




import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
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 PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		lowercase							(     __lowerCAmelCase,__lowerCAmelCase,__lowerCAmelCase,unittest.TestCase	):
     _a											=		AltDiffusionPipeline
     _a											=		TEXT_TO_IMAGE_PARAMS
     _a											=		TEXT_TO_IMAGE_BATCH_PARAMS
     _a											=		TEXT_TO_IMAGE_IMAGE_PARAMS
     _a											=		TEXT_TO_IMAGE_IMAGE_PARAMS

     def       a__			(						self       )		->							Dict:
           torch.manual_seed(0       )
           _A							:  str    =		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	,  )
           _A							:  Tuple    =		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       )
           _A							:  List[str]    =		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	,  )

           # TODO: address the non-deterministic text encoder (fails for save-load tests)
           # torch.manual_seed(0)
           # text_encoder_config = RobertaSeriesConfig(
           #     hidden_size=32,
           #     project_dim=32,
           #     intermediate_size=37,
           #     layer_norm_eps=1e-05,
           #     num_attention_heads=4,
           #     num_hidden_layers=5,
           #     vocab_size=5002,
           # )
           # text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

           torch.manual_seed(0       )
           _A							:  Union[str, Any]    =		CLIPTextConfig(
               bos_token_id=0	,  eos_token_id=2	,  hidden_size=32	,  projection_dim=32	,  intermediate_size=37	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=5002	,  )
           _A							:  List[Any]    =		CLIPTextModel(lowerCamelCase__       )

           _A							:  List[Any]    =		XLMRobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-xlm-roberta"""       )
           _A							:  Tuple    =		77

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

     def       a__			(						self	,  _a	,  _a=0       )		->							Union[str, Any]:






           if str(lowerCamelCase__       ).startswith("""mps"""       ):
                 _A							:  Optional[int]    =		torch.manual_seed(lowerCamelCase__       )
           else:
                 _A							:  Dict    =		torch.Generator(device=lowerCamelCase__       ).manual_seed(lowerCamelCase__       )
           _A							:  Dict    =		{
               '''prompt''': '''A painting of a squirrel eating a burger''',
               '''generator''': generator,
               '''num_inference_steps''': 2,
               '''guidance_scale''': 6.0,
               '''output_type''': '''numpy''',
           }
           return inputs

     def       a__			(						self       )		->							Union[str, Any]:
           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3       )

     def       a__			(						self       )		->							Dict:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3       )

     def       a__			(						self       )		->							Tuple:
           _A							:  Any    =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

           _A							:  Optional[int]    =		self.get_dummy_components()
           torch.manual_seed(0       )
           _A							:  str    =		RobertaSeriesConfig(
               hidden_size=32	,  project_dim=32	,  intermediate_size=37	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  vocab_size=5002	,  )
           # TODO: remove after fixing the non-deterministic text encoder
           _A							:  int    =		RobertaSeriesModelWithTransformation(lowerCamelCase__       )
           _A							:  Any    =		text_encoder

           _A							:  Dict    =		AltDiffusionPipeline(**lowerCamelCase__       )
           _A							:  Any    =		alt_pipe.to(lowerCamelCase__       )
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           _A							:  Dict    =		self.get_dummy_inputs(lowerCamelCase__       )
           _A							:  Optional[int]    =		'''A photo of an astronaut'''
           _A							:  int    =		alt_pipe(**lowerCamelCase__       )
           _A							:  Tuple    =		output.images
           _A							:  str    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           _A							:  List[Any]    =		np.array(
               [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2



     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator

           _A							:  Optional[Any]    =		self.get_dummy_components()
           _A							:  Optional[Any]    =		PNDMScheduler(skip_prk_steps=lowerCamelCase__       )
           torch.manual_seed(0       )
           _A							:  Optional[int]    =		RobertaSeriesConfig(
               hidden_size=32	,  project_dim=32	,  intermediate_size=37	,  layer_norm_eps=1e-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  vocab_size=5002	,  )
           # TODO: remove after fixing the non-deterministic text encoder
           _A							:  int    =		RobertaSeriesModelWithTransformation(lowerCamelCase__       )
           _A							:  str    =		text_encoder
           _A							:  str    =		AltDiffusionPipeline(**lowerCamelCase__       )
           _A							:  Optional[Any]    =		alt_pipe.to(lowerCamelCase__       )
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           _A							:  List[Any]    =		self.get_dummy_inputs(lowerCamelCase__       )
           _A							:  Tuple    =		alt_pipe(**lowerCamelCase__       )
           _A							:  int    =		output.images
           _A							:  Tuple    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 64, 64, 3)
           _A							:  List[Any]    =		np.array(
               [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2







