

import json
import os
import shutil
import tempfile
import unittest

from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin



class   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     CanineTokenizer
			_UpperCAmelCase   :							Dict										=     False



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									super().setUp()
									SCREAMING_SNAKE_CASE_: Any  		=       CanineTokenizer()
									tokenizer.save_pretrained(self.tmpdirname)



			@cached_property
			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									return CanineTokenizer.from_pretrained("google/canine-s")



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						**lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.tokenizer_class.from_pretrained(self.tmpdirname   ,						**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[Any]  		=       1024
									return tokenizer



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.canine_tokenizer
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       ["Life is like a box of chocolates.", "You never know what you're gonna get."]
									# fmt: off
									SCREAMING_SNAKE_CASE_: List[Any]  		=       [5_7344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 5_7345, 0, 0, 0, 0]
									# fmt: on
									SCREAMING_SNAKE_CASE_: Tuple  		=       tokenizer(lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						return_tensors="pt")
									self.assertIsInstance(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: Tuple  		=       list(batch.input_ids.numpy()[0])

									self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

									self.assertEqual((2, 39)   ,						batch.input_ids.shape)
									self.assertEqual((2, 39)   ,						batch.attention_mask.shape)



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.canine_tokenizer
									SCREAMING_SNAKE_CASE_: Tuple  		=       ["Once there was a man.", "He wrote a test in HuggingFace Tranformers."]
									SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer(lowerCAmelCase__   ,						padding=lowerCAmelCase__   ,						return_tensors="pt")
									# check if input_ids, attention_mask and token_type_ids are returned
									self.assertIn("input_ids"   ,						lowerCAmelCase__)
									self.assertIn("attention_mask"   ,						lowerCAmelCase__)
									self.assertIn("token_type_ids"   ,						lowerCAmelCase__)



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.canine_tokenizer
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [
									    "What's the weater?",
									    "It's about 25 degrees.",
									]
									SCREAMING_SNAKE_CASE_: int  		=       tokenizer(
									    text_target=lowerCAmelCase__   ,						max_length=32   ,						padding="max_length"   ,						truncation=lowerCAmelCase__   ,						return_tensors="pt")
									self.assertEqual(32   ,						targets["input_ids"].shape[1])



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									# safety check on max_len default value so we are sure the test works
									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_tokenizers()
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					self.assertNotEqual(tokenizer.model_max_length   ,						42)

        # Now let's start the test
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.get_tokenizers()
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					# Isolate this from the other tests because we save additional tokens/etc
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       tempfile.mkdtemp()

																					SCREAMING_SNAKE_CASE_: List[str]  		=       " He is very happy, UNwant\u00E9d,running"
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					tokenizer.save_pretrained(lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_: Dict  		=       tokenizer.__class__.from_pretrained(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: str  		=       after_tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)

																					shutil.rmtree(lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_tokenizers(model_max_length=42)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					# Isolate this from the other tests because we save additional tokens/etc
																					SCREAMING_SNAKE_CASE_: Dict  		=       tempfile.mkdtemp()

																					SCREAMING_SNAKE_CASE_: List[Any]  		=       " He is very happy, UNwant\u00E9d,running"

																					SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.additional_special_tokens

																					# We can add a new special token for Canine as follows:
																					SCREAMING_SNAKE_CASE_: str  		=       chr(0xE007)
																					additional_special_tokens.append(lowerCAmelCase__)
																					tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
																					SCREAMING_SNAKE_CASE_: Any  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					tokenizer.save_pretrained(lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_: int  		=       tokenizer.__class__.from_pretrained(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       after_tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					self.assertListEqual(lowerCAmelCase__   ,						lowerCAmelCase__)
																					self.assertIn(lowerCAmelCase__   ,						after_tokenizer.additional_special_tokens)
																					self.assertEqual(after_tokenizer.model_max_length   ,						42)

																					SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.__class__.from_pretrained(lowerCAmelCase__   ,						model_max_length=43)
																					self.assertEqual(tokenizer.model_max_length   ,						43)

																					shutil.rmtree(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_tokenizers(do_lower_case=lowerCAmelCase__)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_clean_sequence(lowerCAmelCase__)

																					# a special token for Canine can be defined as follows:
																					SCREAMING_SNAKE_CASE_: int  		=       0xE005
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       chr(lowerCAmelCase__)

																					tokenizer.add_special_tokens({"cls_token": special_token})
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					self.assertEqual(len(lowerCAmelCase__)   ,						1)

																					SCREAMING_SNAKE_CASE_: str  		=       tokenizer.decode(ids + encoded_special_token   ,						clean_up_tokenization_spaces=lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: str  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_: Dict  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Dict  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					self.assertEqual(lowerCAmelCase__   ,						input_encoded + special_token_id)

																					SCREAMING_SNAKE_CASE_: str  		=       tokenizer.decode(lowerCAmelCase__   ,						skip_special_tokens=lowerCAmelCase__)
																					self.assertTrue(special_token not in decoded)



