





'''simple docstring'''


import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict




@pytest.mark.parametrize(
    """files"""				,	[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]				,	)
def       __a       (   UpperCAmelCase				,	UpperCAmelCase			)					->List[str]:



	"""simple docstring"""







	A							     =					tmp_path_factory.mktemp("""dset_infos_dir"""			)
	if "full:README.md" in files:
		with open(dataset_infos_dir / """README.md"""				,	"""w"""			) as f:
			f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""			)
	if "empty:README.md" in files:
		with open(dataset_infos_dir / """README.md"""				,	"""w"""			) as f:
			f.write(""""""			)
    # we want to support dataset_infos.json for backward compatibility
	if "dataset_infos.json" in files:
		with open(dataset_infos_dir / """dataset_infos.json"""				,	"""w"""			) as f:
			f.write("""{\"default\": {\"dataset_size\": 42}}"""			)
	A							     =					DatasetInfosDict.from_directory(_lowerCamelCase			)
	assert dataset_infos
	assert dataset_infos["default"].dataset_size == 42




@pytest.mark.parametrize(
    """dataset_info"""				,	[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""				,	features=Features({"""a""": Value("""int32"""			)}			)				,	builder_name="""builder"""				,	config_name="""config"""				,	version="""1.0.0"""				,	splits=[{"""name""": """train"""}]				,	download_size=42				,	),
    ]				,	)
def       __a       (   UpperCAmelCase				,	UpperCAmelCase			)					->Union[str, Any]:



	"""simple docstring"""







	A							     =					str(_lowerCamelCase			)
	dataset_info.write_to_directory(_lowerCamelCase			)
	A							     =					DatasetInfo.from_directory(_lowerCamelCase			)
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(_lowerCamelCase				,	"""dataset_info.json"""			)			)




def       __a       (   )					->Dict:



	"""simple docstring"""







	A							     =					DatasetInfo(
	    description="""foo"""				,	citation="""bar"""				,	homepage="""https://foo.bar"""				,	license="""CC0"""				,	features=Features({"""a""": Value("""int32"""			)}			)				,	post_processed={}				,	supervised_keys=()				,	task_templates=[]				,	builder_name="""builder"""				,	config_name="""config"""				,	version="""1.0.0"""				,	splits=[{"""name""": """train""", """num_examples""": 42}]				,	download_checksums={}				,	download_size=1337				,	post_processing_size=442				,	dataset_size=1234				,	size_in_bytes=1337 + 442 + 1234				,	)
	A							     =					dataset_info._to_yaml_dict()
	assert sorted(_lowerCamelCase			) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML			)
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		assert key in dataset_info_yaml_dict
		assert isinstance(dataset_info_yaml_dict[key]				,	(list, dict, int, str)			)
	A							     =					yaml.safe_dump(_lowerCamelCase			)
	A							     =					yaml.safe_load(_lowerCamelCase			)
	assert dataset_info_yaml_dict == reloaded




def       __a       (   )					->Tuple:



	"""simple docstring"""







	A							     =					DatasetInfo()
	A							     =					dataset_info._to_yaml_dict()
	assert dataset_info_yaml_dict == {}




@pytest.mark.parametrize(
    """dataset_infos_dict"""				,	[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}			),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}			),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""				,	features=Features({"""a""": Value("""int32"""			)}			)				,	builder_name="""builder"""				,	config_name="""config"""				,	version="""1.0.0"""				,	splits=[{"""name""": """train"""}]				,	download_size=42				,	)
            }			),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42			),
                """v2""": DatasetInfo(dataset_size=1337			),
            }			),
    ]				,	)
def       __a       (   UpperCAmelCase				,	UpperCAmelCase			)					->Optional[Any]:



	"""simple docstring"""







	A							     =					str(_lowerCamelCase			)
	dataset_infos_dict.write_to_directory(_lowerCamelCase			)
	A							     =					DatasetInfosDict.from_directory(_lowerCamelCase			)

	# the config_name of the dataset_infos_dict take over the attribute
	for config_name, dataset_info in dataset_infos_dict.items():
		A							     =					config_name
		# the yaml representation doesn't include fields like description or citation
		# so we just test that we can recover what we can from the yaml
		A							     =					DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()			)
	assert dataset_infos_dict == reloaded

	if dataset_infos_dict:
		assert os.path.exists(os.path.join(_lowerCamelCase				,	"""README.md"""			)			)



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


lowercase  :					List[str]													=       logging.get_logger(__name__)




class  lowerCamelCase__							:




