

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__			(    SCREAMING_SNAKE_CASE_		:    List[Any]	,     SCREAMING_SNAKE_CASE_		:    List[str]	,     SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    List[str]=None	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]=None	,     SCREAMING_SNAKE_CASE_		:    List[str]=None	,     SCREAMING_SNAKE_CASE_		:    List[str]=None	,     SCREAMING_SNAKE_CASE_		:    Dict=None	,     ):



     '''simple docstring'''





     if attention_mask is None:
          lowercase__    :   Optional[int]      =       input_ids.ne(config.pad_token_id			)
     if decoder_attention_mask is None:
          lowercase__    :   List[Any]      =       decoder_input_ids.ne(config.pad_token_id			)
     if head_mask is None:
          lowercase__    :   Any      =       torch.ones(config.encoder_layers	,     config.encoder_attention_heads	,     device=SCREAMING_SNAKE_CASE_			)
     if decoder_head_mask is None:
          lowercase__    :   Optional[Any]      =       torch.ones(config.decoder_layers	,     config.decoder_attention_heads	,     device=SCREAMING_SNAKE_CASE_			)
     if cross_attn_head_mask is None:
          lowercase__    :   Union[str, Any]      =       torch.ones(config.decoder_layers	,     config.decoder_attention_heads	,     device=SCREAMING_SNAKE_CASE_			)
     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       SCREAMING_SNAKE_CASE__					:





    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						,   ):
         lowercase__    :   Any      =       parent
         lowercase__    :   int      =       batch_size
         lowercase__    :   Optional[Any]      =       seq_length
         lowercase__    :   Union[str, Any]      =       is_training
         lowercase__    :   Dict      =       use_labels
         lowercase__    :   Optional[Any]      =       vocab_size
         lowercase__    :   Optional[Any]      =       hidden_size
         lowercase__    :   Any      =       num_hidden_layers
         lowercase__    :   Tuple      =       num_attention_heads
         lowercase__    :   Dict      =       intermediate_size
         lowercase__    :   Dict      =       hidden_act
         lowercase__    :   Tuple      =       hidden_dropout_prob
         lowercase__    :   str      =       attention_probs_dropout_prob
         lowercase__    :   Tuple      =       encoder_layerdrop
         lowercase__    :   int      =       decoder_layerdrop
         lowercase__    :   Any      =       max_position_embeddings
         lowercase__    :   Any      =       eos_token_id
         lowercase__    :   Optional[int]      =       pad_token_id
         lowercase__    :   Optional[Any]      =       bos_token_id





    def       snake_case_				(      self):
         lowercase__    :   Optional[Any]      =       ids_tensor([self.batch_size, self.seq_length]						,   self.vocab_size)
         lowercase__    :   int      =       self.eos_token_id  # Eos Token
         lowercase__    :   Tuple      =       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__    :   Union[str, Any]      =       input_ids.clamp(self.pad_token_id + 1)
         lowercase__    :   Tuple      =       decoder_input_ids.clamp(self.pad_token_id + 1)

         lowercase__    :   Tuple      =       self.get_config()
         lowercase__    :   int      =       prepare_mam_aaa_inputs_dict(a						,   a						,   a)
         return config, inputs_dict





    def       snake_case_				(      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       snake_case_				(      self):
         lowercase__      , lowercase__    :   Optional[Any]      =       self.prepare_config_and_inputs()
         return config, inputs_dict





    def       snake_case_				(      self						,   a						,   a):
         lowercase__    :   List[str]      =       MaMaaaModel(config=a).get_decoder().to(a).eval()
         lowercase__    :   Optional[Any]      =       inputs_dict['input_ids']
         lowercase__    :   int      =       inputs_dict['attention_mask']
         lowercase__    :   List[Any]      =       inputs_dict['head_mask']

         # first forward pass
         lowercase__    :   Optional[int]      =       model(a						,   attention_mask=a						,   head_mask=a						,   use_cache=a)

         lowercase__      , lowercase__    :   Union[str, Any]      =       outputs.to_tuple()

