"""simple docstring"""






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 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					snake_case__				    = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	Any			):
									UpperCAmelCase__             =							TextaTextGenerationPipeline(model=lowerCamelCase__    ,tokenizer=lowerCamelCase__			)
									return generator, ["Something to write", "Something else"]







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Optional[Any]			):
									UpperCAmelCase__             =							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'			)			)

									UpperCAmelCase__             =							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__			)}],
									    ]    ,)

									UpperCAmelCase__             =							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 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							pipeline('text2text-generation'    ,model='patrickvonplaten/t5-tiny-random'    ,framework='pt'			)
									# do_sample=False necessary for reproducibility
									UpperCAmelCase__             =							generator('Something there'    ,do_sample=lowerCamelCase__			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ''}]			)

									UpperCAmelCase__             =							3
									UpperCAmelCase__             =							generator(
									    'Something there'    ,num_return_sequences=lowerCamelCase__    ,num_beams=lowerCamelCase__    ,)
									UpperCAmelCase__             =							[
									    {'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__			)

									UpperCAmelCase__             =							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			)},
									    ]    ,)
									UpperCAmelCase__             =							generator.model.config.eos_token_id
									UpperCAmelCase__             =							'<pad>'
									UpperCAmelCase__             =							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 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							pipeline('text2text-generation'    ,model='patrickvonplaten/t5-tiny-random'    ,framework='tf'			)
									# do_sample=False necessary for reproducibility
									UpperCAmelCase__             =							generator('Something there'    ,do_sample=lowerCamelCase__			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ''}]			)









import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase_									=	logging.get_logger(__name__)





class     lowerCamelCase__:
      def __init__(				self: Any    ,       UpperCamelCase_: str    ,       UpperCamelCase_: Dict   ):
             __lowerCamelCase   =						question_encoder
             __lowerCamelCase   =						generator
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: Optional[Any]   ):
             if os.path.isfile(UpperCamelCase_   ):
                    raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file'   )
             os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """question_encoder_tokenizer"""   )
             __lowerCamelCase   =						os.path.join(UpperCamelCase_    ,       """generator_tokenizer"""   )
             self.question_encoder.save_pretrained(UpperCamelCase_   )
             self.generator.save_pretrained(UpperCamelCase_   )
      @classmethod
      def 			lowerCAmelCase__						(				cls: List[Any]    ,       UpperCamelCase_: Dict    ,       **UpperCamelCase_: Union[str, Any]   ):
             # dynamically import AutoTokenizer
             from ..auto.tokenization_auto import AutoTokenizer

             __lowerCamelCase   =						kwargs.pop("""config"""    ,       UpperCamelCase_   )

             if config is None:
                    __lowerCamelCase   =						RagConfig.from_pretrained(UpperCamelCase_   )

             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.question_encoder    ,       subfolder="""question_encoder_tokenizer"""   )
             __lowerCamelCase   =						AutoTokenizer.from_pretrained(
                 UpperCamelCase_    ,       config=config.generator    ,       subfolder="""generator_tokenizer"""   )
             return cls(question_encoder=UpperCamelCase_    ,       generator=UpperCamelCase_   )
      def __call__(				self: Tuple    ,       *UpperCamelCase_: int    ,       **UpperCamelCase_: int   ):
             return self.current_tokenizer(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Tuple    ,       *UpperCamelCase_: List[Any]    ,       **UpperCamelCase_: List[Any]   ):
             return self.generator.batch_decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: Optional[Any]    ,       *UpperCamelCase_: str    ,       **UpperCamelCase_: Union[str, Any]   ):
             return self.generator.decode(*UpperCamelCase_    ,       **UpperCamelCase_   )
      def 			lowerCAmelCase__						(				self: str   ):
             __lowerCamelCase   =						self.question_encoder
      def 			lowerCAmelCase__						(				self: Optional[int]   ):
             __lowerCamelCase   =						self.generator




      def 			lowerCAmelCase__						(				self: Tuple    ,       UpperCamelCase_: List[str]    ,       UpperCamelCase_: Optional[List[str]] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: Optional[int] = None    ,       UpperCamelCase_: str = "longest"    ,       UpperCamelCase_: str = None    ,       UpperCamelCase_: bool = True    ,       **UpperCamelCase_: int    ,       ):
             warnings.warn(
                 """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                 """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                 """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                 """details"""    ,       UpperCamelCase_    ,       )
             if max_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             if tgt_texts is None:
                    return model_inputs
             # Process tgt_texts
             if max_target_length is None:
                    __lowerCamelCase   =						self.current_tokenizer.model_max_length
             __lowerCamelCase   =						self(
                 text_target=UpperCamelCase_    ,       add_special_tokens=UpperCamelCase_    ,       return_tensors=UpperCamelCase_    ,       padding=UpperCamelCase_    ,       max_length=UpperCamelCase_    ,       truncation=UpperCamelCase_    ,       **UpperCamelCase_    ,       )
             __lowerCamelCase   =						labels["""input_ids"""]
             return model_inputs

