





'''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 lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    str					,       __lowerCamelCase :    Any=None					,       __lowerCamelCase :    List[Any]=None					,       __lowerCamelCase :    Optional[int]=None					,       __lowerCamelCase :    Union[str, Any]=None					,       __lowerCamelCase :    Union[str, Any]=None					,       )						->Any:
    if attention_mask is None:
        _SCREAMING_SNAKE_CASE									=			input_ids.ne(config.pad_token_id    )
    if decoder_attention_mask is None:
        _SCREAMING_SNAKE_CASE									=			decoder_input_ids.ne(config.pad_token_id    )
    if head_mask is None:
        _SCREAMING_SNAKE_CASE									=			torch.ones(config.encoder_layers					,       config.encoder_attention_heads					,       device=__lowerCamelCase    )
    if decoder_head_mask is None:
        _SCREAMING_SNAKE_CASE									=			torch.ones(config.decoder_layers					,       config.decoder_attention_heads					,       device=__lowerCamelCase    )
    if cross_attn_head_mask is None:
        _SCREAMING_SNAKE_CASE									=			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_     :

    '''simple docstring'''


    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 ,		)		->   str:
        _SCREAMING_SNAKE_CASE									=			parent
        _SCREAMING_SNAKE_CASE									=			batch_size
        _SCREAMING_SNAKE_CASE									=			seq_length
        _SCREAMING_SNAKE_CASE									=			is_training
        _SCREAMING_SNAKE_CASE									=			use_labels
        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			encoder_layerdrop
        _SCREAMING_SNAKE_CASE									=			decoder_layerdrop
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			eos_token_id
        _SCREAMING_SNAKE_CASE									=			pad_token_id
        _SCREAMING_SNAKE_CASE									=			bos_token_id


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			ids_tensor([self.batch_size, self.seq_length] ,		self.vocab_size       )
        _SCREAMING_SNAKE_CASE									=			self.eos_token_id  # Eos Token
        _SCREAMING_SNAKE_CASE									=			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
        _SCREAMING_SNAKE_CASE									=			input_ids.clamp(self.pad_token_id + 1       )
        _SCREAMING_SNAKE_CASE									=			decoder_input_ids.clamp(self.pad_token_id + 1       )

        _SCREAMING_SNAKE_CASE									=			self.get_config()
        _SCREAMING_SNAKE_CASE									=			prepare_mam_aaa_inputs_dict(A ,		A ,		A       )
        return config, inputs_dict


    def 			snake_case_(		self       )		->   Any:
        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 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.prepare_config_and_inputs()
        return config, inputs_dict


    def 			snake_case_(		self ,		A ,		A       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			MaMaaaModel(config=A       ).get_decoder().to(A       ).eval()
        _SCREAMING_SNAKE_CASE									=			inputs_dict["""input_ids"""]
        _SCREAMING_SNAKE_CASE									=			inputs_dict["""attention_mask"""]
        _SCREAMING_SNAKE_CASE									=			inputs_dict["""head_mask"""]

        # first forward pass
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		head_mask=A ,		use_cache=A       )

        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			outputs.to_tuple()

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

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

        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A       )["""last_hidden_state"""]
        _SCREAMING_SNAKE_CASE									=			model(A ,		attention_mask=A ,		past_key_values=A       )[
            """last_hidden_state"""
        ]

        # select random slice
        _SCREAMING_SNAKE_CASE									=			ids_tensor((1,) ,		output_from_past.shape[-1]       ).item()
        _SCREAMING_SNAKE_CASE									=			output_from_no_past[:, -3:, random_slice_idx].detach()
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self ,		A ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			MaMaaaModel(config=A       ).to(A       ).eval()
        _SCREAMING_SNAKE_CASE									=			model(**A       )

        _SCREAMING_SNAKE_CASE									=			outputs.encoder_last_hidden_state
        _SCREAMING_SNAKE_CASE									=			outputs.last_hidden_state

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

        _SCREAMING_SNAKE_CASE									=			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:
            _SCREAMING_SNAKE_CASE									=			model.get_decoder()
            decoder.save_pretrained(A       )
            _SCREAMING_SNAKE_CASE									=			MaMaaaDecoder.from_pretrained(A       ).to(A       )

        _SCREAMING_SNAKE_CASE									=			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_     (				snake_case_      ,    snake_case_      ,    snake_case_      ,    unittest.TestCase       ):

    '''simple docstring'''
    UpperCamelCase     = (
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    UpperCamelCase     = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    UpperCamelCase     = (
        {
            '''conversational''': MaMaaaForConditionalGeneration,
            '''feature-extraction''': MaMaaaModel,
            '''summarization''': MaMaaaForConditionalGeneration,
            '''text2text-generation''': MaMaaaForConditionalGeneration,
            '''translation''': MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    UpperCamelCase     = True
    UpperCamelCase     = True
    UpperCamelCase     = False
    UpperCamelCase     = False


    def 			snake_case_(		self ,		A ,		A ,		A ,		A ,		A       )		->   List[Any]:
        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 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			MaMaaaModelTester(self       )
        _SCREAMING_SNAKE_CASE									=			ConfigTester(self ,		config_class=A       )


    def 			snake_case_(		self       )		->   List[str]:
        self.config_tester.run_common_tests()


