





from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class  __A(						__UpperCAmelCase ):

     def __init__(      self,       A,       A = None,       A = None,       A = False,       A = False,       A = None,       A = None,       **A,       ):





            """simple docstring"""

            super().__init__(
                features=A,       cache_dir=A,       keep_in_memory=A,       streaming=A,       num_proc=A,       **A,       )
            _UpperCamelCase              =						Generator(
                cache_dir=A,       features=A,       generator=A,       gen_kwargs=A,       **A,       )



     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            if self.streaming:
                   _UpperCamelCase              =						self.builder.as_streaming_dataset(split='''train'''      )
            # Build regular (map-style) dataset
            else:
                   _UpperCamelCase              =						None
                   _UpperCamelCase              =						None
                   _UpperCamelCase              =						None
                   _UpperCamelCase              =						None

                   self.builder.download_and_prepare(
                       download_config=A,       download_mode=A,       verification_mode=A,       base_path=A,       num_proc=self.num_proc,       )
                   _UpperCamelCase              =						self.builder.as_dataset(
                       split='''train''',       verification_mode=A,       in_memory=self.keep_in_memory      )
            return dataset










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  __A(						unittest.TestCase ):
     __A	  =       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __A	  =       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

     def 	_UpperCamelCase     (      self,       A,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						TextaTextGenerationPipeline(model=A,       tokenizer=A      )
            return generator, ["Something to write", "Something else"]

     def 	_UpperCamelCase     (      self,       A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						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'''      )      )

            _UpperCamelCase              =						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      )}],
                ],       )

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

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

            _UpperCamelCase              =						3
            _UpperCamelCase              =						generator(
                '''Something there''',       num_return_sequences=A,       num_beams=A,       )
            _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(A,       A      )

            _UpperCamelCase              =						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      )},
                ],       )
            _UpperCamelCase              =						generator.model.config.eos_token_id
            _UpperCamelCase              =						'''<pad>'''
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						pipeline('''text2text-generation''',       model='''patrickvonplaten/t5-tiny-random''',       framework='''tf'''      )
            # do_sample=False necessary for reproducibility
            _UpperCamelCase              =						generator('''Something there''',       do_sample=A      )
            self.assertEqual(A,       [{'''generated_text''': ''''''}]      )






