
"""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      lowercase__    (       lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Dict			,					lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       Tuple=None			,					lowerCAmelCase					:       int=None			,					lowerCAmelCase					:       Any=None			,					lowerCAmelCase					:       Optional[int]=None			,					lowerCAmelCase					:       Tuple=None			,					)			->   Tuple:






    """simple docstring"""




    if attention_mask is None:
        UpperCAmelCase          =       input_ids.ne(config.pad_token_id			)
    if decoder_attention_mask is None:
        UpperCAmelCase          =       decoder_input_ids.ne(config.pad_token_id			)
    if head_mask is None:
        UpperCAmelCase          =       torch.ones(config.encoder_layers			,					config.encoder_attention_heads			,					device=lowerCAmelCase			)
    if decoder_head_mask is None:
        UpperCAmelCase          =       torch.ones(config.decoder_layers			,					config.decoder_attention_heads			,					device=lowerCAmelCase			)
    if cross_attn_head_mask is None:
        UpperCAmelCase          =       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   _UpperCAmelCase				:




      def __init__(				self ,							lowercase_ ,							lowercase_=1_3 ,							lowercase_=7 ,							lowercase_=True ,							lowercase_=False ,							lowercase_=9_9 ,							lowercase_=1_6 ,							lowercase_=2 ,							lowercase_=4 ,							lowercase_=4 ,							lowercase_="relu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=0.0 ,							lowercase_=0.0 ,							lowercase_=2_0 ,							lowercase_=2 ,							lowercase_=1 ,							lowercase_=0 ,							)    ->       str:
          UpperCAmelCase          =       parent
          UpperCAmelCase          =       batch_size
          UpperCAmelCase          =       seq_length
          UpperCAmelCase          =       is_training
          UpperCAmelCase          =       use_labels
          UpperCAmelCase          =       vocab_size
          UpperCAmelCase          =       hidden_size
          UpperCAmelCase          =       num_hidden_layers
          UpperCAmelCase          =       num_attention_heads
          UpperCAmelCase          =       intermediate_size
          UpperCAmelCase          =       hidden_act
          UpperCAmelCase          =       hidden_dropout_prob
          UpperCAmelCase          =       attention_probs_dropout_prob
          UpperCAmelCase          =       encoder_layerdrop
          UpperCAmelCase          =       decoder_layerdrop
          UpperCAmelCase          =       max_position_embeddings
          UpperCAmelCase          =       eos_token_id
          UpperCAmelCase          =       pad_token_id
          UpperCAmelCase          =       bos_token_id




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

          UpperCAmelCase          =       self.get_config()
          UpperCAmelCase          =       prepare_mam_aaa_inputs_dict(lowercase_ ,							lowercase_ ,							lowercase_				)
          return config, inputs_dict




      def      a_   (				self				)    ->       str:
          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[Any]:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.prepare_config_and_inputs()
          return config, inputs_dict




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       Optional[Any]:
          UpperCAmelCase          =       MaMaaaModel(config=lowercase_				).get_decoder().to(lowercase_				).eval()
          UpperCAmelCase          =       inputs_dict['input_ids']
          UpperCAmelCase          =       inputs_dict['attention_mask']
          UpperCAmelCase          =       inputs_dict['head_mask']

          # first forward pass
          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							head_mask=lowercase_ ,							use_cache=lowercase_				)

          UpperCAmelCase      ,				UpperCAmelCase          =       outputs.to_tuple()

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

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

          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_				)['last_hidden_state']
          UpperCAmelCase          =       model(lowercase_ ,							attention_mask=lowercase_ ,							past_key_values=lowercase_				)[
              'last_hidden_state'
          ]

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




      def      a_   (				self ,							lowercase_ ,							lowercase_				)    ->       Optional[int]:
          UpperCAmelCase          =       MaMaaaModel(config=lowercase_				).to(lowercase_				).eval()
          UpperCAmelCase          =       model(**lowercase_				)

          UpperCAmelCase          =       outputs.encoder_last_hidden_state
          UpperCAmelCase          =       outputs.last_hidden_state

