


'''simple docstring'''




import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


_lowerCAmelCase	:		Union[str, Any]		  =						logging.get_logger(__name__)



@dataclass
class 			lowerCAmelCase					:
	_lowerCamelCase :       str
	_lowerCamelCase :       str
	_lowerCamelCase :       Optional[str]         =			None
	_lowerCamelCase :       Optional[str]         =			None



	def 					lowercase  (       self     ):
		return json.dumps(dataclasses.asdict(self     )			,       indent=2     ) + "\n"



@dataclass(frozen=a				)
class 			lowerCAmelCase					:
	_lowerCamelCase :       List[int]
	_lowerCamelCase :       Optional[List[int]]         =			None
	_lowerCamelCase :       Optional[List[int]]         =			None
	_lowerCamelCase :       Optional[Union[int, float]]         =			None



	def 					lowercase  (       self     ):
		return json.dumps(dataclasses.asdict(self     )     ) + "\n"



class 			lowerCAmelCase					:



	def 					lowercase  (       self			,       snake_case__     ):
		raise NotImplementedError()



	def 					lowercase  (       self			,       snake_case__     ):
		raise NotImplementedError()



	def 					lowercase  (       self			,       snake_case__     ):
		raise NotImplementedError()



	def 					lowercase  (       self			,       snake_case__     ):
		raise NotImplementedError()



	def 					lowercase  (       self     ):
		raise NotImplementedError()



	def 					lowercase  (       self			,       snake_case__     ):
		if len(self.get_labels()     ) > 1:
			lowerCAmelCase       :     Optional[int]   				=			self.get_labels()[int(example.label     )]
		return example



	@classmethod
	def 					lowercase  (       cls			,       snake_case__			,       snake_case__=None     ):
		with open(snake_case__			,       'r'			,       encoding='utf-8-sig'     ) as f:
			return list(csv.reader(snake_case__			,       delimiter='\t'			,       quotechar=snake_case__     )     )



class 			lowerCAmelCase					(					a				):



	def __init__(       self			,       snake_case__=None			,       snake_case__=None			,       snake_case__="classification"			,       snake_case__=False     ):
		lowerCAmelCase       :     Optional[int]   				=			[] if labels is None else labels
		lowerCAmelCase       :     Dict   				=			[] if examples is None else examples
		lowerCAmelCase       :     List[Any]   				=			mode
		lowerCAmelCase       :     int   				=			verbose



	def __len__(       self     ):
		return len(self.examples     )



	def __getitem__(       self			,       snake_case__     ):
		if isinstance(snake_case__			,       snake_case__     ):
			return SingleSentenceClassificationProcessor(labels=self.labels			,       examples=self.examples[idx]     )
		return self.examples[idx]



	@classmethod
	def 					lowercase  (       cls			,       snake_case__			,       snake_case__=""			,       snake_case__=0			,       snake_case__=1			,       snake_case__=None			,       snake_case__=False			,       **snake_case__     ):
		lowerCAmelCase       :     Optional[int]   				=			cls(**snake_case__     )
		processor.add_examples_from_csv(
		    snake_case__			,       split_name=snake_case__			,       column_label=snake_case__			,       column_text=snake_case__			,       column_id=snake_case__			,       skip_first_row=snake_case__			,       overwrite_labels=snake_case__			,       overwrite_examples=snake_case__			,       )
		return processor



	@classmethod
	def 					lowercase  (       cls			,       snake_case__			,       snake_case__=None			,       **snake_case__     ):
		lowerCAmelCase       :     Tuple   				=			cls(**snake_case__     )
		processor.add_examples(snake_case__			,       labels=snake_case__     )
		return processor



	def 					lowercase  (       self			,       snake_case__			,       snake_case__=""			,       snake_case__=0			,       snake_case__=1			,       snake_case__=None			,       snake_case__=False			,       snake_case__=False			,       snake_case__=False			,       ):
		lowerCAmelCase       :     int   				=			self._read_tsv(snake_case__     )
		if skip_first_row:
			lowerCAmelCase       :     str   				=			lines[1:]
		lowerCAmelCase       :     Optional[int]   				=			[]
		lowerCAmelCase       :     Union[str, Any]   				=			[]
		lowerCAmelCase       :     int   				=			[]
		for i, line in enumerate(snake_case__     ):
			texts.append(line[column_text]     )
			labels.append(line[column_label]     )
			if column_id is not None:
				ids.append(line[column_id]     )
			else:
				lowerCAmelCase       :     Dict   				=			f"{split_name}-{i}" if split_name else str(snake_case__     )
				ids.append(snake_case__     )

