



'''simple docstring'''






import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin


@require_tokenizers
class   _lowercase						(							a		,					unittest.TestCase				):
   _UpperCamelCase       		=   GPTaTokenizer
   _UpperCamelCase       		=   GPTaTokenizerFast
   _UpperCamelCase       		=   True
   _UpperCamelCase       		=   {"""add_prefix_space""": True}
   _UpperCamelCase       		=   False


   def 					snake_case				( self					):
       super().setUp()

       # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
       A       :       Tuple							=						[
           '''l''',
           '''o''',
           '''w''',
           '''e''',
           '''r''',
           '''s''',
           '''t''',
           '''i''',
           '''d''',
           '''n''',
           '''\u0120''',
           '''\u0120l''',
           '''\u0120n''',
           '''\u0120lo''',
           '''\u0120low''',
           '''er''',
           '''\u0120lowest''',
           '''\u0120newer''',
           '''\u0120wider''',
           '''<unk>''',
           '''<|endoftext|>''',
       ]
       A       :       Union[str, Any]							=						dict(zip(_UpperCAmelCase    ,   range(len(_UpperCAmelCase					)					)					)					)
       A       :       Optional[int]							=						['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
       A       :       Optional[Any]							=						{'''unk_token''': '''<unk>'''}

       A       :       str							=						os.path.join(self.tmpdirname    ,   VOCAB_FILES_NAMES['''vocab_file''']					)
       A       :       Tuple							=						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(_UpperCAmelCase					) + '''\n'''					)
       with open(self.merges_file    ,   '''w'''    ,   encoding='''utf-8'''					) as fp:
           fp.write('''\n'''.join(_UpperCAmelCase					)					)


   def 					snake_case				( self    ,   **_UpperCAmelCase					):
       kwargs.update(self.special_tokens_map					)
       return GPTaTokenizer.from_pretrained(self.tmpdirname    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   **_UpperCAmelCase					):
       kwargs.update(self.special_tokens_map					)
       return GPTaTokenizerFast.from_pretrained(self.tmpdirname    ,   **_UpperCAmelCase					)


   def 					snake_case				( self    ,   _UpperCAmelCase					):
       A       :       List[Any]							=						'''lower newer'''
       A       :       List[Any]							=						'''lower newer'''
       return input_text, output_text


   def 					snake_case				( self					):
       A       :       List[str]							=						GPTaTokenizer(self.vocab_file    ,   self.merges_file    ,   **self.special_tokens_map					)
       A       :       Optional[Any]							=						'''lower newer'''
       A       :       Optional[int]							=						['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
       A       :       List[str]							=						tokenizer.tokenize(_UpperCAmelCase    ,   add_prefix_space=_UpperCAmelCase					)
       self.assertListEqual(_UpperCAmelCase    ,   _UpperCAmelCase					)

       A       :       Optional[int]							=						tokens + [tokenizer.unk_token]
       A       :       Tuple							=						[14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)    ,   _UpperCAmelCase					)


   def 					snake_case				( self					):
       if not self.test_rust_tokenizer:
           return

       A       :       Union[str, Any]							=						self.get_tokenizer()
       A       :       Optional[int]							=						self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase					)

       A       :       str							=						'''lower newer'''

       # Testing tokenization
       A       :       Optional[int]							=						tokenizer.tokenize(_UpperCAmelCase    ,   add_prefix_space=_UpperCAmelCase					)
       A       :       Union[str, Any]							=						rust_tokenizer.tokenize(_UpperCAmelCase					)
       self.assertListEqual(_UpperCAmelCase    ,   _UpperCAmelCase					)

       # Testing conversion to ids without special tokens
       A       :       int							=						tokenizer.encode(_UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase    ,   add_prefix_space=_UpperCAmelCase					)
       A       :       str							=						rust_tokenizer.encode(_UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase					)
       self.assertListEqual(_UpperCAmelCase    ,   _UpperCAmelCase					)

       # Testing conversion to ids with special tokens
       A       :       Any							=						self.get_rust_tokenizer(add_prefix_space=_UpperCAmelCase					)
       A       :       int							=						tokenizer.encode(_UpperCAmelCase    ,   add_prefix_space=_UpperCAmelCase					)
       A       :       int							=						rust_tokenizer.encode(_UpperCAmelCase					)
       self.assertListEqual(_UpperCAmelCase    ,   _UpperCAmelCase					)