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

     def       a__			(						self       )		->							List[str]:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def       a__			(						self       )		->							Optional[Any]:
           _A							:  int    =		AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""	,  safety_checker=lowerCamelCase__       )
           _A							:  Union[str, Any]    =		alt_pipe.to(lowerCamelCase__       )
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           _A							:  Any    =		'''A painting of a squirrel eating a burger'''
           _A							:  Optional[int]    =		torch.manual_seed(0       )
           _A							:  Optional[Any]    =		alt_pipe([prompt]	,  generator=lowerCamelCase__	,  guidance_scale=6.0	,  num_inference_steps=20	,  output_type="""np"""       )

           _A							:  Union[str, Any]    =		output.images

           _A							:  Tuple    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           _A							:  int    =		np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2



     def       a__			(						self       )		->							List[Any]:
           _A							:  Any    =		DDIMScheduler.from_pretrained("""BAAI/AltDiffusion"""	,  subfolder="""scheduler"""       )

           _A							:  Optional[int]    =		AltDiffusionPipeline.from_pretrained("""BAAI/AltDiffusion"""	,  scheduler=lowerCamelCase__	,  safety_checker=lowerCamelCase__       )
           _A							:  Dict    =		alt_pipe.to(lowerCamelCase__       )
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase__       )

           _A							:  Optional[int]    =		'''A painting of a squirrel eating a burger'''
           _A							:  Dict    =		torch.manual_seed(0       )

           _A							:  List[str]    =		alt_pipe([prompt]	,  generator=lowerCamelCase__	,  num_inference_steps=2	,  output_type="""numpy"""       )
           _A							:  Optional[int]    =		output.images

           _A							:  Any    =		image[0, -3:, -3:, -1]

           assert image.shape == (1, 512, 512, 3)
           _A							:  str    =		np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]       )

           assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2







import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_snake_case            = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							int:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  List[Any]    =		PegasusTokenizer(_a       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							int:
           return PegasusTokenizer.from_pretrained("""google/pegasus-large"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[Any]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							int:
           _A							:  Dict    =		"""</s>"""
           _A							:  Dict    =		1

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

     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		list(self.get_tokenizer().get_vocab().keys()       )

           self.assertEqual(vocab_keys[0]	,  """<pad>"""       )
           self.assertEqual(vocab_keys[1]	,  """</s>"""       )
           self.assertEqual(vocab_keys[-1]	,  """v"""       )
           self.assertEqual(len(_a       )	,  1103       )

     def       a__			(						self       )		->							Optional[int]:
           self.assertEqual(self.get_tokenizer().vocab_size	,  1103       )

     def       a__			(						self       )		->							Tuple:
           _A							:  Any    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Optional[int]    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  int    =		(
               """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
               """ </s> <pad> <pad> <pad>"""
           )
           _A							:  Optional[int]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  List[Any]    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._large_tokenizer
           # <mask_1> masks whole sentence while <mask_2> masks single word
           _A							:  Optional[int]    =		"""<mask_1> To ensure a <mask_2> flow of bank resolutions."""
           _A							:  Union[str, Any]    =		[2, 413, 615, 114, 3, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Union[str, Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Optional[int]    =		self._large_tokenizer
           # The tracebacks for the following asserts are **better** without messages or self.assertEqual
           assert tokenizer.vocab_size == 9_6103
           assert tokenizer.pad_token_id == 0
           assert tokenizer.eos_token_id == 1
           assert tokenizer.offset == 103
           assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
           assert tokenizer.unk_token == "<unk>"
           assert tokenizer.model_max_length == 1024
           _A							:  Any    =		"""To ensure a smooth flow of bank resolutions."""
           _A							:  Optional[int]    =		[413, 615, 114, 2291, 1971, 113, 1679, 1_0710, 107, 1]
           _A							:  Optional[Any]    =		tokenizer([raw_input_str]	,  return_tensors=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )
           assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]       ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]

