'''simple docstring'''






import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
     from PIL import Image
else:





     class     A__							:





        """simple docstring"""





        @staticmethod
        def 			_lowerCAmelCase		( *lowerCAmelCase__     :		Tuple						,     **lowerCAmelCase__     :		int			)       ->    Tuple:







           """simple docstring"""




           pass






def    __UpperCAmelCase			(  a_:       List[str]							):
   _UpperCAmelCase							:	Union[str, Any]							=   hashlib.mda(image.tobytes()							)
   return m.hexdigest()[:10]






def    __UpperCAmelCase			(  a_:       List[Any]							):
   _UpperCAmelCase							:	Dict							=   np.array(_lowerCAmelCase							)
   _UpperCAmelCase							:	Optional[int]							=   npimg.shape
   return {"hash": hashimage(_lowerCAmelCase							), "shape": shape}






@is_pipeline_test
@require_vision
@require_torch
class     A__							(       unittest.TestCase   ):





   """simple docstring"""





   UpperCamelCase_       :						Tuple    				=     dict(
       (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if MODEL_FOR_MASK_GENERATION_MAPPING else [])   )
   UpperCamelCase_       :						List[str]    				=     dict(
       (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()   ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])   )




   def 			_lowerCAmelCase		( self     :		Optional[int]						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		str						,     lowerCAmelCase__     :		Tuple			)       ->    str:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   MaskGenerationPipeline(model=A_						,     image_processor=A_			)
      return image_segmenter, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]




   def 			_lowerCAmelCase		( self     :		str						,     lowerCAmelCase__     :		Optional[Any]						,     lowerCAmelCase__     :		Dict			)       ->    Union[str, Any]:







      """simple docstring"""




      pass




   @require_tf
   @unittest.skip("Image segmentation not implemented in TF"			)
   def 			_lowerCAmelCase		( self     :		Any			)       ->    int:







      """simple docstring"""




      pass




   @slow
   @require_torch
   def 			_lowerCAmelCase		( self     :		Optional[int]			)       ->    List[str]:







      """simple docstring"""




      _UpperCAmelCase							:	List[Any]							=   pipeline("mask-generation"						,     model="facebook/sam-vit-huge"			)

      _UpperCAmelCase							:	Optional[Any]							=   image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"						,     points_per_batch=2_5_6			)

      # Shortening by hashing
      _UpperCAmelCase							:	Optional[Any]							=   []
      for i, o in enumerate(outputs["masks"]			):
         new_outupt += [{"mask": mask_to_test_readable(A_			), "scores": outputs["scores"][i]}]