       # Testing the unknown token
       A       :       Union[str, Any]							=						tokens + [rust_tokenizer.unk_token]
       A       :       int							=						[14, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_UpperCAmelCase					)    ,   _UpperCAmelCase					)


   def 					snake_case				( self    ,   *_UpperCAmelCase    ,   **_UpperCAmelCase					):
       # It's very difficult to mix/test pretokenization with byte-level
       # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string)
       pass


   def 					snake_case				( self    ,   _UpperCAmelCase=15					):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''					):
               A       :       Optional[Any]							=						self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase    ,   **_UpperCAmelCase					)

               # Simple input
               A       :       Optional[int]							=						'''This is a simple input'''
               A       :       Union[str, Any]							=						['''This is a simple input 1''', '''This is a simple input 2''']
               A       :       Any							=						('''This is a simple input''', '''This is a pair''')
               A       :       Tuple							=						[
                   ('''This is a simple input 1''', '''This is a simple input 2'''),
                   ('''This is a simple pair 1''', '''This is a simple pair 2'''),
               ]

               # Simple input tests
               self.assertRaises(_UpperCAmelCase    ,   tokenizer_r.encode    ,   _UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   padding='''max_length'''					)

               # Simple input
               self.assertRaises(_UpperCAmelCase    ,   tokenizer_r.encode_plus    ,   _UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   padding='''max_length'''					)

               # Simple input
               self.assertRaises(
                   _UpperCAmelCase    ,   tokenizer_r.batch_encode_plus    ,   _UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   padding='''max_length'''    ,   )

               # Pair input
               self.assertRaises(_UpperCAmelCase    ,   tokenizer_r.encode    ,   _UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   padding='''max_length'''					)

               # Pair input
               self.assertRaises(_UpperCAmelCase    ,   tokenizer_r.encode_plus    ,   _UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   padding='''max_length'''					)

               # Pair input
               self.assertRaises(
                   _UpperCAmelCase    ,   tokenizer_r.batch_encode_plus    ,   _UpperCAmelCase    ,   max_length=_UpperCAmelCase    ,   padding='''max_length'''    ,   )


   def 					snake_case				( self					):
       A       :       Tuple							=						GPTaTokenizer.from_pretrained(self.tmpdirname    ,   pad_token='''<pad>'''					)

       # Simple input
       A       :       List[str]							=						'''This is a simple input'''
       A       :       Optional[int]							=						['''This is a simple input looooooooong''', '''This is a simple input''']
       A       :       Optional[int]							=						('''This is a simple input''', '''This is a pair''')
       A       :       Optional[Any]							=						[
           ('''This is a simple input loooooong''', '''This is a simple input'''),
           ('''This is a simple pair loooooong''', '''This is a simple pair'''),
       ]

       A       :       Optional[int]							=						tokenizer.pad_token_id

       A       :       Any							=						tokenizer(_UpperCAmelCase    ,   padding='''max_length'''    ,   max_length=30    ,   return_tensors='''np'''					)
       A       :       Optional[Any]							=						tokenizer(_UpperCAmelCase    ,   padding=_UpperCAmelCase    ,   truncate=_UpperCAmelCase    ,   return_tensors='''np'''					)
       A       :       Optional[int]							=						tokenizer(*_UpperCAmelCase    ,   padding='''max_length'''    ,   max_length=60    ,   return_tensors='''np'''					)
       A       :       str							=						tokenizer(_UpperCAmelCase    ,   padding=_UpperCAmelCase    ,   truncate=_UpperCAmelCase    ,   return_tensors='''np'''					)

       # s
       # test single string max_length padding
       self.assertEqual(out_s['''input_ids'''].shape[-1]    ,   30					)
       self.assertTrue(pad_token_id in out_s['''input_ids''']					)
       self.assertTrue(0 in out_s['''attention_mask''']					)

       # s2
       # test automatic padding
       self.assertEqual(out_sa['''input_ids'''].shape[-1]    ,   33					)
       # long slice doesn't have padding
       self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]					)
       self.assertFalse(0 in out_sa['''attention_mask'''][0]					)
       # short slice does have padding
       self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]					)
       self.assertTrue(0 in out_sa['''attention_mask'''][1]					)

