"""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      UpperCAmelCase__			(							lowerCAmelCase__	:Dict   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:Tuple   ,						lowerCAmelCase__	:int=None   ,						lowerCAmelCase__	:int=None   ,						lowerCAmelCase__	:Any=None   ,						lowerCAmelCase__	:Dict=None   ,						lowerCAmelCase__	:Dict=None   ,						)					->       Optional[Any]:
      '''simple docstring'''


      if attention_mask is None:
            lowercase								=      input_ids.ne(config.pad_token_id						)
      if decoder_attention_mask is None:
            lowercase								=      decoder_input_ids.ne(config.pad_token_id						)
      if head_mask is None:
            lowercase								=      torch.ones(config.encoder_layers   ,						config.encoder_attention_heads   ,						device=lowerCAmelCase__						)
      if decoder_head_mask is None:
            lowercase								=      torch.ones(config.decoder_layers   ,						config.decoder_attention_heads   ,						device=lowerCAmelCase__						)
      if cross_attn_head_mask is None:
            lowercase								=      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						,			__lowerCAmelCase						,			__lowerCAmelCase=13						,			__lowerCAmelCase=7						,			__lowerCAmelCase=True						,			__lowerCAmelCase=False						,			__lowerCAmelCase=99						,			__lowerCAmelCase=16						,			__lowerCAmelCase=2						,			__lowerCAmelCase=4						,			__lowerCAmelCase=4						,			__lowerCAmelCase="relu"						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=20						,			__lowerCAmelCase=2						,			__lowerCAmelCase=1						,			__lowerCAmelCase=0						,			):







            """simple docstring"""



            lowercase								=      parent
            lowercase								=      batch_size
            lowercase								=      seq_length
            lowercase								=      is_training
            lowercase								=      use_labels
            lowercase								=      vocab_size
            lowercase								=      hidden_size
            lowercase								=      num_hidden_layers
            lowercase								=      num_attention_heads
            lowercase								=      intermediate_size
            lowercase								=      hidden_act
            lowercase								=      hidden_dropout_prob
            lowercase								=      attention_probs_dropout_prob
            lowercase								=      encoder_layerdrop
            lowercase								=      decoder_layerdrop
            lowercase								=      max_position_embeddings
            lowercase								=      eos_token_id
            lowercase								=      pad_token_id
            lowercase								=      bos_token_id

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      ids_tensor([self.batch_size, self.seq_length]						,			self.vocab_size		)
            lowercase								=      self.eos_token_id  # Eos Token
            lowercase								=      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
            lowercase								=      input_ids.clamp(self.pad_token_id + 1		)
            lowercase								=      decoder_input_ids.clamp(self.pad_token_id + 1		)

            lowercase								=      self.get_config()
            lowercase								=      prepare_mam_aaa_inputs_dict(__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            return config, inputs_dict

      def 							A__			(		self		):







            """simple docstring"""



            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		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.prepare_config_and_inputs()
            return config, inputs_dict

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MaMaaaModel(config=__lowerCAmelCase		).get_decoder().to(__lowerCAmelCase		).eval()
            lowercase								=      inputs_dict["""input_ids"""]
            lowercase								=      inputs_dict["""attention_mask"""]
            lowercase								=      inputs_dict["""head_mask"""]

            # first forward pass
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			head_mask=__lowerCAmelCase						,			use_cache=__lowerCAmelCase		)

            lowercase				,				lowercase								=      outputs.to_tuple()

            # create hypothetical multiple next token and extent to next_input_ids
            lowercase								=      ids_tensor((self.batch_size, 3)						,			config.vocab_size		)
            lowercase								=      ids_tensor((self.batch_size, 3)						,			2		)

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

            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase		)["""last_hidden_state"""]
            lowercase								=      model(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase						,			past_key_values=__lowerCAmelCase		)[
                """last_hidden_state"""
            ]

            # select random slice
            lowercase								=      ids_tensor((1,)						,			output_from_past.shape[-1]		).item()
            lowercase								=      output_from_no_past[:, -3:, random_slice_idx].detach()
            lowercase								=      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(__lowerCAmelCase						,			__lowerCAmelCase						,			atol=1E-2		)		)







