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

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


_lowerCAmelCase        =       logging.get_logger(__name__)

_lowerCAmelCase        =       {
    'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json',
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 __magic_name__          =   """m2m_100"""
 __magic_name__          =   ["""past_key_values"""]
 __magic_name__          =   {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
 def __init__(    self ,       __magic_name__=12_8112 ,       __magic_name__=1024 ,       __magic_name__=12 ,       __magic_name__=4096 ,       __magic_name__=16 ,       __magic_name__=12 ,       __magic_name__=4096 ,       __magic_name__=16 ,       __magic_name__=0.05 ,       __magic_name__=0.05 ,       __magic_name__=True ,       __magic_name__=True ,       __magic_name__="relu" ,       __magic_name__=1024 ,       __magic_name__=0.1 ,       __magic_name__=0.1 ,       __magic_name__=0.0 ,       __magic_name__=0.02 ,       __magic_name__=2 ,       __magic_name__=True ,       __magic_name__=1 ,       __magic_name__=0 ,       __magic_name__=2 ,       **__magic_name__ ,       ):

        """simple docstring"""

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

        super().__init__(
            pad_token_id=__magic_name__ ,       bos_token_id=__magic_name__ ,       eos_token_id=__magic_name__ ,       is_encoder_decoder=__magic_name__ ,       decoder_start_token_id=__magic_name__ ,       **__magic_name__ ,       )






class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 @property
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				OrderedDict(
            [
                ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
            ]      )

        if self.use_past:
               A_							:       int	      =				{0: '''batch'''}
               A_							:       Optional[Any]	      =				{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
        else:
               A_							:       Optional[int]	      =				{0: '''batch''', 1: '''decoder_sequence'''}
               A_							:       Dict	      =				{0: '''batch''', 1: '''decoder_sequence'''}

        if self.use_past:
               self.fill_with_past_key_values_(__magic_name__ ,       direction='''inputs'''      )
        return common_inputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ = -1 ,       __magic_name__ = -1 ,       __magic_name__ = False ,       __magic_name__ = None ,       ):

        """simple docstring"""

        A_							:       List[Any]	      =				compute_effective_axis_dimension(
            __magic_name__ ,       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
        A_							:       str	      =				tokenizer.num_special_tokens_to_add(__magic_name__      )
        A_							:       Dict	      =				compute_effective_axis_dimension(
            __magic_name__ ,       fixed_dimension=OnnxConfig.default_fixed_sequence ,       num_token_to_add=__magic_name__      )

        # Generate dummy inputs according to compute batch and sequence
        A_							:       Any	      =				[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
        A_							:       Tuple	      =				dict(tokenizer(__magic_name__ ,       return_tensors=__magic_name__      )      )
        return common_inputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__ = -1 ,       __magic_name__ = -1 ,       __magic_name__ = False ,       __magic_name__ = None ,       ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__ ,       __magic_name__      )

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

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

               A_							:       Any	      =				torch.cat(
                   [common_inputs['''decoder_attention_mask'''], torch.ones(__magic_name__ ,       __magic_name__      )] ,       dim=1      )

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

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



from typing import Any, Dict, List, Union

import numpy as np

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 PIL import Image

    from ..image_utils import load_image

if is_torch_available():
    from ..models.auto.modeling_auto import (
        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
        MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
        MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
        MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
    )


_lowerCAmelCase        =       logging.get_logger(__name__)


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






@add_end_docstrings(A__				)
class     __UpperCAmelCase(    A__				):






 """simple docstring"""




 def __init__(    self ,       *__magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        super().__init__(*__magic_name__ ,       **__magic_name__      )

        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_IMAGE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()      )      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        A_							:       str	      =				{}
        A_							:       List[Any]	      =				{}
        if "subtask" in kwargs:
               A_							:       Optional[int]	      =				kwargs['''subtask''']
               A_							:       Dict	      =				kwargs['''subtask''']
        if "threshold" in kwargs:
               A_							:       Tuple	      =				kwargs['''threshold''']
        if "mask_threshold" in kwargs:
               A_							:       int	      =				kwargs['''mask_threshold''']
        if "overlap_mask_area_threshold" in kwargs:
               A_							:       Optional[Any]	      =				kwargs['''overlap_mask_area_threshold''']

        return preprocess_kwargs, {}, postprocess_kwargs
 def __call__(    self ,       __magic_name__ ,       **__magic_name__      ):

