




import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


__UpperCamelCase					:       Optional[int]    					=      get_logger(__name__)


__UpperCamelCase					:       Tuple    					=      R"\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n"

class 		__lowerCAmelCase	:
							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:Dict		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray				):







								'''simple docstring'''







								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'				)







class 		__lowerCAmelCase	:
							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:int		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray				):







								'''simple docstring'''







								raise NotImplementedError(
								    F'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'				)







class 		__lowerCAmelCase	(	__magic_name__ ):
							@add_start_docstrings(__magic_name__				)
							def __call__(						self						:List[Any]		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int		,      **__magic_name__						:Union[str, Any]				):







								'''simple docstring'''







								for processor in self:
									a										=							inspect.signature(processor.__call__				).parameters
									if len(__magic_name__				) > 3:
										if not all(arg in kwargs for arg in list(function_args.keys()				)[2:]				):
											raise ValueError(
											    F'Make sure that all the required parameters: {list(function_args.keys()				)} for '
											    F'{processor.__class__} are passed to the logits processor.'				)
										a										=							processor(__magic_name__		,      __magic_name__		,      __magic_name__		,      **__magic_name__				)
									else:
										a										=							processor(__magic_name__		,      __magic_name__		,      __magic_name__				)
								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Any		,      __magic_name__						:float				):







								'''simple docstring'''







								if not isinstance(__magic_name__		,      __magic_name__				) or not (temperature > 0):
									raise ValueError(F'`temperature` has to be a strictly positive float, but is {temperature}'				)

								a										=							temperature





							def __call__(						self						:List[Any]		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							scores / self.temperature
								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Optional[int]		,      __magic_name__						:float		,      __magic_name__						:float = -float("""Inf"""				)		,      __magic_name__						:int = 1				):







								'''simple docstring'''







								if not isinstance(__magic_name__		,      __magic_name__				) or (top_p < 0 or top_p > 1.0):
									raise ValueError(F'`top_p` has to be a float > 0 and < 1, but is {top_p}'				)
								if not isinstance(__magic_name__		,      __magic_name__				) or (min_tokens_to_keep < 1):
									raise ValueError(F'`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}'				)

								a										=							top_p
								a										=							filter_value
								a										=							min_tokens_to_keep





							def __call__(						self						:List[str]		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								a				, a										=							lax.top_k(__magic_name__		,      scores.shape[-1]				)

								a										=							jnp.full_like(__magic_name__		,      self.filter_value				)
								a										=							jax.nn.softmax(__magic_name__		,      axis=-1				).cumsum(axis=-1				)
								a										=							cumulative_probs < self.top_p

								# include the token that is higher than top_p as well
								a										=							jnp.roll(__magic_name__		,      1				)
								score_mask |= score_mask.at[:, 0].set(__magic_name__				)

								# min tokens to keep
								a										=							score_mask.at[:, : self.min_tokens_to_keep].set(__magic_name__				)

								a										=							jnp.where(__magic_name__		,      __magic_name__		,      __magic_name__				)
								a										=							jax.lax.sort_key_val(__magic_name__		,      __magic_name__				)[-1]

								return next_scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:str		,      __magic_name__						:int		,      __magic_name__						:float = -float("""Inf"""				)		,      __magic_name__						:int = 1				):







								'''simple docstring'''







								if not isinstance(__magic_name__		,      __magic_name__				) or top_k <= 0:
									raise ValueError(F'`top_k` has to be a strictly positive integer, but is {top_k}'				)

								a										=							max(__magic_name__		,      __magic_name__				)
								a										=							filter_value





							def __call__(						self						:Union[str, Any]		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								a				, a										=							scores.shape
								a										=							jnp.full(batch_size * vocab_size		,      self.filter_value				)

								a										=							min(self.top_k		,      scores.shape[-1]				)  # Safety check
								a				, a										=							lax.top_k(__magic_name__		,      __magic_name__				)
								a										=							jnp.broadcast_to((jnp.arange(__magic_name__				) * vocab_size)[:, None]		,      (batch_size, topk)				).flatten()
								a										=							topk_scores.flatten()
								a										=							topk_indices.flatten() + shift

								a										=							next_scores_flat.at[topk_indices_flat].set(__magic_name__				)
								a										=							next_scores_flat.reshape(__magic_name__		,      __magic_name__				)
								return next_scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Any		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							bos_token_id





							def __call__(						self						:Any		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							jnp.full(scores.shape		,      -float("""inf"""				)				)

								a										=							1 - jnp.bool_(cur_len - 1				)

								a										=							jnp.where(__magic_name__		,      new_scores.at[:, self.bos_token_id].set(0				)		,      __magic_name__				)

