'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase    :     Optional[Any]         =       logging.get_logger(__name__)

_UpperCAmelCase    :     List[Any]         =       {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   __lowerCAmelCase         =   "roberta-prelayernorm"
   def __init__(      self      :			Optional[int], UpperCamelCase__      :			List[Any]=5_02_65, UpperCamelCase__      :			int=7_68, UpperCamelCase__      :			str=12, UpperCamelCase__      :			Union[str, Any]=12, UpperCamelCase__      :			Tuple=30_72, UpperCamelCase__      :			Tuple="gelu", UpperCamelCase__      :			List[Any]=0.1, UpperCamelCase__      :			Union[str, Any]=0.1, UpperCamelCase__      :			Any=5_12, UpperCamelCase__      :			Union[str, Any]=2, UpperCamelCase__      :			Optional[Any]=0.02, UpperCamelCase__      :			str=1e-12, UpperCamelCase__      :			str=1, UpperCamelCase__      :			List[str]=0, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Optional[Any]="absolute", UpperCamelCase__      :			Optional[Any]=True, UpperCamelCase__      :			List[str]=None, **UpperCamelCase__      :			Any, )  ->			Dict:
        super().__init__(pad_token_id=UpperCamelCase__, bos_token_id=UpperCamelCase__, eos_token_id=UpperCamelCase__, **UpperCamelCase__     )

        _A    		=  vocab_size
        _A    		=  hidden_size
        _A    		=  num_hidden_layers
        _A    		=  num_attention_heads
        _A    		=  hidden_act
        _A    		=  intermediate_size
        _A    		=  hidden_dropout_prob
        _A    		=  attention_probs_dropout_prob
        _A    		=  max_position_embeddings
        _A    		=  type_vocab_size
        _A    		=  initializer_range
        _A    		=  layer_norm_eps
        _A    		=  position_embedding_type
        _A    		=  use_cache
        _A    		=  classifier_dropout







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   @property
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
             _A    		=  {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
             _A    		=  {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]     )




'''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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   __lowerCAmelCase         =   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			str, UpperCamelCase__      :			Optional[int], UpperCamelCase__      :			Any     )  ->			List[str]:
        _A    		=  TextaTextGenerationPipeline(model=UpperCamelCase__, tokenizer=UpperCamelCase__     )
        return generator, ["Something to write", "Something else"]
   def 	__UpperCAmelCase   (      self      :			Any, UpperCamelCase__      :			Tuple, UpperCamelCase__      :			Optional[Any]     )  ->			List[str]:
        _A    		=  generator('Something there'     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ANY(UpperCamelCase__     )}]     )
        # These are encoder decoder, they don't just append to incoming string
        self.assertFalse(outputs[0]['generated_text'].startswith('Something there'     )     )

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

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

        with self.assertRaises(UpperCamelCase__     ):
             generator(4     )
   @require_torch
   def 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Optional[Any]:
        _A    		=  pipeline('text2text-generation', model='patrickvonplaten/t5-tiny-random', framework='pt'     )
        # do_sample=False necessary for reproducibility
        _A    		=  generator('Something there', do_sample=UpperCamelCase__     )
        self.assertEqual(UpperCamelCase__, [{'generated_text': ''}]     )

        _A    		=  3
        _A    		=  generator(
            'Something there', num_return_sequences=UpperCamelCase__, num_beams=UpperCamelCase__, )
        _A    		=  [
            {'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(UpperCamelCase__, UpperCamelCase__     )

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



