"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import 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
     _a				:		Dict	   =       'platform'

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )





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






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

       _lowerCAmelCase						:	Dict        =					shift_tokens_right(a__    ,					1    ,					2  )

       _lowerCAmelCase						:	Union[str, Any]        =					BlenderbotSmallConfig(
           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=a__    ,					)
       _lowerCAmelCase						:	List[str]        =					prepare_blenderbot_inputs_dict(a__    ,					a__    ,					a__  )
       return config, inputs_dict






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[str]        =					self.prepare_config_and_inputs()
       return config, inputs_dict






     def    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	List[Any]        =					20
       _lowerCAmelCase						:	Any        =					model_class_name(a__  )

       _lowerCAmelCase						:	Optional[int]        =					model.encode(inputs_dict["""input_ids"""]  )

       _lowerCAmelCase      ,     _lowerCAmelCase						:	Dict        =					(
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

       _lowerCAmelCase						:	Union[str, Any]        =					model.init_cache(decoder_input_ids.shape[0]    ,					a__    ,					a__  )
       _lowerCAmelCase						:	List[Any]        =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)    ,					dtype="""i4"""  )

       _lowerCAmelCase						:	int        =					jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]    ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,					)
       _lowerCAmelCase						:	Optional[int]        =					model.decode(
           decoder_input_ids[:, :-1]    ,					a__    ,					decoder_attention_mask=a__    ,					past_key_values=a__    ,					decoder_position_ids=a__    ,					)

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

       _lowerCAmelCase						:	Union[str, Any]        =					model.decode(a__    ,					a__  )

       _lowerCAmelCase						:	List[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    __A  (		self    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Tuple        =					20
       _lowerCAmelCase						:	Any        =					model_class_name(a__  )

       _lowerCAmelCase						:	Optional[int]        =					model.encode(inputs_dict["""input_ids"""]  )

       _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[Any]        =					(
           inputs_dict["""decoder_input_ids"""],
           inputs_dict["""decoder_attention_mask"""],
       )

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

       _lowerCAmelCase						:	List[Any]        =					model.init_cache(decoder_input_ids.shape[0]    ,					a__    ,					a__  )
       _lowerCAmelCase						:	Tuple        =					jnp.broadcast_to(
           jnp.arange(decoder_input_ids.shape[-1] - 1  )[None, :]    ,					(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)    ,					)

       _lowerCAmelCase						:	Union[str, Any]        =					model.decode(
           decoder_input_ids[:, :-1]    ,					a__    ,					decoder_attention_mask=a__    ,					past_key_values=a__    ,					decoder_position_ids=a__    ,					)
       _lowerCAmelCase						:	Tuple        =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]    ,					dtype="""i4"""  )
       _lowerCAmelCase						:	Tuple        =					model.decode(
           decoder_input_ids[:, -1:]    ,					a__    ,					past_key_values=outputs_cache.past_key_values    ,					decoder_attention_mask=a__    ,					decoder_position_ids=a__    ,					)

       _lowerCAmelCase						:	str        =					model.decode(a__    ,					a__    ,					decoder_attention_mask=a__  )

       _lowerCAmelCase						:	Dict        =					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		):
     _UpperCamelCase							:      str		 =		99






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					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    ,					)

       _lowerCAmelCase						:	List[str]        =					input_ids.shape[0]
       _lowerCAmelCase						:	Dict        =					BlenderbotSmallConfig(
           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    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					self._get_config_and_data()
       _lowerCAmelCase						:	Union[str, Any]        =					FlaxBlenderbotSmallForConditionalGeneration(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					lm_model(input_ids=a__  )
       _lowerCAmelCase						:	Optional[Any]        =					(batch_size, input_ids.shape[1], config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					BlenderbotSmallConfig(
           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    ,					)
       _lowerCAmelCase						:	Dict        =					FlaxBlenderbotSmallForConditionalGeneration(a__  )
       _lowerCAmelCase						:	int        =					np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]    ,					dtype=np.intaa  )
       _lowerCAmelCase						:	List[str]        =					np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]    ,					dtype=np.intaa  )
       _lowerCAmelCase						:	List[Any]        =					lm_model(input_ids=a__    ,					decoder_input_ids=a__  )
       _lowerCAmelCase						:	str        =					(*summary.shape, config.vocab_size)
       self.assertEqual(outputs["""logits"""].shape    ,					a__  )






