"""simple docstring"""
import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

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


if is_torch_available():
 import torch

 from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM




@require_torch
class    __snake_case       :



      def __init__(						self    ,			lowercase    ,			lowercase=99    ,			lowercase=13    ,			lowercase=16    ,			lowercase=7    ,			lowercase=True    ,			lowercase=True    ,			lowercase=True    ,			lowercase=False    ,			lowercase=True    ,			lowercase=2    ,			lowercase=32    ,			lowercase=4    ,			lowercase=4    ,			lowercase=30    ,			lowercase=0    ,			lowercase=1    ,			lowercase=2    ,			lowercase=None    ,			)	->			Optional[Any]:





        '''simple docstring'''





        a__:     Any										=	parent
        a__:     Union[str, Any]										=	batch_size
        a__:     str										=	decoder_seq_length
        # For common tests
        a__:     Optional[Any]										=	self.decoder_seq_length
        a__:     Optional[Any]										=	is_training
        a__:     str										=	use_attention_mask
        a__:     Optional[int]										=	use_labels

        a__:     List[Any]										=	vocab_size
        a__:     Optional[int]										=	d_model
        a__:     str										=	d_model
        a__:     int										=	decoder_layers
        a__:     Dict										=	decoder_layers
        a__:     str										=	decoder_ffn_dim
        a__:     Dict										=	decoder_attention_heads
        a__:     Union[str, Any]										=	decoder_attention_heads
        a__:     int										=	eos_token_id
        a__:     Tuple										=	bos_token_id
        a__:     Optional[Any]										=	pad_token_id
        a__:     int										=	decoder_start_token_id
        a__:     Union[str, Any]										=	use_cache
        a__:     Dict										=	max_position_embeddings

        a__:     Union[str, Any]										=	None
        a__:     List[str]										=	decoder_seq_length
        a__:     Tuple										=	2
        a__:     List[str]										=	1



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Any										=	ids_tensor([self.batch_size, self.decoder_seq_length]    ,			self.vocab_size)

        a__:     str										=	None
        if self.use_attention_mask:
          a__:     List[str]										=	ids_tensor([self.batch_size, self.decoder_seq_length]    ,			vocab_size=2)

        a__:     Any										=	None
        if self.use_labels:
          a__:     Any										=	ids_tensor([self.batch_size, self.decoder_seq_length]    ,			self.vocab_size)

        a__:     Optional[int]										=	TrOCRConfig(
            vocab_size=self.vocab_size    ,			d_model=self.d_model    ,			decoder_layers=self.decoder_layers    ,			decoder_ffn_dim=self.decoder_ffn_dim    ,			decoder_attention_heads=self.decoder_attention_heads    ,			eos_token_id=self.eos_token_id    ,			bos_token_id=self.bos_token_id    ,			use_cache=self.use_cache    ,			pad_token_id=self.pad_token_id    ,			decoder_start_token_id=self.decoder_start_token_id    ,			max_position_embeddings=self.max_position_embeddings    ,			)

        return (config, input_ids, attention_mask, lm_labels)



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			)	->			str:





        '''simple docstring'''





        a__:     int										=	True
        a__:     Optional[int]										=	TrOCRDecoder(config=lowercase).to(lowercase).eval()
        a__:     Union[str, Any]										=	input_ids[:2]

        input_ids[input_ids == 0] += 1
        # first forward pass
        a__:     List[Any]										=	model(lowercase    ,			use_cache=lowercase)
        a__:     int										=	model(lowercase)
        a__:     List[str]										=	model(lowercase    ,			use_cache=lowercase)

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

        a__:     Any										=	outputs['past_key_values']

        # create hypothetical next token and extent to next_input_ids
        a__:     int										=	ids_tensor((2, 1)    ,			config.vocab_size - 1) + 1

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

        a__:     Tuple										=	model(lowercase)['last_hidden_state']
        a__:     Optional[Any]										=	model(lowercase    ,			past_key_values=lowercase)['last_hidden_state']

        # select random slice
        a__:     Dict										=	ids_tensor((1,)    ,			output_from_past.shape[-1]).item()
        a__:     Union[str, Any]										=	output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
        a__:     Any										=	output_from_past[:, 0, random_slice_idx].detach()

        # test that outputs are equal for slice
        assert torch.allclose(lowercase    ,			lowercase    ,			atol=1e-3)



      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        a__:     List[str]										=	self.prepare_config_and_inputs()
        a__							,    a__							,    a__							,    a__:     List[str]										=	config_and_inputs

        a__:     Optional[Any]										=	{'input_ids': input_ids, 'attention_mask': attention_mask}
        return config, inputs_dict







@require_torch
class    __snake_case       (     __lowerCAmelCase       ,      __lowerCAmelCase       ,      __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      a__        =		(TrOCRForCausalLM,) if is_torch_available() else ()
      a__        =		{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
      a__        =		True
      a__        =		False



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[int]										=	TrOCRStandaloneDecoderModelTester(self    ,			is_training=lowercase)
        a__:     int										=	ConfigTester(self    ,			config_class=lowercase)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        pass



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        pass



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        pass



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        self.config_tester.run_common_tests()



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





        '''simple docstring'''





        a__:     Any										=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past(*lowercase)



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        return



      @unittest.skip('The model doesn\'t support left padding')  # and it's not used enough to be worth fixing :)
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        pass



"""simple docstring"""
def 						__a   (	_SCREAMING_SNAKE_CASE = 1000000   )	->int:
  a__:     int										=	limit + 1
  a__:     Optional[int]										=	[0] * limit
  for first_term in range(1		,     _SCREAMING_SNAKE_CASE   ):
    for n in range(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ):
      a__:     List[Any]										=	first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  a__:     Any										=	sum(1 for x in frequency[1:limit] if x == 10   )

  return count


if __name__ == "__main__":
 print(f"{solution() = }")


