



'''simple docstring'''





import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class __A     (    A  ,  unittest.TestCase		):



 '''simple docstring'''







 __lowerCamelCase       :			str		=			RobertaTokenizer
 __lowerCamelCase       :			List[Any]		=			RobertaTokenizerFast
 __lowerCamelCase       :			Union[str, Any]		=			True
 __lowerCamelCase       :			Dict		=			{'cls_token': '<s>'}



 def    a__    (self			)       ->	int:


      """simple docstring"""



      super().setUp()

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

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



 def    a__    (self					,  **A			)       ->	Dict:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return self.tokenizer_class.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  **A			)       ->	str:


      """simple docstring"""



      kwargs.update(self.special_tokens_map			)
      return RobertaTokenizerFast.from_pretrained(self.tmpdirname					,  **A			)



 def    a__    (self					,  A			)       ->	Any:


      """simple docstring"""



      _a           =    '''lower newer'''
      _a           =    '''lower newer'''
      return input_text, output_text



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.tokenizer_class(self.vocab_file					,  self.merges_file					,  **self.special_tokens_map			)
      _a           =    '''lower newer'''
      _a           =    ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
      _a           =    tokenizer.tokenize(A			)  # , add_prefix_space=True)
      self.assertListEqual(A					,  A			)

      _a           =    tokens + [tokenizer.unk_token]
      _a           =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A			)					,  A			)



 def    a__    (self			)       ->	int:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      self.assertListEqual(tokenizer.encode('''Hello world!'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 2]			)
      self.assertListEqual(
          tokenizer.encode('''Hello world! cécé herlolip 418'''					,  add_special_tokens=A			)					,  [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]					,  )



 @slow
 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      _a           =    self.tokenizer_class.from_pretrained('''roberta-base'''			)

      _a           =    tokenizer.encode('''sequence builders'''					,  add_special_tokens=A			)
      _a           =    tokenizer.encode('''multi-sequence build'''					,  add_special_tokens=A			)

      _a           =    tokenizer.encode(
          '''sequence builders'''					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.encode(
          '''sequence builders'''					,  '''multi-sequence build'''					,  add_special_tokens=A					,  add_prefix_space=A			)

      _a           =    tokenizer.build_inputs_with_special_tokens(A			)
      _a           =    tokenizer.build_inputs_with_special_tokens(A					,  A			)

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode



 def    a__    (self			)       ->	Dict:


      """simple docstring"""



      _a           =    self.get_tokenizer()

      _a           =    '''Encode this sequence.'''
      _a           =    tokenizer.byte_encoder[''' '''.encode('''utf-8'''			)[0]]

      # Testing encoder arguments
      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertNotEqual(A					,  A			)

      _a           =    tokenizer.encode(A					,  add_special_tokens=A					,  add_prefix_space=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
      self.assertEqual(A					,  A			)

      tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}			)
      _a           =    tokenizer.encode(A					,  add_special_tokens=A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
      self.assertNotEqual(A					,  A			)

      # Testing spaces after special tokens
      _a           =    '''<mask>'''
      tokenizer.add_special_tokens(
          {'''mask_token''': AddedToken(A					,  lstrip=A					,  rstrip=A			)}			)  # mask token has a left space
      _a           =    tokenizer.convert_tokens_to_ids(A			)

      _a           =    '''Encode <mask> sequence'''
      _a           =    '''Encode <mask>sequence'''

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertEqual(A					,  A			)

      _a           =    tokenizer.encode(A			)
      _a           =    encoded.index(A			)
      _a           =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
      self.assertNotEqual(A					,  A			)



 def    a__    (self			)       ->	Optional[int]:


      """simple docstring"""



      pass



 def    a__    (self			)       ->	List[Any]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    self.rust_tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    self.tokenizer_class.from_pretrained(A					,  **A			)
                _a           =    '''A, <mask> AllenNLP sentence.'''
                _a           =    tokenizer_r.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)
                _a           =    tokenizer_p.encode_plus(A					,  add_special_tokens=A					,  return_token_type_ids=A			)

                # token_type_ids should put 0 everywhere
                self.assertEqual(sum(tokens_r['''token_type_ids''']			)					,  sum(tokens_p['''token_type_ids''']			)			)

                # attention_mask should put 1 everywhere, so sum over length should be 1
                self.assertEqual(
                    sum(tokens_r['''attention_mask''']			) / len(tokens_r['''attention_mask''']			)					,  sum(tokens_p['''attention_mask''']			) / len(tokens_p['''attention_mask''']			)					,  )

