
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		,						lowerCamelCase		,						lowerCamelCase=99		,						lowerCamelCase=13		,						lowerCamelCase=7		,						lowerCamelCase=9		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=False		,						lowerCamelCase=32		,						lowerCamelCase=5		,						lowerCamelCase=4		,						lowerCamelCase=37		,						lowerCamelCase=8		,						lowerCamelCase=0.1		,						lowerCamelCase=0.0_0_2		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=0		,						lowerCamelCase=None		,						lowerCamelCase=None		,						):
          snake_case__    =					parent
          snake_case__    =					batch_size
          snake_case__    =					encoder_seq_length
          snake_case__    =					decoder_seq_length
          # For common tests
          snake_case__    =					self.decoder_seq_length
          snake_case__    =					is_training
          snake_case__    =					use_attention_mask
          snake_case__    =					use_labels
          snake_case__    =					vocab_size
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					d_ff
          snake_case__    =					relative_attention_num_buckets
          snake_case__    =					dropout_rate
          snake_case__    =					initializer_factor
          snake_case__    =					eos_token_id
          snake_case__    =					pad_token_id
          snake_case__    =					decoder_start_token_id
          snake_case__    =					None
          snake_case__    =					decoder_layers
      def A_      (      self			):
          return TaConfig.from_pretrained("google/umt5-base"			)
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=None		,						lowerCamelCase=None		,						):
          if attention_mask is None:
              snake_case__    =					input_ids.ne(config.pad_token_id			)
          if decoder_attention_mask is None:
              snake_case__    =					decoder_input_ids.ne(config.pad_token_id			)
          if head_mask is None:
              snake_case__    =					torch.ones(config.num_hidden_layers		,						config.num_attention_heads		,						device=lowerCamelCase			)
          if decoder_head_mask is None:
              snake_case__    =					torch.ones(config.num_decoder_layers		,						config.num_attention_heads		,						device=lowerCamelCase			)
          if cross_attn_head_mask is None:
              snake_case__    =					torch.ones(
                  config.num_decoder_layers		,						config.num_attention_heads		,						device=lowerCamelCase			)
          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			):
          snake_case__    =					ids_tensor([self.batch_size, self.encoder_seq_length]		,						self.vocab_size			)
          snake_case__    =					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
          snake_case__    =					input_ids.clamp(self.pad_token_id + 1			)
          snake_case__    =					decoder_input_ids.clamp(self.pad_token_id + 1			)

          snake_case__    =					self.get_config()
          snake_case__    =					config.num_attention_heads
          snake_case__    =					self.prepare_inputs_dict(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)
          return config, input_dict
      def A_      (      self			):
          snake_case__    ,       snake_case__    =					self.prepare_config_and_inputs()
          return config, inputs_dict
      def A_      (      self			):
          return TaConfig(
              vocab_size=1_66		,						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			):
          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		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						):
          snake_case__    =					UMTaModel(config=lowerCamelCase			)
          model.to(lowerCamelCase			)
          model.eval()
          snake_case__    =					model(
              input_ids=lowerCamelCase		,						decoder_input_ids=lowerCamelCase		,						attention_mask=lowerCamelCase		,						decoder_attention_mask=lowerCamelCase		,						)
          snake_case__    =					model(input_ids=lowerCamelCase		,						decoder_input_ids=lowerCamelCase			)
          snake_case__    =					result.last_hidden_state
          snake_case__    =					result.past_key_values
          snake_case__    =					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(lowerCamelCase			)		,						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		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						):
          snake_case__    =					UMTaModel(config=lowerCamelCase			).get_decoder().to(lowerCamelCase			).eval()
          # first forward pass
          snake_case__    =					model(lowerCamelCase		,						use_cache=lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase			)
          snake_case__    =					model(lowerCamelCase		,						use_cache=lowerCamelCase			)

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

          snake_case__    ,       snake_case__    =					outputs.to_tuple()

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

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

          snake_case__    =					model(lowerCamelCase			)["last_hidden_state"]
          snake_case__    =					model(lowerCamelCase		,						past_key_values=lowerCamelCase			)["last_hidden_state"]

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

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



      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase		,						):
          snake_case__    =					UMTaModel(config=lowerCamelCase			).to(lowerCamelCase			).half().eval()
          snake_case__    =					model(**lowerCamelCase			)["last_hidden_state"]
          self.parent.assertFalse(torch.isnan(lowerCamelCase			).any().item()			)





