

import unittest

from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


A_       : Optional[Any]         =			get_tests_dir('fixtures/test_sentencepiece.model')



@require_sentencepiece
class     _a						(__magic_name__		,   unittest.TestCase				):


					'''simple docstring'''






					UpperCAmelCase__:   List[Any]  							=       XLMProphetNetTokenizer
					UpperCAmelCase__:   Union[str, Any]  							=       False
					UpperCAmelCase__:   Optional[Any]  							=       True






					def   __A			( self			):
						super().setUp()

						# We have a SentencePiece fixture for testing
						A__							:    Any        =		XLMProphetNetTokenizer(A__    ,       keep_accents=A__			)
						tokenizer.save_pretrained(self.tmpdirname			)






					def   __A			( self			):
						A__							:    Optional[Any]        =		"""[PAD]"""
						A__							:    List[Any]        =		0

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(A__			)    ,       A__			)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(A__			)    ,       A__			)






					def   __A			( self			):
						A__							:    int        =		list(self.get_tokenizer().get_vocab().keys()			)

						self.assertEqual(vocab_keys[0]    ,       """[PAD]"""			)
						self.assertEqual(vocab_keys[1]    ,       """[CLS]"""			)
						self.assertEqual(vocab_keys[-1]    ,       """j"""			)
						self.assertEqual(len(A__			)    ,       1012			)






					def   __A			( self			):
						self.assertEqual(self.get_tokenizer().vocab_size    ,       1012			)






					def   __A			( self			):
						A__							:    Union[str, Any]        =		XLMProphetNetTokenizer(A__    ,       keep_accents=A__			)

						A__							:    Tuple        =		tokenizer.tokenize("""This is a test"""			)
						self.assertListEqual(A__    ,       ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]			)

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(A__			)    ,       [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]    ,       )

						A__							:    Union[str, Any]        =		tokenizer.tokenize("""I was born in 92000, and this is falsé."""			)
						self.assertListEqual(
						    A__    ,       [
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """9""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """é""",
						        """.""",
						    ]    ,       )
						A__							:    Optional[int]        =		tokenizer.convert_tokens_to_ids(A__			)
						self.assertListEqual(
						    A__    ,       [
						        value + tokenizer.fairseq_offset
						        for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
						    ]    ,       )

						A__							:    Union[str, Any]        =		tokenizer.convert_ids_to_tokens(A__			)
						self.assertListEqual(
						    A__    ,       [
						        SPIECE_UNDERLINE + """I""",
						        SPIECE_UNDERLINE + """was""",
						        SPIECE_UNDERLINE + """b""",
						        """or""",
						        """n""",
						        SPIECE_UNDERLINE + """in""",
						        SPIECE_UNDERLINE + """""",
						        """[UNK]""",
						        """2""",
						        """0""",
						        """0""",
						        """0""",
						        """,""",
						        SPIECE_UNDERLINE + """and""",
						        SPIECE_UNDERLINE + """this""",
						        SPIECE_UNDERLINE + """is""",
						        SPIECE_UNDERLINE + """f""",
						        """al""",
						        """s""",
						        """[UNK]""",
						        """.""",
						    ]    ,       )






					@cached_property
					def   __A			( self			):
						return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased"""			)






					@slow
					def   __A			( self			):
						A__							:    Optional[int]        =		"""Hello World!"""
						A__							:    List[Any]        =		[3_5389, 6672, 49, 2]
						self.assertListEqual(A__    ,       self.big_tokenizer.encode(A__			)			)






					@slow
					def   __A			( self			):
						# fmt: off
						A__							:    str        =		{"""input_ids""": [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=A__    ,       model_name="""microsoft/xprophetnet-large-wiki100-cased"""    ,       revision="""1acad1643ddd54a44df6a1b797ada8373685d90e"""    ,       )






import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
	import tensorflow as tf

if is_torch_available():
	import torch


A_       : List[Any]         =			logging.get_logger(__name__)



class     _a						:


					'''simple docstring'''






					def __init__( self    ,       A__ = None    ,       A__ = None    ,       A__=None    ,       A__=None			):
						if not conversation_id:
							A__							:    List[Any]        =		uuid.uuida()
						if past_user_inputs is None:
							A__							:    Dict        =		[]
						if generated_responses is None:
							A__							:    int        =		[]

						A__							:    uuid.UUID        =		conversation_id
						A__							:    List[str]        =		past_user_inputs
						A__							:    List[str]        =		generated_responses
						A__							:    Optional[str]        =		text






					def __eq__( self    ,       A__			):
						if not isinstance(A__    ,       A__			):
							return False
						if self.uuid == other.uuid:
							return True
						return (
						    self.new_user_input == other.new_user_input
						    and self.past_user_inputs == other.past_user_inputs
						    and self.generated_responses == other.generated_responses
						)






					def   __A			( self    ,       A__    ,       A__ = False			):
						if self.new_user_input:
							if overwrite:
								logger.warning(
								    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" was overwritten """
								    F"""with: \"{text}\"."""			)
								A__							:    str        =		text
							else:
								logger.warning(
								    F"""User input added while unprocessed input was existing: \"{self.new_user_input}\" new input """
								    F"""ignored: \"{text}\". Set `overwrite` to True to overwrite unprocessed user input"""			)
						else:
							A__							:    Tuple        =		text






					def   __A			( self			):
						if self.new_user_input:
							self.past_user_inputs.append(self.new_user_input			)
						A__							:    Tuple        =		None






					def   __A			( self    ,       A__			):
						self.generated_responses.append(A__			)






					def   __A			( self			):
						for user_input, generated_response in zip(self.past_user_inputs    ,       self.generated_responses			):
							yield True, user_input
							yield False, generated_response
						if self.new_user_input:
							yield True, self.new_user_input






					def __repr__( self			):
						A__							:    Optional[Any]        =		F"""Conversation id: {self.uuid} \n"""
						for is_user, text in self.iter_texts():
							A__							:    str        =		"""user""" if is_user else """bot"""
							output += F"""{name} >> {text} \n"""
						return output