     @require_torch
     def       a__			(						self       )		->							List[str]:
           _A							:  Union[str, Any]    =		["""This is going to be way too long.""" * 150, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Union[str, Any]    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  Tuple    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 1024)
           assert batch.attention_mask.shape == (2, 1024)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     @slow
     def       a__			(						self       )		->							Optional[Any]:
           # fmt: off
           _A							:  List[Any]    =		{"""input_ids""": [[3_8979, 143, 1_8485, 606, 130, 2_6669, 8_7686, 121, 5_4189, 1129, 111, 2_6669, 8_7686, 121, 9114, 1_4787, 121, 1_3249, 158, 592, 956, 121, 1_4621, 3_1576, 143, 6_2613, 108, 9688, 930, 4_3430, 1_1562, 6_2613, 304, 108, 1_1443, 897, 108, 9314, 1_7415, 6_3399, 108, 1_1443, 7614, 1_8316, 118, 4284, 7148, 1_2430, 143, 1400, 2_5703, 158, 111, 4284, 7148, 1_1772, 143, 2_1297, 1064, 158, 122, 204, 3506, 1754, 1133, 1_4787, 1581, 115, 3_3224, 4482, 111, 1355, 110, 2_9173, 317, 5_0833, 108, 2_0147, 9_4665, 111, 7_7198, 107, 1], [110, 6_2613, 117, 638, 112, 1133, 121, 2_0098, 1355, 7_9050, 1_3872, 135, 1596, 5_3541, 1352, 141, 1_3039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 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], [139, 1235, 2799, 1_8289, 1_7780, 204, 109, 9474, 1296, 107, 1, 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, 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, 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, 1, 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], [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, 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]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=_a	,  model_name="""google/bigbird-pegasus-large-arxiv"""	,  revision="""ba85d0851d708441f91440d509690f1ab6353415"""	,  )







@require_sentencepiece
@require_tokenizers
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PegasusTokenizer
     _a											=		PegasusTokenizerFast
     _a											=		True
     _a											=		True

     def       a__			(						self       )		->							Optional[Any]:
           super().setUp()

           # We have a SentencePiece fixture for testing
           _A							:  Tuple    =		PegasusTokenizer(_a	,  offset=0	,  mask_token_sent=_a	,  mask_token="""[MASK]"""       )
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							Optional[Any]:
           return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""       )

     def       a__			(						self	,  **_a       )		->							PegasusTokenizer:
           return PegasusTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							List[str]:
           return ("This is a test", "This is a test")

     def       a__			(						self       )		->							List[Any]:
           _A							:  List[Any]    =		self.rust_tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		self.tokenizer_class.from_pretrained(self.tmpdirname       )
           _A							:  Dict    =		(
               """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
               """ <pad> <pad> <pad>"""
           )
           _A							:  Optional[Any]    =		rust_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           _A							:  int    =		py_tokenizer([raw_input_str]	,  return_tensors=_a	,  add_special_tokens=_a       ).input_ids[0]
           self.assertListEqual(_a	,  _a       )

     @require_torch
     def       a__			(						self       )		->							Optional[int]:
           _A							:  Tuple    =		["""This is going to be way too long.""" * 1000, """short example"""]
           _A							:  Optional[Any]    =		["""not super long but more than 5 tokens""", """tiny"""]
           _A							:  Tuple    =		self._large_tokenizer(_a	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )
           _A							:  str    =		self._large_tokenizer(
               text_target=_a	,  max_length=5	,  padding=_a	,  truncation=_a	,  return_tensors="""pt"""       )

           assert batch.input_ids.shape == (2, 4096)
           assert batch.attention_mask.shape == (2, 4096)
           assert targets["input_ids"].shape == (2, 5)
           assert len(_a       ) == 2  # input_ids, attention_mask.



     def       a__			(						self       )		->							Dict:
           _A							:  Optional[int]    =		(
               """This is an example string that is used to test the original TF implementation against the HF"""
               """ implementation"""
           )

           _A							:  Any    =		self._large_tokenizer(_a       ).input_ids

           self.assertListEqual(
               _a	,  [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 2_5016, 3137, 464, 109, 2_6955, 3137, 1]	,  )

