






import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 		lowerCAmelCase     ( __a      ,       unittest.TestCase			):

					'''simple docstring'''

					_A						:			Union[str, Any]									=      LEDTokenizer
					_A						:			Tuple									=      LEDTokenizerFast
					_A						:			Tuple									=      True



					def       lowerCAmelCase				(  self				:	Tuple							)			->  Union[str, Any]:


												"""simple docstring"""

												super().setUp()
												__lowercase      :							str				=     [
												    """l""",
												    """o""",
												    """w""",
												    """e""",
												    """r""",
												    """s""",
												    """t""",
												    """i""",
												    """d""",
												    """n""",
												    """\u0120""",
												    """\u0120l""",
												    """\u0120n""",
												    """\u0120lo""",
												    """\u0120low""",
												    """er""",
												    """\u0120lowest""",
												    """\u0120newer""",
												    """\u0120wider""",
												    """<unk>""",
												]
												__lowercase      :							int				=     dict(zip(__a		,     range(len(__a							)							)							)							)
												__lowercase      :							Any				=     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
												__lowercase      :							List[str]				=     {"""unk_token""": """<unk>"""}

												__lowercase      :							Optional[Any]				=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]							)
												__lowercase      :							List[Any]				=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""merges_file"""]							)
												with open(self.vocab_file		,     """w"""		,     encoding="""utf-8"""							) as fp:
																			fp.write(json.dumps(__a							) + """\n"""							)
												with open(self.merges_file		,     """w"""		,     encoding="""utf-8"""							) as fp:
																			fp.write("""\n""".join(__a							)							)



					def       lowerCAmelCase				(  self				:	Any		,     **__a				:	Optional[int]							)			->  Tuple:


												"""simple docstring"""

												kwargs.update(self.special_tokens_map							)
												return self.tokenizer_class.from_pretrained(self.tmpdirname		,     **__a							)



					def       lowerCAmelCase				(  self				:	Tuple		,     **__a				:	str							)			->  Optional[int]:


												"""simple docstring"""

												kwargs.update(self.special_tokens_map							)
												return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,     **__a							)



					def       lowerCAmelCase				(  self				:	str		,     __a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												return "lower newer", "lower newer"



					@cached_property
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Any:


												"""simple docstring"""

												return LEDTokenizer.from_pretrained("""allenai/led-base-16384"""							)



					@cached_property
					def       lowerCAmelCase				(  self				:	Optional[Any]							)			->  List[Any]:


												"""simple docstring"""

												return LEDTokenizerFast.from_pretrained("""allenai/led-base-16384"""							)



					@require_torch
					def       lowerCAmelCase				(  self				:	Dict							)			->  str:


												"""simple docstring"""

												__lowercase      :							Any				=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												__lowercase      :							Optional[Any]				=     [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__lowercase      :							Union[str, Any]				=     tokenizer(__a		,     max_length=len(__a							)		,     padding=__a		,     return_tensors="""pt"""							)
																			self.assertIsInstance(__a		,     __a							)

																			self.assertEqual((2, 9)		,     batch.input_ids.shape							)
																			self.assertEqual((2, 9)		,     batch.attention_mask.shape							)
																			__lowercase      :							List[str]				=     batch.input_ids.tolist()[0]
																			self.assertListEqual(__a		,     __a							)



					@require_torch
					def       lowerCAmelCase				(  self				:	Tuple							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[str]				=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__lowercase      :							Dict				=     tokenizer(__a		,     padding=__a		,     return_tensors="""pt"""							)
																			self.assertIn("""input_ids"""		,     __a							)
																			self.assertIn("""attention_mask"""		,     __a							)
																			self.assertNotIn("""labels"""		,     __a							)
																			self.assertNotIn("""decoder_attention_mask"""		,     __a							)



					@require_torch
					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  str:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     [
												    """Summary of the text.""",
												    """Another summary.""",
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__lowercase      :							Dict				=     tokenizer(text_target=__a		,     max_length=32		,     padding="""max_length"""		,     return_tensors="""pt"""							)
																			self.assertEqual(32		,     targets["""input_ids"""].shape[1]							)



					@require_torch
					def       lowerCAmelCase				(  self				:	str							)			->  Optional[Any]:


												"""simple docstring"""