@add_end_docstrings(
    __magic_name__		,   r'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    '''		,   )
class     _a						(__magic_name__				):


					'''simple docstring'''






					def __init__( self    ,       *A__    ,       **A__			):
						super().__init__(*A__    ,       **A__			)
						if self.tokenizer.pad_token_id is None:
							A__							:    Tuple        =		self.tokenizer.eos_token






					def   __A			( self    ,       A__=None    ,       A__=None    ,       A__=None    ,       **A__			):
						A__							:    Tuple        =		{}
						A__							:    List[str]        =		{}
						A__							:    Union[str, Any]        =		{}

						if min_length_for_response is not None:
							A__							:    str        =		min_length_for_response
						if minimum_tokens is not None:
							A__							:    List[str]        =		minimum_tokens

						if "max_length" in generate_kwargs:
							A__							:    List[Any]        =		generate_kwargs["""max_length"""]
							# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
						if clean_up_tokenization_spaces is not None:
							A__							:    Optional[int]        =		clean_up_tokenization_spaces

						if generate_kwargs:
							forward_params.update(A__			)
						return preprocess_params, forward_params, postprocess_params






					def __call__( self    ,       A__    ,       A__=0    ,       **A__			):
						A__							:    Optional[Any]        =		super().__call__(A__    ,       num_workers=A__    ,       **A__			)
						if isinstance(A__    ,       A__			) and len(A__			) == 1:
							return outputs[0]
						return outputs






					def   __A			( self    ,       A__    ,       A__=32			):
						if not isinstance(A__    ,       A__			):
							raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""			)
						if conversation.new_user_input is None:
							raise ValueError(
							    F"""Conversation with UUID {type(conversation.uuid			)} does not contain new user input to process. """
							    """Add user inputs with the conversation's `add_user_input` method"""			)
						if hasattr(self.tokenizer    ,       """_build_conversation_input_ids"""			):
							A__							:    List[str]        =		self.tokenizer._build_conversation_input_ids(A__			)
						else:
							# If the tokenizer cannot handle conversations, we default to only the old version
							A__							:    Tuple        =		self._legacy_parse_and_tokenize(A__			)

						if self.framework == "pt":
							A__							:    List[str]        =		torch.LongTensor([input_ids]			)
						elif self.framework == "tf":
							A__							:    Optional[Any]        =		tf.constant([input_ids]			)
						return {"input_ids": input_ids, "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=10    ,       **A__			):
						A__							:    List[Any]        =		generate_kwargs.get("""max_length"""    ,       self.model.config.max_length			)

						A__							:    Optional[int]        =		model_inputs["""input_ids"""].shape[1]
						if max_length - minimum_tokens < n:
							logger.warning(F"""Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})"""			)
							A__							:    Dict        =		max_length - minimum_tokens
							A__							:    Optional[int]        =		model_inputs["""input_ids"""][:, -trim:]
							if "attention_mask" in model_inputs:
								A__							:    str        =		model_inputs["""attention_mask"""][:, -trim:]
						A__							:    List[str]        =		model_inputs.pop("""conversation"""			)
						A__							:    Dict        =		max_length
						A__							:    str        =		self.model.generate(**A__    ,       **A__			)
						if self.model.config.is_encoder_decoder:
							A__							:    Union[str, Any]        =		1
						else:
							A__							:    Optional[Any]        =		n
						return {"output_ids": output_ids[:, start_position:], "conversation": conversation}






					def   __A			( self    ,       A__    ,       A__=True			):
						A__							:    Dict        =		model_outputs["""output_ids"""]
						A__							:    str        =		self.tokenizer.decode(
						    output_ids[0]    ,       skip_special_tokens=A__    ,       clean_up_tokenization_spaces=A__    ,       )
						A__							:    Optional[int]        =		model_outputs["""conversation"""]
						conversation.mark_processed()
						conversation.append_response(A__			)
						return conversation






					def   __A			( self    ,       A__			):
						A__							:    str        =		self.tokenizer.eos_token_id
						A__							:    Tuple        =		[]
						for is_user, text in conversation.iter_texts():
							if eos_token_id is not None:
								input_ids.extend(self.tokenizer.encode(A__    ,       add_special_tokens=A__			) + [eos_token_id]			)
							else:
								input_ids.extend(self.tokenizer.encode(A__    ,       add_special_tokens=A__			)			)

						if len(A__			) > self.tokenizer.model_max_length:
							A__							:    str        =		input_ids[-self.tokenizer.model_max_length :]
						return input_ids



