


"""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   snake_case		(     UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						Tuple			,				UpperCamelCase__       :						Dict=None			,				UpperCamelCase__       :						str=None			,				UpperCamelCase__       :						List[Any]=None			,				UpperCamelCase__       :						int=None			,				UpperCamelCase__       :						Any=None			,				)   ->      Optional[Any]:
      if attention_mask is None:
            lowerCamelCase					:					Any        =	input_ids.ne(config.pad_token_id    )
      if decoder_attention_mask is None:
            lowerCamelCase					:					int        =	decoder_input_ids.ne(config.pad_token_id    )
      if head_mask is None:
            lowerCamelCase					:					List[Any]        =	torch.ones(config.encoder_layers			,				config.encoder_attention_heads			,				device=UpperCamelCase__    )
      if decoder_head_mask is None:
            lowerCamelCase					:					List[str]        =	torch.ones(config.decoder_layers			,				config.decoder_attention_heads			,				device=UpperCamelCase__    )
      if cross_attn_head_mask is None:
            lowerCamelCase					:					List[Any]        =	torch.ones(config.decoder_layers			,				config.decoder_attention_heads			,				device=UpperCamelCase__    )
      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:						Dict     ,		__a:						Any     ,		__a:						Optional[int]=13     ,		__a:						Tuple=7     ,		__a:						Optional[int]=True     ,		__a:						Optional[int]=False     ,		__a:						List[str]=99     ,		__a:						str=16     ,		__a:						str=2     ,		__a:						List[str]=4     ,		__a:						Union[str, Any]=4     ,		__a:						Dict="relu"     ,		__a:						int=0.1     ,		__a:						str=0.1     ,		__a:						Union[str, Any]=0.0     ,		__a:						Optional[Any]=0.0     ,		__a:						List[Any]=20     ,		__a:						str=2     ,		__a:						int=1     ,		__a:						Optional[Any]=0     ,		)->    Optional[int]:
           lowerCamelCase					:					Any        =	parent
           lowerCamelCase					:					Any        =	batch_size
           lowerCamelCase					:					List[str]        =	seq_length
           lowerCamelCase					:					Optional[int]        =	is_training
           lowerCamelCase					:					str        =	use_labels
           lowerCamelCase					:					Tuple        =	vocab_size
           lowerCamelCase					:					Optional[Any]        =	hidden_size
           lowerCamelCase					:					Tuple        =	num_hidden_layers
           lowerCamelCase					:					List[Any]        =	num_attention_heads
           lowerCamelCase					:					Tuple        =	intermediate_size
           lowerCamelCase					:					str        =	hidden_act
           lowerCamelCase					:					Optional[Any]        =	hidden_dropout_prob
           lowerCamelCase					:					int        =	attention_probs_dropout_prob
           lowerCamelCase					:					Optional[int]        =	encoder_layerdrop
           lowerCamelCase					:					Dict        =	decoder_layerdrop
           lowerCamelCase					:					List[Any]        =	max_position_embeddings
           lowerCamelCase					:					List[str]        =	eos_token_id
           lowerCamelCase					:					List[str]        =	pad_token_id
           lowerCamelCase					:					Tuple        =	bos_token_id

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

           lowerCamelCase					:					List[str]        =	self.get_config()
           lowerCamelCase					:					Tuple        =	prepare_mam_aaa_inputs_dict(__a     ,		__a     ,		__a	)
           return config, inputs_dict

     def 		a__						(	self:						str	)->    int:
           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:						List[str]	)->    Optional[Any]:
           lowerCamelCase     ,			lowerCamelCase					:					List[str]        =	self.prepare_config_and_inputs()
           return config, inputs_dict

     def 		a__						(	self:						Any     ,		__a:						Optional[int]     ,		__a:						List[Any]	)->    List[str]:
           lowerCamelCase					:					Dict        =	MaMaaaModel(config=__a	).get_decoder().to(__a	).eval()
           lowerCamelCase					:					Any        =	inputs_dict["""input_ids"""]
           lowerCamelCase					:					Union[str, Any]        =	inputs_dict["""attention_mask"""]
           lowerCamelCase					:					Dict        =	inputs_dict["""head_mask"""]

           # first forward pass
           lowerCamelCase					:					Dict        =	model(__a     ,		attention_mask=__a     ,		head_mask=__a     ,		use_cache=__a	)

           lowerCamelCase     ,			lowerCamelCase					:					Dict        =	outputs.to_tuple()

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

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

           lowerCamelCase					:					Tuple        =	model(__a     ,		attention_mask=__a	)["""last_hidden_state"""]
           lowerCamelCase					:					Optional[Any]        =	model(__a     ,		attention_mask=__a     ,		past_key_values=__a	)[
               """last_hidden_state"""
           ]

