


'''simple docstring'''

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel





class 						__SCREAMING_SNAKE_CASE					:






     def __init__(			self     ,							__UpperCamelCase     ,							__UpperCamelCase=99     ,							__UpperCamelCase=13     ,							__UpperCamelCase=7     ,							__UpperCamelCase=9     ,							__UpperCamelCase=True     ,							__UpperCamelCase=True     ,							__UpperCamelCase=False     ,							__UpperCamelCase=32     ,							__UpperCamelCase=5     ,							__UpperCamelCase=4     ,							__UpperCamelCase=37     ,							__UpperCamelCase=8     ,							__UpperCamelCase=0.1     ,							__UpperCamelCase=0.0_02     ,							__UpperCamelCase=1     ,							__UpperCamelCase=0     ,							__UpperCamelCase=0     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							)       ->    List[Any]:
          _a						=		parent
          _a						=		batch_size
          _a						=		encoder_seq_length
          _a						=		decoder_seq_length
          # For common tests
          _a						=		self.decoder_seq_length
          _a						=		is_training
          _a						=		use_attention_mask
          _a						=		use_labels
          _a						=		vocab_size
          _a						=		hidden_size
          _a						=		num_hidden_layers
          _a						=		num_attention_heads
          _a						=		d_ff
          _a						=		relative_attention_num_buckets
          _a						=		dropout_rate
          _a						=		initializer_factor
          _a						=		eos_token_id
          _a						=		pad_token_id
          _a						=		decoder_start_token_id
          _a						=		None
          _a						=		decoder_layers






     def  a_					(			self		)       ->    int:
          return TaConfig.from_pretrained("google/umt5-base"		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							__UpperCamelCase=None     ,							)       ->    int:
          if attention_mask is None:
               _a						=		input_ids.ne(config.pad_token_id		)
          if decoder_attention_mask is None:
               _a						=		decoder_input_ids.ne(config.pad_token_id		)
          if head_mask is None:
               _a						=		torch.ones(config.num_hidden_layers     ,							config.num_attention_heads     ,							device=__UpperCamelCase		)
          if decoder_head_mask is None:
               _a						=		torch.ones(config.num_decoder_layers     ,							config.num_attention_heads     ,							device=__UpperCamelCase		)
          if cross_attn_head_mask is None:
               _a						=		torch.ones(
                   config.num_decoder_layers     ,							config.num_attention_heads     ,							device=__UpperCamelCase		)
          return {
              "input_ids": input_ids,
              "decoder_input_ids": decoder_input_ids,
              "attention_mask": attention_mask,
              "decoder_attention_mask": decoder_attention_mask,
              "head_mask": head_mask,
              "decoder_head_mask": decoder_head_mask,
              "cross_attn_head_mask": cross_attn_head_mask,
          }






     def  a_					(			self		)       ->    Optional[int]:
          _a						=		ids_tensor([self.batch_size, self.encoder_seq_length]     ,							self.vocab_size		)
          _a						=		ids_tensor([self.batch_size, self.decoder_seq_length]     ,							self.vocab_size		)

          # we need to clamp the input ids here to avoid having pad token in between
          # this is because for NllbMoe the position_ids are prepared such that
          # all pad tokens have pos id = 2 and rest are between 2..seq_length
          # and the seq_length here is seq_length - num_pad_tokens
          # but when using past, there is no way of knowing if the past input ids had
          # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
          # position_ids being off by num_pad_tokens in past input
          _a						=		input_ids.clamp(self.pad_token_id + 1		)
          _a						=		decoder_input_ids.clamp(self.pad_token_id + 1		)

          _a						=		self.get_config()
          _a						=		config.num_attention_heads
          _a						=		self.prepare_inputs_dict(__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase		)
          return config, input_dict






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






     def  a_					(			self		)       ->    int:
          return TaConfig(
              vocab_size=166     ,							d_model=self.hidden_size     ,							d_ff=self.d_ff     ,							d_kv=self.hidden_size // self.num_attention_heads     ,							num_layers=self.num_hidden_layers     ,							num_decoder_layers=self.decoder_layers     ,							num_heads=self.num_attention_heads     ,							relative_attention_num_buckets=self.relative_attention_num_buckets     ,							dropout_rate=self.dropout_rate     ,							initializer_factor=self.initializer_factor     ,							eos_token_id=self.eos_token_id     ,							bos_token_id=self.pad_token_id     ,							pad_token_id=self.pad_token_id     ,							decoder_start_token_id=self.decoder_start_token_id     ,							)