        """simple docstring"""

        return super().__call__(__magic_name__ ,       **__magic_name__      )
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None      ):

        """simple docstring"""

        A_							:       Optional[int]	      =				load_image(__magic_name__      )
        A_							:       int	      =				[(image.height, image.width)]
        if self.model.config.__class__.__name__ == "OneFormerConfig":
               if subtask is None:
                      A_							:       int	      =				{}
               else:
                      A_							:       str	      =				{'''task_inputs''': [subtask]}
               A_							:       List[Any]	      =				self.image_processor(images=[image] ,       return_tensors='''pt''' ,       **__magic_name__      )
               A_							:       int	      =				self.tokenizer(
                   inputs['''task_inputs'''] ,       padding='''max_length''' ,       max_length=self.model.config.task_seq_len ,       return_tensors=self.framework ,       )['''input_ids''']
        else:
               A_							:       Any	      =				self.image_processor(images=[image] ,       return_tensors='''pt'''      )
        A_							:       Optional[int]	      =				target_size
        return inputs
 def  UpperCAmelCase   (    self ,       __magic_name__      ):

        """simple docstring"""

        A_							:       Dict	      =				model_inputs.pop('''target_size'''      )
        A_							:       Union[str, Any]	      =				self.model(**__magic_name__      )
        A_							:       Any	      =				target_size
        return model_outputs
 def  UpperCAmelCase   (    self ,       __magic_name__ ,       __magic_name__=None ,       __magic_name__=0.9 ,       __magic_name__=0.5 ,       __magic_name__=0.5      ):

        """simple docstring"""

        A_							:       Tuple	      =				None
        if subtask in {"panoptic", None} and hasattr(self.image_processor ,       '''post_process_panoptic_segmentation'''      ):
               A_							:       Dict	      =				self.image_processor.post_process_panoptic_segmentation
        elif subtask in {"instance", None} and hasattr(self.image_processor ,       '''post_process_instance_segmentation'''      ):
               A_							:       Tuple	      =				self.image_processor.post_process_instance_segmentation

        if fn is not None:
               A_							:       Dict	      =				fn(
                   __magic_name__ ,       threshold=__magic_name__ ,       mask_threshold=__magic_name__ ,       overlap_mask_area_threshold=__magic_name__ ,       target_sizes=model_outputs['''target_size'''] ,       )[0]

               A_							:       Optional[Any]	      =				[]
               A_							:       Optional[int]	      =				outputs['''segmentation''']

               for segment in outputs["segments_info"]:
                      A_							:       Optional[int]	      =				(segmentation == segment['''id''']) * 255
                      A_							:       int	      =				Image.fromarray(mask.numpy().astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       Union[str, Any]	      =				self.model.config.idalabel[segment['''label_id''']]
                      A_							:       Optional[int]	      =				segment['''score''']
                      annotation.append({'''score''': score, '''label''': label, '''mask''': mask}      )

        elif subtask in {"semantic", None} and hasattr(self.image_processor ,       '''post_process_semantic_segmentation'''      ):
               A_							:       int	      =				self.image_processor.post_process_semantic_segmentation(
                   __magic_name__ ,       target_sizes=model_outputs['''target_size''']      )[0]

               A_							:       Tuple	      =				[]
               A_							:       Any	      =				outputs.numpy()
               A_							:       Optional[Any]	      =				np.unique(__magic_name__      )

               for label in labels:
                      A_							:       Any	      =				(segmentation == label) * 255
                      A_							:       str	      =				Image.fromarray(mask.astype(np.uinta      ) ,       mode='''L'''      )
                      A_							:       List[str]	      =				self.model.config.idalabel[label]
                      annotation.append({'''score''': None, '''label''': label, '''mask''': mask}      )
        else:
               raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model      )}"""      )
        return annotation