           # select random slice
           lowerCamelCase					:					List[Any]        =	ids_tensor((1,)     ,		output_from_past.shape[-1]	).item()
           lowerCamelCase					:					Dict        =	output_from_no_past[:, -3:, random_slice_idx].detach()
           lowerCamelCase					:					Any        =	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:						Union[str, Any]     ,		__a:						str     ,		__a:						Dict	)->    Any:
           lowerCamelCase					:					Union[str, Any]        =	MaMaaaModel(config=__a	).to(__a	).eval()
           lowerCamelCase					:					Any        =	model(**__a	)

           lowerCamelCase					:					Any        =	outputs.encoder_last_hidden_state
           lowerCamelCase					:					Dict        =	outputs.last_hidden_state

           with tempfile.TemporaryDirectory() as tmpdirname:
                 lowerCamelCase					:					List[Any]        =	model.get_encoder()
                 encoder.save_pretrained(__a	)
                 lowerCamelCase					:					Union[str, Any]        =	MaMaaaEncoder.from_pretrained(__a	).to(__a	)

           lowerCamelCase					:					List[Any]        =	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					:					Dict        =	model.get_decoder()
                 decoder.save_pretrained(__a	)
                 lowerCamelCase					:					Dict        =	MaMaaaDecoder.from_pretrained(__a	).to(__a	)

           lowerCamelCase					:					int        =	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__	(						__lowercase ,     __lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=(
         (
             MaMaaaModel,
             MaMaaaForConditionalGeneration,
         )
         if is_torch_available()
         else ()
     )
     snake_case__	:					Optional[int]													=(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
     snake_case__	:					int													=(
         {
             '''conversational''': MaMaaaForConditionalGeneration,
             '''feature-extraction''': MaMaaaModel,
             '''summarization''': MaMaaaForConditionalGeneration,
             '''text2text-generation''': MaMaaaForConditionalGeneration,
             '''translation''': MaMaaaForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     snake_case__	:					List[Any]													=True
     snake_case__	:					Optional[Any]													=True
     snake_case__	:					Dict													=False
     snake_case__	:					Optional[Any]													=False

     def 		a__						(	self:						Tuple     ,		__a:						Dict     ,		__a:						List[Any]     ,		__a:						Union[str, Any]     ,		__a:						Any     ,		__a:						Optional[Any]	)->    Dict:
           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:						Any	)->    List[str]:
           lowerCamelCase					:					Any        =	MaMaaaModelTester(self	)
           lowerCamelCase					:					Union[str, Any]        =	ConfigTester(self     ,		config_class=__a	)

     def 		a__						(	self:						str	)->    str:
           self.config_tester.run_common_tests()

     def 		a__						(	self:						Optional[int]	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					List[Any]        =	self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                 lowerCamelCase					:					Optional[Any]        =	model_class(__a	)

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

     def 		a__						(	self:						Dict	)->    Tuple:
           lowerCamelCase					:					Dict        =	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_decoder_model_past_large_inputs(*__a	)

     def 		a__						(	self:						Union[str, Any]	)->    str:
           lowerCamelCase					:					str        =	self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_encoder_decoder_model_standalone(*__a	)

     def 		a__						(	self:						Tuple	)->    Any:
           lowerCamelCase     ,			lowerCamelCase					:					Union[str, Any]        =	self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                 lowerCamelCase					:					Tuple        =	model_class(__a	)
                 model.to(__a	)
                 model.eval()

                 lowerCamelCase					:					Tuple        =	copy.deepcopy(self._prepare_for_class(__a     ,		__a	)	)

                 if not self.is_encoder_decoder:
                       lowerCamelCase					:					str        =	inputs["""input_ids"""]
                       del inputs["input_ids"]
                 else:
                       lowerCamelCase					:					List[Any]        =	inputs["""input_ids"""]
                       lowerCamelCase					:					int        =	inputs.get("""decoder_input_ids"""     ,		__a	)
                       del inputs["input_ids"]
                       inputs.pop("""decoder_input_ids"""     ,		__a	)

                 lowerCamelCase					:					Dict        =	model.get_input_embeddings()
                 if not self.is_encoder_decoder:
                       lowerCamelCase					:					Any        =	wte(__a	)
                 else:
                       lowerCamelCase					:					Optional[int]        =	wte(__a	)
                       lowerCamelCase					:					Tuple        =	wte(__a	)

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







     def 		a__						(	self:						List[str]	)->    List[str]:
           lowerCamelCase     ,			lowerCamelCase					:					Optional[int]        =	self.model_tester.prepare_config_and_inputs()
           lowerCamelCase					:					List[str]        =	input_dict["""input_ids"""]
           lowerCamelCase					:					int        =	input_ids.ne(1	).to(__a	)
           lowerCamelCase					:					Any        =	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   snake_case		(     UpperCamelCase__       :						Dict    )   ->      Optional[Any]:
      return torch.tensor(UpperCamelCase__			,				dtype=torch.long			,				device=UpperCamelCase__    )