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







@require_flax
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase   ,     SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Dict		 =		True
     _UpperCamelCase							:      Union[str, Any]		 =		(
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     _UpperCamelCase							:      Dict		 =		(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					FlaxBlenderbotSmallModelTester(self  )






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         self.model_tester.check_use_cache_forward(a__    ,					a__    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	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(a__    ,					a__    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	str        =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__  ):
           _lowerCAmelCase						:	Optional[Any]        =					self._prepare_for_class(a__    ,					a__  )
           _lowerCAmelCase						:	Dict        =					model_class(a__  )

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

           with self.subTest("""JIT Enabled"""  ):
             _lowerCAmelCase						:	Optional[Any]        =					encode_jitted(**a__  ).to_tuple()

           with self.subTest("""JIT Disabled"""  ):
             with jax.disable_jit():
               _lowerCAmelCase						:	Optional[Any]        =					encode_jitted(**a__  ).to_tuple()

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






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__  ):
           _lowerCAmelCase						:	List[Any]        =					model_class(a__  )
           _lowerCAmelCase						:	Tuple        =					model.encode(inputs_dict["""input_ids"""]    ,					inputs_dict["""attention_mask"""]  )

           _lowerCAmelCase						:	Optional[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(a__    ,					a__    ,					a__  ):
             return model.decode(
                 decoder_input_ids=a__    ,					decoder_attention_mask=a__    ,					encoder_outputs=a__    ,					)

           with self.subTest("""JIT Enabled"""  ):
             _lowerCAmelCase						:	Union[str, Any]        =					decode_jitted(**a__  ).to_tuple()

           with self.subTest("""JIT Disabled"""  ):
             with jax.disable_jit():
               _lowerCAmelCase						:	List[Any]        =					decode_jitted(**a__  ).to_tuple()

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






     @slow
     def    __A  (		self  ):
       for model_class_name in self.all_model_classes:
         _lowerCAmelCase						:	Tuple        =					model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""  )
         # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
         _lowerCAmelCase						:	Optional[Any]        =					np.ones((1, 1)  ) * model.config.eos_token_id
         _lowerCAmelCase						:	Optional[int]        =					model(a__  )
         self.assertIsNotNone(a__  )





"""simple docstring"""




import math





def 							SCREAMING_SNAKE_CASE					(      )	->				None:
  _lowerCAmelCase						:	Any        =					input("""Enter message: """     )
  _lowerCAmelCase						:	List[Any]        =					int(input(f"Enter key [2-{len(_lowerCamelCase     ) - 1}]: "     )     )
  _lowerCAmelCase						:	Optional[Any]        =					input("""Encryption/Decryption [e/d]: """     )

  if mode.lower().startswith("""e"""     ):
    _lowerCAmelCase						:	Tuple        =					encrypt_message(_lowerCamelCase      ,_lowerCamelCase     )
  elif mode.lower().startswith("""d"""     ):
    _lowerCAmelCase						:	Dict        =					decrypt_message(_lowerCamelCase      ,_lowerCamelCase     )

  # Append pipe symbol (vertical bar) to identify spaces at the end.
  print(f"Output:\n{text + '|'}"     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	Dict        =					[""""""] * key
  for col in range(_lowerCamelCase     ):
    _lowerCAmelCase						:	List[str]        =					col
    while pointer < len(_lowerCamelCase     ):
      cipher_text[col] += message[pointer]
      pointer += key
  return "".join(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	int      ,_lowerCamelCase						:	str     )	->				str:
  _lowerCAmelCase						:	str        =					math.ceil(len(_lowerCamelCase     ) / key     )
  _lowerCAmelCase						:	Union[str, Any]        =					key
  _lowerCAmelCase						:	Any        =					(num_cols * num_rows) - len(_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					[""""""] * num_cols
  _lowerCAmelCase						:	Optional[Any]        =					0
  _lowerCAmelCase						:	Dict        =					0

  for symbol in message:
    plain_text[col] += symbol
    col += 1

    if (
        (col == num_cols)
        or (col == num_cols - 1)
        and (row >= num_rows - num_shaded_boxes)
    ):
      _lowerCAmelCase						:	str        =					0
      row += 1

  return "".join(_lowerCamelCase     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




