


'''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
   lowercase__				     =		"platform"

   import jax
   import jax.numpy as jnp

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






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: Optional[Any]      ,  __lowerCamelCase		: str=None      ,  __lowerCamelCase		: List[Any]=None      ,  __lowerCamelCase		: str=None      ,  __lowerCamelCase		: int=None      ,  __lowerCamelCase		: Union[str, Any]=None      ,  __lowerCamelCase		: int=None      ,  )      ->     Tuple:







     '''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     ,							__UpperCamelCase     ,							__UpperCamelCase=13     ,							__UpperCamelCase=7     ,							__UpperCamelCase=True     ,							__UpperCamelCase=False     ,							__UpperCamelCase=99     ,							__UpperCamelCase=16     ,							__UpperCamelCase=2     ,							__UpperCamelCase=4     ,							__UpperCamelCase=4     ,							__UpperCamelCase="gelu"     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=32     ,							__UpperCamelCase=2     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=0.02     ,							)       ->    Optional[Any]:
          _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  a_					(			self		)       ->    int:
          _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(__UpperCamelCase     ,							1     ,							2		)

          _a						=		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=__UpperCamelCase     ,							)
          _a						=		prepare_blenderbot_inputs_dict(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)
          return config, inputs_dict






     def  a_					(			self		)       ->    List[Any]:
          _a			,			_a						=		self.prepare_config_and_inputs()
          return config, inputs_dict






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    Any:
          _a						=		20
          _a						=		model_class_name(__UpperCamelCase		)

          _a						=		model.encode(inputs_dict["input_ids"]		)

          _a			,			_a						=		(
              inputs_dict["decoder_input_ids"],
              inputs_dict["decoder_attention_mask"],
          )

          _a						=		model.init_cache(decoder_input_ids.shape[0]     ,							__UpperCamelCase     ,							__UpperCamelCase		)
          _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]     ,							__UpperCamelCase     ,							decoder_attention_mask=__UpperCamelCase     ,							past_key_values=__UpperCamelCase     ,							decoder_position_ids=__UpperCamelCase     ,							)

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

          _a						=		model.decode(__UpperCamelCase     ,							__UpperCamelCase		)

          _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  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)       ->    List[str]:
          _a						=		20
          _a						=		model_class_name(__UpperCamelCase		)

          _a						=		model.encode(inputs_dict["input_ids"]		)

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

          _a						=		model.decode(__UpperCamelCase     ,							__UpperCamelCase     ,							decoder_attention_mask=__UpperCamelCase		)

          _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 ):
     UpperCAmelCase 					=				99






     def  a_					(			self		)       ->    Any:
          _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						=		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		)       ->    Optional[int]:
          _a			,			_a			,			_a						=		self._get_config_and_data()
          _a						=		FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase		)
          _a						=		lm_model(input_ids=__UpperCamelCase		)
          _a						=		(batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs["logits"].shape     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    Optional[Any]:
          _a						=		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     ,							)
          _a						=		FlaxBlenderbotSmallForConditionalGeneration(__UpperCamelCase		)
          _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=__UpperCamelCase     ,							decoder_input_ids=__UpperCamelCase		)
          _a						=		(*summary.shape, config.vocab_size)
          self.assertEqual(outputs["logits"].shape     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    Dict:
          _a						=		np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]     ,							dtype=np.intaa		)
          _a						=		shift_tokens_right(__UpperCamelCase     ,							1     ,							2		)
          _a						=		np.equal(__UpperCamelCase     ,							1		).astype(np.floataa		).sum()
          _a						=		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 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			unittest.TestCase ,			lowerCamelCase__ ):
     UpperCAmelCase 					=				True
     UpperCAmelCase 					=				(
         (
             FlaxBlenderbotSmallModel,
             FlaxBlenderbotSmallForConditionalGeneration,
         )
         if is_flax_available()
         else ()
     )
     UpperCAmelCase 					=				(FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






     def  a_					(			self		)       ->    str:
          _a						=		FlaxBlenderbotSmallModelTester(self		)






     def  a_					(			self		)       ->    List[Any]:
          _a			,			_a						=		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  a_					(			self		)       ->    int:
          _a			,			_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(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)






     def  a_					(			self		)       ->    Dict:
          _a			,			_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(__UpperCamelCase     ,							__UpperCamelCase		)
                    _a						=		model_class(__UpperCamelCase		)

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

                    with self.subTest("JIT Enabled"		):
                         _a						=		encode_jitted(**__UpperCamelCase		).to_tuple()

                    with self.subTest("JIT Disabled"		):
                         with jax.disable_jit():
                              _a						=		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  a_					(			self		)       ->    List[Any]:
          _a			,			_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(__UpperCamelCase		)
                    _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(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		):
                         return model.decode(
                             decoder_input_ids=__UpperCamelCase     ,							decoder_attention_mask=__UpperCamelCase     ,							encoder_outputs=__UpperCamelCase     ,							)

                    with self.subTest("JIT Enabled"		):
                         _a						=		decode_jitted(**__UpperCamelCase		).to_tuple()

                    with self.subTest("JIT Disabled"		):
                         with jax.disable_jit():
                              _a						=		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  a_					(			self		)       ->    Tuple:
          for model_class_name in self.all_model_classes:
               _a						=		model_class_name.from_pretrained("facebook/blenderbot_small-90M"		)
               # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
               _a						=		np.ones((1, 1)		) * model.config.eos_token_id
               _a						=		model(__UpperCamelCase		)
               self.assertIsNotNone(__UpperCamelCase		)





'''simple docstring'''

lowercase__				     =		256
# Modulus to hash a string
lowercase__				     =		1_000_003






def __UpperCamelCase   (			__lowerCamelCase		: str      ,  __lowerCamelCase		: str				)      ->     bool:







     '''simple docstring'''






     _a						=		len(__lowerCamelCase				)
     _a						=		len(__lowerCamelCase				)
     if p_len > t_len:
          return False

     _a						=		0
     _a						=		0
     _a						=		1

     # Calculating the hash of pattern and substring of text
     for i in range(__lowerCamelCase				):
          _a						=		(ord(pattern[i]				) + p_hash * alphabet_size) % modulus
          _a						=		(ord(text[i]				) + text_hash * alphabet_size) % modulus
          if i == p_len - 1:
               continue
          _a						=		(modulus_power * alphabet_size) % modulus

     for i in range(0      ,  t_len - p_len + 1				):
          if text_hash == p_hash and text[i : i + p_len] == pattern:
               return True
          if i == t_len - p_len:
               continue
          # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
          _a						=		(
              (text_hash - ord(text[i]				) * modulus_power) * alphabet_size
              + ord(text[i + p_len]				)
          ) % modulus
     return False






def __UpperCamelCase   (			)      ->     None:







     '''simple docstring'''






     _a						=		"abc1abc12"
     _a						=		"alskfjaldsabc1abc1abc12k23adsfabcabc"
     _a						=		"alskfjaldsk23adsfabcabc"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				) and not rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 2)
     _a						=		"ABABX"
     _a						=		"ABABZABABYABABX"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 3)
     _a						=		"AAAB"
     _a						=		"ABAAAAAB"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 4)
     _a						=		"abcdabcy"
     _a						=		"abcxabcdabxabcdabcdabcy"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)

     # Test 5)
     _a						=		"Lü"
     _a						=		"Lüsai"
     assert rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)
     _a						=		"Lue"
     assert not rabin_karp(__lowerCamelCase      ,  __lowerCamelCase				)
     print("Success."				)


if __name__ == "__main__":
   test_rabin_karp()