								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Optional[int]		,      __magic_name__						:int		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							max_length
								a										=							eos_token_id





							def __call__(						self						:Tuple		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							jnp.full(scores.shape		,      -float("""inf"""				)				)

								a										=							1 - jnp.bool_(cur_len - self.max_length + 1				)

								a										=							jnp.where(__magic_name__		,      new_scores.at[:, self.eos_token_id].set(0				)		,      __magic_name__				)

								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:int		,      __magic_name__						:int		,      __magic_name__						:int				):







								'''simple docstring'''







								if not isinstance(__magic_name__		,      __magic_name__				) or min_length < 0:
									raise ValueError(F'`min_length` has to be a positive integer, but is {min_length}'				)

								if not isinstance(__magic_name__		,      __magic_name__				) or eos_token_id < 0:
									raise ValueError(F'`eos_token_id` has to be a positive integer, but is {eos_token_id}'				)

								a										=							min_length
								a										=							eos_token_id





							def __call__(						self						:List[str]		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							1 - jnp.clip(cur_len - self.min_length		,      0		,      1				)

								a										=							jnp.where(__magic_name__		,      scores.at[:, self.eos_token_id].set(-float("""inf"""				)				)		,      __magic_name__				)

								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Optional[Any]		,      __magic_name__						:Optional[int]		,      __magic_name__						:List[str]				):







								'''simple docstring'''







								a										=							list(__magic_name__				)
								a										=							begin_index





							def __call__(						self						:Optional[Any]		,      __magic_name__						:List[str]		,      __magic_name__						:Optional[int]		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							1 - jnp.bool_(cur_len - self.begin_index				)

								a										=							jnp.where(__magic_name__		,      scores.at[:, self.begin_suppress_tokens].set(-float("""inf"""				)				)		,      __magic_name__				)

								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Dict		,      __magic_name__						:list				):







								'''simple docstring'''







								a										=							list(__magic_name__				)





							def __call__(						self						:int		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								a										=							scores.at[..., self.suppress_tokens].set(-float("""inf"""				)				)

								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Dict		,      __magic_name__						:Tuple				):







								'''simple docstring'''







								a										=							dict(__magic_name__				)
								# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
								# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
								# Indexes without forced tokens will have a negative value.
								a										=							jnp.ones((max(force_token_map.keys()				) + 1)		,      dtype=jnp.intaa				) * -1
								for index, token in force_token_map.items():
									if token is not None:
										a										=							force_token_array.at[index].set(__magic_name__				)
								a										=							jnp.intaa(__magic_name__				)





							def __call__(						self						:Optional[Any]		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:jnp.ndarray		,      __magic_name__						:int				):







								'''simple docstring'''







								def _force_token(__magic_name__						:Dict				):
									a										=							scores.shape[0]
									a										=							self.force_token_array[generation_idx]

									a										=							jnp.ones_like(__magic_name__		,      dtype=scores.dtype				) * -float("""inf"""				)
									a										=							jnp.zeros((batch_size, 1)		,      dtype=scores.dtype				)
									a										=							lax.dynamic_update_slice(__magic_name__		,      __magic_name__		,      (0, current_token)				)
									return new_scores

								a										=							lax.cond(
								    cur_len >= self.force_token_array.shape[0]		,      lambda: scores		,      lambda: lax.cond(
								        self.force_token_array[cur_len] >= 0		,      lambda: _force_token(__magic_name__				)		,      lambda: scores		,      )		,      )
								return scores







class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:Dict		,      __magic_name__						:Optional[Any]		,      __magic_name__						:Union[str, Any]		,      __magic_name__						:Dict				):







