




'''simple docstring'''





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-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json''',
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	'''blenderbot'''
      __lowercase					=	['''past_key_values''']
      __lowercase					=	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






      def __init__(self			,	a__=8_0_0_8			,	a__=1_2_8			,	a__=2			,	a__=1_0_2_4_0			,	a__=3_2			,	a__=2_4			,	a__=1_0_2_4_0			,	a__=3_2			,	a__=0.0			,	a__=0.0			,	a__=True			,	a__=True			,	a__="gelu"			,	a__=2_5_6_0			,	a__=0.1			,	a__=0.0			,	a__=0.0			,	a__=0.02			,	a__=1			,	a__=False			,	a__=0			,	a__=1			,	a__=2			,	a__=3			,	a__=2			,	**a__			,	)							-> Optional[Any]:
         SCREAMING_SNAKE_CASE__      :					List[str]							    =			vocab_size
         SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			max_position_embeddings
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			d_model
         SCREAMING_SNAKE_CASE__      :					int							    =			encoder_ffn_dim
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			encoder_layers
         SCREAMING_SNAKE_CASE__      :					Any							    =			encoder_attention_heads
         SCREAMING_SNAKE_CASE__      :					str							    =			decoder_ffn_dim
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			decoder_layers
         SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			decoder_attention_heads
         SCREAMING_SNAKE_CASE__      :					int							    =			dropout
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			attention_dropout
         SCREAMING_SNAKE_CASE__      :					Dict							    =			activation_dropout
         SCREAMING_SNAKE_CASE__      :					Dict							    =			activation_function
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			init_std
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			encoder_layerdrop
         SCREAMING_SNAKE_CASE__      :					str							    =			decoder_layerdrop
         SCREAMING_SNAKE_CASE__      :					int							    =			use_cache
         SCREAMING_SNAKE_CASE__      :					str							    =			encoder_layers
         SCREAMING_SNAKE_CASE__      :					int							    =			scale_embedding  # scale factor will be sqrt(d_model) if True

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






class     lowercase  ( UpperCAmelCase_):






      @property
      def  A__(self       )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ]       )
            if self.use_past:
               SCREAMING_SNAKE_CASE__      :					Any							    =			{0: '''batch'''}
               SCREAMING_SNAKE_CASE__      :					str							    =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            else:
               SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			{0: '''batch''', 1: '''decoder_sequence'''}
               SCREAMING_SNAKE_CASE__      :					str							    =			{0: '''batch''', 1: '''decoder_sequence'''}
            if self.use_past:
               self.fill_with_past_key_values_(a__			,	direction='''inputs'''       )
         elif self.task == "causal-lm":
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			OrderedDict(
                [
                    ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                    ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                ]       )
            if self.use_past:
               SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.num_layers
               for i in range(a__       ):
                  SCREAMING_SNAKE_CASE__      :					Tuple							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
                  SCREAMING_SNAKE_CASE__      :					Tuple							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
         else:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			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
      # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
      def  A__(self       )							-> Mapping[str, Mapping[int, str]]:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			super().outputs
         else:
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			super(a__			,	self       ).outputs
            if self.use_past:
               SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Dict							    =			self.num_layers
               for i in range(a__       ):
                  SCREAMING_SNAKE_CASE__      :					Tuple							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
                  SCREAMING_SNAKE_CASE__      :					List[str]							    =			{0: '''batch''', 2: '''past_sequence + sequence'''}
         return common_outputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Any							    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__			,	a__			,	a__			,	a__			,	a__       )
         # Generate decoder inputs
         SCREAMING_SNAKE_CASE__      :					List[Any]							    =			seq_length if not self.use_past else 1
         SCREAMING_SNAKE_CASE__      :					str							    =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             a__			,	a__			,	a__			,	a__			,	a__       )
         SCREAMING_SNAKE_CASE__      :					Any							    =			{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
         SCREAMING_SNAKE_CASE__      :					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
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			common_inputs['''input_ids'''].shape
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			common_inputs['''decoder_input_ids'''].shape[1]
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			self.num_attention_heads
            SCREAMING_SNAKE_CASE__      :					Any							    =			(
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            SCREAMING_SNAKE_CASE__      :					Dict							    =			decoder_seq_length
            SCREAMING_SNAKE_CASE__      :					Dict							    =			(
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.cat(
                [common_inputs['''decoder_attention_mask'''], torch.ones(a__			,	a__       )]			,	dim=1       )
            SCREAMING_SNAKE_CASE__      :					str							    =			[]
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			self.num_layers