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      MaMaaaModel(config=__lowerCAmelCase		).to(__lowerCAmelCase		).eval()
            lowercase								=      model(**__lowerCAmelCase		)

            lowercase								=      outputs.encoder_last_hidden_state
            lowercase								=      outputs.last_hidden_state

            with tempfile.TemporaryDirectory() as tmpdirname:
                  lowercase								=      model.get_encoder()
                  encoder.save_pretrained(__lowerCAmelCase		)
                  lowercase								=      MaMaaaEncoder.from_pretrained(__lowerCAmelCase		).to(__lowerCAmelCase		)

            lowercase								=      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:
                  lowercase								=      model.get_decoder()
                  decoder.save_pretrained(__lowerCAmelCase		)
                  lowercase								=      MaMaaaDecoder.from_pretrained(__lowerCAmelCase		).to(__lowerCAmelCase		)

            lowercase								=      decoder(
                input_ids=inputs_dict["""decoder_input_ids"""]						,			attention_mask=inputs_dict["""decoder_attention_mask"""]						,			encoder_hidden_states=__lowerCAmelCase						,			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		(			lowerCAmelCase    ,       lowerCAmelCase    ,       lowerCAmelCase    ,       unittest.TestCase      ):
      snake_case__     :  int				    =      (
          (
              MaMaaaModel,
              MaMaaaForConditionalGeneration,
          )
          if is_torch_available()
          else ()
      )
      snake_case__     :  str				    =      (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
      snake_case__     :  Dict				    =      (
          {
              'conversational': MaMaaaForConditionalGeneration,
              'feature-extraction': MaMaaaModel,
              'summarization': MaMaaaForConditionalGeneration,
              'text2text-generation': MaMaaaForConditionalGeneration,
              'translation': MaMaaaForConditionalGeneration,
          }
          if is_torch_available()
          else {}
      )
      snake_case__     :  Union[str, Any]				    =      True
      snake_case__     :  Optional[int]				    =      True
      snake_case__     :  int				    =      False
      snake_case__     :  Optional[int]				    =      False

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            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		):







            """simple docstring"""



            lowercase								=      MaMaaaModelTester(self		)
            lowercase								=      ConfigTester(self						,			config_class=__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            self.config_tester.run_common_tests()

      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs()
            for model_class in self.all_model_classes:
                  lowercase								=      model_class(__lowerCAmelCase		)

                  with tempfile.TemporaryDirectory() as tmpdirname:
                        model.save_pretrained(__lowerCAmelCase		)
                        lowercase				,				lowercase								=      model_class.from_pretrained(__lowerCAmelCase						,			output_loading_info=__lowerCAmelCase		)
                  self.assertEqual(info["""missing_keys"""]						,			[]		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_encoder_decoder_model_standalone(*__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                  lowercase								=      model_class(__lowerCAmelCase		)
                  model.to(__lowerCAmelCase		)
                  model.eval()

                  lowercase								=      copy.deepcopy(self._prepare_for_class(__lowerCAmelCase						,			__lowerCAmelCase		)		)

                  if not self.is_encoder_decoder:
                        lowercase								=      inputs["""input_ids"""]
                        del inputs["input_ids"]
                  else:
                        lowercase								=      inputs["""input_ids"""]
                        lowercase								=      inputs.get("""decoder_input_ids"""						,			__lowerCAmelCase		)
                        del inputs["input_ids"]
                        inputs.pop("""decoder_input_ids"""						,			__lowerCAmelCase		)

                  lowercase								=      model.get_input_embeddings()
                  if not self.is_encoder_decoder:
                        lowercase								=      wte(__lowerCAmelCase		)
                  else:
                        lowercase								=      wte(__lowerCAmelCase		)
                        lowercase								=      wte(__lowerCAmelCase		)

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







      def 							A__			(		self		):







            """simple docstring"""



            lowercase				,				lowercase								=      self.model_tester.prepare_config_and_inputs()
            lowercase								=      input_dict["""input_ids"""]
            lowercase								=      input_ids.ne(1		).to(__lowerCAmelCase		)
            lowercase								=      MaMaaaForConditionalGeneration(__lowerCAmelCase		).eval().to(__lowerCAmelCase		)
            if torch_device == "cuda":
                  model.half()
            model.generate(__lowerCAmelCase						,			attention_mask=__lowerCAmelCase		)
            model.generate(num_beams=4						,			do_sample=__lowerCAmelCase						,			early_stopping=__lowerCAmelCase						,			num_return_sequences=3		)






def      UpperCAmelCase__			(							lowerCAmelCase__	:Optional[Any]						)					->       List[Any]:
      '''simple docstring'''


      return torch.tensor(lowerCAmelCase__   ,						dtype=torch.long   ,						device=lowerCAmelCase__						)