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

          UpperCAmelCase          =       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:
              UpperCAmelCase          =       model.get_decoder()
              decoder.save_pretrained(lowercase_				)
              UpperCAmelCase          =       MaMaaaDecoder.from_pretrained(lowercase_				).to(lowercase_				)

          UpperCAmelCase          =       decoder(
              input_ids=inputs_dict['decoder_input_ids'] ,							attention_mask=inputs_dict['decoder_attention_mask'] ,							encoder_hidden_states=lowercase_ ,							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   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_  ,      unittest.TestCase				):
      __SCREAMING_SNAKE_CASE :				Dict				   =						(
          (
              MaMaaaModel,
              MaMaaaForConditionalGeneration,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE :				int				   =						(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE :				Any				   =						(
          {
              "conversational": MaMaaaForConditionalGeneration,
              "feature-extraction": MaMaaaModel,
              "summarization": MaMaaaForConditionalGeneration,
              "text2text-generation": MaMaaaForConditionalGeneration,
              "translation": MaMaaaForConditionalGeneration,
          }
          if is_torch_available()
          else {}
      )
      __SCREAMING_SNAKE_CASE :				Dict				   =						True
      __SCREAMING_SNAKE_CASE :				List[str]				   =						True
      __SCREAMING_SNAKE_CASE :				List[str]				   =						False
      __SCREAMING_SNAKE_CASE :				int				   =						False




      def      a_   (				self ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_ ,							lowercase_				)    ->       str:
          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				)    ->       Optional[int]:
          UpperCAmelCase          =       MaMaaaModelTester(self				)
          UpperCAmelCase          =       ConfigTester(self ,							config_class=lowercase_				)




      def      a_   (				self				)    ->       Optional[int]:
          self.config_tester.run_common_tests()




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
              UpperCAmelCase          =       model_class(lowercase_				)

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




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs_for_common()
          self.model_tester.check_encoder_decoder_model_standalone(*lowercase_				)




      def      a_   (				self				)    ->       int:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
              UpperCAmelCase          =       model_class(lowercase_				)
              model.to(lowercase_				)
              model.eval()

              UpperCAmelCase          =       copy.deepcopy(self._prepare_for_class(lowercase_ ,							lowercase_				)				)

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

              UpperCAmelCase          =       model.get_input_embeddings()
              if not self.is_encoder_decoder:
                  UpperCAmelCase          =       wte(lowercase_				)
              else:
                  UpperCAmelCase          =       wte(lowercase_				)
                  UpperCAmelCase          =       wte(lowercase_				)

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




      def      a_   (				self				)    ->       Any:
          UpperCAmelCase      ,				UpperCAmelCase          =       self.model_tester.prepare_config_and_inputs()
          UpperCAmelCase          =       input_dict['input_ids']
          UpperCAmelCase          =       input_ids.ne(1				).to(lowercase_				)
          UpperCAmelCase          =       MaMaaaForConditionalGeneration(lowercase_				).eval().to(lowercase_				)
          if torch_device == "cuda":
              model.half()
          model.generate(lowercase_ ,							attention_mask=lowercase_				)
          model.generate(num_beams=4 ,							do_sample=lowercase_ ,							early_stopping=lowercase_ ,							num_return_sequences=3				)




def      lowercase__    (       lowerCAmelCase					:       Optional[Any]			)			->   Optional[int]:






    """simple docstring"""




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


SCREAMING_SNAKE_CASE_            =    1e-4







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




      @cached_property
      def      a_   (				self				)    ->       Optional[int]:
          return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       MaMaaaModel.from_pretrained('facebook/m2m100_418M'				).to(lowercase_				)
          UpperCAmelCase          =       _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]				)
          UpperCAmelCase          =       _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]				)
          UpperCAmelCase          =       prepare_mam_aaa_inputs_dict(model.config ,							lowercase_ ,							lowercase_				)
          with torch.no_grad():
              UpperCAmelCase          =       model(**lowercase_				)[0]
          UpperCAmelCase          =       torch.Size((1, 1_1, 1_0_2_4)				)
          self.assertEqual(output.shape ,							lowercase_				)
          # change to expected output here
          UpperCAmelCase          =       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=lowercase_				)
          self.assertTrue(torch.allclose(output[:, :3, :3] ,							lowercase_ ,							atol=lowercase_				)				)




      def      a_   (				self				)    ->       Optional[Any]:
          UpperCAmelCase          =       MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'				).to(lowercase_				)