            for _ in range(a__       ):
               common_inputs["past_key_values"].append(
                   (
                       torch.zeros(a__       ),
                       torch.zeros(a__       ),
                       torch.zeros(a__       ),
                       torch.zeros(a__       ),
                   )       )
         return common_inputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         SCREAMING_SNAKE_CASE__      :					Any							    =			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
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Tuple							    =			common_inputs['''input_ids'''].shape
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			seqlen
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					str							    =			self.num_layers
            SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			self.num_attention_heads
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			(
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            SCREAMING_SNAKE_CASE__      :					Any							    =			common_inputs['''attention_mask'''].dtype
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			torch.cat(
                [common_inputs['''attention_mask'''], torch.ones(a__			,	a__			,	dtype=a__       )]			,	dim=1       )
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[
                (torch.zeros(a__       ), torch.zeros(a__       )) for _ in range(a__       )
            ]
         return common_inputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         # Copied from OnnxConfig.generate_dummy_inputs
         # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
         # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
         SCREAMING_SNAKE_CASE__      :					List[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
         SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			tokenizer.num_special_tokens_to_add(a__       )
         SCREAMING_SNAKE_CASE__      :					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
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[''' '''.join([tokenizer.unk_token]       ) * seq_length] * batch_size
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			dict(tokenizer(a__			,	return_tensors=a__       )       )
         return common_inputs






      def  A__(self			,	a__			,	a__ = -1			,	a__ = -1			,	a__ = False			,	a__ = None			,	)							-> Mapping[str, Any]:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					Any							    =			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":
            SCREAMING_SNAKE_CASE__      :					Any							    =			self._generate_dummy_inputs_for_causal_lm(
                a__			,	batch_size=a__			,	seq_length=a__			,	is_pair=a__			,	framework=a__       )
         else:
            SCREAMING_SNAKE_CASE__      :					Any							    =			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  A__(self			,	a__			,	a__			,	a__			,	a__       )							-> Any:
         if self.task in ["default", "seq2seq-lm"]:
            SCREAMING_SNAKE_CASE__      :					Dict							    =			super()._flatten_past_key_values_(a__			,	a__			,	a__			,	a__       )
         else:
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			super(a__			,	self       )._flatten_past_key_values_(
                a__			,	a__			,	a__			,	a__       )






      def  A__(self			,	a__			,	a__       )							-> Tuple:
         if direction not in ["inputs", "outputs"]:
            raise ValueError(f"""direction must either be \"inputs\" or \"outputs\", but {direction} was given"""       )

         SCREAMING_SNAKE_CASE__      :					int							    =			'''past_key_values''' if direction == '''inputs''' else '''present'''
         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					List[Any]							    =			self.num_layers

         SCREAMING_SNAKE_CASE__      :					Any							    =			'''past_encoder_sequence'''
         SCREAMING_SNAKE_CASE__      :					Dict							    =			'''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

         for i in range(a__       ):
            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			{0: '''batch''', 2: decoder_sequence}
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			{0: '''batch''', 2: decoder_sequence}
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			{0: '''batch''', 2: encoder_sequence}
            SCREAMING_SNAKE_CASE__      :					Optional[int]							    =			{0: '''batch''', 2: encoder_sequence}







