
"""simple docstring"""






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 				lowercase__(				_UpperCAmelCase		,						unittest.TestCase   ):
	'''simple docstring'''

	UpperCamelCase				=  LEDTokenizer
	UpperCamelCase				=  LEDTokenizerFast
	UpperCamelCase				=  True





	def   __lowerCAmelCase (						self    :str					)							->			List[Any]:







			'''simple docstring'''




			super().setUp()
			SCREAMING_SNAKE_CASE       :     Optional[int]				= [
			    '''l''',
			    '''o''',
			    '''w''',
			    '''e''',
			    '''r''',
			    '''s''',
			    '''t''',
			    '''i''',
			    '''d''',
			    '''n''',
			    '''\u0120''',
			    '''\u0120l''',
			    '''\u0120n''',
			    '''\u0120lo''',
			    '''\u0120low''',
			    '''er''',
			    '''\u0120lowest''',
			    '''\u0120newer''',
			    '''\u0120wider''',
			    '''<unk>''',
			]
			SCREAMING_SNAKE_CASE       :     List[str]				= dict(zip(lowerCamelCase_		, range(len(lowerCamelCase_					)					)					)					)
			SCREAMING_SNAKE_CASE       :     Dict				= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
			SCREAMING_SNAKE_CASE       :     str				= {'''unk_token''': '''<unk>'''}

			SCREAMING_SNAKE_CASE       :     int				= os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES['''vocab_file''']					)
			SCREAMING_SNAKE_CASE       :     Optional[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(lowerCamelCase_					) + '''\n'''					)
			with open(self.merges_file		, '''w'''		, encoding='''utf-8'''					) as fp:
					fp.write('''\n'''.join(lowerCamelCase_					)					)





	def   __lowerCAmelCase (						self    :int		, **lowerCamelCase_    :Optional[int]					)							->			List[Any]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Optional[int]		, **lowerCamelCase_    :Optional[int]					)							->			Union[str, Any]:







			'''simple docstring'''




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





	def   __lowerCAmelCase (						self    :Optional[Any]		, lowerCamelCase_    :int					)							->			Optional[int]:







			'''simple docstring'''




			return "lower newer", "lower newer"





	@cached_property
	def   __lowerCAmelCase (						self    :Dict					)							->			int:







			'''simple docstring'''




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





	@cached_property
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			str:







			'''simple docstring'''




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





	@require_torch
	def   __lowerCAmelCase (						self    :List[str]					)							->			Tuple:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			SCREAMING_SNAKE_CASE       :     Any				= [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     str				= tokenizer(lowerCamelCase_		, max_length=len(lowerCamelCase_					)		, padding=lowerCamelCase_		, return_tensors='''pt'''					)
					self.assertIsInstance(lowerCamelCase_		, lowerCamelCase_					)

					self.assertEqual((2, 9)		, batch.input_ids.shape					)
					self.assertEqual((2, 9)		, batch.attention_mask.shape					)
					SCREAMING_SNAKE_CASE       :     List[Any]				= batch.input_ids.tolist()[0]
					self.assertListEqual(lowerCamelCase_		, lowerCamelCase_					)





	@require_torch
	def   __lowerCAmelCase (						self    :Tuple					)							->			Any:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Any				= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Tuple				= tokenizer(lowerCamelCase_		, padding=lowerCamelCase_		, return_tensors='''pt'''					)
					self.assertIn('''input_ids'''		, lowerCamelCase_					)
					self.assertIn('''attention_mask'''		, lowerCamelCase_					)
					self.assertNotIn('''labels'''		, lowerCamelCase_					)
					self.assertNotIn('''decoder_attention_mask'''		, lowerCamelCase_					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= [
			    '''Summary of the text.''',
			    '''Another summary.''',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     int				= tokenizer(text_target=lowerCamelCase_		, max_length=32		, padding='''max_length'''		, return_tensors='''pt'''					)
					self.assertEqual(32		, targets['''input_ids'''].shape[1]					)





	@require_torch
	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Optional[int]:







			'''simple docstring'''




			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= tokenizer(
					    ['''I am a small frog''' * 10_24, '''I am a small frog''']		, padding=lowerCamelCase_		, truncation=lowerCamelCase_		, return_tensors='''pt'''					)
					self.assertIsInstance(lowerCamelCase_		, lowerCamelCase_					)
					self.assertEqual(batch.input_ids.shape		, (2, 51_22)					)





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







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= ['''A long paragraph for summarization.''']
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= [
			    '''Summary of the text.''',
			]
			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Optional[int]				= tokenizer(lowerCamelCase_		, return_tensors='''pt'''					)
					SCREAMING_SNAKE_CASE       :     Dict				= tokenizer(text_target=lowerCamelCase_		, return_tensors='''pt'''					)
					SCREAMING_SNAKE_CASE       :     str				= inputs['''input_ids''']
					SCREAMING_SNAKE_CASE       :     List[str]				= 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]					)							->			Any:







