case		:	int					     =     model_class_name(lowercase__		)

        _snake_case		:	Union[str, Any]					     =     model.encode(inputs_dict['''input_ids''']		)

        _snake_case     ,							_snake_case		:	Optional[Any]					     =     (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

        _snake_case		:	int					     =     model.init_cache(decoder_input_ids.shape[0]     ,     lowercase__     ,     lowercase__		)
        _snake_case		:	Dict					     =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,     dtype='''i4'''		)

        _snake_case		:	Optional[int]					     =     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		:	List[Any]					     =     model.decode(
            decoder_input_ids[:, :-1]     ,     lowercase__     ,     decoder_attention_mask=lowercase__     ,     past_key_values=lowercase__     ,     decoder_position_ids=lowercase__     ,     )

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

        _snake_case		:	Optional[Any]					     =     model.decode(lowercase__     ,     lowercase__		)

        _snake_case		:	Optional[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}'''		)







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__     ,     lowercase__		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Dict					     =     20
        _snake_case		:	Any					     =     model_class_name(lowercase__		)

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

        _snake_case     ,							_snake_case		:	List[str]					     =     (
            inputs_dict['''decoder_input_ids'''],
            inputs_dict['''decoder_attention_mask'''],
        )

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

        _snake_case		:	Tuple					     =     model.init_cache(decoder_input_ids.shape[0]     ,     lowercase__     ,     lowercase__		)
        _snake_case		:	Optional[int]					     =     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		:	List[str]					     =     model.decode(
            decoder_input_ids[:, :-1]     ,     lowercase__     ,     decoder_attention_mask=lowercase__     ,     past_key_values=lowercase__     ,     decoder_position_ids=lowercase__     ,     )
        _snake_case		:	Optional[int]					     =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,     dtype='''i4'''		)
        _snake_case		:	List[str]					     =     model.decode(
            decoder_input_ids[:, -1:]     ,     lowercase__     ,     past_key_values=outputs_cache.past_key_values     ,     decoder_attention_mask=lowercase__     ,     decoder_position_ids=lowercase__     ,     )

        _snake_case		:	Optional[Any]					     =     model.decode(lowercase__     ,     lowercase__     ,     decoder_attention_mask=lowercase__		)

        _snake_case		:	int					     =     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 lowerCamelCase						(unittest.TestCase					):
  _lowercase  :	Optional[Any]       					=				99







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






        """simple docstring"""





        _snake_case		:	Dict					     =     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		:	Union[str, Any]					     =     input_ids.shape[0]
        _snake_case		:	Union[str, Any]					     =     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"""





        _snake_case     ,							_snake_case     ,							_snake_case		:	Tuple					     =     self._get_config_and_data()
        _snake_case		:	Dict					     =     FlaxBlenderbotForConditionalGeneration(lowercase__		)
        _snake_case		:	List[Any]					     =     lm_model(input_ids=lowercase__		)
        _snake_case		:	Optional[Any]					     =     (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case		:	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     ,     )
        _snake_case		:	Optional[Any]					     =     FlaxBlenderbotForConditionalGeneration(lowercase__		)
        _snake_case		:	Dict					     =     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,     dtype=np.intaa		)
        _snake_case		:	Union[str, Any]					     =     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,     dtype=np.intaa		)
        _snake_case		:	List[str]					     =     lm_model(input_ids=lowercase__     ,     decoder_input_ids=lowercase__		)
        _snake_case		:	Any					     =     (*summary.shape, config.vocab_size)
        self.assertEqual(outputs['''logits'''].shape     ,     lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Any:






        """simple docstring"""





        _snake_case		:	Any					     =     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,     dtype=np.intaa		)
        _snake_case		:	Optional[int]					     =     shift_tokens_right(lowercase__     ,     1     ,     2		)
        _snake_case		:	Optional[int]					     =     np.equal(lowercase__     ,     1		).astype(np.floataa		).sum()
        _snake_case		:	Dict					     =     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 lowerCamelCase						(a__						,      unittest.TestCase						,      a__					):
  _lowercase  :	Optional[Any]       					=				True
  _lowercase  :	List[str]       					=				(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  _lowercase  :	Tuple       					=				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        _snake_case		:	Any					     =     FlaxBlenderbotModelTester(self		)







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






        """simple docstring"""





        _snake_case     ,							_snake_case		:	Optional[int]					     =     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"""





        _snake_case     ,							_snake_case		:	Optional[int]					     =     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"""





        _snake_case     ,							_snake_case		:	int					     =     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		:	Dict					     =     self._prepare_for_class(lowercase__     ,     lowercase__		)
                    _snake_case		:	List[str]					     =     model_class(lowercase__		)

