

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class        A_		(       SCREAMING_SNAKE_CASE			,							unittest.TestCase				):
							_UpperCAmelCase    :  Dict						      =      FunnelTokenizer
							_UpperCAmelCase    :  List[str]						      =      FunnelTokenizerFast
							_UpperCAmelCase    :  str						      =      True
							_UpperCAmelCase    :  Optional[int]						      =      True
							def     lowerCAmelCase     (		self		:			Any):
								super().setUp()

								__lowerCamelCase				:				Union[str, Any]					       = [
								    '<unk>',
								    '<cls>',
								    '<sep>',
								    'want',
								    '##want',
								    '##ed',
								    'wa',
								    'un',
								    'runn',
								    '##ing',
								    ',',
								    'low',
								    'lowest',
								]
								__lowerCamelCase				:				Optional[Any]					       = os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['vocab_file'])
								with open(self.vocab_file   ,'w'   ,encoding='utf-8') as vocab_writer:
									vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]))
							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,**SCREAMING_SNAKE_CASE__		:			List[Any]):
								return FunnelTokenizer.from_pretrained(self.tmpdirname   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			List[Any]   ,**SCREAMING_SNAKE_CASE__		:			List[str]):
								return FunnelTokenizerFast.from_pretrained(self.tmpdirname   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Optional[int]   ,SCREAMING_SNAKE_CASE__		:			str):
								__lowerCamelCase				:				Dict					       = 'UNwant\u00E9d,running'
								__lowerCamelCase				:				Tuple					       = 'unwanted, running'
								return input_text, output_text
							def     lowerCAmelCase     (		self		:			List[Any]):
								__lowerCamelCase				:				Tuple					       = self.tokenizer_class(self.vocab_file)

								__lowerCamelCase				:				Any					       = tokenizer.tokenize('UNwant\u00E9d,running')
								self.assertListEqual(SCREAMING_SNAKE_CASE__   ,['un', '##want', '##ed', ',', 'runn', '##ing'])
								self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__)   ,[7, 4, 5, 1_0, 8, 9])


							def     lowerCAmelCase     (		self		:			Tuple):
								__lowerCamelCase				:				int					       = self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__)
								for tokenizer in tokenizers:
									__lowerCamelCase				:				Dict					       = tokenizer('UNwant\u00E9d,running')
									__lowerCamelCase				:				int					       = len(inputs['input_ids']) - 1
									self.assertListEqual(inputs['token_type_ids']   ,[2] + [0] * sentence_len)

									__lowerCamelCase				:				Dict					       = tokenizer('UNwant\u00E9d,running'   ,'UNwant\u00E9d,running')
									self.assertListEqual(inputs['token_type_ids']   ,[2] + [0] * sentence_len + [1] * sentence_len)










import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
  import torch







@is_pipeline_test
class 						lowercase							(   unittest.TestCase		):
     _a							 =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     _a							 =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 					a__	(  self		,     _a		,     _a		,     _a				)						->							int:
           _A							:     str			=						TextaTextGenerationPipeline(model=_a		,     tokenizer=_a				)
           return generator, ["Something to write", "Something else"]

     def 					a__	(  self		,     _a		,     _a				)						->							Dict:
           _A							:     Any			=						generator("""Something there"""				)
           self.assertEqual(_a		,     [{"""generated_text""": ANY(_a				)}]				)
           # These are encoder decoder, they don't just append to incoming string
           self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there"""				)				)

           _A							:     List[Any]			=						generator(["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           _A							:     Optional[int]			=						generator(
               ["""This is great !""", """Something else"""]		,     num_return_sequences=2		,     batch_size=2		,     do_sample=_a				)
           self.assertEqual(
               _a		,     [
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
                   [{"""generated_text""": ANY(_a				)}, {"""generated_text""": ANY(_a				)}],
               ]		,     )

           with self.assertRaises(_a				):
                 generator(4				)

     @require_torch
     def 					a__	(  self				)						->							List[str]:
           _A							:     Any			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""pt"""				)
           # do_sample=False necessary for reproducibility
           _A							:     Dict			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

           _A							:     Any			=						3
           _A							:     Any			=						generator(
               """Something there"""		,     num_return_sequences=_a		,     num_beams=_a		,     )
           _A							:     Optional[int]			=						[
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """Beide Beide Beide Beide Beide Beide Beide Beide"""},
               {"""generated_text""": """"""},
           ]
           self.assertEqual(_a		,     _a				)

           _A							:     Dict			=						generator("""This is a test"""		,     do_sample=_a		,     num_return_sequences=2		,     return_tensors=_a				)
           self.assertEqual(
               _a		,     [
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
                   {"""generated_token_ids""": ANY(torch.Tensor				)},
               ]		,     )
           _A							:     Dict			=						generator.model.config.eos_token_id
           _A							:     List[str]			=						"""<pad>"""
           _A							:     Dict			=						generator(
               ["""This is a test""", """This is a second test"""]		,     do_sample=_a		,     num_return_sequences=2		,     batch_size=2		,     return_tensors=_a		,     )
           self.assertEqual(
               _a		,     [
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
                   [
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                       {"""generated_token_ids""": ANY(torch.Tensor				)},
                   ],
               ]		,     )



     @require_tf
     def 					a__	(  self				)						->							int:
           _A							:     Optional[Any]			=						pipeline("""text2text-generation"""		,     model="""patrickvonplaten/t5-tiny-random"""		,     framework="""tf"""				)
           # do_sample=False necessary for reproducibility
           _A							:     str			=						generator("""Something there"""		,     do_sample=_a				)
           self.assertEqual(_a		,     [{"""generated_text""": """"""}]				)

