'''simple docstring'''

import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


A_     :						Any    							=logging.get_logger(__name__)

A_     :						Optional[Any]    							={'vocab_file': 'vocab.txt'}

A_     :						Tuple    							={
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

A_     :						Union[str, Any]    							={
    'facebook/esm2_t6_8M_UR50D': 10_24,
    'facebook/esm2_t12_35M_UR50D': 10_24,
}



def snake_case_      (					__snake_case  :							List[Any])			->					Any:







   with open(__snake_case	,   '''r''') as f:
      lowerCAmelCase_			=      f.read().splitlines()
      return [l.strip() for l in lines]






class     __UpperCAmelCase       ( lowercase_		):
  __A :				Union[str, Any]											= VOCAB_FILES_NAMES
  __A :				Dict											= PRETRAINED_VOCAB_FILES_MAP
  __A :				str											= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __A :				Tuple											= ["""input_ids""", """attention_mask"""]





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase="<unk>"    ,     _lowerCamelCase="<cls>"    ,     _lowerCamelCase="<pad>"    ,     _lowerCamelCase="<mask>"    ,     _lowerCamelCase="<eos>"    ,     **_lowerCamelCase    ,     ):
     super().__init__(**_lowerCamelCase )
     lowerCAmelCase_			=      load_vocab_file(_lowerCamelCase )
     lowerCAmelCase_			=      dict(enumerate(self.all_tokens ) )
     lowerCAmelCase_			=      {tok: ind for ind, tok in enumerate(self.all_tokens )}
     lowerCAmelCase_			=      unk_token
     lowerCAmelCase_			=      cls_token
     lowerCAmelCase_			=      pad_token
     lowerCAmelCase_			=      mask_token
     lowerCAmelCase_			=      eos_token
     lowerCAmelCase_			=      self.all_tokens
     self._create_trie(self.unique_no_split_tokens )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return self._id_to_token.get(_lowerCamelCase    ,     self.unk_token )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return self._token_to_id.get(_lowerCamelCase    ,     self._token_to_id.get(self.unk_token ) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     return text.split()





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase=False ):
     return len(self._id_to_token )





  def 						UpperCAmelCase_	(	self ):
     return {token: i for i, token in enumerate(self.all_tokens )}





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return self._token_to_id.get(_lowerCamelCase    ,     self._token_to_id.get(self.unk_token ) )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase ):
     return self._id_to_token.get(_lowerCamelCase    ,     self.unk_token )





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      [self.cls_token_id]
     lowerCAmelCase_			=      [self.eos_token_id]  # No sep token in ESM vocabulary
     if token_ids_a is None:
        if self.eos_token_id is None:
           return cls + token_ids_a
        else:
           return cls + token_ids_a + sep
     elif self.eos_token_id is None:
        raise ValueError('''Cannot tokenize multiple sequences when EOS token is not set!''' )
     return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None    ,     _lowerCamelCase = False ):







     if already_has_special_tokens:
        if token_ids_a is not None:
           raise ValueError(
               '''You should not supply a second sequence if the provided sequence of '''
               '''ids is already formatted with special tokens for the model.''' )

        return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
     lowerCAmelCase_			=      [1] + ([0] * len(_lowerCamelCase )) + [1]
     if token_ids_a is not None:
        mask += [0] * len(_lowerCamelCase ) + [1]
     return mask





  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     lowerCAmelCase_			=      os.path.join(_lowerCamelCase    ,     (filename_prefix + '''-''' if filename_prefix else '''''') + '''vocab.txt''' )
     with open(_lowerCamelCase    ,     '''w''' ) as f:
        f.write('''\n'''.join(self.all_tokens ) )
     return (vocab_file,)





  @property
  def 						UpperCAmelCase_	(	self ):
     return self.get_vocab_size(with_added_tokens=_lowerCamelCase )







  def 						UpperCAmelCase_	(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = False ):
     return super()._add_tokens(_lowerCamelCase    ,     special_tokens=_lowerCamelCase )









'''simple docstring'''

import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:
 class     __lowerCAmelCase		:





       '''simple docstring'''






       @staticmethod
       def  UpperCamelCase__						(*UpperCamelCase       :       Optional[Any]  ,		**UpperCamelCase       :       Tuple					):

