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_			(					__A,  __A,  __A,  __A=None,  __A=None,  __A=None,  __A=None,  __A=None,  )					->    List[str]:







      '''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=__A							)
      if decoder_head_mask is None:
            UpperCAmelCase__								=  torch.ones(config.decoder_layers,  config.decoder_attention_heads,  device=__A							)
      if cross_attn_head_mask is None:
            UpperCAmelCase__								=  torch.ones(config.decoder_layers,  config.decoder_attention_heads,  device=__A							)
      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					:						Optional[int]	,  __UpperCAmelCase					:						Tuple	,  __UpperCAmelCase					:						Dict=1_3	,  __UpperCAmelCase					:						List[str]=7	,  __UpperCAmelCase					:						Tuple=True	,  __UpperCAmelCase					:						Tuple=False	,  __UpperCAmelCase					:						Optional[int]=9_9	,  __UpperCAmelCase					:						Optional[int]=1_6	,  __UpperCAmelCase					:						Any=2	,  __UpperCAmelCase					:						List[Any]=4	,  __UpperCAmelCase					:						Optional[Any]=4	,  __UpperCAmelCase					:						int="relu"	,  __UpperCAmelCase					:						int=0.1	,  __UpperCAmelCase					:						Dict=0.1	,  __UpperCAmelCase					:						Optional[Any]=0.0	,  __UpperCAmelCase					:						Any=0.0	,  __UpperCAmelCase					:						int=2_0	,  __UpperCAmelCase					:						str=2	,  __UpperCAmelCase					:						Union[str, Any]=1	,  __UpperCAmelCase					:						Dict=0	,  )						->    Optional[int]:
         """simple docstring"""



         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       lowercase_				(self					:						Dict		)						->    Optional[int]:
         """simple docstring"""



         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(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
         return config, inputs_dict


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[Any]:
         """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       lowercase_				(self					:						Tuple		)						->    Dict:
         """simple docstring"""



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


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



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

         # first forward pass
         UpperCAmelCase__								=  model(__UpperCAmelCase	,  attention_mask=__UpperCAmelCase	,  head_mask=__UpperCAmelCase	,  use_cache=__UpperCAmelCase		)

         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(__UpperCAmelCase	,  attention_mask=__UpperCAmelCase		)["last_hidden_state"]
         UpperCAmelCase__								=  model(__UpperCAmelCase	,  attention_mask=__UpperCAmelCase	,  past_key_values=__UpperCAmelCase		)[
             "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(__UpperCAmelCase	,  __UpperCAmelCase	,  atol=1E-2		)		)





   def       lowercase_				(self					:						Tuple	,  __UpperCAmelCase					:						str	,  __UpperCAmelCase					:						str		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  MaMaaaModel(config=__UpperCAmelCase		).to(__UpperCAmelCase		).eval()
         UpperCAmelCase__								=  model(**__UpperCAmelCase		)

         UpperCAmelCase__								=  outputs.encoder_last_hidden_state
         UpperCAmelCase__								=  outputs.last_hidden_state

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

         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(__UpperCAmelCase		)
               UpperCAmelCase__								=  MaMaaaDecoder.from_pretrained(__UpperCAmelCase		).to(__UpperCAmelCase		)

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


   def       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Union[str, Any]	,  __UpperCAmelCase					:						Optional[Any]	,  __UpperCAmelCase					:						Optional[int]		)						->    str:
         """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       lowercase_				(self					:						Any		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  MaMaaaModelTester(self		)
         UpperCAmelCase__								=  ConfigTester(self	,  config_class=__UpperCAmelCase		)


   def       lowercase_				(self					:						str		)						->    Optional[int]:
         """simple docstring"""



         self.config_tester.run_common_tests()


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         for model_class in self.all_model_classes:
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)

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


   def       lowercase_				(self					:						Optional[int]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_decoder_model_past_large_inputs(*__UpperCAmelCase		)


   def       lowercase_				(self					:						List[str]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_encoder_decoder_model_standalone(*__UpperCAmelCase		)


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
               UpperCAmelCase__								=  model_class(__UpperCAmelCase		)
               model.to(__UpperCAmelCase		)
               model.eval()

               UpperCAmelCase__								=  copy.deepcopy(self._prepare_for_class(__UpperCAmelCase	,  __UpperCAmelCase		)		)

               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"	,  __UpperCAmelCase		)
                     del inputs["input_ids"]
                     inputs.pop("decoder_input_ids"	,  __UpperCAmelCase		)

               UpperCAmelCase__								=  model.get_input_embeddings()
               if not self.is_encoder_decoder:
                     UpperCAmelCase__								=  wte(__UpperCAmelCase		)
               else:
                     UpperCAmelCase__								=  wte(__UpperCAmelCase		)
                     UpperCAmelCase__								=  wte(__UpperCAmelCase		)

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





   def       lowercase_				(self					:						Dict		)						->    Any:
         """simple docstring"""



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




def        lowerCAmelCase_			(					__A							)					->    Dict:







      '''simple docstring'''


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


UpperCamelCase__    							= 1e-4







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


   @cached_property
   def       lowercase_				(self					:						Optional[int]		)						->    int:
         """simple docstring"""



         return MaMaaaTokenizer.from_pretrained("facebook/m2m100_418M"		)


   def       lowercase_				(self					:						List[Any]		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  MaMaaaModel.from_pretrained("facebook/m2m100_418M"		).to(__UpperCAmelCase		)
         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	,  __UpperCAmelCase	,  __UpperCAmelCase		)
         with torch.no_grad():
               UpperCAmelCase__								=  model(**__UpperCAmelCase		)[0]
         UpperCAmelCase__								=  torch.Size((1, 1_1, 1_0_2_4)		)
         self.assertEqual(output.shape	,  __UpperCAmelCase		)
         # change to expected output here
         UpperCAmelCase__								=  torch.tensor(
             [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]	,  device=__UpperCAmelCase		)
         self.assertTrue(torch.allclose(output[:, :3, :3]	,  __UpperCAmelCase	,  atol=__UpperCAmelCase		)		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"		).to(__UpperCAmelCase		)