     def  a_					(			self		)       ->    List[Any]:
          return TaConfig(
              vocab_size=self.vocab_size     ,							d_model=self.hidden_size     ,							d_ff=self.d_ff     ,							d_kv=self.hidden_size // self.num_attention_heads     ,							num_layers=self.num_hidden_layers     ,							num_decoder_layers=self.decoder_layers     ,							num_heads=self.num_attention_heads     ,							relative_attention_num_buckets=self.relative_attention_num_buckets     ,							dropout_rate=self.dropout_rate     ,							initializer_factor=self.initializer_factor     ,							eos_token_id=self.eos_token_id     ,							bos_token_id=self.pad_token_id     ,							pad_token_id=self.pad_token_id     ,							decoder_start_token_id=self.decoder_start_token_id     ,							)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							)       ->    str:
          _a						=		UMTaModel(config=__UpperCamelCase		)
          model.to(__UpperCamelCase		)
          model.eval()
          _a						=		model(
              input_ids=__UpperCamelCase     ,							decoder_input_ids=__UpperCamelCase     ,							attention_mask=__UpperCamelCase     ,							decoder_attention_mask=__UpperCamelCase     ,							)
          _a						=		model(input_ids=__UpperCamelCase     ,							decoder_input_ids=__UpperCamelCase		)
          _a						=		result.last_hidden_state
          _a						=		result.past_key_values
          _a						=		result.encoder_last_hidden_state

          self.parent.assertEqual(encoder_output.size()     ,							(self.batch_size, self.encoder_seq_length, self.hidden_size)		)
          self.parent.assertEqual(decoder_output.size()     ,							(self.batch_size, self.decoder_seq_length, self.hidden_size)		)
          # There should be `num_layers` key value embeddings stored in decoder_past
          self.parent.assertEqual(len(__UpperCamelCase		)     ,							config.num_layers		)
          # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
          self.parent.assertEqual(len(decoder_past[0]		)     ,							4		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							)       ->    Any:
          _a						=		UMTaModel(config=__UpperCamelCase		).get_decoder().to(__UpperCamelCase		).eval()
          # first forward pass
          _a						=		model(__UpperCamelCase     ,							use_cache=__UpperCamelCase		)
          _a						=		model(__UpperCamelCase		)
          _a						=		model(__UpperCamelCase     ,							use_cache=__UpperCamelCase		)

          self.parent.assertTrue(len(__UpperCamelCase		) == len(__UpperCamelCase		)		)
          self.parent.assertTrue(len(__UpperCamelCase		) == len(__UpperCamelCase		) + 1		)

          _a			,			_a						=		outputs.to_tuple()

          # create hypothetical next token and extent to next_input_ids
          _a						=		ids_tensor((self.batch_size, 1)     ,							config.vocab_size		)

          # append to next input_ids and
          _a						=		torch.cat([input_ids, next_tokens]     ,							dim=-1		)

          _a						=		model(__UpperCamelCase		)["last_hidden_state"]
          _a						=		model(__UpperCamelCase     ,							past_key_values=__UpperCamelCase		)["last_hidden_state"]

          # select random slice
          _a						=		ids_tensor((1,)     ,							output_from_past.shape[-1]		).item()
          _a						=		output_from_no_past[:, -1, random_slice_idx].detach()
          _a						=		output_from_past[:, 0, random_slice_idx].detach()

          # test that outputs are equal for slice
          self.parent.assertTrue(torch.allclose(__UpperCamelCase     ,							__UpperCamelCase     ,							atol=1e-3		)		)






     def  a_					(			self     ,							__UpperCamelCase     ,							__UpperCamelCase     ,							)       ->    Optional[int]:
          _a						=		UMTaModel(config=__UpperCamelCase		).to(__UpperCamelCase		).half().eval()
          _a						=		model(**__UpperCamelCase		)["last_hidden_state"]
          self.parent.assertFalse(torch.isnan(__UpperCamelCase		).any().item()		)







@require_torch
class 						__SCREAMING_SNAKE_CASE					(      lowerCamelCase__ ,			lowerCamelCase__ ,			lowerCamelCase__ ,			unittest.TestCase ):
     UpperCAmelCase 					=				(
         (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
     )
     UpperCAmelCase 					=				(UMTaForConditionalGeneration,) if is_torch_available() else ()
     UpperCAmelCase 					=				(
         {
             '''conversational''': UMTaForConditionalGeneration,
             '''feature-extraction''': UMTaModel,
             '''summarization''': UMTaForConditionalGeneration,
             '''text2text-generation''': UMTaForConditionalGeneration,
             '''translation''': UMTaForConditionalGeneration,
             '''question-answering''': UMTaForQuestionAnswering,
         }
         if is_torch_available()
         else {}
     )
     UpperCAmelCase 					=				True
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				False
     UpperCAmelCase 					=				True
     UpperCAmelCase 					=				True
     # The small UMT5 model needs higher percentages for CPU/MP tests
     UpperCAmelCase 					=				[0.8, 0.9]






     def  a_					(			self		)       ->    Any:
          _a						=		UMTaModelTester(self		)