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizers(do_lower_case=lowerCAmelCase__)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					SCREAMING_SNAKE_CASE_: Dict  		=       chr(0xE005)
																					SCREAMING_SNAKE_CASE_: Tuple  		=       chr(0xE006)

																					# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
																					tokenizer.add_tokens([SPECIAL_TOKEN_1]   ,						special_tokens=lowerCAmelCase__)
																					# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
																					# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
																					tokenizer.add_special_tokens({"additional_special_tokens": [SPECIAL_TOKEN_2]})

																					SCREAMING_SNAKE_CASE_: int  		=       tokenizer.tokenize(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       tokenizer.tokenize(lowerCAmelCase__)

																					self.assertEqual(len(lowerCAmelCase__)   ,						1)
																					self.assertEqual(len(lowerCAmelCase__)   ,						1)
																					self.assertEqual(token_a[0]   ,						lowerCAmelCase__)
																					self.assertEqual(token_a[0]   ,						lowerCAmelCase__)



			@require_tokenizers
			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_tokenizers(do_lower_case=lowerCAmelCase__)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					# a special token for Canine can be defined as follows:
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       0xE006
																					SCREAMING_SNAKE_CASE_: int  		=       chr(lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_: int  		=       AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__)
																					tokenizer.add_special_tokens({"additional_special_tokens": [new_token]})

																					with tempfile.TemporaryDirectory() as tmp_dir_name:
																											tokenizer.save_pretrained(lowerCAmelCase__)
																											tokenizer.from_pretrained(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									SCREAMING_SNAKE_CASE_: str  		=       []
									if self.test_slow_tokenizer:
															tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))

									if self.test_rust_tokenizer:
															tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))

									for tokenizer_class, tokenizer_utils in tokenizer_list:
															with tempfile.TemporaryDirectory() as tmp_dir:
																					tokenizer_utils.save_pretrained(lowerCAmelCase__)

																					with open(os.path.join(lowerCAmelCase__   ,						"special_tokens_map.json")   ,						encoding="utf-8") as json_file:
																											SCREAMING_SNAKE_CASE_: Optional[int]  		=       json.load(lowerCAmelCase__)

																					with open(os.path.join(lowerCAmelCase__   ,						"tokenizer_config.json")   ,						encoding="utf-8") as json_file:
																											SCREAMING_SNAKE_CASE_: int  		=       json.load(lowerCAmelCase__)

																					# a special token for Canine can be defined as follows:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       0xE006
																					SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       chr(lowerCAmelCase__)

																					SCREAMING_SNAKE_CASE_: Any  		=       [new_token_a]
																					SCREAMING_SNAKE_CASE_: List[Any]  		=       [new_token_a]

																					with open(os.path.join(lowerCAmelCase__   ,						"special_tokens_map.json")   ,						"w"   ,						encoding="utf-8") as outfile:
																											json.dump(lowerCAmelCase__   ,						lowerCAmelCase__)
																					with open(os.path.join(lowerCAmelCase__   ,						"tokenizer_config.json")   ,						"w"   ,						encoding="utf-8") as outfile:
																											json.dump(lowerCAmelCase__   ,						lowerCAmelCase__)

																					# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
																					# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
																					# "special_tokens_map.json" files
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       tokenizer_class.from_pretrained(lowerCAmelCase__   ,						extra_ids=0)
																					self.assertIn(lowerCAmelCase__   ,						tokenizer_without_change_in_init.additional_special_tokens)
																					# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
																					self.assertEqual(
																					    [new_token_a]   ,						tokenizer_without_change_in_init.convert_ids_to_tokens(
																					        tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a]))   ,						)

																					SCREAMING_SNAKE_CASE_: int  		=       0xE007
																					SCREAMING_SNAKE_CASE_: Dict  		=       chr(lowerCAmelCase__)
																					# Now we test that we can change the value of additional_special_tokens in the from_pretrained
																					SCREAMING_SNAKE_CASE_: Optional[Any]  		=       [AddedToken(lowerCAmelCase__   ,						lstrip=lowerCAmelCase__)]
																					SCREAMING_SNAKE_CASE_: Tuple  		=       tokenizer_class.from_pretrained(
																					    lowerCAmelCase__   ,						additional_special_tokens=lowerCAmelCase__   ,						extra_ids=0)

																					self.assertIn(lowerCAmelCase__   ,						tokenizer.additional_special_tokens)
																					# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
																					self.assertEqual(
																					    [new_token_a]   ,						tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a])))



			@require_tokenizers
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_tokenizers(do_lower_case=lowerCAmelCase__)
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       "hello world"
																					if self.space_between_special_tokens:
																											SCREAMING_SNAKE_CASE_: Tuple  		=       "[CLS] hello world [SEP]"
																					else:
																											SCREAMING_SNAKE_CASE_: List[str]  		=       input
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       tokenizer.encode(lowerCAmelCase__   ,						add_special_tokens=lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_: List[str]  		=       tokenizer.decode(lowerCAmelCase__   ,						spaces_between_special_tokens=self.space_between_special_tokens)
																					self.assertIn(lowerCAmelCase__   ,						[output, output.lower()])



