
import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np





class 				UpperCAmelCase :




	'''simple docstring'''





	def 							UpperCamelCase_    (			self   :					Any					):
					torch.manual_seed(0					)
					__A       =					TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					UNetaDConditionModel(
					    sample_size=32				,layers_per_block=1				,block_out_channels=[32, 64]				,down_block_types=[
					        "ResnetDownsampleBlock2D",
					        "SimpleCrossAttnDownBlock2D",
					    ]				,mid_block_type="UNetMidBlock2DSimpleCrossAttn"				,up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]				,in_channels=3				,out_channels=6				,cross_attention_dim=32				,encoder_hid_dim=32				,attention_head_dim=8				,addition_embed_type="text"				,addition_embed_type_num_heads=2				,cross_attention_norm="group_norm"				,resnet_time_scale_shift="scale_shift"				,act_fn="gelu"				,)
					unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					torch.manual_seed(0					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="squaredcos_cap_v2"				,beta_start=0.00_01				,beta_end=0.02				,thresholding=A				,dynamic_thresholding_ratio=0.95				,sample_max_value=1.0				,prediction_type="epsilon"				,variance_type="learned_range"				,)

					torch.manual_seed(0					)
					__A       =					IFWatermarker()

					return {
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "watermarker": watermarker,
					    "safety_checker": None,
					    "feature_extractor": None,
					}






	def 							UpperCamelCase_    (			self   :					int					):
					torch.manual_seed(0					)
					__A       =					TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5"					)

					torch.manual_seed(0					)
					__A       =					UNetaDConditionModel(
					    sample_size=32				,layers_per_block=[1, 2]				,block_out_channels=[32, 64]				,down_block_types=[
					        "ResnetDownsampleBlock2D",
					        "SimpleCrossAttnDownBlock2D",
					    ]				,mid_block_type="UNetMidBlock2DSimpleCrossAttn"				,up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"]				,in_channels=6				,out_channels=6				,cross_attention_dim=32				,encoder_hid_dim=32				,attention_head_dim=8				,addition_embed_type="text"				,addition_embed_type_num_heads=2				,cross_attention_norm="group_norm"				,resnet_time_scale_shift="scale_shift"				,act_fn="gelu"				,class_embed_type="timestep"				,mid_block_scale_factor=1.4_14				,time_embedding_act_fn="gelu"				,time_embedding_dim=32				,)
					unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					torch.manual_seed(0					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="squaredcos_cap_v2"				,beta_start=0.00_01				,beta_end=0.02				,thresholding=A				,dynamic_thresholding_ratio=0.95				,sample_max_value=1.0				,prediction_type="epsilon"				,variance_type="learned_range"				,)

					torch.manual_seed(0					)
					__A       =					DDPMScheduler(
					    num_train_timesteps=10_00				,beta_schedule="squaredcos_cap_v2"				,beta_start=0.00_01				,beta_end=0.02				,)

					torch.manual_seed(0					)
					__A       =					IFWatermarker()

					return {
					    "text_encoder": text_encoder,
					    "tokenizer": tokenizer,
					    "unet": unet,
					    "scheduler": scheduler,
					    "image_noising_scheduler": image_noising_scheduler,
					    "watermarker": watermarker,
					    "safety_checker": None,
					    "feature_extractor": None,
					}






	def 							UpperCamelCase_    (			self   :					List[str]					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					self.get_dummy_inputs(A					)

					__A       =					inputs["prompt"]
					__A       =					inputs["generator"]
					__A       =					inputs["num_inference_steps"]
					__A       =					inputs["output_type"]

					if "image" in inputs:
									__A       =					inputs["image"]
					else:
									__A       =					None

					if "mask_image" in inputs:
									__A       =					inputs["mask_image"]
					else:
									__A       =					None

					if "original_image" in inputs:
									__A       =					inputs["original_image"]
					else:
									__A       =					None

					__A     ,				__A       =					pipe.encode_prompt(A					)

					# inputs with prompt converted to embeddings
					__A       =					{
					    "prompt_embeds": prompt_embeds,
					    "negative_prompt_embeds": negative_prompt_embeds,
					    "generator": generator,
					    "num_inference_steps": num_inference_steps,
					    "output_type": output_type,
					}

					if image is not None:
									__A       =					image

					if mask_image is not None:
									__A       =					mask_image

					if original_image is not None:
									__A       =					original_image

					# set all optional components to None
					for optional_component in pipe._optional_components:
									setattr(A				,A				,A					)

					__A       =					pipe(**A					)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(A					)
									__A       =					self.pipeline_class.from_pretrained(A					)
									pipe_loaded.to(A					)
									pipe_loaded.set_progress_bar_config(disable=A					)

					pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					for optional_component in pipe._optional_components:
									self.assertTrue(
									    getattr(A				,A					) is None				,f'''`{optional_component}` did not stay set to None after loading.'''				,)

					__A       =					self.get_dummy_inputs(A					)

					__A       =					inputs["generator"]
					__A       =					inputs["num_inference_steps"]
					__A       =					inputs["output_type"]

