


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			: Dict           =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
		UpperCamelCase			: Union[str, Any]           =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







		def  __A					(      self						,	A						,	A						,	A     )   ->      Optional[Any]:







					'''simple docstring'''







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







		def  __A					(      self						,	A						,	A     )   ->      Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase  						=   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"""     )     )

					lowerCamelCase  						=   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     )}],
					    ]						,	)

					lowerCamelCase  						=   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     )   ->      Optional[Any]:







					'''simple docstring'''







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

					lowerCamelCase  						=   3
					lowerCamelCase  						=   generator(
					    """Something there"""						,	num_return_sequences=A						,	num_beams=A						,	)
					lowerCamelCase  						=   [
					    {"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     )

					lowerCamelCase  						=   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     )},
					    ]						,	)
					lowerCamelCase  						=   generator.model.config.eos_token_id
					lowerCamelCase  						=   "<pad>"
					lowerCamelCase  						=   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     )   ->      Union[str, Any]:







					'''simple docstring'''







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








'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase					:     Tuple												=logging.get_logger(__name__)


lowerCAmelCase					:     List[str]												={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase					:     Optional[int]												={
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase					:     List[Any]												={
    '''RUCAIBox/mvp''': 1_024,
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			["input_ids", "attention_mask"]
     __A      =			MvpTokenizer





     def __init__( self		:					Optional[Any]	,      lowercase		:					Any=None	,      lowercase		:					List[Any]=None	,      lowercase		:					Dict=None	,      lowercase		:					int="replace"	,      lowercase		:					int="<s>"	,      lowercase		:					List[str]="</s>"	,      lowercase		:					Optional[Any]="</s>"	,      lowercase		:					List[str]="<s>"	,      lowercase		:					List[str]="<unk>"	,      lowercase		:					List[str]="<pad>"	,      lowercase		:					Tuple="<mask>"	,      lowercase		:					Tuple=False	,      lowercase		:					Dict=True	,      **lowercase		:					List[str]	,      ):





        """simple docstring"""




        super().__init__(
            lowercase	,      lowercase	,      tokenizer_file=lowercase	,      errors=lowercase	,      bos_token=lowercase	,      eos_token=lowercase	,      sep_token=lowercase	,      cls_token=lowercase	,      unk_token=lowercase	,      pad_token=lowercase	,      mask_token=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase	,      **lowercase	,      )

        lowercase_			:Dict    			= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
           lowercase_			:List[str]    			= getattr(lowercase	,      pre_tok_state.pop("type"						)						)
           lowercase_			:int    			= add_prefix_space
           lowercase_			:Optional[int]    			= pre_tok_class(**lowercase						)

        lowercase_			:Any    			= add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        lowercase_			:List[Any]    			= "post_processor"
        lowercase_			:str    			= getattr(self.backend_tokenizer	,      lowercase	,      lowercase						)
        if tokenizer_component_instance:
           lowercase_			:Any    			= json.loads(tokenizer_component_instance.__getstate__()						)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              lowercase_			:int    			= tuple(state["sep"]						)
           if "cls" in state:
              lowercase_			:Any    			= tuple(state["cls"]						)

           lowercase_			:int    			= False

           if state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
              lowercase_			:Union[str, Any]    			= add_prefix_space
              lowercase_			:int    			= True

           if state.get("trim_offsets"	,      lowercase						) != trim_offsets:
              lowercase_			:Any    			= trim_offsets
              lowercase_			:int    			= True

           if changes_to_apply:
              lowercase_			:Tuple    			= getattr(lowercase	,      state.pop("type"						)						)
              lowercase_			:Any    			= component_class(**lowercase						)
              setattr(self.backend_tokenizer	,      lowercase	,      lowercase						)





     @property
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        if self._mask_token is None:
           if self.verbose:
              logger.error("Using mask_token, but it is not set yet."						)
           return None
        return str(self._mask_token						)





     @mask_token.setter
     def 							lowercase__      ( self		:					int	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:List[str]    			= AddedToken(lowercase	,      lstrip=lowercase	,      rstrip=lowercase						) if isinstance(lowercase	,      lowercase						) else value
        lowercase_			:Union[str, Any]    			= value





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					List[Any]	,      **lowercase		:					Any						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._batch_encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					Optional[int]	,      **lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:str    			= self._tokenizer.model.save(lowercase	,      name=lowercase						)
        return tuple(lowercase						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict	,      lowercase		:					int=None						):





        """simple docstring"""




        lowercase_			:List[str]    			= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







     def 							lowercase__      ( self		:					int	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= [self.sep_token_id]
        lowercase_			:Tuple    			= [self.cls_token_id]

        if token_ids_a is None:
           return len(cls + token_ids_a + sep						) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]


