'''simple docstring'''

from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  import torch

  from transformers.modeling_outputs import BaseModelOutput

  from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__a: Any          =   logging.get_logger(__name__)



@add_end_docstrings(a__					)
class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   **__lowerCAmelCase )  ->  Any:
        super().__init__(**__lowerCAmelCase )

        if self.framework == "tf":
         raise ValueError(F"""The {self.__class__} is only available in PyTorch.""" )

        requires_backends(self	,   '''vision''' )
        self.check_model_type(__lowerCAmelCase )

       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  Union[str, Any]:
        if "text_queries" in kwargs:
         lowercase__				:    Any  =					kwargs.pop('''text_queries''' )

        if isinstance(__lowerCAmelCase	,   (str, Image.Image) ):
         lowercase__				:    Any  =					{'''image''': image, '''candidate_labels''': candidate_labels}
        else:
         lowercase__				:    Optional[Any]  =					image
        lowercase__				:    Optional[Any]  =					super().__call__(__lowerCAmelCase	,   **__lowerCAmelCase )
        return results

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  int:
        lowercase__				:    Union[str, Any]  =					{}
        if "threshold" in kwargs:
         lowercase__				:    List[str]  =					kwargs['''threshold''']
        if "top_k" in kwargs:
         lowercase__				:    Optional[int]  =					kwargs['''top_k''']
        return {}, {}, postprocess_params

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Dict  =					load_image(inputs['''image'''] )
        lowercase__				:    List[str]  =					inputs['''candidate_labels''']
        if isinstance(__lowerCAmelCase	,   __lowerCAmelCase ):
         lowercase__				:    Optional[Any]  =					candidate_labels.split(''',''' )

        lowercase__				:    Union[str, Any]  =					torch.tensor([[image.height, image.width]]	,   dtype=torch.intaa )
        for i, candidate_label in enumerate(__lowerCAmelCase ):
         lowercase__				:    int  =					self.tokenizer(__lowerCAmelCase	,   return_tensors=self.framework )
         lowercase__				:    Optional[Any]  =					self.image_processor(__lowerCAmelCase	,   return_tensors=self.framework )
         yield {
             "is_last": i == len(__lowerCAmelCase ) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Union[str, Any]  =					model_inputs.pop('''target_size''' )
        lowercase__				:    Optional[int]  =					model_inputs.pop('''candidate_label''' )
        lowercase__				:    Optional[int]  =					model_inputs.pop('''is_last''' )

        lowercase__				:    Any  =					self.model(**__lowerCAmelCase )

        lowercase__				:    Tuple  =					{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
        return model_outputs

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase=0.1	,   __lowerCAmelCase=None )  ->  Union[str, Any]:
        lowercase__				:    Union[str, Any]  =					[]
        for model_output in model_outputs:
         lowercase__				:    Optional[int]  =					model_output['''candidate_label''']
         lowercase__				:    Any  =					BaseModelOutput(__lowerCAmelCase )
         lowercase__				:    Any  =					self.image_processor.post_process_object_detection(
             outputs=__lowerCAmelCase	,   threshold=__lowerCAmelCase	,   target_sizes=model_output['''target_size'''] )[0]

         for index in outputs["scores"].nonzero():
          lowercase__				:    Any  =					outputs['''scores'''][index].item()
          lowercase__				:    Any  =					self._get_bounding_box(outputs['''boxes'''][index][0] )

          lowercase__				:    List[Any]  =					{'''score''': score, '''label''': label, '''box''': box}
          results.append(__lowerCAmelCase )

        lowercase__				:    str  =					sorted(__lowerCAmelCase	,   key=lambda __lowerCAmelCase : x["score"]	,   reverse=__lowerCAmelCase )
        if top_k:
         lowercase__				:    Optional[int]  =					results[:top_k]

        return results


       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  Dict[str, int]:
        if self.framework != "pt":
         raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.''' )
        lowercase__ ,							lowercase__ ,							lowercase__ ,							lowercase__				:    List[Any]  =					box.int().tolist()
        lowercase__				:    Tuple  =					{
            '''xmin''': xmin,
            '''ymin''': ymin,
            '''xmax''': xmax,
            '''ymax''': ymax,
        }
        return bbox


'''simple docstring'''

import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    BlenderbotSmallTokenizer
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Optional[int]:
        super().setUp()

        lowercase__				:    List[Any]  =					['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
        lowercase__				:    Any  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )

        lowercase__				:    Optional[Any]  =					['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
        lowercase__				:    Dict  =					{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

        lowercase__				:    List[str]  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Any  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  Tuple:
        kwargs.update(self.special_tokens_map )
        return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  int:
        lowercase__				:    str  =					'''adapt act apte'''
        lowercase__				:    Any  =					'''adapt act apte'''
        return input_text, output_text

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    Optional[int]  =					BlenderbotSmallTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Tuple  =					'''adapt act apte'''
        lowercase__				:    Dict  =					['''adapt''', '''act''', '''ap@@''', '''te''']
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    Any  =					[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

        lowercase__				:    int  =					[0, 1, 2, 3, 4, 5]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

       def _lowerCAmelCase(						self )  ->  str:
        lowercase__				:    int  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        assert tok('''sam''' ).input_ids == [1384]
        lowercase__				:    str  =					'''I am a small frog.'''
        lowercase__				:    Union[str, Any]  =					tok([src_text]	,   padding=__lowerCAmelCase	,   truncation=__lowerCAmelCase )['''input_ids''']
        lowercase__				:    List[str]  =					tok.batch_decode(__lowerCAmelCase	,   skip_special_tokens=__lowerCAmelCase	,   clean_up_tokenization_spaces=__lowerCAmelCase )[0]
        assert src_text != decoded  # I wish it did!
        assert decoded == "i am a small frog ."


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M''' )
        lowercase__				:    Optional[Any]  =					'''I am a small frog .'''
        lowercase__				:    Any  =					'''.'''
        lowercase__				:    List[Any]  =					tok(__lowerCAmelCase )['''input_ids''']
        lowercase__				:    Optional[Any]  =					tok(__lowerCAmelCase )['''input_ids''']

        assert encoded[-1] == encoded_dot[0]