      # fmt: off
      self.assertEqual(
          nested_simplify(A_						,     decimals=4			)						,     [
              {"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.0444},
              {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.021},
              {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0167},
              {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0132},
              {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0053},
              {"mask": {"hash": "e2d0b7a0b7", "shape": (4_8_0, 6_4_0)}, "scores": 0.9967},
              {"mask": {"hash": "453c7844bd", "shape": (4_8_0, 6_4_0)}, "scores": 0.993},
              {"mask": {"hash": "3d44f2926d", "shape": (4_8_0, 6_4_0)}, "scores": 0.9909},
              {"mask": {"hash": "64033ddc3f", "shape": (4_8_0, 6_4_0)}, "scores": 0.9879},
              {"mask": {"hash": "801064ff79", "shape": (4_8_0, 6_4_0)}, "scores": 0.9834},
              {"mask": {"hash": "6172f276ef", "shape": (4_8_0, 6_4_0)}, "scores": 0.9716},
              {"mask": {"hash": "b49e60e084", "shape": (4_8_0, 6_4_0)}, "scores": 0.9612},
              {"mask": {"hash": "a811e775fd", "shape": (4_8_0, 6_4_0)}, "scores": 0.9599},
              {"mask": {"hash": "a6a8ebcf4b", "shape": (4_8_0, 6_4_0)}, "scores": 0.9552},
              {"mask": {"hash": "9d8257e080", "shape": (4_8_0, 6_4_0)}, "scores": 0.9532},
              {"mask": {"hash": "32de6454a8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9516},
              {"mask": {"hash": "af3d4af2c8", "shape": (4_8_0, 6_4_0)}, "scores": 0.9499},
              {"mask": {"hash": "3c6db475fb", "shape": (4_8_0, 6_4_0)}, "scores": 0.9483},
              {"mask": {"hash": "c290813fb9", "shape": (4_8_0, 6_4_0)}, "scores": 0.9464},
              {"mask": {"hash": "b6f0b8f606", "shape": (4_8_0, 6_4_0)}, "scores": 0.943},
              {"mask": {"hash": "92ce16bfdf", "shape": (4_8_0, 6_4_0)}, "scores": 0.943},
              {"mask": {"hash": "c749b25868", "shape": (4_8_0, 6_4_0)}, "scores": 0.9408},
              {"mask": {"hash": "efb6cab859", "shape": (4_8_0, 6_4_0)}, "scores": 0.9335},
              {"mask": {"hash": "1ff2eafb30", "shape": (4_8_0, 6_4_0)}, "scores": 0.9326},
              {"mask": {"hash": "788b798e24", "shape": (4_8_0, 6_4_0)}, "scores": 0.9262},
              {"mask": {"hash": "abea804f0e", "shape": (4_8_0, 6_4_0)}, "scores": 0.8999},
              {"mask": {"hash": "7b9e8ddb73", "shape": (4_8_0, 6_4_0)}, "scores": 0.8986},
              {"mask": {"hash": "cd24047c8a", "shape": (4_8_0, 6_4_0)}, "scores": 0.8984},
              {"mask": {"hash": "6943e6bcbd", "shape": (4_8_0, 6_4_0)}, "scores": 0.8873},
              {"mask": {"hash": "b5f47c9191", "shape": (4_8_0, 6_4_0)}, "scores": 0.8871}
          ]						,     )
      # fmt: on




   @require_torch
   @slow
   def 			_lowerCAmelCase		( self     :		Optional[Any]			)       ->    List[Any]:







      """simple docstring"""




      _UpperCAmelCase							:	Optional[Any]							=   "facebook/sam-vit-huge"
      _UpperCAmelCase							:	Optional[Any]							=   pipeline("mask-generation"						,     model=A_			)

      _UpperCAmelCase							:	Optional[Any]							=   image_segmenter(
          "http://images.cocodataset.org/val2017/000000039769.jpg"						,     pred_iou_thresh=1						,     points_per_batch=2_5_6			)

      # Shortening by hashing
      _UpperCAmelCase							:	Dict							=   []
      for i, o in enumerate(outputs["masks"]			):
         new_outupt += [{"mask": mask_to_test_readable(A_			), "scores": outputs["scores"][i]}]

      self.assertEqual(
          nested_simplify(A_						,     decimals=4			)						,     [
              {"mask": {"hash": "115ad19f5f", "shape": (4_8_0, 6_4_0)}, "scores": 1.0444},
              {"mask": {"hash": "6affa964c6", "shape": (4_8_0, 6_4_0)}, "scores": 1.0210},
              {"mask": {"hash": "dfe28a0388", "shape": (4_8_0, 6_4_0)}, "scores": 1.0167},
              {"mask": {"hash": "c0a5f4a318", "shape": (4_8_0, 6_4_0)}, "scores": 1.0132},
              {"mask": {"hash": "fe8065c197", "shape": (4_8_0, 6_4_0)}, "scores": 1.0053},
          ]						,     )






import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__lowerCamelCase       :     Union[str, Any]   =     logging.get_logger(__name__)