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            _SCREAMING_SNAKE_CASE									=			model_class(A       )

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


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*A       )


    def 			snake_case_(		self       )		->   List[str]:
        _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*A       )


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
            _SCREAMING_SNAKE_CASE									=			model_class(A       )
            model.to(A       )
            model.eval()

            _SCREAMING_SNAKE_CASE									=			copy.deepcopy(self._prepare_for_class(A ,		A       )       )

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

            _SCREAMING_SNAKE_CASE									=			model.get_input_embeddings()
            if not self.is_encoder_decoder:
                _SCREAMING_SNAKE_CASE									=			wte(A       )
            else:
                _SCREAMING_SNAKE_CASE									=			wte(A       )
                _SCREAMING_SNAKE_CASE									=			wte(A       )

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





    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			self.model_tester.prepare_config_and_inputs()
        _SCREAMING_SNAKE_CASE									=			input_dict["""input_ids"""]
        _SCREAMING_SNAKE_CASE									=			input_ids.ne(1       ).to(A       )
        _SCREAMING_SNAKE_CASE									=			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 lowerCamelCase						(     __lowerCamelCase :    Any    )						->Tuple:
    return torch.tensor(__lowerCamelCase					,       dtype=torch.long					,       device=__lowerCamelCase    )


lowercase_					= 1e-4






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

    '''simple docstring'''


    @cached_property
    def 			snake_case_(		self       )		->   Any:
        return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""       )


    def 			snake_case_(		self       )		->   Union[str, Any]:
        _SCREAMING_SNAKE_CASE									=			MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""       ).to(A       )
        _SCREAMING_SNAKE_CASE									=			_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
        _SCREAMING_SNAKE_CASE									=			_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
        _SCREAMING_SNAKE_CASE									=			prepare_mam_aaa_inputs_dict(model.config ,		A ,		A       )
        with torch.no_grad():
            _SCREAMING_SNAKE_CASE									=			model(**A       )[0]
        _SCREAMING_SNAKE_CASE									=			torch.Size((1, 11, 1024)       )
        self.assertEqual(output.shape ,		A       )
        # change to expected output here
        _SCREAMING_SNAKE_CASE									=			torch.tensor(
            [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] ,		device=A       )
        self.assertTrue(torch.allclose(output[:, :3, :3] ,		A ,		atol=A       )       )


    def 			snake_case_(		self       )		->   int:
        _SCREAMING_SNAKE_CASE									=			MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""       ).to(A       )

        # change to intended input
        _SCREAMING_SNAKE_CASE									=			_long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]]       )
        _SCREAMING_SNAKE_CASE									=			_long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]]       )
        _SCREAMING_SNAKE_CASE									=			prepare_mam_aaa_inputs_dict(model.config ,		A ,		A       )
        with torch.no_grad():
            _SCREAMING_SNAKE_CASE									=			model(**A       )[0]
        _SCREAMING_SNAKE_CASE									=			torch.Size((1, 11, model.config.vocab_size)       )
        self.assertEqual(output.shape ,		A       )
        # change to expected output here
        _SCREAMING_SNAKE_CASE									=			torch.tensor(
            [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] ,		device=A       )
        self.assertTrue(torch.allclose(output[:, :3, :3] ,		A ,		atol=A       )       )





    def 			snake_case_(		self       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""       ).to(A       )
        _SCREAMING_SNAKE_CASE									=			MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" ,		src_lang="""fr""" ,		tgt_lang="""en"""       )

        _SCREAMING_SNAKE_CASE									=			[
            """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
        _SCREAMING_SNAKE_CASE									=			tokenizer(A ,		padding=A ,		return_tensors="""pt"""       )

        _SCREAMING_SNAKE_CASE									=			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"""       ) ,		)

        _SCREAMING_SNAKE_CASE									=			[
            """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.""",
        ]

        _SCREAMING_SNAKE_CASE									=			tokenizer.batch_decode(
            hypotheses_batch.tolist() ,		clean_up_tokenization_spaces=A ,		skip_special_tokens=A       )
        assert generated == expected_en










'''simple docstring'''

from collections.abc import Sequence





def lowerCamelCase						(     __lowerCamelCase :    Sequence[float]					,       __lowerCamelCase :    bool = False    )						->float:
    if not arr:
        return 0

    _SCREAMING_SNAKE_CASE									=			0 if allow_empty_subarrays else float("""-inf"""    )
    _SCREAMING_SNAKE_CASE									=			0.0
    for num in arr:
        _SCREAMING_SNAKE_CASE									=			max(0 if allow_empty_subarrays else num					,       curr_sum + num    )
        _SCREAMING_SNAKE_CASE									=			max(__lowerCamelCase					,       __lowerCamelCase    )

    return max_sum


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      lowercase_					= [-2, 1, -3, 4, -1, 2, 1, -5, 4]
      print(f"""{max_subarray_sum(nums) = }""")