          # change to intended input
          UpperCAmelCase          =       _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]				)
          UpperCAmelCase          =       _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]				)
          UpperCAmelCase          =       prepare_mam_aaa_inputs_dict(model.config ,							lowercase_ ,							lowercase_				)
          with torch.no_grad():
              UpperCAmelCase          =       model(**lowercase_				)[0]
          UpperCAmelCase          =       torch.Size((1, 1_1, model.config.vocab_size)				)
          self.assertEqual(output.shape ,							lowercase_				)
          # change to expected output here
          UpperCAmelCase          =       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=lowercase_				)
          self.assertTrue(torch.allclose(output[:, :3, :3] ,							lowercase_ ,							atol=lowercase_				)				)




      def      a_   (				self				)    ->       List[str]:
          UpperCAmelCase          =       MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'				).to(lowercase_				)
          UpperCAmelCase          =       MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M' ,							src_lang='fr' ,							tgt_lang='en'				)

          UpperCAmelCase          =       [
              '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
          UpperCAmelCase          =       tokenizer(lowercase_ ,							padding=lowercase_ ,							return_tensors='pt'				)

          UpperCAmelCase          =       model.generate(
              input_ids=dct['input_ids'].to(lowercase_				) ,							attention_mask=dct['attention_mask'].to(lowercase_				) ,							num_beams=5 ,							forced_bos_token_id=tokenizer.get_lang_id('en'				) ,							)

          UpperCAmelCase          =       [
              '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.',
          ]

          UpperCAmelCase          =       tokenizer.batch_decode(
              hypotheses_batch.tolist() ,							clean_up_tokenization_spaces=lowercase_ ,							skip_special_tokens=lowercase_				)
          assert generated == expected_en





"""simple docstring"""

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class   _UpperCAmelCase				(		SCREAMING_SNAKE_CASE_				):
      __SCREAMING_SNAKE_CASE :				List[Any]				   =						["image_processor", "tokenizer"]
      __SCREAMING_SNAKE_CASE :				Dict				   =						"AutoImageProcessor"
      __SCREAMING_SNAKE_CASE :				Optional[int]				   =						"AutoTokenizer"




      def __init__(				self ,							lowercase_ ,							lowercase_				)    ->       List[str]:
          super().__init__(lowercase_ ,							lowercase_				)
          UpperCAmelCase          =       self.image_processor




      def __call__(				self ,							lowercase_=None ,							lowercase_=None ,							lowercase_=None ,							**lowercase_				)    ->       Any:

          if text is None and images is None:
              raise ValueError('You have to specify either text or images. Both cannot be none.'				)

          if text is not None:
              UpperCAmelCase          =       self.tokenizer(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if images is not None:
              UpperCAmelCase          =       self.image_processor(lowercase_ ,							return_tensors=lowercase_ ,							**lowercase_				)

          if text is not None and images is not None:
              UpperCAmelCase          =       image_features.pixel_values
              return encoding
          elif text is not None:
              return encoding
          else:
              return BatchEncoding(data=dict(**lowercase_				) ,							tensor_type=lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[Any]:
          return self.tokenizer.batch_decode(*lowercase_ ,							**lowercase_				)




      def      a_   (				self ,							*lowercase_ ,							**lowercase_				)    ->       Optional[int]:
          return self.tokenizer.decode(*lowercase_ ,							**lowercase_				)




      @property
      def      a_   (				self				)    ->       Optional[int]:
          return ["input_ids", "attention_mask", "pixel_values"]