@require_torch
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase							,       __UpperCamelCase							,       __UpperCamelCase							,       unittest.TestCase			):
      _A    :		Optional[Any]     						=  (
          (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
      )
      _A    :		List[Any]     						=  (UMTaForConditionalGeneration,) if is_torch_available() else ()
      _A    :		Dict     						=  (
          {
              'conversational': UMTaForConditionalGeneration,
              'feature-extraction': UMTaModel,
              'summarization': UMTaForConditionalGeneration,
              'text2text-generation': UMTaForConditionalGeneration,
              'translation': UMTaForConditionalGeneration,
              'question-answering': UMTaForQuestionAnswering,
          }
          if is_torch_available()
          else {}
      )
      _A    :		int     						=  True
      _A    :		Tuple     						=  False
      _A    :		Optional[int]     						=  False
      _A    :		Optional[int]     						=  True
      _A    :		Dict     						=  True
      # The small UMT5 model needs higher percentages for CPU/MP tests
      _A    :		str     						=  [0.8, 0.9]
      def A_      (      self			):
          snake_case__    =					UMTaModelTester(self			)
      @unittest.skip("Test has a segmentation fault on torch 1.8.0"			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          snake_case__    =					UMTaModel(config_and_inputs[0]			).to(lowerCamelCase			)
          with tempfile.TemporaryDirectory() as tmpdirname:
              torch.onnx.export(
                  lowerCamelCase		,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])		,						F"""{tmpdirname}/t5_test.onnx"""		,						export_params=lowerCamelCase		,						opset_version=9		,						input_names=["input_ids", "decoder_input_ids"]		,						)
      @unittest.skipIf(torch_device == "cpu"		,						"Cant do half precision"			)
      def A_      (      self			):
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model_fpaa_forward(*lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					["encoder_attentions", "decoder_attentions", "cross_attentions"]
          snake_case__    =					self.model_tester.prepare_config_and_inputs()
          snake_case__    =					config_and_inputs[0]
          snake_case__    =					UMTaForConditionalGeneration(lowerCamelCase			).eval()
          model.to(lowerCamelCase			)

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

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

              snake_case__    =					model.generate(
                  config_and_inputs[1]["input_ids"]		,						num_beams=1		,						max_length=3		,						output_attentions=lowerCamelCase		,						return_dict_in_generate=lowerCamelCase		,						**lowerCamelCase		,						)
              # We check the state of decoder_attentions and cross_attentions just from the last step
              snake_case__    =					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			):
          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			):
          snake_case__    =					UMTaForConditionalGeneration.from_pretrained("google/umt5-small"		,						return_dict=lowerCamelCase			).to(lowerCamelCase			)
          snake_case__    =					AutoTokenizer.from_pretrained("google/umt5-small"		,						use_fast=lowerCamelCase		,						legacy=lowerCamelCase			)
          snake_case__    =					[
              "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>.",
          ]
          snake_case__    =					tokenizer(lowerCamelCase		,						return_tensors="pt"		,						padding=lowerCamelCase			).input_ids
          # fmt: off
          snake_case__    =					torch.tensor(
              [
                  [ 3_85_30, 21_07_03, 25_62_99, 14_10, 25_62_98, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   8_26, 3_21, 6_71, 2_59_22, 25_62_99, 2_74, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [  14_60, 3_39, 3_12, 1_90_14, 1_06_20, 7_58, 25_62_99, 23_55,2_74, 1, 0, 0, 0, 0, 0, 0,0, 0],
                  [   5_17, 25_62_99, 1_48_69, 2_81, 3_01, 25_62_98, 2_75, 11_99_83,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                  [   3_20, 25_62_99, 1_48_69, 2_81, 22_34, 2_89, 22_75, 3_33,6_13_91, 2_89, 25_62_98, 5_43, 25_62_97, 16_87_14, 3_29, 25_62_96,2_74, 1],
              ]			)
          # fmt: on
          torch.testing.assert_allclose(lowerCamelCase		,						lowerCamelCase			)

          snake_case__    =					model.generate(input_ids.to(lowerCamelCase			)			)
          snake_case__    =					[
              "<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>",
          ]
          snake_case__    =					tokenizer.batch_decode(lowerCamelCase			)
          self.assertEqual(lowerCamelCase		,						lowerCamelCase			)





from __future__ import annotations

import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5     ,		int(math.sqrt(__lowerCAmelCase				) + 1				)     ,		6				):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					str(__lowerCAmelCase				)
    snake_case__    =					[n]
    for i in range(1     ,		len(__lowerCAmelCase				)				):
        list_nums.append(int(str_num[i:]				)				)
        list_nums.append(int(str_num[:-i]				)				)
    return list_nums





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if len(str(__lowerCAmelCase				)				) > 3:
        if not is_prime(int(str(__lowerCAmelCase				)[-3:]				)				) or not is_prime(int(str(__lowerCAmelCase				)[:3]				)				):
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase = 11				):
    snake_case__    =					[]
    snake_case__    =					13
    while len(__lowerCAmelCase				) != count:
        if validate(__lowerCAmelCase				):
            snake_case__    =					list_truncated_nums(__lowerCAmelCase				)
            if all(is_prime(__lowerCAmelCase				) for i in list_nums				):
                list_truncated_primes.append(__lowerCAmelCase				)
        num += 2
    return list_truncated_primes





def 						SCREAMING_SNAKE_CASE__     (   ):
    return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(F'''{sum(compute_truncated_primes(11)) = }''')



