

import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


_UpperCAmelCase		:   Tuple										=       """<<<<<<< This should probably be modified because it mentions: """

_UpperCAmelCase		:   Dict										=       """=======
>>>>>>>
"""

_UpperCAmelCase		:   Any										=       [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

_UpperCAmelCase		:   Tuple										=       [
    # (pattern, replacement)
    # Order is important here for some replacements
    (R"""tfds\.core""", R"""datasets"""),
    (R"""tf\.io\.gfile\.GFile""", R"""open"""),
    (R"""tf\.([\w\d]+)""", R"""datasets.Value('\1')"""),
    (R"""tfds\.features\.Text\(\)""", R"""datasets.Value('string')"""),
    (R"""tfds\.features\.Text\(""", R"""datasets.Value('string'),"""),
    (R"""features\s*=\s*tfds.features.FeaturesDict\(""", R"""features=datasets.Features("""),
    (R"""tfds\.features\.FeaturesDict\(""", R"""dict("""),
    (R"""The TensorFlow Datasets Authors""", R"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
    (R"""tfds\.""", R"""datasets."""),
    (R"""dl_manager\.manual_dir""", R"""self.config.data_dir"""),
    (R"""self\.builder_config""", R"""self.config"""),
]





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return ConvertCommand(args.tfds_path   ,							args.datasets_directory      )







class  lowercase				(       lowercase_			):




				@staticmethod
				def     a							(					snake_case       ):
											snake_case_							=    parser.add_parser(
											    'convert'      ,   help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'      ,   )
											train_parser.add_argument(
											    '--tfds_path'      ,   type=snake_case      ,   required=snake_case      ,   help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'      ,   )
											train_parser.add_argument(
											    '--datasets_directory'      ,   type=snake_case      ,   required=snake_case      ,   help='Path to the HuggingFace Datasets folder.'       )
											train_parser.set_defaults(func=snake_case       )




				def __init__(					self      ,   snake_case      ,   snake_case      ,   *snake_case       ):
											snake_case_							=    get_logger('datasets-cli/converting'       )

											snake_case_							=    tfds_path
											snake_case_							=    datasets_directory




				def     a							(					self       ):
											if os.path.isdir(self._tfds_path       ):
																		snake_case_							=    os.path.abspath(self._tfds_path       )
											elif os.path.isfile(self._tfds_path       ):
																		snake_case_							=    os.path.dirname(self._tfds_path       )
											else:
																		raise ValueError('--tfds_path is neither a directory nor a file. Please check path.'       )

											snake_case_							=    os.path.abspath(self._datasets_directory       )

											self._logger.info(F'''Converting datasets from {abs_tfds_path} to {abs_datasets_path}'''       )

											snake_case_							=    []
											snake_case_							=    []
											snake_case_							=    {}

											if os.path.isdir(self._tfds_path       ):
																		snake_case_							=    os.listdir(snake_case       )
											else:
																		snake_case_							=    [os.path.basename(self._tfds_path       )]

											for f_name in file_names:
																		self._logger.info(F'''Looking at file {f_name}'''       )
																		snake_case_							=    os.path.join(snake_case      ,   snake_case       )
																		snake_case_							=    os.path.join(snake_case      ,   snake_case       )

																		if not os.path.isfile(snake_case       ) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																									self._logger.info('Skipping file'       )
																									continue

																		with open(snake_case      ,   encoding='utf-8'       ) as f:
																									snake_case_							=    f.readlines()

																		snake_case_							=    []
																		snake_case_							=    False
																		snake_case_							=    False
																		snake_case_							=    []
																		for line in lines:
																									snake_case_							=    line

																									# Convert imports
																									if "import tensorflow.compat.v2 as tf" in out_line:
																																continue
																									elif "@tfds.core" in out_line:
																																continue
																									elif "builder=self" in out_line:
																																continue
																									elif "import tensorflow_datasets.public_api as tfds" in out_line:
																																snake_case_							=    'import datasets\n'
																									elif "import tensorflow" in out_line:
																																# order is important here
																																snake_case_							=    ''
																																continue
																									elif "from absl import logging" in out_line:
																																snake_case_							=    'from datasets import logging\n'
																									elif "getLogger" in out_line:
																																snake_case_							=    out_line.replace('getLogger'      ,   'get_logger'       )
																									elif any(expression in out_line for expression in TO_HIGHLIGHT       ):
																																snake_case_							=    True
																																snake_case_							=    list(filter(lambda snake_case       : e in out_line      ,   snake_case       )       )
																																out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(snake_case       ) + '\n'       )
																																out_lines.append(snake_case       )
																																out_lines.append(snake_case       )
																																continue
																									else:
																																for pattern, replacement in TO_CONVERT:
																																							snake_case_							=    re.sub(snake_case      ,   snake_case      ,   snake_case       )