__lowerCAmelCase       :			List[Any]   	=1E-4






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

      @cached_property
      def 							A__			(		self		):







            """simple docstring"""



            return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""		).to(__lowerCAmelCase		)
            lowercase								=      _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]		)
            lowercase								=      _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]		)
            lowercase								=      prepare_mam_aaa_inputs_dict(model.config						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            with torch.no_grad():
                  lowercase								=      model(**__lowerCAmelCase		)[0]
            lowercase								=      torch.Size((1, 11, 1024)		)
            self.assertEqual(output.shape						,			__lowerCAmelCase		)
            # change to expected output here
            lowercase								=      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=__lowerCAmelCase		)
            self.assertTrue(torch.allclose(output[:, :3, :3]						,			__lowerCAmelCase						,			atol=__lowerCAmelCase		)		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""		).to(__lowerCAmelCase		)

            # change to intended input
            lowercase								=      _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]		)
            lowercase								=      _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]		)
            lowercase								=      prepare_mam_aaa_inputs_dict(model.config						,			__lowerCAmelCase						,			__lowerCAmelCase		)
            with torch.no_grad():
                  lowercase								=      model(**__lowerCAmelCase		)[0]
            lowercase								=      torch.Size((1, 11, model.config.vocab_size)		)
            self.assertEqual(output.shape						,			__lowerCAmelCase		)
            # change to expected output here
            lowercase								=      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=__lowerCAmelCase		)
            self.assertTrue(torch.allclose(output[:, :3, :3]						,			__lowerCAmelCase						,			atol=__lowerCAmelCase		)		)







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""		).to(__lowerCAmelCase		)
            lowercase								=      MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""						,			src_lang="""fr"""						,			tgt_lang="""en"""		)

            lowercase								=      [
                """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
            lowercase								=      tokenizer(__lowerCAmelCase						,			padding=__lowerCAmelCase						,			return_tensors="""pt"""		)

            lowercase								=      model.generate(
                input_ids=dct["""input_ids"""].to(__lowerCAmelCase		)						,			attention_mask=dct["""attention_mask"""].to(__lowerCAmelCase		)						,			num_beams=5						,			forced_bos_token_id=tokenizer.get_lang_id("""en"""		)						,			)

            lowercase								=      [
                """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.""",
            ]

            lowercase								=      tokenizer.batch_decode(
                hypotheses_batch.tolist()						,			clean_up_tokenization_spaces=__lowerCAmelCase						,			skip_special_tokens=__lowerCAmelCase		)
            assert generated == expected_en




"""simple docstring"""






from copy import deepcopy





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None		):







            """simple docstring"""



            if arr is None and size is not None:
                  lowercase								=      size
                  lowercase								=      [0] * size
            elif arr is not None:
                  self.init(__lowerCAmelCase		)
            else:
                  raise ValueError("""Either arr or size must be specified"""		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      len(__lowerCAmelCase		)
            lowercase								=      deepcopy(__lowerCAmelCase		)
            for i in range(1						,			self.size		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        self.tree[j] += self.tree[i]

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.tree[:]
            for i in range(self.size - 1						,			0						,			-1		):
                  lowercase								=      self.next_(__lowerCAmelCase		)
                  if j < self.size:
                        arr[j] -= arr[i]
            return arr

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index + (index & (-index))

      @staticmethod
      def 							A__			(		__lowerCAmelCase		):







            """simple docstring"""



            return index - (index & (-index))

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            if index == 0:
                  self.tree[0] += value
                  return
            while index < self.size:
                  self.tree[index] += value
                  lowercase								=      self.next_(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            self.add(__lowerCAmelCase						,			value - self.get(__lowerCAmelCase		)		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if right == 0:
                  return 0
            lowercase								=      self.tree[0]
            right -= 1  # make right inclusive
            while right > 0:
                  result += self.tree[right]
                  lowercase								=      self.prev(__lowerCAmelCase		)
            return result

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.prefix(__lowerCAmelCase		) - self.prefix(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.query(__lowerCAmelCase						,			index + 1		)







      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            value -= self.tree[0]
            if value < 0:
                  return -1

            lowercase								=      1  # Largest power of 2 <= size
            while j * 2 < self.size:
                  j *= 2

            lowercase								=      0

            while j > 0:
                  if i + j < self.size and self.tree[i + j] <= value:
                        value -= self.tree[i + j]
                        i += j
                  j //= 2
            return i


if __name__ == "__main__":
       import doctest

       doctest.testmod()