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       self.get_tokenizers()
									for tokenizer in tokenizers:
															with self.subTest(F"{tokenizer.__class__.__name__}"):
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       [
																					    "bos_token",
																					    "eos_token",
																					    "unk_token",
																					    "sep_token",
																					    "pad_token",
																					    "cls_token",
																					    "mask_token",
																					]

									SCREAMING_SNAKE_CASE_: str  		=       "a"
									SCREAMING_SNAKE_CASE_: List[Any]  		=       ord(lowerCAmelCase__)

									for attr in attributes_list:
															setattr(lowerCAmelCase__   ,						attr + "_id"   ,						lowerCAmelCase__)
															self.assertEqual(getattr(lowerCAmelCase__   ,						lowerCAmelCase__)   ,						lowerCAmelCase__)
															self.assertEqual(getattr(lowerCAmelCase__   ,						attr + "_id")   ,						lowerCAmelCase__)

															setattr(lowerCAmelCase__   ,						attr + "_id"   ,						lowerCAmelCase__)
															self.assertEqual(getattr(lowerCAmelCase__   ,						lowerCAmelCase__)   ,						lowerCAmelCase__)
															self.assertEqual(getattr(lowerCAmelCase__   ,						attr + "_id")   ,						lowerCAmelCase__)

									setattr(lowerCAmelCase__   ,						"additional_special_tokens_ids"   ,						[])
									self.assertListEqual(getattr(lowerCAmelCase__   ,						"additional_special_tokens")   ,						[])
									self.assertListEqual(getattr(lowerCAmelCase__   ,						"additional_special_tokens_ids")   ,						[])

									SCREAMING_SNAKE_CASE_: List[Any]  		=       0xE006
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       chr(lowerCAmelCase__)
									setattr(lowerCAmelCase__   ,						"additional_special_tokens_ids"   ,						[additional_special_token_id])
									self.assertListEqual(getattr(lowerCAmelCase__   ,						"additional_special_tokens")   ,						[additional_special_token])
									self.assertListEqual(getattr(lowerCAmelCase__   ,						"additional_special_tokens_ids")   ,						[additional_special_token_id])



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									pass



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									pass




			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									pass









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	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[Any]										=     MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			_UpperCAmelCase   :							str										=     TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



			def        _SCREAMING_SNAKE_CASE			( self  :     Any   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     List[str]   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: Any  		=       TextaTextGenerationPipeline(model=lowerCAmelCase__   ,						tokenizer=lowerCAmelCase__)
									return generator, ["Something to write", "Something else"]



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Dict   ,						lowerCAmelCase__  :     Any):
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there")
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ANY(lowerCAmelCase__)}])
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))

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

									SCREAMING_SNAKE_CASE_: Dict  		=       generator(
									    ["This is great !", "Something else"]   ,						num_return_sequences=2   ,						batch_size=2   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									        [{"generated_text": ANY(lowerCAmelCase__)}, {"generated_text": ANY(lowerCAmelCase__)}],
									    ]   ,						)

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



			@require_torch
			def        _SCREAMING_SNAKE_CASE			( self  :     Tuple):
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="pt")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       3
									SCREAMING_SNAKE_CASE_: Any  		=       generator(
									    "Something there"   ,						num_return_sequences=lowerCAmelCase__   ,						num_beams=lowerCAmelCase__   ,						)
									SCREAMING_SNAKE_CASE_: Any  		=       [
									    {"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(lowerCAmelCase__   ,						lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       generator("This is a test"   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						return_tensors=lowerCAmelCase__)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        {"generated_token_ids": ANY(torch.Tensor)},
									        {"generated_token_ids": ANY(torch.Tensor)},
									    ]   ,						)
									SCREAMING_SNAKE_CASE_: str  		=       generator.model.config.eos_token_id
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       "<pad>"
									SCREAMING_SNAKE_CASE_: Tuple  		=       generator(
									    ["This is a test", "This is a second test"]   ,						do_sample=lowerCAmelCase__   ,						num_return_sequences=2   ,						batch_size=2   ,						return_tensors=lowerCAmelCase__   ,						)
									self.assertEqual(
									    lowerCAmelCase__   ,						[
									        [
									            {"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        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       pipeline("text2text-generation"   ,						model="patrickvonplaten/t5-tiny-random"   ,						framework="tf")
									# do_sample=False necessary for reproducibility
									SCREAMING_SNAKE_CASE_: List[Any]  		=       generator("Something there"   ,						do_sample=lowerCAmelCase__)
									self.assertEqual(lowerCAmelCase__   ,						[{"generated_text": ""}])