     @unittest.skip("Test has a segmentation fault on torch 1.8.0"		)
     def  a_					(			self		)       ->    Any:
          _a						=		self.model_tester.prepare_config_and_inputs()
          _a						=		UMTaModel(config_and_inputs[0]		).to(__UpperCamelCase		)
          with tempfile.TemporaryDirectory() as tmpdirname:
               torch.onnx.export(
                   __UpperCamelCase     ,							(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])     ,							f"{tmpdirname}/t5_test.onnx"     ,							export_params=__UpperCamelCase     ,							opset_version=9     ,							input_names=["input_ids", "decoder_input_ids"]     ,							)






     @unittest.skipIf(torch_device == "cpu"     ,							"Cant do half precision"		)
     def  a_					(			self		)       ->    str:
          _a						=		self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*__UpperCamelCase		)






     def  a_					(			self		)       ->    Dict:
          _a						=		["encoder_attentions", "decoder_attentions", "cross_attentions"]
          _a						=		self.model_tester.prepare_config_and_inputs()
          _a						=		config_and_inputs[0]
          _a						=		UMTaForConditionalGeneration(__UpperCamelCase		).eval()
          model.to(__UpperCamelCase		)

          _a						=		{
              "head_mask": torch.zeros(config.num_layers     ,							config.num_heads     ,							device=__UpperCamelCase		),
              "decoder_head_mask": torch.zeros(config.num_decoder_layers     ,							config.num_heads     ,							device=__UpperCamelCase		),
              "cross_attn_head_mask": torch.zeros(config.num_decoder_layers     ,							config.num_heads     ,							device=__UpperCamelCase		),
          }

          for attn_name, (name, mask) in zip(__UpperCamelCase     ,							head_masking.items()		):
               _a						=		{name: mask}
               # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
               if name == "head_mask":
                    _a						=		torch.ones(
                        config.num_decoder_layers     ,							config.num_heads     ,							device=__UpperCamelCase		)

               _a						=		model.generate(
                   config_and_inputs[1]["input_ids"]     ,							num_beams=1     ,							max_length=3     ,							output_attentions=__UpperCamelCase     ,							return_dict_in_generate=__UpperCamelCase     ,							**__UpperCamelCase     ,							)
               # We check the state of decoder_attentions and cross_attentions just from the last step
               _a						=		out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
               self.assertEqual(sum([w.sum().item() for w in attn_weights]		)     ,							0.0		)






     @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."		)
     def  a_					(			self		)       ->    List[str]:
          pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 						__SCREAMING_SNAKE_CASE					(      unittest.TestCase ):






     @slow
     @unittest.skip(
         "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"		)
     def  a_					(			self		)       ->    Tuple:
          _a						=		UMTaForConditionalGeneration.from_pretrained("google/umt5-small"     ,							return_dict=__UpperCamelCase		).to(__UpperCamelCase		)
          _a						=		AutoTokenizer.from_pretrained("google/umt5-small"     ,							use_fast=__UpperCamelCase     ,							legacy=__UpperCamelCase		)
          _a						=		[
              "Bonjour monsieur <extra_id_0> bien <extra_id_1>.",
              "No se como puedo <extra_id_0>.",
              "This is the reason why we <extra_id_0> them.",
              "The <extra_id_0> walks in <extra_id_1>, seats",
              "A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.",
          ]
          _a						=		tokenizer(__UpperCamelCase     ,							return_tensors="pt"     ,							padding=__UpperCamelCase		).input_ids
          # fmt: off
          _a						=		torch.tensor(
              [
                  [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
              ]		)
          # fmt: on
          torch.testing.assert_allclose(__UpperCamelCase     ,							__UpperCamelCase		)

          _a						=		model.generate(input_ids.to(__UpperCamelCase		)		)
          _a						=		[
              "<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>",
              "<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
              "<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
              "<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
              "<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>",
          ]
          _a						=		tokenizer.batch_decode(__UpperCamelCase		)
          self.assertEqual(__UpperCamelCase     ,							__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()