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

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

                    with self.subTest('''JIT Disabled'''		):
                          with jax.disable_jit():
                                _snake_case		:	Tuple					     =     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"""





        _snake_case     ,							_snake_case		:	Dict					     =     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		:	Optional[int]					     =     model_class(lowercase__		)
                    _snake_case		:	Tuple					     =     model.encode(inputs_dict['''input_ids''']     ,     inputs_dict['''attention_mask''']		)

                    _snake_case		:	List[Any]					     =     {
                        '''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(lowercase__     ,     lowercase__     ,     lowercase__		):
                          return model.decode(
                              decoder_input_ids=lowercase__     ,     decoder_attention_mask=lowercase__     ,     encoder_outputs=lowercase__     ,     )

                    with self.subTest('''JIT Enabled'''		):
                          _snake_case		:	List[str]					     =     decode_jitted(**lowercase__		).to_tuple()

                    with self.subTest('''JIT Disabled'''		):
                          with jax.disable_jit():
                                _snake_case		:	Any					     =     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:
              _snake_case		:	Dict					     =     model_class_name.from_pretrained('''facebook/blenderbot-400M-distill'''		)
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              _snake_case		:	List[Any]					     =     np.ones((1, 1)		) * model.config.eos_token_id
              _snake_case		:	Union[str, Any]					     =     model(lowercase__		)
              self.assertIsNotNone(lowercase__		)







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






        """simple docstring"""





        _snake_case		:	str					     =     {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
        _snake_case		:	Tuple					     =     {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

        _snake_case		:	Optional[Any]					     =     FlaxBlenderbotForConditionalGeneration.from_pretrained('''facebook/blenderbot-3B'''     ,     from_pt=lowercase__		)
        _snake_case		:	int					     =     BlenderbotTokenizer.from_pretrained('''facebook/blenderbot-3B'''		)

        _snake_case		:	Any					     =     ['''Sam''']
        _snake_case		:	List[str]					     =     tokenizer(lowercase__     ,     return_tensors='''jax'''		)

        _snake_case		:	Union[str, Any]					     =     model.generate(**lowercase__     ,     **lowercase__		)
        _snake_case		:	Tuple					     =     '''Sam is a great name. It means "sun" in Gaelic.'''

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









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	List[Any]					     =     0
      if start < end:
            _snake_case		:	List[Any]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
            _snake_case		:	Any					     =     a[end]
            _snake_case		:	List[str]					     =     a[pivot]
            _snake_case		:	Optional[int]					     =     temp

            _snake_case     ,							_snake_case		:	List[Any]					     =     _in_place_partition(lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      lowerCAmelCase_			,      p - 1	)
            count += _in_place_quick_sort(lowerCAmelCase_			,      p + 1			,      lowerCAmelCase_	)
      return count




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     0
      _snake_case		:	Optional[int]					     =     randint(lowerCAmelCase_			,      lowerCAmelCase_	)
      _snake_case		:	Tuple					     =     a[end]
      _snake_case		:	Optional[Any]					     =     a[pivot]
      _snake_case		:	Union[str, Any]					     =     temp
      _snake_case		:	Union[str, Any]					     =     start - 1
      for index in range(lowerCAmelCase_			,      lowerCAmelCase_	):
            count += 1
            if a[index] < a[end]:  # check if current val is less than pivot value
                  _snake_case		:	Optional[int]					     =     new_pivot_index + 1
                  _snake_case		:	Optional[Any]					     =     a[new_pivot_index]
                  _snake_case		:	Tuple					     =     a[index]
                  _snake_case		:	str					     =     temp

      _snake_case		:	Any					     =     a[new_pivot_index + 1]
      _snake_case		:	str					     =     a[end]
      _snake_case		:	Optional[int]					     =     temp
      return new_pivot_index + 1, count


UpperCAmelCase					:		Dict		=							TemporaryFile()
UpperCAmelCase					:		Dict		=							1_0_0  # 1000 elements are to be sorted


UpperCAmelCase,       UpperCAmelCase					:		str		=							0, 1  # mean and standard deviation
UpperCAmelCase					:		Optional[Any]		=							np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('The array is')
print(X)


outfile.seek(0)  # using the same array
UpperCAmelCase					:		int		=							np.load(outfile)
UpperCAmelCase					:		Optional[int]		=							len(M) - 1
UpperCAmelCase					:		str		=							_in_place_quick_sort(M, 0, r)

print(
    'No of Comparisons for 100 elements selected from a standard normal distribution'
    'is :'
)
print(z)