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

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

         lowercase__    :   List[str]      =       model(a						,   attention_mask=a)['last_hidden_state']
         lowercase__    :   int      =       model(a						,   attention_mask=a						,   past_key_values=a)[
             'last_hidden_state'
         ]

         # select random slice
         lowercase__    :   Optional[Any]      =       ids_tensor((1,)						,   output_from_past.shape[-1]).item()
         lowercase__    :   int      =       output_from_no_past[:, -3:, random_slice_idx].detach()
         lowercase__    :   Union[str, 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       snake_case_				(      self						,   a						,   a):
         lowercase__    :   int      =       MaMaaaModel(config=a).to(a).eval()
         lowercase__    :   Any      =       model(**a)

         lowercase__    :   int      =       outputs.encoder_last_hidden_state
         lowercase__    :   Union[str, Any]      =       outputs.last_hidden_state

         with tempfile.TemporaryDirectory() as tmpdirname:
              lowercase__    :   int      =       model.get_encoder()
              encoder.save_pretrained(a)
              lowercase__    :   List[Any]      =       MaMaaaEncoder.from_pretrained(a).to(a)

         lowercase__    :   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:
              lowercase__    :   Dict      =       model.get_decoder()
              decoder.save_pretrained(a)
              lowercase__    :   List[str]      =       MaMaaaDecoder.from_pretrained(a).to(a)

         lowercase__    :   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       SCREAMING_SNAKE_CASE__					(__snake_case		,       __snake_case		,       __snake_case		,       unittest.TestCase     ):
    __lowerCamelCase						:       str														=     (
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    __lowerCamelCase						:       Optional[int]														=     (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    __lowerCamelCase						:       Optional[int]														=     (
        {
            """conversational""": MaMaaaForConditionalGeneration,
            """feature-extraction""": MaMaaaModel,
            """summarization""": MaMaaaForConditionalGeneration,
            """text2text-generation""": MaMaaaForConditionalGeneration,
            """translation""": MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    __lowerCamelCase						:       List[str]														=     True
    __lowerCamelCase						:       Union[str, Any]														=     True
    __lowerCamelCase						:       Union[str, Any]														=     False
    __lowerCamelCase						:       int														=     False





    def       snake_case_				(      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       snake_case_				(      self):
         lowercase__    :   Optional[Any]      =       MaMaaaModelTester(self)
         lowercase__    :   int      =       ConfigTester(self						,   config_class=a)





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





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   Any      =       self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
              lowercase__    :   Tuple      =       model_class(a)

              with tempfile.TemporaryDirectory() as tmpdirname:
                   model.save_pretrained(a)
                   lowercase__      , lowercase__    :   Dict      =       model_class.from_pretrained(a						,   output_loading_info=a)
              self.assertEqual(info['missing_keys']						,   [])





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*a)





    def       snake_case_				(      self):
         lowercase__    :   Any      =       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*a)





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   Optional[int]      =       self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
              lowercase__    :   str      =       model_class(a)
              model.to(a)
              model.eval()

              lowercase__    :   Optional[int]      =       copy.deepcopy(self._prepare_for_class(a						,   a))

              if not self.is_encoder_decoder:
                   lowercase__    :   Optional[Any]      =       inputs['input_ids']
                   del inputs["input_ids"]
              else:
                   lowercase__    :   List[Any]      =       inputs['input_ids']
                   lowercase__    :   Any      =       inputs.get('decoder_input_ids'						,   a)
                   del inputs["input_ids"]
                   inputs.pop('decoder_input_ids'						,   a)

              lowercase__    :   Any      =       model.get_input_embeddings()
              if not self.is_encoder_decoder:
                   lowercase__    :   Any      =       wte(a)
              else:
                   lowercase__    :   Optional[Any]      =       wte(a)
                   lowercase__    :   int      =       wte(a)

