

import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

lowercase_       		=  logging.get_logger(__name__)


class  A						(							enum.Enum ):







    """simple docstring"""





    lowerCamelCase			=							0
    lowerCamelCase			=							1




@add_end_docstrings(_UpperCAmelCase )
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'generated'


    def __init__(      self :       Optional[Any],*lowercase_ :       str,**lowercase_ :       List[Any]		)->    int:





        '''simple docstring'''







        super().__init__(*lowercase_,**lowercase_		)

        self.check_model_type(
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
            if self.framework == 'tf'
            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING		)


    def 						snake_case__     (      self :       List[str],lowercase_ :       str=None,lowercase_ :       str=None,lowercase_ :       Optional[int]=None,lowercase_ :       Tuple=None,lowercase_ :       Dict=None,lowercase_ :       Optional[Any]=None,**lowercase_ :       str,)->    List[str]:





        '''simple docstring'''







        A__          =   {}
        if truncation is not None:
            A__          =   truncation

        A__          =   generate_kwargs

        A__          =   {}
        if return_tensors is not None and return_type is None:
            A__          =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
            A__          =   return_type

        if clean_up_tokenization_spaces is not None:
            A__          =   clean_up_tokenization_spaces

        if stop_sequence is not None:
            A__          =   self.tokenizer.encode(lowercase_,add_special_tokens=lowercase_		)
            if len(lowercase_		) > 1:
                warnings.warn(
                    'Stopping on a multiple token sequence is not yet supported on transformers. The first token of'
                    ' the stop sequence will be used as the stop sequence string in the interim.'		)
            A__          =   stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params


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





        '''simple docstring'''







        return True


    def 						snake_case__     (      self :       Optional[Any],*lowercase_ :       int,lowercase_ :       Dict		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.model.config.prefix if self.model.config.prefix is not None else ''
        if isinstance(args[0],lowercase_		):
            if self.tokenizer.pad_token_id is None:
                raise ValueError('Please make sure that the tokenizer has a pad_token_id when using a batch input'		)
            A__          =   ([prefix + arg for arg in args[0]],)
            A__          =   True

        elif isinstance(args[0],lowercase_		):
            A__          =   (prefix + args[0],)
            A__          =   False
        else:
            raise ValueError(
                F' `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`'		)
        A__          =   self.tokenizer(*lowercase_,padding=lowercase_,truncation=lowercase_,return_tensors=self.framework		)
        # This is produced by tokenizers but is an invalid generate kwargs
        if "token_type_ids" in inputs:
            del inputs["token_type_ids"]
        return inputs


    def __call__(      self :       Dict,*lowercase_ :       Dict,**lowercase_ :       int		)->    Dict:





        '''simple docstring'''







        A__          =   super().__call__(*lowercase_,**lowercase_		)
        if (
            isinstance(args[0],lowercase_		)
            and all(isinstance(lowercase_,lowercase_		) for el in args[0]		)
            and all(len(lowercase_		) == 1 for res in result		)
        ):
            return [res[0] for res in result]
        return result


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       List[Any],lowercase_ :       List[str]=TruncationStrategy.DO_NOT_TRUNCATE,**lowercase_ :       Any		)->    Tuple:





        '''simple docstring'''







        A__          =   self._parse_and_tokenize(lowercase_,truncation=lowercase_,**lowercase_		)
        return inputs


    def 						snake_case__     (      self :       Any,lowercase_ :       int,**lowercase_ :       Any		)->    int:





        '''simple docstring'''







        if self.framework == "pt":
            A__      ,						A__          =   model_inputs['input_ids'].shape
        elif self.framework == "tf":
            A__      ,						A__          =   tf.shape(model_inputs['input_ids']		).numpy()

        A__          =   generate_kwargs.get('min_length',self.model.config.min_length		)
        A__          =   generate_kwargs.get('max_length',self.model.config.max_length		)
        self.check_inputs(lowercase_,generate_kwargs['min_length'],generate_kwargs['max_length']		)
        A__          =   self.model.generate(**lowercase_,**lowercase_		)
        A__          =   output_ids.shape[0]
        if self.framework == "pt":
            A__          =   output_ids.reshape(lowercase_,out_b // in_b,*output_ids.shape[1:]		)
        elif self.framework == "tf":
            A__          =   tf.reshape(lowercase_,(in_b, out_b // in_b, *output_ids.shape[1:])		)
        return {"output_ids": output_ids}


    def 						snake_case__     (      self :       Union[str, Any],lowercase_ :       Optional[Any],lowercase_ :       Union[str, Any]=ReturnType.TEXT,lowercase_ :       List[Any]=False		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   []
        for output_ids in model_outputs["output_ids"][0]:
            if return_type == ReturnType.TENSORS:
                A__          =   {F'{self.return_name}_token_ids': output_ids}
            elif return_type == ReturnType.TEXT:
                A__          =   {
                    F'{self.return_name}_text': self.tokenizer.decode(
                        lowercase_,skip_special_tokens=lowercase_,clean_up_tokenization_spaces=lowercase_,)
                }
            records.append(lowercase_		)
        return records