       # p
       # test single pair max_length padding
       self.assertEqual(out_p['''input_ids'''].shape[-1]    ,   60					)
       self.assertTrue(pad_token_id in out_p['''input_ids''']					)
       self.assertTrue(0 in out_p['''attention_mask''']					)

       # p2
       # test automatic padding pair
       self.assertEqual(out_pa['''input_ids'''].shape[-1]    ,   52					)
       # long slice pair doesn't have padding
       self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]					)
       self.assertFalse(0 in out_pa['''attention_mask'''][0]					)
       # short slice pair does have padding
       self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]					)
       self.assertTrue(0 in out_pa['''attention_mask'''][1]					)


   def 					snake_case				( self					):
       A       :       Any							=						'''$$$'''
       A       :       int							=						GPTaTokenizer.from_pretrained(self.tmpdirname    ,   bos_token=_UpperCAmelCase    ,   add_bos_token=_UpperCAmelCase					)

       A       :       Any							=						'''This is a simple input'''
       A       :       Union[str, Any]							=						['''This is a simple input 1''', '''This is a simple input 2''']

       A       :       int							=						tokenizer.bos_token_id

       A       :       int							=						tokenizer(_UpperCAmelCase					)
       A       :       Dict							=						tokenizer(_UpperCAmelCase					)

       self.assertEqual(out_s.input_ids[0]    ,   _UpperCAmelCase					)
       self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids					)					)

       A       :       List[Any]							=						tokenizer.decode(out_s.input_ids					)
       A       :       Dict							=						tokenizer.batch_decode(out_sa.input_ids					)

       self.assertEqual(decode_s.split()[0]    ,   _UpperCAmelCase					)
       self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa					)					)


   def 					snake_case				( self					):
       pass


   def 					snake_case				( self					):
       # TODO: change to self.get_tokenizers() when the fast version is implemented
       A       :       str							=						[self.get_tokenizer(do_lower_case=_UpperCAmelCase    ,   add_bos_token=_UpperCAmelCase					)]
       for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''					):
               A       :       Optional[int]							=						'''Encode this.'''
               A       :       Dict							=						'''This one too please.'''
               A       :       Dict							=						tokenizer.encode(_UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase					)
               encoded_sequence += tokenizer.encode(_UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase					)
               A       :       int							=						tokenizer.encode_plus(
                   _UpperCAmelCase    ,   _UpperCAmelCase    ,   add_special_tokens=_UpperCAmelCase    ,   return_special_tokens_mask=_UpperCAmelCase    ,   )
               A       :       Dict							=						encoded_sequence_dict['''input_ids''']
               A       :       Tuple							=						encoded_sequence_dict['''special_tokens_mask''']
               self.assertEqual(len(_UpperCAmelCase					)    ,   len(_UpperCAmelCase					)					)

               A       :       Optional[int]							=						[
                   (x if not special_tokens_mask[i] else None) for i, x in enumerate(_UpperCAmelCase					)
               ]
               A       :       str							=						[x for x in filtered_sequence if x is not None]
               self.assertEqual(_UpperCAmelCase    ,   _UpperCAmelCase					)







@require_tokenizers
class   _lowercase						(							unittest.TestCase				):


   def 					snake_case				( self					):
       # More context:
       # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1
       # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519
       # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439

       A       :       str							=						AutoTokenizer.from_pretrained('''facebook/opt-350m'''    ,   from_slow=_UpperCAmelCase					)
       A       :       Optional[Any]							=						'''A photo of a cat'''

       A       :       Optional[int]							=						tokenizer.encode(
           _UpperCAmelCase    ,   )
       self.assertEqual(_UpperCAmelCase    ,   [2, 250, 1_345, 9, 10, 4_758]					)
       tokenizer.save_pretrained('''test_opt'''					)

       A       :       List[Any]							=						AutoTokenizer.from_pretrained('''./test_opt'''					)
       A       :       Tuple							=						tokenizer.encode(
           _UpperCAmelCase    ,   )
       self.assertEqual(_UpperCAmelCase    ,   [2, 250, 1_345, 9, 10, 4_758]					)


   def 					snake_case				( self					):
       A       :       Tuple							=						AutoTokenizer.from_pretrained('''facebook/opt-350m'''    ,   use_slow=_UpperCAmelCase					)
       A       :       int							=						'''A photo of a cat'''