		return self.add_examples(
		    snake_case__			,       snake_case__			,       snake_case__			,       overwrite_labels=snake_case__			,       overwrite_examples=snake_case__     )



	def 					lowercase  (       self			,       snake_case__			,       snake_case__=None			,       snake_case__=None			,       snake_case__=False			,       snake_case__=False     ):
		if labels is not None and len(snake_case__     ) != len(snake_case__     ):
			raise ValueError(
			    f"Text and labels have mismatched lengths {len(snake_case__     )} and {len(snake_case__     )}"     )
		if ids is not None and len(snake_case__     ) != len(snake_case__     ):
			raise ValueError(f"Text and ids have mismatched lengths {len(snake_case__     )} and {len(snake_case__     )}"     )
		if ids is None:
			lowerCAmelCase       :     str   				=			[None] * len(snake_case__     )
		if labels is None:
			lowerCAmelCase       :     Optional[Any]   				=			[None] * len(snake_case__     )
		lowerCAmelCase       :     Dict   				=			[]
		lowerCAmelCase       :     Optional[Any]   				=			set()
		for text_or_text_and_label, label, guid in zip(snake_case__			,       snake_case__			,       snake_case__     ):
			if isinstance(snake_case__			,       (tuple, list)     ) and label is None:
				lowerCAmelCase				, lowerCAmelCase       :     List[Any]   				=			text_or_text_and_label
			else:
				lowerCAmelCase       :     Optional[int]   				=			text_or_text_and_label
			added_labels.add(snake_case__     )
			examples.append(InputExample(guid=snake_case__			,       text_a=snake_case__			,       text_b=snake_case__			,       label=snake_case__     )     )

		# Update examples
		if overwrite_examples:
			lowerCAmelCase       :     str   				=			examples
		else:
			self.examples.extend(snake_case__     )

		# Update labels
		if overwrite_labels:
			lowerCAmelCase       :     Optional[Any]   				=			list(snake_case__     )
		else:
			lowerCAmelCase       :     Optional[Any]   				=			list(set(self.labels     ).union(snake_case__     )     )

		return self.examples



	def 					lowercase  (       self			,       snake_case__			,       snake_case__=None			,       snake_case__=False			,       snake_case__=0			,       snake_case__=True			,       snake_case__=None			,       ):
		if max_length is None:
			lowerCAmelCase       :     List[str]   				=			tokenizer.max_len

		lowerCAmelCase       :     List[str]   				=			{label: i for i, label in enumerate(self.labels     )}

		lowerCAmelCase       :     List[Any]   				=			[]
		for ex_index, example in enumerate(self.examples     ):
			if ex_index % 1_0000 == 0:
				logger.info(f"Tokenizing example {ex_index}"     )

			lowerCAmelCase       :     Optional[int]   				=			tokenizer.encode(
			    example.text_a			,       add_special_tokens=snake_case__			,       max_length=min(snake_case__			,       tokenizer.max_len     )			,       )
			all_input_ids.append(snake_case__     )

		lowerCAmelCase       :     List[Any]   				=			max(len(snake_case__     ) for input_ids in all_input_ids     )

		lowerCAmelCase       :     Optional[Any]   				=			[]
		for ex_index, (input_ids, example) in enumerate(zip(snake_case__			,       self.examples     )     ):
			if ex_index % 1_0000 == 0:
				logger.info(f"Writing example {ex_index}/{len(self.examples     )}"     )
			# The mask has 1 for real tokens and 0 for padding tokens. Only real
			# tokens are attended to.
			lowerCAmelCase       :     Dict   				=			[1 if mask_padding_with_zero else 0] * len(snake_case__     )

