


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 	UpperCamelCase__(       _UpperCamelCase						,    unittest.TestCase	):
				__magic_name__    :				Any  		=	LEDTokenizer
				__magic_name__    :				int  		=	LEDTokenizerFast
				__magic_name__    :				Dict  		=	True




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

										"""simple docstring"""




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

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




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

										"""simple docstring"""




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




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

										"""simple docstring"""




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




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

										"""simple docstring"""




										return "lower newer", "lower newer"




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

										"""simple docstring"""




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




				@cached_property
				def 					a__(		self      :			Tuple				)->					Optional[int]:

										"""simple docstring"""




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




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

										"""simple docstring"""




										UpperCAmelCase					    =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										UpperCAmelCase					    =     [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

																self.assertEqual((2, 9)				,   batch.input_ids.shape				)
																self.assertEqual((2, 9)				,   batch.attention_mask.shape				)
																UpperCAmelCase					    =     batch.input_ids.tolist()[0]
																self.assertListEqual(_SCREAMING_SNAKE_CASE				,   _SCREAMING_SNAKE_CASE				)




				@require_torch
				def 					a__(		self      :			Union[str, Any]				)->					List[str]:

										"""simple docstring"""




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




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

										"""simple docstring"""




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




				@require_torch
				def 					a__(		self      :			str				)->					str:

										"""simple docstring"""




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




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

										"""simple docstring"""




										UpperCAmelCase					    =     ['''A long paragraph for summarization.''']
										UpperCAmelCase					    =     [
										    '''Summary of the text.''',
										]
										for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																UpperCAmelCase					    =     tokenizer(_SCREAMING_SNAKE_CASE				,   return_tensors='''pt'''				)
																UpperCAmelCase					    =     tokenizer(text_target=_SCREAMING_SNAKE_CASE				,   return_tensors='''pt'''				)
																UpperCAmelCase					    =     inputs['''input_ids''']
																UpperCAmelCase					    =     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 					a__(		self      :			str				)->					str:

										"""simple docstring"""




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

																UpperCAmelCase					    =     tokenizer(_SCREAMING_SNAKE_CASE				,   padding=_SCREAMING_SNAKE_CASE				)
																UpperCAmelCase					    =     [[0] * len(_SCREAMING_SNAKE_CASE				) for x in encoded_output['''input_ids''']]
																UpperCAmelCase					    =     tokenizer.pad(_SCREAMING_SNAKE_CASE				)
																self.assertSequenceEqual(outputs['''global_attention_mask''']				,   _SCREAMING_SNAKE_CASE				)




				def 					a__(		self      :			Tuple				)->					Tuple:

										"""simple docstring"""




										pass





				def 					a__(		self      :			List[str]				)->					Optional[int]:

										"""simple docstring"""




										for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																						UpperCAmelCase					    =     self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE				,   **_SCREAMING_SNAKE_CASE				)
																						UpperCAmelCase					    =     self.tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE				,   **_SCREAMING_SNAKE_CASE				)
																						UpperCAmelCase					    =     '''A, <mask> AllenNLP sentence.'''
																						UpperCAmelCase					    =     tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE				,   add_special_tokens=_SCREAMING_SNAKE_CASE				,   return_token_type_ids=_SCREAMING_SNAKE_CASE				)
																						UpperCAmelCase					    =     tokenizer_p.encode_plus(_SCREAMING_SNAKE_CASE				,   add_special_tokens=_SCREAMING_SNAKE_CASE				,   return_token_type_ids=_SCREAMING_SNAKE_CASE				)
																						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''']				)				,   )

																						UpperCAmelCase					    =     tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']				)
																						UpperCAmelCase					    =     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(
																						    _SCREAMING_SNAKE_CASE				,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)
																						self.assertSequenceEqual(
																						    _SCREAMING_SNAKE_CASE				,   ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']				)









'''simple docstring'''



