

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

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json",
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'blenderbot-small'
    lowerCamelCase			=							['past_key_values']
    lowerCamelCase			=							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(      self :       Optional[Any],lowercase_ :       List[str]=5_0_2_6_5,lowercase_ :       List[str]=5_1_2,lowercase_ :       str=8,lowercase_ :       Any=2_0_4_8,lowercase_ :       List[Any]=1_6,lowercase_ :       Tuple=8,lowercase_ :       Dict=2_0_4_8,lowercase_ :       int=1_6,lowercase_ :       Optional[int]=0.0,lowercase_ :       str=0.0,lowercase_ :       Optional[Any]=True,lowercase_ :       List[Any]=True,lowercase_ :       Optional[int]="gelu",lowercase_ :       Tuple=5_1_2,lowercase_ :       Union[str, Any]=0.1,lowercase_ :       List[str]=0.0,lowercase_ :       str=0.0,lowercase_ :       Optional[int]=0.02,lowercase_ :       Dict=1,lowercase_ :       List[Any]=False,lowercase_ :       List[str]=0,lowercase_ :       Tuple=1,lowercase_ :       List[Any]=2,lowercase_ :       Dict=2,**lowercase_ :       str,)->    str:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   max_position_embeddings
        A__          =   d_model
        A__          =   encoder_ffn_dim
        A__          =   encoder_layers
        A__          =   encoder_attention_heads
        A__          =   decoder_ffn_dim
        A__          =   decoder_layers
        A__          =   decoder_attention_heads
        A__          =   dropout
        A__          =   attention_dropout
        A__          =   activation_dropout
        A__          =   activation_function
        A__          =   init_std
        A__          =   encoder_layerdrop
        A__          =   decoder_layerdrop
        A__          =   use_cache
        A__          =   encoder_layers
        A__          =   scale_embedding  # scale factor will be sqrt(d_model) if True

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




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    @property
    def 						snake_case__     (      self :       int		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







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

            if self.use_past:
                A__          =   {0: 'batch'}
                A__          =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                A__          =   {0: 'batch', 1: 'decoder_sequence'}
                A__          =   {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                self.fill_with_past_key_values_(lowercase_,direction='inputs'		)
        elif self.task == "causal-lm":
            # TODO: figure this case out.
            A__          =   OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)
            if self.use_past:
                A__      ,						A__          =   self.num_layers
                for i in range(lowercase_		):
                    A__          =   {0: 'batch', 2: 'past_sequence + sequence'}
                    A__          =   {0: 'batch', 2: 'past_sequence + sequence'}
        else:
            A__          =   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 						snake_case__     (      self :       List[Any]		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Tuple,lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        A__          =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        # Generate decoder inputs
        A__          =   seq_length if not self.use_past else 1
        A__          =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)
        A__          =   {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        A__          =   dict(**lowercase_,**lowercase_		)

        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__          =   common_inputs['input_ids'].shape
            A__          =   common_inputs['decoder_input_ids'].shape[1]
            A__      ,						A__          =   self.num_attention_heads
            A__          =   (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            A__          =   decoder_seq_length + 3
            A__          =   (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            A__          =   torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_		)],dim=1		)

            A__          =   []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            A__      ,						A__          =   self.num_layers
            A__          =   min(lowercase_,lowercase_		)
            A__          =   max(lowercase_,lowercase_		) - min_num_layers
            A__          =   'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

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


    def 						snake_case__     (      self :       str,lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        A__          =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        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__          =   common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A__          =   seqlen + 2
            A__      ,						A__          =   self.num_layers
            A__      ,						A__          =   self.num_attention_heads
            A__          =   (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            A__          =   common_inputs['attention_mask'].dtype
            A__          =   torch.cat(
                [common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_		)],dim=1		)
            A__          =   [
                (torch.zeros(lowercase_		), torch.zeros(lowercase_		)) for _ in range(lowercase_		)
            ]
        return common_inputs


    def 						snake_case__     (      self :       List[Any],lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        A__          =   compute_effective_axis_dimension(
            lowercase_,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__          =   tokenizer.num_special_tokens_to_add(lowercase_		)
        A__          =   compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=lowercase_		)

        # Generate dummy inputs according to compute batch and sequence
        A__          =   [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
        A__          =   dict(tokenizer(lowercase_,return_tensors=lowercase_		)		)
        return common_inputs


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       PreTrainedTokenizer,lowercase_ :       int = -1,lowercase_ :       int = -1,lowercase_ :       bool = False,lowercase_ :       Optional[TensorType] = None,)->    Mapping[str, Any]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__          =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        elif self.task == "causal-lm":
            A__          =   self._generate_dummy_inputs_for_causal_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)
        else:
            A__          =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        return common_inputs


    def 						snake_case__     (      self :       str,lowercase_ :       Any,lowercase_ :       List[Any],lowercase_ :       List[str],lowercase_ :       List[str]		)->    Any:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__          =   super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_		)
        else:
            A__          =   super(lowercase_,self		)._flatten_past_key_values_(
                lowercase_,lowercase_,lowercase_,lowercase_		)