         # 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	,  __UpperCAmelCase	,  __UpperCAmelCase		)
         with torch.no_grad():
               UpperCAmelCase__								=  model(**__UpperCAmelCase		)[0]
         UpperCAmelCase__								=  torch.Size((1, 1_1, model.config.vocab_size)		)
         self.assertEqual(output.shape	,  __UpperCAmelCase		)
         # change to expected output here
         UpperCAmelCase__								=  torch.tensor(
             [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]	,  device=__UpperCAmelCase		)
         self.assertTrue(torch.allclose(output[:, :3, :3]	,  __UpperCAmelCase	,  atol=__UpperCAmelCase		)		)





   def       lowercase_				(self					:						Union[str, Any]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  MaMaaaForConditionalGeneration.from_pretrained("facebook/m2m100_418M"		).to(__UpperCAmelCase		)
         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(__UpperCAmelCase	,  padding=__UpperCAmelCase	,  return_tensors="pt"		)

         UpperCAmelCase__								=  model.generate(
             input_ids=dct["input_ids"].to(__UpperCAmelCase		)	,  attention_mask=dct["attention_mask"].to(__UpperCAmelCase		)	,  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=__UpperCAmelCase	,  skip_special_tokens=__UpperCAmelCase		)
         assert generated == expected_en




from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

UpperCamelCase__    							= logging.get_logger(__name__)


UpperCamelCase__    							= Dict[str, Any]
UpperCamelCase__    							= List[Prediction]





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

         if self.framework == "tf":
               raise ValueError(f"""The {self.__class__} is only available in PyTorch."""		)

         requires_backends(self	,  "vision"		)
         self.check_model_type(
             dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()		)		)


   def       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



         UpperCAmelCase__								=  {}
         if "threshold" in kwargs:
               UpperCAmelCase__								=  kwargs["threshold"]
         return {}, {}, postprocess_kwargs


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



         return super().__call__(*__UpperCAmelCase	,  **__UpperCAmelCase		)


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.IntTensor([[image.height, image.width]]		)
         UpperCAmelCase__								=  self.image_processor(images=[image]	,  return_tensors="pt"		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  self.tokenizer(text=inputs["words"]	,  boxes=inputs["boxes"]	,  return_tensors="pt"		)
         UpperCAmelCase__								=  target_size
         return inputs


   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_inputs.pop("target_size"		)
         UpperCAmelCase__								=  self.model(**__UpperCAmelCase		)
         UpperCAmelCase__								=  outputs.__class__({"target_size": target_size, **outputs}		)
         if self.tokenizer is not None:
               UpperCAmelCase__								=  model_inputs["bbox"]
         return model_outputs


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  model_outputs["target_size"]
         if self.tokenizer is not None:
               # This is a LayoutLMForTokenClassification variant.
               # The OCR got the boxes and the model classified the words.
               UpperCAmelCase__		,				UpperCAmelCase__								=  target_size[0].tolist()

               def unnormalize(__UpperCAmelCase					:						List[str]		):
                     return self._get_bounding_box(
                         torch.Tensor(
                             [
                                 (width * bbox[0] / 1_0_0_0),
                                 (height * bbox[1] / 1_0_0_0),
                                 (width * bbox[2] / 1_0_0_0),
                                 (height * bbox[3] / 1_0_0_0),
                             ]		)		)

               UpperCAmelCase__		,				UpperCAmelCase__								=  model_outputs["logits"].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
               UpperCAmelCase__								=  [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
               UpperCAmelCase__								=  [unnormalize(__UpperCAmelCase		) for bbox in model_outputs["bbox"].squeeze(0		)]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		) for vals in zip(scores.tolist()	,  __UpperCAmelCase	,  __UpperCAmelCase		) if vals[0] > threshold]
         else:
               # This is a regular ForObjectDetectionModel
               UpperCAmelCase__								=  self.image_processor.post_process_object_detection(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
               UpperCAmelCase__								=  raw_annotations[0]
               UpperCAmelCase__								=  raw_annotation["scores"]
               UpperCAmelCase__								=  raw_annotation["labels"]
               UpperCAmelCase__								=  raw_annotation["boxes"]

               UpperCAmelCase__								=  scores.tolist()
               UpperCAmelCase__								=  [self.model.config.idalabel[label.item()] for label in labels]
               UpperCAmelCase__								=  [self._get_bounding_box(__UpperCAmelCase		) for box in boxes]

               # {"scores": [...], ...} --> [{"score":x, ...}, ...]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [
                   dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		)
                   for vals in zip(raw_annotation["scores"]	,  raw_annotation["labels"]	,  raw_annotation["boxes"]		)
               ]

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"torch.Tensor"		)						->    Dict[str, int]:
         """simple docstring"""



         if self.framework != "pt":
               raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."		)
         UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__		,				UpperCAmelCase__								=  box.int().tolist()
         UpperCAmelCase__								=  {
             "xmin": xmin,
             "ymin": ymin,
             "xmax": xmax,
             "ymax": ymax,
         }
         return bbox