__lowerCamelCase      :List[Any]	    =		1e-4






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


     """simple docstring"""

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

     def 		a__						(	self:						Optional[Any]	)->    int:
           lowerCamelCase					:					Dict        =	MaMaaaModel.from_pretrained("""facebook/m2m100_418M"""	).to(__a	)
           lowerCamelCase					:					List[Any]        =	_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]	)
           lowerCamelCase					:					int        =	_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]	)
           lowerCamelCase					:					Any        =	prepare_mam_aaa_inputs_dict(model.config     ,		__a     ,		__a	)
           with torch.no_grad():
                 lowerCamelCase					:					Optional[Any]        =	model(**__a	)[0]
           lowerCamelCase					:					List[Any]        =	torch.Size((1, 11, 1_024)	)
           self.assertEqual(output.shape     ,		__a	)
           # change to expected output here
           lowerCamelCase					:					int        =	torch.tensor(
               [[-0.77_80, -0.16_76, 0.10_38], [-6.75_56, -1.39_92, 0.05_67], [-7.53_83, -0.59_20, -0.27_79]]     ,		device=__a	)
           self.assertTrue(torch.allclose(output[:, :3, :3]     ,		__a     ,		atol=__a	)	)

     def 		a__						(	self:						Optional[Any]	)->    Optional[Any]:
           lowerCamelCase					:					Optional[Any]        =	MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""	).to(__a	)

           # change to intended input
           lowerCamelCase					:					Union[str, Any]        =	_long_tensor([[128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38, 2]]	)
           lowerCamelCase					:					Optional[Any]        =	_long_tensor([[2, 128_028, 98, 12, 30_527, 2_732, 159, 7_755, 61_904, 39_144, 38]]	)
           lowerCamelCase					:					Any        =	prepare_mam_aaa_inputs_dict(model.config     ,		__a     ,		__a	)
           with torch.no_grad():
                 lowerCamelCase					:					str        =	model(**__a	)[0]
           lowerCamelCase					:					Dict        =	torch.Size((1, 11, model.config.vocab_size)	)
           self.assertEqual(output.shape     ,		__a	)
           # change to expected output here
           lowerCamelCase					:					str        =	torch.tensor(
               [[-1.04_48, -1.04_11, 3.79_92], [-3.21_91, -3.23_86, -1.34_51], [-3.62_10, -3.59_93, 0.49_25]]     ,		device=__a	)
           self.assertTrue(torch.allclose(output[:, :3, :3]     ,		__a     ,		atol=__a	)	)







     def 		a__						(	self:						str	)->    Optional[int]:
           lowerCamelCase					:					Union[str, Any]        =	MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M"""	).to(__a	)
           lowerCamelCase					:					Any        =	MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M"""     ,		src_lang="""fr"""     ,		tgt_lang="""en"""	)

           lowerCamelCase					:					List[str]        =	[
               """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					:					int        =	tokenizer(__a     ,		padding=__a     ,		return_tensors="""pt"""	)

           lowerCamelCase					:					str        =	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					:					Tuple        =	[
               """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					:					Optional[int]        =	tokenizer.batch_decode(
               hypotheses_batch.tolist()     ,		clean_up_tokenization_spaces=__a     ,		skip_special_tokens=__a	)
           assert generated == expected_en








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__lowerCamelCase      :List[str]	    =		{
    'configuration_owlvit': [
        'OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'OwlViTConfig',
        'OwlViTOnnxConfig',
        'OwlViTTextConfig',
        'OwlViTVisionConfig',
    ],
    'processing_owlvit': ['OwlViTProcessor'],
}


try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[int]	    =		['OwlViTFeatureExtractor']
    __lowerCamelCase      :List[str]	    =		['OwlViTImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase      :Optional[Any]	    =		[
        'OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'OwlViTModel',
        'OwlViTPreTrainedModel',
        'OwlViTTextModel',
        'OwlViTVisionModel',
        'OwlViTForObjectDetection',
    ]

if TYPE_CHECKING:
    from .configuration_owlvit import (
        OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        OwlViTConfig,
        OwlViTOnnxConfig,
        OwlViTTextConfig,
        OwlViTVisionConfig,
    )
    from .processing_owlvit import OwlViTProcessor

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_owlvit import OwlViTFeatureExtractor
        from .image_processing_owlvit import OwlViTImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_owlvit import (
            OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
            OwlViTForObjectDetection,
            OwlViTModel,
            OwlViTPreTrainedModel,
            OwlViTTextModel,
            OwlViTVisionModel,
        )

else:
    import sys

    __lowerCamelCase      :Dict	    =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