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






class 	UpperCamelCase__(       lowerCAmelCase						,    lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				List[str]  		=	StableDiffusionSAGPipeline
				__magic_name__    :				str  		=	TEXT_TO_IMAGE_PARAMS
				__magic_name__    :				Any  		=	TEXT_TO_IMAGE_BATCH_PARAMS
				__magic_name__    :				Optional[int]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				List[Any]  		=	TEXT_TO_IMAGE_IMAGE_PARAMS
				__magic_name__    :				str  		=	False




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

										"""simple docstring"""




										torch.manual_seed(0				)
										UpperCAmelCase					    =     UNetaDConditionModel(
										    block_out_channels=(32, 64)				,   layers_per_block=2				,   sample_size=32				,   in_channels=4				,   out_channels=4				,   down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,   up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,   cross_attention_dim=32				,   )
										UpperCAmelCase					    =     DDIMScheduler(
										    beta_start=0.00085				,   beta_end=0.012				,   beta_schedule='''scaled_linear'''				,   clip_sample=lowerCAmelCase				,   set_alpha_to_one=lowerCAmelCase				,   )
										torch.manual_seed(0				)
										UpperCAmelCase					    =     AutoencoderKL(
										    block_out_channels=[32, 64]				,   in_channels=3				,   out_channels=3				,   down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,   up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,   latent_channels=4				,   )
										torch.manual_seed(0				)
										UpperCAmelCase					    =     CLIPTextConfig(
										    bos_token_id=0				,   eos_token_id=2				,   hidden_size=32				,   intermediate_size=37				,   layer_norm_eps=1E-05				,   num_attention_heads=4				,   num_hidden_layers=5				,   pad_token_id=1				,   vocab_size=1000				,   )
										UpperCAmelCase					    =     CLIPTextModel(lowerCAmelCase				)
										UpperCAmelCase					    =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

										UpperCAmelCase					    =     {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''safety_checker''': None,
										    '''feature_extractor''': None,
										}
										return components




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Tuple=0				)->					str:

										"""simple docstring"""




										if str(lowerCAmelCase				).startswith('''mps'''				):
																UpperCAmelCase					    =     torch.manual_seed(lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     torch.Generator(device=lowerCAmelCase				).manual_seed(lowerCAmelCase				)
										UpperCAmelCase					    =     {
										    '''prompt''': '''.''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 1.0,
										    '''sag_scale''': 1.0,
										    '''output_type''': '''numpy''',
										}
										return inputs





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

										"""simple docstring"""




										super().test_inference_batch_single_identical(expected_max_diff=3E-3				)







@slow
@require_torch_gpu
class 	UpperCamelCase__(       unittest.TestCase	):




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

										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()




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

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.1568, 0.1738, 0.1695, 0.1693, 0.1507, 0.1705, 0.1547, 0.1751, 0.1949]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2




				def 					a__(		self      :			int				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				)

										UpperCAmelCase					    =     output.images

										UpperCAmelCase					    =     image[0, -3:, -3:, -1]

										assert image.shape == (1, 512, 512, 3)
										UpperCAmelCase					    =     np.array([0.3459, 0.2876, 0.2537, 0.3002, 0.2671, 0.2160, 0.3026, 0.2262, 0.2371]				)

										assert np.abs(image_slice.flatten() - expected_slice				).max() < 5E-2





				def 					a__(		self      :			Optional[Any]				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     StableDiffusionSAGPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''				)
										UpperCAmelCase					    =     sag_pipe.to(lowerCAmelCase				)
										sag_pipe.set_progress_bar_config(disable=lowerCAmelCase				)

										UpperCAmelCase					    =     '''.'''
										UpperCAmelCase					    =     torch.manual_seed(0				)
										UpperCAmelCase					    =     sag_pipe(
										    [prompt]				,   width=768				,   height=512				,   generator=lowerCAmelCase				,   guidance_scale=7.5				,   sag_scale=1.0				,   num_inference_steps=20				,   output_type='''np'''				,   )

										UpperCAmelCase					    =     output.images

										assert image.shape == (1, 512, 768, 3)





