"""simple docstring"""




import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

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 import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
     from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       List[str]			,_lowerCamelCase						:       Tuple			,_lowerCamelCase						:       Optional[int]=None			,_lowerCamelCase						:       List[str]=None			,_lowerCamelCase						:       str=None			,_lowerCamelCase						:       List[str]=None			,_lowerCamelCase						:       Any=None			,)       ->      Dict:
  if attention_mask is None:
    _lowerCAmelCase						:       int  			=     input_ids.ne(config.pad_token_id	)
  if decoder_attention_mask is None:
    _lowerCAmelCase						:       Optional[Any]  			=     decoder_input_ids.ne(config.pad_token_id	)
  if head_mask is None:
    _lowerCAmelCase						:       str  			=     torch.ones(config.encoder_layers			,config.encoder_attention_heads			,device=_lowerCamelCase	)
  if decoder_head_mask is None:
    _lowerCAmelCase						:       Any  			=     torch.ones(config.decoder_layers			,config.decoder_attention_heads			,device=_lowerCamelCase	)
  if cross_attn_head_mask is None:
    _lowerCAmelCase						:       Optional[int]  			=     torch.ones(config.decoder_layers			,config.decoder_attention_heads			,device=_lowerCamelCase	)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
      "cross_attn_head_mask": cross_attn_head_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__="relu"				,     a__=0.1				,     a__=0.1				,     a__=0.0				,     a__=0.0				,     a__=20				,     a__=2				,     a__=1				,     a__=0				,     ):
       _lowerCAmelCase						:       int  			=     parent
       _lowerCAmelCase						:       Optional[Any]  			=     batch_size
       _lowerCAmelCase						:       int  			=     seq_length
       _lowerCAmelCase						:       List[str]  			=     is_training
       _lowerCAmelCase						:       int  			=     use_labels
       _lowerCAmelCase						:       Any  			=     vocab_size
       _lowerCAmelCase						:       Dict  			=     hidden_size
       _lowerCAmelCase						:       int  			=     num_hidden_layers
       _lowerCAmelCase						:       List[Any]  			=     num_attention_heads
       _lowerCAmelCase						:       List[str]  			=     intermediate_size
       _lowerCAmelCase						:       List[Any]  			=     hidden_act
       _lowerCAmelCase						:       Union[str, Any]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       List[str]  			=     encoder_layerdrop
       _lowerCAmelCase						:       Union[str, Any]  			=     decoder_layerdrop
       _lowerCAmelCase						:       List[Any]  			=     max_position_embeddings
       _lowerCAmelCase						:       List[str]  			=     eos_token_id
       _lowerCAmelCase						:       Tuple  			=     pad_token_id
       _lowerCAmelCase						:       int  			=     bos_token_id






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )
       _lowerCAmelCase						:       List[Any]  			=     self.eos_token_id  # Eos Token
       _lowerCAmelCase						:       Any  			=     ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size  )

       # we need to clamp the input ids here to avoid having pad token in between
       # this is because for M2M100 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
       _lowerCAmelCase						:       Optional[Any]  			=     input_ids.clamp(self.pad_token_id + 1  )
       _lowerCAmelCase						:       Tuple  			=     decoder_input_ids.clamp(self.pad_token_id + 1  )

       _lowerCAmelCase						:       List[Any]  			=     self.get_config()
       _lowerCAmelCase						:       Dict  			=     prepare_mam_aaa_inputs_dict(a__				,     a__				,     a__  )
       return config, inputs_dict






     def     __A  (		self  ):
       return MaMaaaConfig(
           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				,     encoder_layerdrop=self.encoder_layerdrop				,     decoder_layerdrop=self.decoder_layerdrop				,     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				,     )






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






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       List[str]  			=     MaMaaaModel(config=a__  ).get_decoder().to(a__  ).eval()
       _lowerCAmelCase						:       str  			=     inputs_dict["""input_ids"""]
       _lowerCAmelCase						:       Dict  			=     inputs_dict["""attention_mask"""]
       _lowerCAmelCase						:       int  			=     inputs_dict["""head_mask"""]

       # first forward pass
       _lowerCAmelCase						:       Tuple  			=     model(a__				,     attention_mask=a__				,     head_mask=a__				,     use_cache=a__  )

       _lowerCAmelCase			,	_lowerCAmelCase						:       List[Any]  			=     outputs.to_tuple()

       # create hypothetical multiple next token and extent to next_input_ids
       _lowerCAmelCase						:       Tuple  			=     ids_tensor((self.batch_size, 3)				,     config.vocab_size  )
       _lowerCAmelCase						:       Union[str, Any]  			=     ids_tensor((self.batch_size, 3)				,     2  )