			# Zero-pad up to the sequence length.
			lowerCAmelCase       :     str   				=			batch_length - len(snake_case__     )
			if pad_on_left:
				lowerCAmelCase       :     List[str]   				=			([pad_token] * padding_length) + input_ids
				lowerCAmelCase       :     str   				=			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
			else:
				lowerCAmelCase       :     Optional[int]   				=			input_ids + ([pad_token] * padding_length)
				lowerCAmelCase       :     str   				=			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

			if len(snake_case__     ) != batch_length:
				raise ValueError(f"Error with input length {len(snake_case__     )} vs {batch_length}"     )
			if len(snake_case__     ) != batch_length:
				raise ValueError(f"Error with input length {len(snake_case__     )} vs {batch_length}"     )

			if self.mode == "classification":
				lowerCAmelCase       :     Any   				=			label_map[example.label]
			elif self.mode == "regression":
				lowerCAmelCase       :     List[str]   				=			float(example.label     )
			else:
				raise ValueError(self.mode     )

			if ex_index < 5 and self.verbose:
				logger.info('*** Example ***'     )
				logger.info(f"guid: {example.guid}"     )
				logger.info(f"input_ids: {' '.join([str(snake_case__     ) for x in input_ids]     )}"     )
				logger.info(f"attention_mask: {' '.join([str(snake_case__     ) for x in attention_mask]     )}"     )
				logger.info(f"label: {example.label} (id = {label})"     )

			features.append(InputFeatures(input_ids=snake_case__			,       attention_mask=snake_case__			,       label=snake_case__     )     )

		if return_tensors is None:
			return features
		elif return_tensors == "tf":
			if not is_tf_available():
				raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'     )
			import tensorflow as tf

			def gen():
				for ex in features:
					yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

			lowerCAmelCase       :     int   				=			tf.data.Dataset.from_generator(
			    snake_case__			,       ({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)			,       ({'input_ids': tf.TensorShape([None]     ), 'attention_mask': tf.TensorShape([None]     )}, tf.TensorShape([]     ))			,       )
			return dataset
		elif return_tensors == "pt":
			if not is_torch_available():
				raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'     )
			import torch
			from torch.utils.data import TensorDataset

			lowerCAmelCase       :     Optional[int]   				=			torch.tensor([f.input_ids for f in features]			,       dtype=torch.long     )
			lowerCAmelCase       :     Optional[Any]   				=			torch.tensor([f.attention_mask for f in features]			,       dtype=torch.long     )
			if self.mode == "classification":
				lowerCAmelCase       :     Optional[Any]   				=			torch.tensor([f.label for f in features]			,       dtype=torch.long     )
			elif self.mode == "regression":
				lowerCAmelCase       :     List[Any]   				=			torch.tensor([f.label for f in features]			,       dtype=torch.float     )

			lowerCAmelCase       :     Any   				=			TensorDataset(snake_case__			,       snake_case__			,       snake_case__     )
			return dataset
		else:
			raise ValueError('return_tensors should be one of \'tf\' or \'pt\''     )




'''simple docstring'''




import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax



@require_flax
class 			lowerCAmelCase					:



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Union[str, Any]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Any   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     Tuple   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     int   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     List[str]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase				, lowerCAmelCase       :     Optional[int]   				=			self.prepare_init_args_and_inputs_for_common()

		lowerCAmelCase       :     Tuple   				=			16
		lowerCAmelCase       :     Dict   				=			(16, 32)

		lowerCAmelCase       :     Dict   				=			self.model_class(**snake_case__     )
		lowerCAmelCase       :     int   				=			model.init(inputs_dict['prng_key']			,       inputs_dict['sample']     )
		jax.lax.stop_gradient(snake_case__     )

		lowerCAmelCase       :     int   				=			model.apply(snake_case__			,       inputs_dict['sample']     )

		if isinstance(snake_case__			,       snake_case__     ):
			lowerCAmelCase       :     Dict   				=			output.sample

		self.assertIsNotNone(snake_case__     )
		lowerCAmelCase       :     Union[str, Any]   				=			inputs_dict['sample'].shape
		self.assertEqual(output.shape			,       snake_case__			,       'Input and output shapes do not match'     )



	def 					lowercase  (       self     ):
		lowerCAmelCase       :     Any   				=			'kwargs' in inspect.signature(self.model_class.__init__     ).parameters
		lowerCAmelCase       :     Optional[Any]   				=			len(self.model_class._deprecated_kwargs     ) > 0

		if has_kwarg_in_model_class and not has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
			    ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
			    ' [<deprecated_argument>]`'     )

		if not has_kwarg_in_model_class and has_deprecated_kwarg:
			raise ValueError(
			    f"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
			    ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
			    f" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
			    ' from `_deprecated_kwargs = [<deprecated_argument>]`'     )