__lowerCamelCase       :     Any   =     {
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:Dict						    =   'bart'
      _UpperCAmelCase	:str						    =   ['past_key_values']
      _UpperCAmelCase	:Any						    =   {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





      def __init__( self       ,							A_=5_0265       ,							A_=1024       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=12       ,							A_=4096       ,							A_=16       ,							A_=0.0       ,							A_=0.0       ,							A_="gelu"       ,							A_=1024       ,							A_=0.1       ,							A_=0.0       ,							A_=0.0       ,							A_=0.02       ,							A_=0.0       ,							A_=False       ,							A_=True       ,							A_=3       ,							A_=1       ,							A_=0       ,							A_=2       ,							A_=True       ,							A_=2       ,							A_=2       ,							**A_       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   vocab_size
       UpperCamelCase	:					List[Any]       	=   max_position_embeddings
       UpperCamelCase	:					Any       	=   d_model
       UpperCamelCase	:					Optional[Any]       	=   encoder_ffn_dim
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   encoder_attention_heads
       UpperCamelCase	:					Optional[int]       	=   decoder_ffn_dim
       UpperCamelCase	:					List[str]       	=   decoder_layers
       UpperCamelCase	:					Optional[int]       	=   decoder_attention_heads
       UpperCamelCase	:					int       	=   dropout
       UpperCamelCase	:					int       	=   attention_dropout
       UpperCamelCase	:					Tuple       	=   activation_dropout
       UpperCamelCase	:					Tuple       	=   activation_function
       UpperCamelCase	:					int       	=   init_std
       UpperCamelCase	:					List[Any]       	=   encoder_layerdrop
       UpperCamelCase	:					List[str]       	=   decoder_layerdrop
       UpperCamelCase	:					Dict       	=   classifier_dropout
       UpperCamelCase	:					Optional[int]       	=   use_cache
       UpperCamelCase	:					List[Any]       	=   encoder_layers
       UpperCamelCase	:					int       	=   scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           num_labels=A_       ,							pad_token_id=A_       ,							bos_token_id=A_       ,							eos_token_id=A_       ,							is_encoder_decoder=A_       ,							decoder_start_token_id=A_       ,							forced_eos_token_id=A_       ,							**A_       ,							)

       # ensure backward compatibility for BART CNN models
       if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"       ,							A_   ):
        UpperCamelCase	:					int       	=   self.bos_token_id
        warnings.warn(
            F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
            "The config can simply be saved and uploaded again to be fixed."   )



class 					A__   (       __snake_case			):





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )

        if self.use_past:
         UpperCamelCase	:					List[str]       	=   {0: "batch"}
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "past_decoder_sequence + sequence"}
        else:
         UpperCamelCase	:					Dict       	=   {0: "batch", 1: "decoder_sequence"}
         UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 1: "decoder_sequence"}

        if self.use_past:
         self.fill_with_past_key_values_(A_       ,							direction="inputs"   )
       elif self.task == "causal-lm":
        # TODO: figure this case out.
        UpperCamelCase	:					Any       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
            ]   )
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					Optional[Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Union[str, Any]       	=   {0: "batch", 2: "past_sequence + sequence"}
       else:
        UpperCamelCase	:					Optional[Any]       	=   OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
                ("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
                ("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
            ]   )

       return common_inputs





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Tuple       	=   super().outputs
       else:
        UpperCamelCase	:					Dict       	=   super(A_       ,							self   ).outputs
        if self.use_past:
         UpperCamelCase	,    UpperCamelCase	:					int       	=   self.num_layers
         for i in range(A_   ):
          UpperCamelCase	:					int       	=   {0: "batch", 2: "past_sequence + sequence"}
          UpperCamelCase	:					Tuple       	=   {0: "batch", 2: "past_sequence + sequence"}
       return common_outputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					List[Any]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       # Generate decoder inputs
       UpperCamelCase	:					List[Any]       	=   seq_length if not self.use_past else 1
       UpperCamelCase	:					Tuple       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )
       UpperCamelCase	:					Optional[int]       	=   {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
       UpperCamelCase	:					List[Any]       	=   dict(**A_       ,							**A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Optional[Any]       	=   common_inputs["input_ids"].shape
        UpperCamelCase	:					List[Any]       	=   common_inputs["decoder_input_ids"].shape[1]
        UpperCamelCase	,    UpperCamelCase	:					List[str]       	=   self.num_attention_heads
        UpperCamelCase	:					int       	=   (
            batch,
            num_encoder_attention_heads,
            encoder_seq_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )
        UpperCamelCase	:					List[Any]       	=   decoder_seq_length + 3
        UpperCamelCase	:					str       	=   (
            batch,
            num_decoder_attention_heads,
            decoder_past_length,
            self._config.hidden_size // num_decoder_attention_heads,
        )