	'''simple docstring'''




	def __init__(					self		:str     ,   a		:str = None     ,   a		:uuid.UUID = None     ,   a		:Tuple=None     ,   a		:Optional[Any]=None						)		-> str:
								if not conversation_id:
															__UpperCamelCase	:   Dict			   =					uuid.uuida()
								if past_user_inputs is None:
															__UpperCamelCase	:   List[Any]			   =					[]
								if generated_responses is None:
															__UpperCamelCase	:   Any			   =					[]

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







	def __eq__(					self		:Optional[int]     ,   a		:Optional[int]						)		-> Union[str, Any]:
								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		:Optional[int]     ,   a		:str     ,   a		:bool = False						)		-> str:
								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}".'						)
																						__UpperCamelCase	:   Any			   =					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:
															__UpperCamelCase	:   int			   =					text







	def    _lowerCamelCase				(					self		:List[str]						)		-> int:
								if self.new_user_input:
															self.past_user_inputs.append(self.new_user_input						)
								__UpperCamelCase	:   Dict			   =					None







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:str						)		-> Optional[int]:
								self.generated_responses.append(a						)







	def    _lowerCamelCase				(					self		:int						)		-> Optional[Any]:
								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		:List[str]						)		-> List[Any]:
								__UpperCamelCase	:   Any			   =					f'Conversation id: {self.uuid} \n'
								for is_user, text in self.iter_texts():
															__UpperCamelCase	:   str			   =					"user" if is_user else "bot"
															output += f'{name} >> {text} \n'
								return output




@add_end_docstrings(
    __lowercase     ,						R'\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    '     ,						)
class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	def __init__(					self		:Tuple     ,   *a		:Tuple     ,   **a		:List[str]						)		-> Tuple:
								super().__init__(*a     ,   **a						)
								if self.tokenizer.pad_token_id is None:
															__UpperCamelCase	:   int			   =					self.tokenizer.eos_token







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:List[Any]=None     ,   a		:str=None     ,   a		:int=None     ,   **a		:str						)		-> List[str]:
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   List[str]			   =					{}
								__UpperCamelCase	:   str			   =					{}

								if min_length_for_response is not None:
															__UpperCamelCase	:   Optional[Any]			   =					min_length_for_response
								if minimum_tokens is not None:
															__UpperCamelCase	:   List[str]			   =					minimum_tokens

								if "max_length" in generate_kwargs:
															__UpperCamelCase	:   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:
															__UpperCamelCase	:   List[Any]			   =					clean_up_tokenization_spaces

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







	def __call__(					self		:Dict     ,   a		:Union[Conversation, List[Conversation]]     ,   a		:List[Any]=0     ,   **a		:Any						)		-> Union[str, Any]:
								__UpperCamelCase	:   Optional[int]			   =					super().__call__(a     ,   num_workers=a     ,   **a						)
								if isinstance(a     ,   a						) and len(a						) == 1:
															return outputs[0]
								return outputs







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Conversation     ,   a		:Dict=3_2						)		-> Dict[str, Any]:
								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"						):
															__UpperCamelCase	:   str			   =					self.tokenizer._build_conversation_input_ids(a						)
								else:
															# If the tokenizer cannot handle conversations, we default to only the old version
															__UpperCamelCase	:   Optional[Any]			   =					self._legacy_parse_and_tokenize(a						)

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







	def    _lowerCamelCase				(					self		:Any     ,   a		:List[Any]     ,   a		:Optional[Any]=1_0     ,   **a		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   Union[str, Any]			   =					generate_kwargs.get("max_length"     ,   self.model.config.max_length						)

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







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str     ,   a		:Optional[int]=True						)		-> Union[str, Any]:
								__UpperCamelCase	:   List[str]			   =					model_outputs["output_ids"]
								__UpperCamelCase	:   Any			   =					self.tokenizer.decode(
								    output_ids[0]     ,   skip_special_tokens=a     ,   clean_up_tokenization_spaces=a     ,   )
								__UpperCamelCase	:   int			   =					model_outputs["conversation"]
								conversation.mark_processed()
								conversation.append_response(a						)
								return conversation







	def    _lowerCamelCase				(					self		:str     ,   a		:Conversation						)		-> Dict:
								__UpperCamelCase	:   int			   =					self.tokenizer.eos_token_id
								__UpperCamelCase	:   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:
															__UpperCamelCase	:   Union[str, Any]			   =					input_ids[-self.tokenizer.model_max_length :]
								return input_ids