"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
   import PIL

if is_torch_available():
   import torch


_A			:       List[Any]   		=       logging.get_logger(__name__)

class 		a__						(      a_						):
   __lowerCAmelCase            =				["""pixel_values"""]
   def __init__(			self	,    _a = True	,    _a = None	,    _a = PILImageResampling.BILINEAR	,    _a = True	,    _a = 1 / 255	,    _a = True	,    _a = None	,    _a = True	,    **_a	,    ):
          super().__init__(**_a				)
          lowercase    :				Optional[Any]       		=							size if size is not None else {"shortest_edge": 224}
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				str       		=							crop_size if crop_size is not None else {"height": 256, "width": 256}
          lowercase    :				List[str]       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							do_resize
          lowercase    :				Optional[int]       		=							size
          lowercase    :				str       		=							resample
          lowercase    :				List[Any]       		=							do_rescale
          lowercase    :				Union[str, Any]       		=							rescale_factor
          lowercase    :				Optional[int]       		=							do_center_crop
          lowercase    :				Union[str, Any]       		=							crop_size
          lowercase    :				Optional[Any]       		=							do_flip_channel_order
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = PIL.Image.BILINEAR	,    _a = None	,    **_a	,    ):
          lowercase    :				List[Any]       		=							get_size_dict(_a	,    default_to_square=_a				)
          if "shortest_edge" not in size:
                 raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""				)
          lowercase    :				Union[str, Any]       		=							get_resize_output_image_size(_a	,    size=size["shortest_edge"]	,    default_to_square=_a				)
          return resize(_a	,    size=_a	,    resample=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          lowercase    :				str       		=							get_size_dict(_a				)
          if "height" not in size or "width" not in size:
                 raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""				)
          return center_crop(_a	,    size=(size["height"], size["width"])	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a	,    _a = None	,    **_a	,    ):
          return rescale(_a	,    scale=_a	,    data_format=_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          return flip_channel_order(_a	,    data_format=_a				)
   def 			__magic_name__			(			self	,    _a	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = None	,    _a = ChannelDimension.FIRST	,    **_a	,    ):
          lowercase    :				Optional[Any]       		=							do_resize if do_resize is not None else self.do_resize
          lowercase    :				Tuple       		=							resample if resample is not None else self.resample
          lowercase    :				List[str]       		=							do_rescale if do_rescale is not None else self.do_rescale
          lowercase    :				List[Any]       		=							rescale_factor if rescale_factor is not None else self.rescale_factor
          lowercase    :				Union[str, Any]       		=							do_center_crop if do_center_crop is not None else self.do_center_crop
          lowercase    :				Optional[int]       		=							(
              do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
          )

          lowercase    :				str       		=							size if size is not None else self.size
          lowercase    :				Any       		=							get_size_dict(_a	,    default_to_square=_a				)
          lowercase    :				int       		=							crop_size if crop_size is not None else self.crop_size
          lowercase    :				Any       		=							get_size_dict(_a	,    param_name="crop_size"				)

          lowercase    :				int       		=							make_list_of_images(_a				)

          if not valid_images(_a				):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray."				)

          if do_resize and size is None:
                 raise ValueError("Size must be specified if do_resize is True."				)

          if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True."				)

          if do_center_crop and crop_size is None:
                 raise ValueError("Crop size must be specified if do_center_crop is True."				)

          # All transformations expect numpy arrays.
          lowercase    :				Any       		=							[to_numpy_array(_a				) for image in images]

          if do_resize:
                 lowercase    :				Optional[int]       		=							[self.resize(image=_a	,    size=_a	,    resample=_a				) for image in images]

          if do_center_crop:
                 lowercase    :				str       		=							[self.center_crop(image=_a	,    size=_a				) for image in images]

          if do_rescale:
                 lowercase    :				Union[str, Any]       		=							[self.rescale(image=_a	,    scale=_a				) for image in images]

          # the pretrained checkpoints assume images are BGR, not RGB
          if do_flip_channel_order:
                 lowercase    :				int       		=							[self.flip_channel_order(image=_a				) for image in images]

          lowercase    :				int       		=							[to_channel_dimension_format(_a	,    _a				) for image in images]

          lowercase    :				Optional[Any]       		=							{"pixel_values": images}
          return BatchFeature(data=_a	,    tensor_type=_a				)

   def 			__magic_name__			(			self	,    _a	,    _a = None				):
          lowercase    :				Optional[int]       		=							outputs.logits

          # Resize logits and compute semantic segmentation maps
          if target_sizes is not None:
                 if len(_a				) != len(_a				):
                        raise ValueError(
                            "Make sure that you pass in as many target sizes as the batch dimension of the logits"				)

                 if is_torch_tensor(_a				):
                        lowercase    :				Tuple       		=							target_sizes.numpy()

                 lowercase    :				List[Any]       		=							[]

                 for idx in range(len(_a				)				):
                        lowercase    :				Dict       		=							torch.nn.functional.interpolate(
                            logits[idx].unsqueeze(dim=0				)	,    size=target_sizes[idx]	,    mode="bilinear"	,    align_corners=_a				)
                        lowercase    :				Tuple       		=							resized_logits[0].argmax(dim=0				)
                        semantic_segmentation.append(_a				)
          else:
                 lowercase    :				str       		=							logits.argmax(dim=1				)
                 lowercase    :				Dict       		=							[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

          return semantic_segmentation