								'''simple docstring'''







								a										=							generate_config.eos_token_id
								a										=							generate_config.no_timestamps_token_id
								a										=							generate_config.no_timestamps_token_id + 1

								a										=							decoder_input_length + 1

								if generate_config.is_multilingual:
									# room for language token and task token
									self.begin_index += 2
								if hasattr(__magic_name__		,      """max_initial_timestamp_index"""				):
									a										=							generate_config.max_initial_timestamp_index
								else:
									a										=							model_config.vocab_size
								if self.max_initial_timestamp_index is None:
									a										=							model_config.vocab_size





							def __call__(						self						:str		,      __magic_name__						:int		,      __magic_name__						:Dict		,      __magic_name__						:Dict				):







								'''simple docstring'''







								a										=							scores.at[:, self.no_timestamps_token_id].set(-float("""inf"""				)				)

								def handle_pairs(__magic_name__						:List[str]		,      __magic_name__						:str				):
									a										=							jnp.where((cur_len - self.begin_index) >= 1		,      __magic_name__		,      __magic_name__				)
									a										=							jnp.where(
									    input_ids_k[cur_len - 1] >= self.timestamp_begin		,      True and last_was_timestamp		,      __magic_name__		,      )

									a										=							jnp.where((cur_len - self.begin_index) < 2		,      __magic_name__		,      __magic_name__				)
									a										=							jnp.where(
									    input_ids_k[cur_len - 2] >= self.timestamp_begin		,      __magic_name__		,      __magic_name__		,      )

									return jnp.where(
									    __magic_name__		,      jnp.where(
									        penultimate_was_timestamp > 0		,      scores_k.at[self.timestamp_begin :].set(-float("""inf"""				)				)		,      scores_k.at[: self.eos_token_id].set(-float("""inf"""				)				)		,      )		,      __magic_name__		,      )

								a										=							jax.vmap(__magic_name__				)(__magic_name__		,      __magic_name__				)

								a										=							jnp.where(cur_len == self.begin_index		,      __magic_name__		,      __magic_name__				)
								a										=							jnp.where(
								    self.max_initial_timestamp_index is not None		,      True and apply_max_initial_timestamp		,      __magic_name__		,      )

								a										=							self.timestamp_begin + self.max_initial_timestamp_index

								a										=							jnp.where(
								    __magic_name__		,      scores.at[:, last_allowed + 1 :].set(-float("""inf"""				)				)		,      __magic_name__		,      )

								# if sum of probability over timestamps is above any other token, sample timestamp
								a										=							jax.nn.log_softmax(__magic_name__		,      axis=-1				)

								def handle_cumulative_probs(__magic_name__						:List[str]		,      __magic_name__						:List[Any]				):
									a										=							jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]		,      axis=-1				)
									a										=							jnp.max(logprobs_k[: self.timestamp_begin]				)
									return jnp.where(
									    timestamp_logprob > max_text_token_logprob		,      scores_k.at[: self.timestamp_begin].set(-float("""inf"""				)				)		,      __magic_name__		,      )

								a										=							jax.vmap(__magic_name__				)(__magic_name__		,      __magic_name__				)

								return scores







import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionAttendAndExcitePipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import 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


__UpperCamelCase					:       Optional[Any]    					=      False

@skip_mps
class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__      ,       __magic_name__      ,       unittest.TestCase ):
							UpperCamelCase__					     =  StableDiffusionAttendAndExcitePipeline
							UpperCamelCase__					     =  False
							UpperCamelCase__					     =  TEXT_TO_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							UpperCamelCase__					     =  TEXT_TO_IMAGE_IMAGE_PARAMS
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Union[str, Any]				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								torch.manual_seed(0				)
								a										=							UNetaDConditionModel(
								    block_out_channels=(32, 64)		,      layers_per_block=1		,      sample_size=32		,      in_channels=4		,      out_channels=4		,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")		,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")		,      cross_attention_dim=32		,      attention_head_dim=(2, 4)		,      use_linear_projection=__magic_name__		,      )
								a										=							DDIMScheduler(
								    beta_start=0.00085		,      beta_end=0.012		,      beta_schedule="""scaled_linear"""		,      clip_sample=__magic_name__		,      set_alpha_to_one=__magic_name__		,      )
								torch.manual_seed(0				)
								a										=							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		,      sample_size=128		,      )
								torch.manual_seed(0				)
								a										=							CLIPTextConfig(
								    bos_token_id=0		,      eos_token_id=2		,      hidden_size=32		,      intermediate_size=37		,      layer_norm_eps=1E-0_5		,      num_attention_heads=4		,      num_hidden_layers=5		,      pad_token_id=1		,      vocab_size=1000		,      hidden_act="""gelu"""		,      projection_dim=512		,      )
								a										=							CLIPTextModel(__magic_name__				)
								a										=							CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