       # append to next input_ids and
       _lowerCAmelCase						:       Optional[Any]  			=     torch.cat([input_ids, next_tokens]				,     dim=-1  )
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.cat([attention_mask, next_attn_mask]				,     dim=-1  )

       _lowerCAmelCase						:       Optional[Any]  			=     model(a__				,     attention_mask=a__  )["""last_hidden_state"""]
       _lowerCAmelCase						:       Any  			=     model(a__				,     attention_mask=a__				,     past_key_values=a__  )[
           """last_hidden_state"""
       ]

       # select random slice
       _lowerCAmelCase						:       List[str]  			=     ids_tensor((1,)				,     output_from_past.shape[-1]  ).item()
       _lowerCAmelCase						:       int  			=     output_from_no_past[:, -3:, random_slice_idx].detach()
       _lowerCAmelCase						:       Optional[int]  			=     output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(a__				,     a__				,     atol=1e-2  )  )






     def     __A  (		self				,     a__				,     a__  ):
       _lowerCAmelCase						:       Tuple  			=     MaMaaaModel(config=a__  ).to(a__  ).eval()
       _lowerCAmelCase						:       str  			=     model(**a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     outputs.encoder_last_hidden_state
       _lowerCAmelCase						:       List[Any]  			=     outputs.last_hidden_state

       with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase						:       str  			=     model.get_encoder()
         encoder.save_pretrained(a__  )
         _lowerCAmelCase						:       str  			=     MaMaaaEncoder.from_pretrained(a__  ).to(a__  )

       _lowerCAmelCase						:       Tuple  			=     encoder(inputs_dict["""input_ids"""]				,     attention_mask=inputs_dict["""attention_mask"""]  )[
           0
       ]

       self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1e-3  )

       with tempfile.TemporaryDirectory() as tmpdirname:
         _lowerCAmelCase						:       List[str]  			=     model.get_decoder()
         decoder.save_pretrained(a__  )
         _lowerCAmelCase						:       int  			=     MaMaaaDecoder.from_pretrained(a__  ).to(a__  )

       _lowerCAmelCase						:       Any  			=     decoder(
           input_ids=inputs_dict["""decoder_input_ids"""]				,     attention_mask=inputs_dict["""decoder_attention_mask"""]				,     encoder_hidden_states=a__				,     encoder_attention_mask=inputs_dict["""attention_mask"""]				,     )[0]

       self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1e-3  )







@require_torch
class 						__A							( SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Tuple							=		(
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:			Tuple							=		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     _UpperCamelCase							:			Union[str, Any]							=		(
         {
             "conversational": MaMaaaForConditionalGeneration,
             "feature-extraction": MaMaaaModel,
             "summarization": MaMaaaForConditionalGeneration,
             "text2text-generation": MaMaaaForConditionalGeneration,
             "translation": MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     _UpperCamelCase							:			Optional[int]							=		True
     _UpperCamelCase							:			Any							=		True
     _UpperCamelCase							:			Any							=		False
     _UpperCamelCase							:			List[Any]							=		False






     def     __A  (		self				,     a__				,     a__				,     a__				,     a__				,     a__  ):
       if pipeline_test_casse_name == "TranslationPipelineTests":
         # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
         # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
         return True

       return False






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     MaMaaaModelTester(self  )
       _lowerCAmelCase						:       Optional[int]  			=     ConfigTester(self				,     config_class=a__  )






     def     __A  (		self  ):
       self.config_tester.run_common_tests()






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     self.model_tester.prepare_config_and_inputs()
       for model_class in self.all_model_classes:
         _lowerCAmelCase						:       Any  			=     model_class(a__  )

         with tempfile.TemporaryDirectory() as tmpdirname:
           model.save_pretrained(a__  )
           _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     model_class.from_pretrained(a__				,     output_loading_info=a__  )
         self.assertEqual(info["""missing_keys"""]				,     []  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(*a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_encoder_decoder_model_standalone(*a__  )






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

       for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
         _lowerCAmelCase						:       List[str]  			=     model_class(a__  )
         model.to(a__  )
         model.eval()

         _lowerCAmelCase						:       List[Any]  			=     copy.deepcopy(self._prepare_for_class(a__				,     a__  )  )

         if not self.is_encoder_decoder:
           _lowerCAmelCase						:       Union[str, Any]  			=     inputs["""input_ids"""]
           del inputs["input_ids"]
         else:
           _lowerCAmelCase						:       Tuple  			=     inputs["""input_ids"""]
           _lowerCAmelCase						:       Union[str, Any]  			=     inputs.get("""decoder_input_ids"""				,     a__  )
           del inputs["input_ids"]
           inputs.pop("""decoder_input_ids"""				,     a__  )