'''simple docstring'''





from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
       import torch






class     lowercase  ( UpperCAmelCase_):
      __lowercase					=	['''image_processor''', '''tokenizer''']
      __lowercase					=	'''OneFormerImageProcessor'''
      __lowercase					=	('''CLIPTokenizer''', '''CLIPTokenizerFast''')






      def __init__(self			,	a__=None			,	a__=None			,	a__ = 7_7			,	a__ = 7_7			,	**a__       )							-> str:
         if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''       )
         if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''       )

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			max_seq_length
         SCREAMING_SNAKE_CASE__      :					Tuple							    =			task_seq_length

         super().__init__(a__			,	a__       )






      def  A__(self			,	a__=None			,	a__=7_7       )							-> str:
         if text_list is None:
            raise ValueError('''tokens cannot be None.'''       )

         SCREAMING_SNAKE_CASE__      :					int							    =			self.tokenizer(a__			,	padding='''max_length'''			,	max_length=a__			,	truncation=a__       )

         SCREAMING_SNAKE_CASE__    ,					SCREAMING_SNAKE_CASE__      :					Any							    =			tokens['''attention_mask'''], tokens['''input_ids''']

         SCREAMING_SNAKE_CASE__      :					Tuple							    =			[]
         for attn_mask, input_id in zip(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.tensor(a__       ) * torch.tensor(a__       )
            token_inputs.append(token.unsqueeze(0       )       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			torch.cat(a__			,	dim=0       )
         return token_inputs






      def __call__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> str:

         if task_inputs is None:
            raise ValueError('''You have to specify the task_input. Found None.'''       )
         elif images is None:
            raise ValueError('''You have to specify the image. Found None.'''       )

         if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs       ):
            raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''       )

         SCREAMING_SNAKE_CASE__      :					Dict							    =			self.image_processor(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					List[str]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					Any							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Tuple							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					int							    =			self._preprocess_text(a__			,	max_length=self.task_seq_length       )
         else:
            raise TypeError('''Task Inputs should be a string or a list of strings.'''       )

         if hasattr(a__			,	'''text_inputs'''       ):
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					int							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					List[str]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	a__=None			,	a__=None			,	a__=None			,	**a__       )							-> Any:

         if task_inputs is None:
            raise ValueError('''You have to specify the task_input. Found None.'''       )
         elif images is None:
            raise ValueError('''You have to specify the image. Found None.'''       )

         if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs       ):
            raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''       )

         SCREAMING_SNAKE_CASE__      :					List[str]							    =			self.image_processor.encode_inputs(a__			,	a__			,	a__			,	**a__       )

         if isinstance(a__			,	a__       ):
            SCREAMING_SNAKE_CASE__      :					Optional[Any]							    =			[task_inputs]

         if isinstance(a__			,	a__       ) and all(isinstance(a__			,	a__       ) for task_input in task_inputs       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for task in task_inputs:
               SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			f"""the task is {task}"""
               task_token_inputs.append(a__       )
            SCREAMING_SNAKE_CASE__      :					Tuple							    =			self._preprocess_text(a__			,	max_length=self.task_seq_length       )
         else:
            raise TypeError('''Task Inputs should be a string or a list of strings.'''       )

         if hasattr(a__			,	'''text_inputs'''       ):
            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			encoded_inputs.text_inputs

            SCREAMING_SNAKE_CASE__      :					List[Any]							    =			[]
            for texts in texts_list:
               SCREAMING_SNAKE_CASE__      :					Any							    =			self._preprocess_text(a__			,	max_length=self.max_seq_length       )
               text_inputs.append(text_input_list.unsqueeze(0       )       )

            SCREAMING_SNAKE_CASE__      :					Union[str, Any]							    =			torch.cat(a__			,	dim=0       )

         return encoded_inputs






      def  A__(self			,	*a__			,	**a__       )							-> str:
         return self.image_processor.post_process_semantic_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Optional[Any]:
         return self.image_processor.post_process_instance_segmentation(*a__			,	**a__       )






      def  A__(self			,	*a__			,	**a__       )							-> Tuple:
         return self.image_processor.post_process_panoptic_segmentation(*a__			,	**a__       )