								a										=							{
								    """unet""": unet,
								    """scheduler""": scheduler,
								    """vae""": vae,
								    """text_encoder""": text_encoder,
								    """tokenizer""": tokenizer,
								    """safety_checker""": None,
								    """feature_extractor""": None,
								}

								return components
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:Optional[int]		,      __magic_name__						:Optional[int]=0				):







								'''simple docstring'''







								if str(__magic_name__				).startswith("""mps"""				):
									a										=							torch.manual_seed(__magic_name__				)
								else:
									a										=							torch.Generator(device=__magic_name__				).manual_seed(__magic_name__				)
								a										=							a										=							{
								    """prompt""": """a cat and a frog""",
								    """token_indices""": [2, 5],
								    """generator""": generator,
								    """num_inference_steps""": 1,
								    """guidance_scale""": 6.0,
								    """output_type""": """numpy""",
								    """max_iter_to_alter""": 2,
								    """thresholds""": {0: 0.7},
								}
								return inputs
							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								a										=							"""cpu"""

								a										=							self.get_dummy_components()
								a										=							self.pipeline_class(**__magic_name__				)
								pipe.to(__magic_name__				)
								pipe.set_progress_bar_config(disable=__magic_name__				)

								a										=							self.get_dummy_inputs(__magic_name__				)
								a										=							pipe(**__magic_name__				).images
								a										=							image[0, -3:, -3:, -1]

								self.assertEqual(image.shape		,      (1, 64, 64, 3)				)
								a										=							np.array(
								    [0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496]				)
								a										=							np.abs(image_slice.flatten() - expected_slice				).max()
								self.assertLessEqual(__magic_name__		,      1E-3				)
							def       lowerCamelCase__							(						self						:Tuple				):







								'''simple docstring'''







								super().test_cpu_offload_forward_pass(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[Any]				):







								'''simple docstring'''







								self._test_inference_batch_consistent(batch_sizes=[1, 2]				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								self._test_inference_batch_single_identical(batch_size=2		,      expected_max_diff=7E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3				)
							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4				)
							def       lowerCamelCase__							(						self						:List[str]				):







								'''simple docstring'''







								super().test_save_load_local(expected_max_difference=5E-4				)





							def       lowerCamelCase__							(						self						:str				):







								'''simple docstring'''







								super().test_save_load_optional_components(expected_max_difference=4E-4				)







@require_torch_gpu
@slow
class 		__lowerCAmelCase	(	unittest.TestCase ):
							@classmethod
							def       lowerCamelCase__							(						cls						:int				):







								'''simple docstring'''







								super().setUpClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							@classmethod
							def       lowerCamelCase__							(						cls						:Any				):







								'''simple docstring'''







								super().tearDownClass()
								torch.use_deterministic_algorithms(__magic_name__				)
							def       lowerCamelCase__							(						self						:Union[str, Any]				):







								'''simple docstring'''







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





							def       lowerCamelCase__							(						self						:int				):







								'''simple docstring'''







								a										=							torch.manual_seed(51				)

								a										=							StableDiffusionAttendAndExcitePipeline.from_pretrained(
								    """CompVis/stable-diffusion-v1-4"""		,      safety_checker=__magic_name__		,      torch_dtype=torch.floataa				)
								pipe.to("""cuda"""				)

								a										=							"""a painting of an elephant with glasses"""
								a										=							[5, 7]

								a										=							pipe(
								    prompt=__magic_name__		,      token_indices=__magic_name__		,      guidance_scale=7.5		,      generator=__magic_name__		,      num_inference_steps=5		,      max_iter_to_alter=5		,      output_type="""numpy"""		,      ).images[0]

								a										=							load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy"""				)
								assert np.abs((expected_image - image).max()				) < 5E-1

