





'''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
      __lowerCamelCase						: Dict		=   '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   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      __SCREAMING_SNAKE_CASE=None  ,      ):


  """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 		UpperCAmelCase   :


  """simple docstring"""







  def __init__(				self    :					int							,	UpperCamelCase__    :					List[str]							,	UpperCamelCase__    :					int=13							,	UpperCamelCase__    :					List[str]=7							,	UpperCamelCase__    :					str=True							,	UpperCamelCase__    :					int=False							,	UpperCamelCase__    :					Optional[Any]=99							,	UpperCamelCase__    :					Optional[Any]=16							,	UpperCamelCase__    :					Dict=2							,	UpperCamelCase__    :					Union[str, Any]=4							,	UpperCamelCase__    :					List[Any]=4							,	UpperCamelCase__    :					List[str]="gelu"							,	UpperCamelCase__    :					int=0.1							,	UpperCamelCase__    :					Optional[int]=0.1							,	UpperCamelCase__    :					Any=32							,	UpperCamelCase__    :					Dict=2							,	UpperCamelCase__    :					Dict=1							,	UpperCamelCase__    :					int=0							,	UpperCamelCase__    :					List[str]=0.02							,	)     ->						Tuple:
    _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   UpperCamelCase__      (				self    :					Tuple  )     ->						int:
    _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(UpperCamelCase__							,	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=UpperCamelCase__							,	)
    _UpperCamelCase       			=prepare_blenderbot_inputs_dict(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )
    return config, inputs_dict


  def   UpperCamelCase__      (				self    :					str  )     ->						Any:
    _UpperCamelCase			,     _UpperCamelCase       			=self.prepare_config_and_inputs()
    return config, inputs_dict


  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					str							,	UpperCamelCase__    :					str  )     ->						Optional[Any]:
    _UpperCamelCase       			=20
    _UpperCamelCase       			=model_class_name(UpperCamelCase__  )

    _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]							,	UpperCamelCase__							,	UpperCamelCase__  )
    _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]							,	UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__							,	past_key_values=UpperCamelCase__							,	decoder_position_ids=UpperCamelCase__							,	)

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

    _UpperCamelCase       			=model.decode(UpperCamelCase__							,	UpperCamelCase__  )

    _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   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					Any							,	UpperCamelCase__    :					Dict  )     ->						Any:
    _UpperCamelCase       			=20
    _UpperCamelCase       			=model_class_name(UpperCamelCase__  )

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

    _UpperCamelCase       			=model.decode(UpperCamelCase__							,	UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__  )

    _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 		UpperCAmelCase   (				unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				99


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						Tuple:
    _UpperCamelCase       			=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       			=input_ids.shape[0]
    _UpperCamelCase       			=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  )     ->						List[str]:
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=self._get_config_and_data()
    _UpperCamelCase       			=FlaxBlenderbotForConditionalGeneration(UpperCamelCase__  )
    _UpperCamelCase       			=lm_model(input_ids=UpperCamelCase__  )
    _UpperCamelCase       			=(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs['''logits'''].shape							,	UpperCamelCase__  )


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




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







@require_flax
class 		UpperCAmelCase   (				lowercase_   ,     unittest.TestCase   ,     lowercase_):


  """simple docstring"""







  lowerCAmelCase_					=				True
  lowerCAmelCase_					=				(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  lowerCAmelCase_					=				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()


  def   UpperCamelCase__      (				self    :					Tuple  )     ->						Any:
    _UpperCamelCase       			=FlaxBlenderbotModelTester(self  )


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


  def   UpperCamelCase__      (				self    :					int  )     ->						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(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Union[str, Any]:
    _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(UpperCamelCase__							,	UpperCamelCase__  )
        _UpperCamelCase       			=model_class(UpperCamelCase__  )

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

        with self.subTest('''JIT Enabled'''  ):
          _UpperCamelCase       			=encode_jitted(**UpperCamelCase__  ).to_tuple()

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

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


  def   UpperCamelCase__      (				self    :					int  )     ->						List[str]:
    _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(UpperCamelCase__  )
        _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(UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					Dict							,	UpperCamelCase__    :					Tuple  ):
          return model.decode(
              decoder_input_ids=UpperCamelCase__							,	decoder_attention_mask=UpperCamelCase__							,	encoder_outputs=UpperCamelCase__							,	)

        with self.subTest('''JIT Enabled'''  ):
          _UpperCamelCase       			=decode_jitted(**UpperCamelCase__  ).to_tuple()

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

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


  @slow
  def   UpperCamelCase__      (				self    :					List[str]  )     ->						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(UpperCamelCase__  )
      self.assertIsNotNone(UpperCamelCase__  )




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

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

    _UpperCamelCase       			=['''Sam''']
    _UpperCamelCase       			=tokenizer(UpperCamelCase__							,	return_tensors='''jax'''  )

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

    _UpperCamelCase       			=tokenizer.batch_decode(UpperCamelCase__							,	**UpperCamelCase__  )
    assert generated_txt[0].strip() == tgt_text













'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1 or len(__SCREAMING_SNAKE_CASE   ) <= key:
    return input_string

  for position, character in enumerate(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[''''''.join(__SCREAMING_SNAKE_CASE   ) for row in temp_grid]
  _UpperCamelCase       			=''''''.join(__SCREAMING_SNAKE_CASE   )

  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1:
    return input_string

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]  # generates template
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append('''*'''   )

  _UpperCamelCase       			=0
  for row in temp_grid:  # fills in the characters
    _UpperCamelCase       			=input_string[counter : counter + len(__SCREAMING_SNAKE_CASE   )]
    grid.append(list(__SCREAMING_SNAKE_CASE   )   )
    counter += len(__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=''''''  # reads as zigzag
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    output_string += grid[num][0]
    grid[num].pop(0   )
  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			={}
  for key_guess in range(1  ,      len(__SCREAMING_SNAKE_CASE   )   ):  # tries every key
    _UpperCamelCase       			=decrypt(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return results


if __name__ == "__main__":
      import doctest

      doctest.testmod()