			'''simple docstring'''




			for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
					SCREAMING_SNAKE_CASE       :     Any				= ['''Summary of the text.''', '''Another summary.''']
					SCREAMING_SNAKE_CASE       :     int				= [[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= tokenizer(lowerCamelCase_		, padding=lowerCamelCase_					)
					SCREAMING_SNAKE_CASE       :     str				= [[0] * len(lowerCamelCase_					) for x in encoded_output['''input_ids''']]
					SCREAMING_SNAKE_CASE       :     Optional[Any]				= tokenizer.pad(lowerCamelCase_					)
					self.assertSequenceEqual(outputs['''global_attention_mask''']		, lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Optional[Any]					)							->			Dict:







			'''simple docstring'''




			pass







	def   __lowerCAmelCase (						self    :int					)							->			Optional[int]:







			'''simple docstring'''




			for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"					):
							SCREAMING_SNAKE_CASE       :     str				= self.rust_tokenizer_class.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= self.tokenizer_class.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     List[str]				= '''A, <mask> AllenNLP sentence.'''
							SCREAMING_SNAKE_CASE       :     str				= tokenizer_r.encode_plus(lowerCamelCase_		, add_special_tokens=lowerCamelCase_		, return_token_type_ids=lowerCamelCase_					)
							SCREAMING_SNAKE_CASE       :     Optional[Any]				= tokenizer_p.encode_plus(lowerCamelCase_		, add_special_tokens=lowerCamelCase_		, return_token_type_ids=lowerCamelCase_					)
							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''']					)		, )

							SCREAMING_SNAKE_CASE       :     List[Any]				= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']					)
							SCREAMING_SNAKE_CASE       :     Dict				= tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']					)
							self.assertSequenceEqual(tokens_p['''input_ids''']		, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)
							self.assertSequenceEqual(tokens_r['''input_ids''']		, [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]					)

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







"""simple docstring"""






import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


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




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	def __init__(						self    :List[str]		, lowerCamelCase_    :Union[List[ControlNetModel], Tuple[ControlNetModel]]					)							->			Union[str, Any]:







			'''simple docstring'''




			super().__init__()
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= nn.ModuleList(lowerCamelCase_					)





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :torch.FloatTensor		, lowerCamelCase_    :Union[torch.Tensor, float, int]		, lowerCamelCase_    :torch.Tensor		, lowerCamelCase_    :List[torch.tensor]		, lowerCamelCase_    :List[float]		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[torch.Tensor] = None		, lowerCamelCase_    :Optional[Dict[str, Any]] = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :bool = True		, )							->			Union[ControlNetOutput, Tuple]:







			'''simple docstring'''




			for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase_		, lowerCamelCase_		, self.nets					)					):
					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     List[Any]				= controlnet(
					    lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, lowerCamelCase_		, )

					# merge samples
					if i == 0:
							SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE       :     int				= down_samples, mid_sample
					else:
							SCREAMING_SNAKE_CASE       :     Optional[int]				= [
							    samples_prev + samples_curr
							    for samples_prev, samples_curr in zip(lowerCamelCase_		, lowerCamelCase_					)
							]
							mid_block_res_sample += mid_sample

			return down_block_res_samples, mid_block_res_sample





	def   __lowerCAmelCase (						self    :Any		, lowerCamelCase_    :Union[str, os.PathLike]		, lowerCamelCase_    :bool = True		, lowerCamelCase_    :Callable = None		, lowerCamelCase_    :bool = False		, lowerCamelCase_    :Optional[str] = None		, )							->			Optional[int]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[Any]				= 0
			SCREAMING_SNAKE_CASE       :     Any				= save_directory
			for controlnet in self.nets:
					controlnet.save_pretrained(
					    lowerCamelCase_		, is_main_process=lowerCamelCase_		, save_function=lowerCamelCase_		, safe_serialization=lowerCamelCase_		, variant=lowerCamelCase_		, )

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= model_path_to_save + f"_{idx}"







	@classmethod
	def   __lowerCAmelCase (						cls    :Dict		, lowerCamelCase_    :Optional[Union[str, os.PathLike]]		, **lowerCamelCase_    :Tuple					)							->			List[Any]:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     str				= 0
			SCREAMING_SNAKE_CASE       :     Optional[int]				= []

			# load controlnet and append to list until no controlnet directory exists anymore
			# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
			# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
			SCREAMING_SNAKE_CASE       :     Dict				= pretrained_model_path
			while os.path.isdir(lowerCamelCase_					):
					SCREAMING_SNAKE_CASE       :     Tuple				= ControlNetModel.from_pretrained(lowerCamelCase_		, **lowerCamelCase_					)
					controlnets.append(lowerCamelCase_					)

					idx += 1
					SCREAMING_SNAKE_CASE       :     Union[str, Any]				= pretrained_model_path + f"_{idx}"

			logger.info(f"{len(lowerCamelCase_					)} controlnets loaded from {pretrained_model_path}."					)

			if len(lowerCamelCase_					) == 0:
					raise ValueError(
					    f"No ControlNets found under {os.path.dirname(lowerCamelCase_					)}. Expected at least {pretrained_model_path + '_0'}."					)

			return cls(lowerCamelCase_					)