              '''simple docstring'''







              pass
@is_pipeline_test
@require_vision
@require_torch
class     __lowerCAmelCase		(unittest.TestCase	):





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



      def  UpperCamelCase__						(self       :       int  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







             lowercase__      =					pipeline(
                 '''zero-shot-object-detection'''  ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''					)

             lowercase__      =					[
                 {
                     '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                     '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                 }
             ]
             return object_detector, examples



      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







             lowercase__      =					object_detector(examples[0]  ,		threshold=0.0					)

             lowercase__      =					len(UpperCamelCase					)
             self.assertGreater(UpperCamelCase  ,		0					)
             self.assertEqual(
                 UpperCamelCase  ,		[
                     {
                         '''score''': ANY(UpperCamelCase					),
                         '''label''': ANY(UpperCamelCase					),
                         '''box''': {'''xmin''': ANY(UpperCamelCase					), '''ymin''': ANY(UpperCamelCase					), '''xmax''': ANY(UpperCamelCase					), '''ymax''': ANY(UpperCamelCase					)},
                     }
                     for i in range(UpperCamelCase					)
                 ]  ,		)



      @require_tf
      @unittest.skip('''Zero Shot Object Detection not implemented in TF'''					)
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      @require_torch
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					pipeline(
                 '''zero-shot-object-detection'''  ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''					)

             lowercase__      =					object_detector(
                 '''./tests/fixtures/tests_samples/COCO/000000039769.png'''  ,		candidate_labels=['''cat''', '''remote''', '''couch''']  ,		threshold=0.64  ,		)

             self.assertEqual(
                 nested_simplify(UpperCamelCase  ,		decimals=4					)  ,		[
                     {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                     {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                     {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                     {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                     {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                     {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                     {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                     {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                     {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                 ]  ,		)

             lowercase__      =					object_detector(
                 [
                     {
                         '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                         '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                     }
                 ]  ,		threshold=0.64  ,		)

             self.assertEqual(
                 nested_simplify(UpperCamelCase  ,		decimals=4					)  ,		[
                     [
                         {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                         {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                         {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                         {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                         {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                         {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                         {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                         {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                         {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                     ]
                 ]  ,		)



      @require_torch
      @slow
      def  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             lowercase__      =					pipeline('''zero-shot-object-detection'''					)

             lowercase__      =					object_detector(
                 '''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,		candidate_labels=['''cat''', '''remote''', '''couch''']  ,		)
             self.assertEqual(
                 nested_simplify(UpperCamelCase  ,		decimals=4					)  ,		[
                     {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                     {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                     {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                     {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                     {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                 ]  ,		)

             lowercase__      =					object_detector(
                 [
                     {
                         '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                         '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                     },
                     {
                         '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                         '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                     },
                 ]  ,		)
             self.assertEqual(
                 nested_simplify(UpperCamelCase  ,		decimals=4					)  ,		[
                     [
                         {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                         {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                         {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                         {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                         {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                     ],
                     [
                         {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                         {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                         {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                         {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                         {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                     ],
                 ]  ,		)



      @require_tf
      @unittest.skip('''Zero Shot Object Detection not implemented in TF'''					)
      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             pass



      @require_torch
      @slow
      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







             lowercase__      =					0.2
             lowercase__      =					pipeline('''zero-shot-object-detection'''					)

             lowercase__      =					object_detector(
                 '''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,		candidate_labels=['''cat''', '''remote''', '''couch''']  ,		threshold=UpperCamelCase  ,		)
             self.assertEqual(
                 nested_simplify(UpperCamelCase  ,		decimals=4					)  ,		[
                     {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                     {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                     {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                 ]  ,		)






      @require_torch
      @slow
      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             lowercase__      =					2
             lowercase__      =					pipeline('''zero-shot-object-detection'''					)

             lowercase__      =					object_detector(
                 '''http://images.cocodataset.org/val2017/000000039769.jpg'''  ,		candidate_labels=['''cat''', '''remote''', '''couch''']  ,		top_k=UpperCamelCase  ,		)
             self.assertEqual(
                 nested_simplify(UpperCamelCase  ,		decimals=4					)  ,		[
                     {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                     {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                 ]  ,		)