                # Take care of saving utilities (to later move them together with main script)
																									if "tensorflow_datasets" in out_line:
																																snake_case_							=    re.match(R'from\stensorflow_datasets.*import\s([^\.\r\n]+)'      ,   snake_case       )
																																tfds_imports.extend(imp.strip() for imp in match.group(1       ).split(','       )       )
																																snake_case_							=    'from . import ' + match.group(1       )

																									# Check we have not forget anything
																									if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																																raise ValueError(F'''Error converting {out_line.strip()}'''       )

																									if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																																snake_case_							=    True
																									out_lines.append(snake_case       )

																		if is_builder or "wmt" in f_name:
																									# We create a new directory for each dataset
																									snake_case_							=    f_name.replace('.py'      ,   ''       )
																									snake_case_							=    os.path.join(snake_case      ,   snake_case       )
																									snake_case_							=    os.path.join(snake_case      ,   snake_case       )
																									os.makedirs(snake_case      ,   exist_ok=snake_case       )
																									self._logger.info(F'''Adding directory {output_dir}'''       )
																									imports_to_builder_map.update({imp: output_dir for imp in tfds_imports}       )
																		else:
																									# Utilities will be moved at the end
																									utils_files.append(snake_case       )

																		if needs_manual_update:
																									with_manual_update.append(snake_case       )

																		with open(snake_case      ,   'w'      ,   encoding='utf-8'       ) as f:
																									f.writelines(snake_case       )
																		self._logger.info(F'''Converted in {output_file}'''       )

											for utils_file in utils_files:
																		try:
																									snake_case_							=    os.path.basename(snake_case       )
																									snake_case_							=    imports_to_builder_map[f_name.replace('.py'      ,   ''       )]
																									self._logger.info(F'''Moving {dest_folder} to {utils_file}'''       )
																									shutil.copy(snake_case      ,   snake_case       )
																		except KeyError:
																									self._logger.error(F'''Cannot find destination folder for {utils_file}. Please copy manually.'''       )

											if with_manual_update:
																		for file_path in with_manual_update:
																									self._logger.warning(
																									    F'''You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.'''       )







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


_UpperCAmelCase		:   Dict										=       logging.get_logger(__name__)







class  lowercase				:




				def __init__(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case=None      ,   snake_case=None       ):
											if not conversation_id:
																		snake_case_							=    uuid.uuida()
											if past_user_inputs is None:
																		snake_case_							=    []
											if generated_responses is None:
																		snake_case_							=    []

											snake_case_							=    conversation_id
											snake_case_							=    past_user_inputs
											snake_case_							=    generated_responses
											snake_case_							=    text




				def __eq__(					self      ,   snake_case       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		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      ,   snake_case      ,   snake_case = 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}".'''       )
																									snake_case_							=    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:
																		snake_case_							=    text




				def     a							(					self       ):
											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input       )
											snake_case_							=    None




				def     a							(					self      ,   snake_case       ):
											self.generated_responses.append(snake_case       )




				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       ):
											snake_case_							=    F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		snake_case_							=    'user' if is_user else 'bot'
																		output += F'''{name} >> {text} \n'''
											return output







@add_end_docstrings(
    lowercase_	,  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  lowercase				(       lowercase_			):




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




				def     a							(					self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   **snake_case       ):
											snake_case_							=    {}
											snake_case_							=    {}
											snake_case_							=    {}

											if min_length_for_response is not None:
																		snake_case_							=    min_length_for_response
											if minimum_tokens is not None:
																		snake_case_							=    minimum_tokens

											if "max_length" in generate_kwargs:
																		snake_case_							=    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:
																		snake_case_							=    clean_up_tokenization_spaces

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




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




				def     a							(					self      ,   snake_case      ,   snake_case=32       ):
											if not isinstance(snake_case      ,   snake_case       ):
																		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'       ):
																		snake_case_							=    self.tokenizer._build_conversation_input_ids(snake_case       )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		snake_case_							=    self._legacy_parse_and_tokenize(snake_case       )

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




				def     a							(					self      ,   snake_case      ,   snake_case=10      ,   **snake_case       ):
											snake_case_							=    generate_kwargs.get('max_length'      ,   self.model.config.max_length       )

											snake_case_							=    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})'''       )
																		snake_case_							=    max_length - minimum_tokens
																		snake_case_							=    model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									snake_case_							=    model_inputs['attention_mask'][:, -trim:]
											snake_case_							=    model_inputs.pop('conversation'       )
											snake_case_							=    max_length
											snake_case_							=    self.model.generate(**snake_case      ,   **snake_case       )
											if self.model.config.is_encoder_decoder:
																		snake_case_							=    1
											else:
																		snake_case_							=    n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}




				def     a							(					self      ,   snake_case      ,   snake_case=True       ):
											snake_case_							=    model_outputs['output_ids']
											snake_case_							=    self.tokenizer.decode(
											    output_ids[0]      ,   skip_special_tokens=snake_case      ,   clean_up_tokenization_spaces=snake_case      ,   )
											snake_case_							=    model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(snake_case       )
											return conversation




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

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