					# inputs with prompt converted to embeddings
					__A       =					{
					    "prompt_embeds": prompt_embeds,
					    "negative_prompt_embeds": negative_prompt_embeds,
					    "generator": generator,
					    "num_inference_steps": num_inference_steps,
					    "output_type": output_type,
					}

					if image is not None:
									__A       =					image

					if mask_image is not None:
									__A       =					mask_image

					if original_image is not None:
									__A       =					original_image

					__A       =					pipe_loaded(**A					)[0]

					__A       =					np.abs(to_np(A					) - to_np(A					)					).max()
					self.assertLess(A				,1E-4					)







	def 							UpperCamelCase_    (			self   :					Any					):
					__A       =					self.get_dummy_components()
					__A       =					self.pipeline_class(**A					)
					pipe.to(A					)
					pipe.set_progress_bar_config(disable=A					)

					__A       =					self.get_dummy_inputs(A					)
					__A       =					pipe(**A					)[0]

					with tempfile.TemporaryDirectory() as tmpdir:
									pipe.save_pretrained(A					)
									__A       =					self.pipeline_class.from_pretrained(A					)
									pipe_loaded.to(A					)
									pipe_loaded.set_progress_bar_config(disable=A					)

					pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()					)  # For reproducibility tests

					__A       =					self.get_dummy_inputs(A					)
					__A       =					pipe_loaded(**A					)[0]

					__A       =					np.abs(to_np(A					) - to_np(A					)					).max()
					self.assertLess(A				,1E-4					)





'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__lowerCAmelCase		     =  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class       __magic_name__		(       _UpperCamelCase     ,		unittest.TestCase  ):
							lowerCAmelCase			:       Tuple  							=   GPTSwaTokenizer
							lowerCAmelCase			:       Tuple  							=   False
							lowerCAmelCase			:       Optional[Any]  							=   True
							lowerCAmelCase			:       Any  							=   False







							def 				__lowercase     (				self   :			Any						):
									super().setUp()

									# We have a SentencePiece fixture for testing
									_a	: int								=				GPTSwaTokenizer(_UpperCAmelCase      ,eos_token='<unk>'      ,bos_token='<unk>'      ,pad_token='<unk>'						)

									tokenizer.save_pretrained(self.tmpdirname						)







							def 				__lowercase     (				self   :			Optional[Any]      ,_UpperCAmelCase   :			int						):
									_a	: Any								=				'This is a test'
									_a	: Any								=				'This is a test'
									return input_text, output_text







							def 				__lowercase     (				self   :			str						):
									_a	: Optional[Any]								=				'<s>'
									_a	: str								=				1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)      ,_UpperCAmelCase						)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							def 				__lowercase     (				self   :			Union[str, Any]						):
									_a	: Dict								=				list(self.get_tokenizer().get_vocab().keys()						)

									self.assertEqual(vocab_keys[0]      ,'<unk>'						)
									self.assertEqual(vocab_keys[1]      ,'<s>'						)
									self.assertEqual(vocab_keys[-1]      ,'j'						)
									self.assertEqual(len(_UpperCAmelCase						)      ,2000						)







							def 				__lowercase     (				self   :			Optional[int]						):
									self.assertEqual(self.get_tokenizer().vocab_size      ,2000						)







							def 				__lowercase     (				self   :			List[Any]						):
									_a	: Union[str, Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)

									_a	: str								=				tokenizer.tokenize('This is a test'						)
									self.assertListEqual(_UpperCAmelCase      ,['▁This', '▁is', '▁a', '▁t', 'est']						)

									self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)      ,[465, 287, 265, 631, 842]						)

									_a	: Any								=				tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']      ,)
									# fmt: on

									_a	: Optional[Any]								=				tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
									self.assertListEqual(
									    _UpperCAmelCase      ,[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]      ,)

									_a	: List[str]								=				tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
									# fmt: off
									self.assertListEqual(
									    _UpperCAmelCase      ,['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
									# fmt: on







							def 				__lowercase     (				self   :			str						):
									_a	: List[Any]								=				GPTSwaTokenizer(_UpperCAmelCase						)
									_a	: Optional[Any]								=				['This is a test', 'I was born in 92000, and this is falsé.']
									_a	: Dict								=				[
									    [465, 287, 265, 631, 842],
									    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
									]

									# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
									for text, expected_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)

									# Test that decode_fast returns the input text
									for text, token_ids in zip(_UpperCAmelCase      ,_UpperCAmelCase						):
											self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)      ,_UpperCAmelCase						)







							@slow
							def 				__lowercase     (				self   :			Optional[Any]						):
									_a	: Optional[int]								=				[
									    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
									    'Hey there, how are you doing this fine day?',
									    'This is a text with a trailing spaces followed by a dot     .',
									    'Häj sväjs lillebrör! =)',
									    'Det är inget fel på Mr. Cool',
									]

									# fmt: off
									_a	: Tuple								=				{'input_ids': [[63423, 5, 6811, 14954, 282, 816, 3821, 63466, 63425, 63462, 18, 63978, 678, 301, 1320, 63423, 63455, 63458, 18, 63982, 4246, 3940, 1901, 47789, 5547, 18994], [19630, 1100, 63446, 1342, 633, 544, 4488, 593, 5102, 2416, 63495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 58593, 22413, 9106, 546, 268, 33213, 63979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55130, 63450, 924, 63449, 2249, 4062, 1558, 318, 63504, 21498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 63443, 26801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
									# fmt: on
									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCAmelCase      ,model_name='AI-Sweden/gpt-sw3-126m'      ,sequences=_UpperCAmelCase      ,)