                _a           =    tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
                _a           =    tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']			)

                # Rust correctly handles the space before the mask while python doesnt
                self.assertSequenceEqual(tokens_p['''input_ids''']					,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)
                self.assertSequenceEqual(tokens_r['''input_ids''']					,  [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)

                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
                self.assertSequenceEqual(
                    A					,  ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)



 def    a__    (self			)       ->	Tuple:


      """simple docstring"""



      for trim_offsets, add_prefix_space in itertools.product([True, False]					,  repeat=2			):
           _a           =    self.rust_tokenizer_class.from_pretrained(
               self.tmpdirname					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)

           _a           =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
           _a           =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

           self.assertEqual(pre_tokenizer_state['''add_prefix_space''']					,  A			)

           self.assertEqual(post_processor_state['''add_prefix_space''']					,  A			)
           self.assertEqual(post_processor_state['''trim_offsets''']					,  A			)



 def    a__    (self			)       ->	List[str]:


      """simple docstring"""



      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''			):
                _a           =    '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
                _a           =    f'''{text_of_1_token} {text_of_1_token}'''

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			) + 1, len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (len(A			), len(A			) + 1 + len(A			))					,  )

                _a           =    f''' {text}'''

                # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
                #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
                # )
                # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
                # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
                # self.assertEqual(
                #     encoding.offset_mapping[1],
                #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
                # )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (1, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			) + 1, 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )

                _a           =    self.rust_tokenizer_class.from_pretrained(
                    A					,  use_fast=A					,  add_prefix_space=A					,  trim_offsets=A			)
                _a           =    tokenizer_r(A					,  return_offsets_mapping=A					,  add_special_tokens=A			)
                self.assertEqual(encoding.offset_mapping[0]					,  (0, 1 + len(A			))			)
                self.assertEqual(
                    encoding.offset_mapping[1]					,  (1 + len(A			), 1 + len(A			) + 1 + len(A			))					,  )







'''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


lowerCAmelCase   : List[Any]             =				["""text""", """image""", """audio"""]




def 	_A  (       A							)  ->				Dict:
	lowercase						:		str   					=		[]

	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((5_1_2, 5_1_2)							)							)
		elif input_type == "audio":
			inputs.append(torch.ones(3_0_0_0							)							)
		elif isinstance(A							,A							):
			inputs.append(create_inputs(A							)							)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''							)

	return inputs




def 	_A  (       A							)  ->				str:
	lowercase						:		Tuple   					=		[]

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

	return output_types






@is_tool_test
class        _UpperCamelCase	:


				'''simple docstring'''


				def 	a__		(     self							)    ->  Optional[Any]:
					self.assertTrue(hasattr(self.tool							,      "inputs"							)							)
					self.assertTrue(hasattr(self.tool							,      "outputs"							)							)

					lowercase						:		Optional[Any]   					=		self.tool.inputs
					for _input in inputs:
						if isinstance(_input							,      a_							):
							for __input in _input:
								self.assertTrue(__input in authorized_types							)
						else:
							self.assertTrue(_input in authorized_types							)

					lowercase						:		Any   					=		self.tool.outputs
					for _output in outputs:
						self.assertTrue(_output in authorized_types							)

				def 	a__		(     self							)    ->  Any:
					lowercase						:		Any   					=		create_inputs(self.tool.inputs							)
					lowercase						:		Tuple   					=		self.tool(*a_							)

					# There is a single output
					if len(self.tool.outputs							) == 1:
						lowercase						:		Any   					=		[outputs]

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

				def 	a__		(     self							)    ->  List[str]:
					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 	a__		(     self							)    ->  int:
					lowercase						:		str   					=		create_inputs(self.tool.inputs							)
					lowercase						:		str   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		[outputs]

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

					for output, output_type in zip(a_							,      self.tool.outputs							):
						lowercase						:		List[str]   					=		AGENT_TYPE_MAPPING[output_type]
						self.assertTrue(isinstance(a_							,      a_							)							)


				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		int   					=		create_inputs(self.tool.inputs							)

					lowercase						:		str   					=		[]

					for _input, input_type in zip(a_							,      self.tool.inputs							):
						if isinstance(a_							,      a_							):
							_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
					lowercase						:		Optional[int]   					=		self.tool(*a_							)

					if not isinstance(a_							,      a_							):
						lowercase						:		str   					=		[outputs]

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