@add_end_docstrings(_UpperCAmelCase )
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'summary'


    def __call__(      self :       str,*lowercase_ :       List[Any],**lowercase_ :       Union[str, Any]		)->    int:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       int,lowercase_ :       int,lowercase_ :       int		)->    bool:





        '''simple docstring'''







        if max_length < min_length:
            logger.warning(F'Your min_length={min_length} must be inferior than your max_length={max_length}.'		)

        if input_length < max_length:
            logger.warning(
                F'Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is '
                'a summarization task, where outputs shorter than the input are typically wanted, you might '
                F'consider decreasing max_length manually, e.g. summarizer(\'...\', max_length={input_length//2})'		)




@add_end_docstrings(_UpperCAmelCase )
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'translation'


    def 						snake_case__     (      self :       Optional[Any],lowercase_ :       int,lowercase_ :       int,lowercase_ :       int		)->    int:





        '''simple docstring'''







        if input_length > 0.9 * max_length:
            logger.warning(
                F'Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider '
                'increasing your max_length manually, e.g. translator(\'...\', max_length=400)'		)
        return True


    def 						snake_case__     (      self :       List[Any],*lowercase_ :       Dict,lowercase_ :       str=TruncationStrategy.DO_NOT_TRUNCATE,lowercase_ :       Any=None,lowercase_ :       Optional[Any]=None		)->    str:





        '''simple docstring'''







        if getattr(self.tokenizer,'_build_translation_inputs',lowercase_		):
            return self.tokenizer._build_translation_inputs(
                *lowercase_,return_tensors=self.framework,truncation=lowercase_,src_lang=lowercase_,tgt_lang=lowercase_		)
        else:
            return super()._parse_and_tokenize(*lowercase_,truncation=lowercase_		)


    def 						snake_case__     (      self :       List[str],lowercase_ :       Tuple=None,lowercase_ :       str=None,**lowercase_ :       List[str]		)->    Union[str, Any]:





        '''simple docstring'''







        A__      ,						A__      ,						A__          =   super()._sanitize_parameters(**lowercase_		)
        if src_lang is not None:
            A__          =   src_lang
        if tgt_lang is not None:
            A__          =   tgt_lang
        if src_lang is None and tgt_lang is None:
            # Backward compatibility, direct arguments use is preferred.
            A__          =   kwargs.get('task',self.task		)
            A__          =   task.split('_'		)
            if task and len(lowercase_		) == 4:
                # translation, XX, to YY
                A__          =   items[1]
                A__          =   items[3]
        return preprocess_params, forward_params, postprocess_params


    def __call__(      self :       Optional[int],*lowercase_ :       Optional[Any],**lowercase_ :       Dict		)->    Union[str, Any]:





        '''simple docstring'''







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




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}


class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'resnet'
    lowerCamelCase			=							['basic', 'bottleneck']


    def __init__(      self :       Optional[Any],lowercase_ :       int=3,lowercase_ :       List[str]=6_4,lowercase_ :       int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8],lowercase_ :       Tuple=[3, 4, 6, 3],lowercase_ :       Union[str, Any]="bottleneck",lowercase_ :       List[str]="relu",lowercase_ :       Tuple=False,lowercase_ :       List[str]=None,lowercase_ :       List[Any]=None,**lowercase_ :       str,)->    Optional[Any]:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
        A__          =   num_channels
        A__          =   embedding_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   layer_type
        A__          =   hidden_act
        A__          =   downsample_in_first_stage
        A__          =   ['stem'] + [F'stage{idx}' for idx in range(1,len(lowercase_		) + 1		)]
        A__      ,						A__          =   get_aligned_output_features_output_indices(
            out_features=lowercase_,out_indices=lowercase_,stage_names=self.stage_names		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							version.parse('1.11' )


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





        '''simple docstring'''







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]		)


    @property
    def 						snake_case__     (      self :       Any		)->    float:





        '''simple docstring'''







        return 1E-3