         _lowerCAmelCase						:       Tuple  			=     model.get_input_embeddings()
         if not self.is_encoder_decoder:
           _lowerCAmelCase						:       Optional[int]  			=     wte(a__  )
         else:
           _lowerCAmelCase						:       Dict  			=     wte(a__  )
           _lowerCAmelCase						:       Dict  			=     wte(a__  )

         with torch.no_grad():
           model(**a__  )[0]






     def     __A  (		self  ):
       _lowerCAmelCase			,	_lowerCAmelCase						:       Dict  			=     self.model_tester.prepare_config_and_inputs()
       _lowerCAmelCase						:       List[Any]  			=     input_dict["""input_ids"""]
       _lowerCAmelCase						:       Union[str, Any]  			=     input_ids.ne(1  ).to(a__  )
       _lowerCAmelCase						:       List[str]  			=     MaMaaaForConditionalGeneration(a__  ).eval().to(a__  )
       if torch_device == "cuda":
         model.half()
       model.generate(a__				,     attention_mask=a__  )
       model.generate(num_beams=4				,     do_sample=a__				,     early_stopping=a__				,     num_return_sequences=3  )







def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       str	)       ->      Tuple:
  return torch.tensor(_lowerCamelCase			,dtype=torch.long			,device=_lowerCamelCase	)


_a      :		Optional[Any]           =   1e-4






@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 						__A							( unittest.TestCase		):






     @cached_property
     def     __A  (		self  ):
       return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""  ).to(a__  )
       _lowerCAmelCase						:       Optional[Any]  			=     _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]  )
       _lowerCAmelCase						:       Union[str, Any]  			=     _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]  )
       _lowerCAmelCase						:       Union[str, Any]  			=     prepare_mam_aaa_inputs_dict(model.config				,     a__				,     a__  )
       with torch.no_grad():
         _lowerCAmelCase						:       Tuple  			=     model(**a__  )[0]
       _lowerCAmelCase						:       Union[str, Any]  			=     torch.Size((1, 11, 1024)  )
       self.assertEqual(output.shape				,     a__  )
       # change to expected output here
       _lowerCAmelCase						:       Tuple  			=     torch.tensor(
           [[-0.7_7_8_0, -0.1_6_7_6, 0.1_0_3_8], [-6.7_5_5_6, -1.3_9_9_2, 0.0_5_6_7], [-7.5_3_8_3, -0.5_9_2_0, -0.2_7_7_9]]				,     device=a__  )
       self.assertTrue(torch.allclose(output[:, :3, :3]				,     a__				,     atol=a__  )  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Union[str, Any]  			=     MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""  ).to(a__  )

       # change to intended input
       _lowerCAmelCase						:       Dict  			=     _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]]  )
       _lowerCAmelCase						:       Union[str, Any]  			=     _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]]  )
       _lowerCAmelCase						:       str  			=     prepare_mam_aaa_inputs_dict(model.config				,     a__				,     a__  )
       with torch.no_grad():
         _lowerCAmelCase						:       str  			=     model(**a__  )[0]
       _lowerCAmelCase						:       int  			=     torch.Size((1, 11, model.config.vocab_size)  )
       self.assertEqual(output.shape				,     a__  )
       # change to expected output here
       _lowerCAmelCase						:       List[Any]  			=     torch.tensor(
           [[-1.0_4_4_8, -1.0_4_1_1, 3.7_9_9_2], [-3.2_1_9_1, -3.2_3_8_6, -1.3_4_5_1], [-3.6_2_1_0, -3.5_9_9_3, 0.4_9_2_5]]				,     device=a__  )
       self.assertTrue(torch.allclose(output[:, :3, :3]				,     a__				,     atol=a__  )  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""  ).to(a__  )
       _lowerCAmelCase						:       List[str]  			=     MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""				,     src_lang="""fr"""				,     tgt_lang="""en"""  )

       _lowerCAmelCase						:       int  			=     [
           """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
           """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
           """Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
           """ Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
           """ l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
       ]

       # The below article tests that we don't add any hypotheses outside of the top n_beams
       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer(a__				,     padding=a__				,     return_tensors="""pt"""  )

       _lowerCAmelCase						:       Tuple  			=     model.generate(
           input_ids=dct["""input_ids"""].to(a__  )				,     attention_mask=dct["""attention_mask"""].to(a__  )				,     num_beams=5				,     forced_bos_token_id=tokenizer.get_lang_id("""en"""  )				,     )

       _lowerCAmelCase						:       Optional[Any]  			=     [
           """The NSA case highlights the total absence of intelligence debate""",
           """I think there are two levels of response from the French government.""",
           """When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
           """ Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
           """ communications in France.""",
       ]

       _lowerCAmelCase						:       List[str]  			=     tokenizer.batch_decode(
           hypotheses_batch.tolist()				,     clean_up_tokenization_spaces=a__				,     skip_special_tokens=a__  )
       assert generated == expected_en











def    __lowerCamelCase					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   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__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


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