        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["decoder_attention_mask"], torch.ones(A_       ,							A_   )]       ,							dim=1   )

        UpperCamelCase	:					int       	=   []
        # If the number of encoder and decoder layers are present in the model configuration, both are considered
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   self.num_layers
        UpperCamelCase	:					Any       	=   min(A_       ,							A_   )
        UpperCamelCase	:					List[str]       	=   max(A_       ,							A_   ) - min_num_layers
        UpperCamelCase	:					Dict       	=   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

        for _ in range(A_   ):
         common_inputs["past_key_values"].append(
             (
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
                 torch.zeros(A_   ),
             )   )
        # TODO: test this.
        UpperCamelCase	:					Optional[Any]       	=   encoder_shape if remaining_side_name == "encoder" else decoder_shape
        for _ in range(A_       ,							A_   ):
         common_inputs["past_key_values"].append((torch.zeros(A_   ), torch.zeros(A_   ))   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           A_       ,							A_       ,							A_       ,							A_       ,							A_   )

       if self.use_past:
        if not is_torch_available():
         raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."   )
        else:
         import torch
        UpperCamelCase	,    UpperCamelCase	:					Union[str, Any]       	=   common_inputs["input_ids"].shape
        # Not using the same length for past_key_values
        UpperCamelCase	:					Optional[Any]       	=   seqlen + 2
        UpperCamelCase	,    UpperCamelCase	:					List[Any]       	=   self.num_layers
        UpperCamelCase	,    UpperCamelCase	:					Optional[int]       	=   self.num_attention_heads
        UpperCamelCase	:					str       	=   (
            batch,
            num_encoder_attention_heads,
            past_key_values_length,
            self._config.hidden_size // num_encoder_attention_heads,
        )

        UpperCamelCase	:					Optional[Any]       	=   common_inputs["attention_mask"].dtype
        UpperCamelCase	:					int       	=   torch.cat(
            [common_inputs["attention_mask"], torch.ones(A_       ,							A_       ,							dtype=A_   )]       ,							dim=1   )
        UpperCamelCase	:					Optional[Any]       	=   [
            (torch.zeros(A_   ), torch.zeros(A_   )) for _ in range(A_   )
        ]
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Optional[Any]       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_batch       ,							num_token_to_add=0   )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCamelCase	:					Union[str, Any]       	=   tokenizer.num_special_tokens_to_add(A_   )
       UpperCamelCase	:					int       	=   compute_effective_axis_dimension(
           A_       ,							fixed_dimension=OnnxConfig.default_fixed_sequence       ,							num_token_to_add=A_   )

       # Generate dummy inputs according to compute batch and sequence
       UpperCamelCase	:					int       	=   [" ".join([tokenizer.unk_token]   ) * seq_length] * batch_size
       UpperCamelCase	:					Dict       	=   dict(tokenizer(A_       ,							return_tensors=A_   )   )
       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_ = -1       ,							A_ = -1       ,							A_ = False       ,							A_ = None       ,							):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[int]       	=   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       elif self.task == "causal-lm":
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_causal_lm(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )
       else:
        UpperCamelCase	:					List[str]       	=   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            A_       ,							batch_size=A_       ,							seq_length=A_       ,							is_pair=A_       ,							framework=A_   )

       return common_inputs





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       if self.task in ["default", "seq2seq-lm"]:
        UpperCamelCase	:					Optional[Any]       	=   super()._flatten_past_key_values_(A_       ,							A_       ,							A_       ,							A_   )
       else:
        UpperCamelCase	:					Optional[Any]       	=   super(A_       ,							self   )._flatten_past_key_values_(
            A_       ,							A_       ,							A_       ,							A_   )