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





    def       snake_case_				(      self):
         lowercase__      , lowercase__    :   List[Any]      =       self.model_tester.prepare_config_and_inputs()
         lowercase__    :   str      =       input_dict['input_ids']
         lowercase__    :   Optional[Any]      =       input_ids.ne(1).to(a)
         lowercase__    :   Optional[int]      =       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__			(    SCREAMING_SNAKE_CASE_		:    str			):



     '''simple docstring'''





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


snake_case_			  = 1e-4


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





    @cached_property
    def       snake_case_				(      self):
         return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M')





    def       snake_case_				(      self):
         lowercase__    :   List[Any]      =       MaMaaaModel.from_pretrained('facebook/m2m100_418M').to(a)
         lowercase__    :   int      =       _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]])
         lowercase__    :   int      =       _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]])
         lowercase__    :   Dict      =       prepare_mam_aaa_inputs_dict(model.config						,   a						,   a)
         with torch.no_grad():
              lowercase__    :   Optional[Any]      =       model(**a)[0]
         lowercase__    :   str      =       torch.Size((1, 11, 1024))
         self.assertEqual(output.shape						,   a)
         # change to expected output here
         lowercase__    :   str      =       torch.tensor(
             [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]]						,   device=a)
         self.assertTrue(torch.allclose(output[:, :3, :3]						,   a						,   atol=a))





    def       snake_case_				(      self):
         lowercase__    :   Any      =       MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M').to(a)

         # change to intended input
         lowercase__    :   Union[str, Any]      =       _long_tensor([[12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38, 2]])
         lowercase__    :   Dict      =       _long_tensor([[2, 12_8028, 98, 12, 3_0527, 2732, 159, 7755, 6_1904, 3_9144, 38]])
         lowercase__    :   Dict      =       prepare_mam_aaa_inputs_dict(model.config						,   a						,   a)
         with torch.no_grad():
              lowercase__    :   int      =       model(**a)[0]
         lowercase__    :   Tuple      =       torch.Size((1, 11, model.config.vocab_size))
         self.assertEqual(output.shape						,   a)
         # change to expected output here
         lowercase__    :   Tuple      =       torch.tensor(
             [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]]						,   device=a)
         self.assertTrue(torch.allclose(output[:, :3, :3]						,   a						,   atol=a))





    def       snake_case_				(      self):
         lowercase__    :   Dict      =       MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M').to(a)
         lowercase__    :   List[Any]      =       MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'						,   src_lang='fr'						,   tgt_lang='en')

         lowercase__    :   Optional[Any]      =       [
             '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__    :   Optional[Any]      =       tokenizer(a						,   padding=a						,   return_tensors='pt')

         lowercase__    :   Any      =       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')						,   )

         lowercase__    :   List[str]      =       [
             '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__    :   Union[str, Any]      =       tokenizer.batch_decode(
             hypotheses_batch.tolist()						,   clean_up_tokenization_spaces=a						,   skip_special_tokens=a)
         assert generated == expected_en




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			  = {
    '''configuration_instructblip''': [
        '''INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''InstructBlipConfig''',
        '''InstructBlipQFormerConfig''',
        '''InstructBlipVisionConfig''',
    ],
    '''processing_instructblip''': ['''InstructBlipProcessor'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_			  = [
           '''INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''InstructBlipQFormerModel''',
           '''InstructBlipPreTrainedModel''',
           '''InstructBlipForConditionalGeneration''',
           '''InstructBlipVisionModel''',
       ]

if TYPE_CHECKING:
       from .configuration_instructblip import (
           INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           InstructBlipConfig,
           InstructBlipQFormerConfig,
           InstructBlipVisionConfig,
       )
       from .processing_instructblip import InstructBlipProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_instructblip import (
                  INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  InstructBlipForConditionalGeneration,
                  InstructBlipPreTrainedModel,
                  InstructBlipQFormerModel,
                  InstructBlipVisionModel,
              )

else:
       import sys

       snake_case_			  = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