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__lowercase      :							Optional[int]				=     tokenizer(
																			    ["""I am a small frog""" * 1024, """I am a small frog"""]		,     padding=__a		,     truncation=__a		,     return_tensors="""pt"""							)
																			self.assertIsInstance(__a		,     __a							)
																			self.assertEqual(batch.input_ids.shape		,     (2, 5122)							)



					@require_torch
					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     ["""A long paragraph for summarization."""]
												__lowercase      :							List[str]				=     [
												    """Summary of the text.""",
												]
												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__lowercase      :							Optional[int]				=     tokenizer(__a		,     return_tensors="""pt"""							)
																			__lowercase      :							str				=     tokenizer(text_target=__a		,     return_tensors="""pt"""							)
																			__lowercase      :							int				=     inputs["""input_ids"""]
																			__lowercase      :							List[Any]				=     targets["""input_ids"""]
																			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()							)
																			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()							)
																			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()							)
																			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()							)



					@require_torch
					def       lowerCAmelCase				(  self				:	List[str]							)			->  Optional[Any]:


												"""simple docstring"""

												for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																			__lowercase      :							int				=     ["""Summary of the text.""", """Another summary."""]
																			__lowercase      :							Tuple				=     [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

																			__lowercase      :							str				=     tokenizer(__a		,     padding=__a							)
																			__lowercase      :							Optional[Any]				=     [[0] * len(__a							) for x in encoded_output["""input_ids"""]]
																			__lowercase      :							Tuple				=     tokenizer.pad(__a							)
																			self.assertSequenceEqual(outputs["""global_attention_mask"""]		,     __a							)



					def       lowerCAmelCase				(  self				:	List[str]							)			->  List[str]:


												"""simple docstring"""

												pass



					def       lowerCAmelCase				(  self				:	Dict							)			->  List[Any]:


												"""simple docstring"""

												for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																			with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"							):
																										__lowercase      :							Any				=     self.rust_tokenizer_class.from_pretrained(__a		,     **__a							)
																										__lowercase      :							Optional[Any]				=     self.tokenizer_class.from_pretrained(__a		,     **__a							)
																										__lowercase      :							Dict				=     """A, <mask> AllenNLP sentence."""
																										__lowercase      :							Optional[int]				=     tokenizer_r.encode_plus(__a		,     add_special_tokens=__a		,     return_token_type_ids=__a							)
																										__lowercase      :							Tuple				=     tokenizer_p.encode_plus(__a		,     add_special_tokens=__a		,     return_token_type_ids=__a							)
																										self.assertEqual(sum(tokens_r["""token_type_ids"""]							)		,     sum(tokens_p["""token_type_ids"""]							)							)
																										self.assertEqual(
																										    sum(tokens_r["""attention_mask"""]							) / len(tokens_r["""attention_mask"""]							)		,     sum(tokens_p["""attention_mask"""]							) / len(tokens_p["""attention_mask"""]							)		,     )

																										__lowercase      :							Optional[Any]				=     tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]							)
																										__lowercase      :							List[str]				=     tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""]							)
																										self.assertSequenceEqual(tokens_p["""input_ids"""]		,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)
																										self.assertSequenceEqual(tokens_r["""input_ids"""]		,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]							)

																										self.assertSequenceEqual(
																										    __a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)
																										self.assertSequenceEqual(
																										    __a		,     ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""]							)







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


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

class 		lowerCAmelCase     :

					'''simple docstring'''

					def __init__(  self				:	List[str]		,     __a				:	str = None		,     __a				:	uuid.UUID = None		,     __a				:	Any=None		,     __a				:	List[Any]=None							)			->  List[Any]:


												"""simple docstring"""

												if not conversation_id:
																			__lowercase      :							Any				=     uuid.uuida()
												if past_user_inputs is None:
																			__lowercase      :							Dict				=     []
												if generated_responses is None:
																			__lowercase      :							Dict				=     []

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



					def __eq__(  self				:	Dict		,     __a				:	Dict							)			->  Any:


												"""simple docstring"""

												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       lowerCAmelCase				(  self				:	List[str]		,     __a				:	str		,     __a				:	bool = False							)			->  Dict:


												"""simple docstring"""