       A       :       Optional[Any]							=						tokenizer.encode(
           _UpperCAmelCase    ,   )
       # Same as above
       self.assertEqual(_UpperCAmelCase    ,   [2, 250, 1_345, 9, 10, 4_758]					)


   @unittest.skip('''This test is failing because of a bug in the fast tokenizer'''					)
   def 					snake_case				( self					):
       A       :       Union[str, Any]							=						AutoTokenizer.from_pretrained('''facebook/opt-350m'''    ,   from_slow=_UpperCAmelCase					)

       A       :       str							=						'''bos'''
       A       :       Optional[Any]							=						tokenizer.get_vocab()['''bos''']

       A       :       List[str]							=						'''A photo of a cat'''
       A       :       List[Any]							=						tokenizer.encode(
           _UpperCAmelCase    ,   )
       # We changed the bos token
       self.assertEqual(_UpperCAmelCase    ,   [31_957, 250, 1_345, 9, 10, 4_758]					)
       tokenizer.save_pretrained('''./tok'''					)
       A       :       List[str]							=						AutoTokenizer.from_pretrained('''./tok'''					)
       self.assertTrue(tokenizer.is_fast					)
       A       :       List[str]							=						tokenizer.encode(
           _UpperCAmelCase    ,   )
       self.assertEqual(_UpperCAmelCase    ,   [31_957, 250, 1_345, 9, 10, 4_758]					)







'''simple docstring'''






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


snake_case_     =	["""text""", """image""", """audio"""]
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    A       :       int							=						[]

    for input_type in input_types:
        if input_type == "text":
            inputs.append('''Text input'''		)
        elif input_type == "image":
            inputs.append(
                Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		).resize((512, 512)		)		)
        elif input_type == "audio":
            inputs.append(torch.ones(3_000		)		)
        elif isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
            inputs.append(create_inputs(UpperCamelCase__		)		)
        else:
            raise ValueError(F'''Invalid type requested: {input_type}'''		)

    return inputs
def 							_lowerCamelCase(  UpperCamelCase__    : List		)		->  Tuple:
    A       :       Optional[int]							=						[]

    for output in outputs:
        if isinstance(UpperCamelCase__    ,       (str, AgentText)		):
            output_types.append('''text'''		)
        elif isinstance(UpperCamelCase__    ,       (Image.Image, AgentImage)		):
            output_types.append('''image'''		)
        elif isinstance(UpperCamelCase__    ,       (torch.Tensor, AgentAudio)		):
            output_types.append('''audio'''		)
        else:
            raise ValueError(F'''Invalid output: {output}'''		)

    return output_types





@is_tool_test
class   _lowercase						:


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''inputs'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''outputs'''					)					)

       A       :       Any							=						self.tool.inputs
       for _input in inputs:
           if isinstance(_input    ,   _UpperCAmelCase					):
               for __input in _input:
                   self.assertTrue(__input in authorized_types					)
           else:
               self.assertTrue(_input in authorized_types					)

       A       :       Tuple							=						self.tool.outputs
       for _output in outputs:
           self.assertTrue(_output in authorized_types					)


   def 					snake_case				( self					):
       A       :       Any							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       # There is a single output
       if len(self.tool.outputs					) == 1:
           A       :       Optional[int]							=						[outputs]

       self.assertListEqual(output_types(_UpperCAmelCase					)    ,   self.tool.outputs					)


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''description'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''default_checkpoint'''					)					)
       self.assertTrue(self.tool.description.startswith('''This is a tool that'''					)					)


   def 					snake_case				( self					):
       A       :       List[str]							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       int							=						[outputs]

       self.assertEqual(len(_UpperCAmelCase					)    ,   len(self.tool.outputs					)					)

       for output, output_type in zip(_UpperCAmelCase    ,   self.tool.outputs					):
           A       :       Tuple							=						AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)					)


   def 					snake_case				( self					):
       A       :       Tuple							=						create_inputs(self.tool.inputs					)

       A       :       Dict							=						[]

       for _input, input_type in zip(_UpperCAmelCase    ,   self.tool.inputs					):
           if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
           else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)

        # Should not raise an error
       A       :       List[Any]							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Any							=						[outputs]

       self.assertEqual(len(_UpperCAmelCase					)    ,   len(self.tool.outputs					)					)