												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}\"."							)
																										__lowercase      :							Optional[int]				=     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:
																			__lowercase      :							Dict				=     text



					def       lowerCAmelCase				(  self				:	int							)			->  Optional[Any]:


												"""simple docstring"""

												if self.new_user_input:
																			self.past_user_inputs.append(self.new_user_input							)
												__lowercase      :							Dict				=     None



					def       lowerCAmelCase				(  self				:	Optional[int]		,     __a				:	str							)			->  List[Any]:


												"""simple docstring"""

												self.generated_responses.append(__a							)



					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[Any]:


												"""simple docstring"""

												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				:	int							)			->  str:


												"""simple docstring"""

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







@add_end_docstrings(
    __a      ,       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 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					def __init__(  self				:	Any		,     *__a				:	int		,     **__a				:	str							)			->  Union[str, Any]:


												"""simple docstring"""

												super().__init__(*__a		,     **__a							)
												if self.tokenizer.pad_token_id is None:
																			__lowercase      :							List[Any]				=     self.tokenizer.eos_token



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	int=None		,     __a				:	Tuple=None		,     __a				:	Any=None		,     **__a				:	List[str]							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     {}
												__lowercase      :							Tuple				=     {}
												__lowercase      :							List[str]				=     {}

												if min_length_for_response is not None:
																			__lowercase      :							Dict				=     min_length_for_response
												if minimum_tokens is not None:
																			__lowercase      :							Union[str, Any]				=     minimum_tokens

												if "max_length" in generate_kwargs:
																			__lowercase      :							Union[str, 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:
																			__lowercase      :							Union[str, Any]				=     clean_up_tokenization_spaces

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



					def __call__(  self				:	Optional[int]		,     __a				:	Union[Conversation, List[Conversation]]		,     __a				:	Dict=0		,     **__a				:	List[Any]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     super().__call__(__a		,     num_workers=__a		,     **__a							)
												if isinstance(__a		,     __a							) and len(__a							) == 1:
																			return outputs[0]
												return outputs



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     __a				:	Conversation		,     __a				:	Tuple=32							)			->  Dict[str, Any]:


												"""simple docstring"""

												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"""							):
																			__lowercase      :							List[Any]				=     self.tokenizer._build_conversation_input_ids(__a							)
												else:
																			# If the tokenizer cannot handle conversations, we default to only the old version
																			__lowercase      :							Tuple				=     self._legacy_parse_and_tokenize(__a							)

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



					def       lowerCAmelCase				(  self				:	Any		,     __a				:	Dict		,     __a				:	Any=10		,     **__a				:	Dict							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     generate_kwargs.get("""max_length"""		,     self.model.config.max_length							)

												__lowercase      :							List[Any]				=     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})"							)
																			__lowercase      :							Any				=     max_length - minimum_tokens
																			__lowercase      :							int				=     model_inputs["""input_ids"""][:, -trim:]
																			if "attention_mask" in model_inputs:
																										__lowercase      :							Dict				=     model_inputs["""attention_mask"""][:, -trim:]
												__lowercase      :							Union[str, Any]				=     model_inputs.pop("""conversation"""							)
												__lowercase      :							Tuple				=     max_length
												__lowercase      :							int				=     self.model.generate(**__a		,     **__a							)
												if self.model.config.is_encoder_decoder:
																			__lowercase      :							Optional[int]				=     1
												else:
																			__lowercase      :							str				=     n
												return {"output_ids": output_ids[:, start_position:], "conversation": conversation}



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Tuple		,     __a				:	List[Any]=True							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							int				=     model_outputs["""output_ids"""]
												__lowercase      :							Union[str, Any]				=     self.tokenizer.decode(
												    output_ids[0]		,     skip_special_tokens=__a		,     clean_up_tokenization_spaces=__a		,     )
												__lowercase      :							List[str]				=     model_outputs["""conversation"""]
												conversation.mark_processed()
												conversation.append_response(__a							)
												return conversation



					def       lowerCAmelCase				(  self				:	int		,     __a				:	Conversation							)			->  Dict:


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     self.tokenizer.eos_token_id
												__lowercase      :							Optional[Any]				=     []
												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:
																			__lowercase      :							List[Any]				=     input_ids[-self.tokenizer.model_max_length :]
												return input_ids