
"""simple docstring"""

import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
					import torch

					from ..models.auto.modeling_auto import (
					    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
					)

if is_tf_available() and is_tensorflow_probability_available():
					import tensorflow as tf
					import tensorflow_probability as tfp

					from ..models.auto.modeling_tf_auto import (
					    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
					    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
					)






class 			_lowercase  (							__UpperCAmelCase						):




	def __call__(					self						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					**UpperCAmelCase_   )						->				Dict:
			# Returns tqa_pipeline_inputs of shape:
			# [
			#   {"table": pd.DataFrame, "query": List[str]},
			#   ...,
			#   {"table": pd.DataFrame, "query" : List[str]}
			# ]
			requires_backends(self						,					'pandas'   )
			import pandas as pd

			if table is None:
					raise ValueError('Keyword argument `table` cannot be None.'   )
			elif query is None:
					if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) and table.get('query'   ) is not None and table.get('table'   ) is not None:
							lowerCamelCase	:     Any            =       [table]
					elif isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) and len(UpperCAmelCase_   ) > 0:
							if not all(isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) for d in table   ):
									raise ValueError(
									    F"""Keyword argument `table` should be a list of dict, but is {(type(UpperCAmelCase_   ) for d in table)}"""   )

							if table[0].get('query'   ) is not None and table[0].get('table'   ) is not None:
									lowerCamelCase	:     List[Any]            =       table
							else:
									raise ValueError(
									    'If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`'
									    F""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""   )
					elif Dataset is not None and isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) or isinstance(UpperCAmelCase_						,					types.GeneratorType   ):
							return table
					else:
							raise ValueError(
							    'Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but '
							    F"""is {type(UpperCAmelCase_   )})"""   )
			else:
					lowerCamelCase	:     List[str]            =       [{'table': table, 'query': query}]

			for tqa_pipeline_input in tqa_pipeline_inputs:
					if not isinstance(tqa_pipeline_input['table']						,					pd.DataFrame   ):
							if tqa_pipeline_input["table"] is None:
									raise ValueError('Table cannot be None.'   )

							lowerCamelCase	:     Optional[int]            =       pd.DataFrame(tqa_pipeline_input['table']   )

			return tqa_pipeline_inputs






@add_end_docstrings(__UpperCAmelCase						)
class 			_lowercase  (							__UpperCAmelCase						):
	lowercase_								=      'table,query'




	def __init__(					self						,					UpperCAmelCase_=TableQuestionAnsweringArgumentHandler()						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				int:
			super().__init__(*UpperCAmelCase_						,					**UpperCAmelCase_   )
			lowerCamelCase	:     List[str]            =       args_parser

			self.check_model_type(
			    dict(
			        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
			        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()   )
			    if self.framework == 'tf'
			    else dict(
			        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()   )   )

			lowerCamelCase	:     Dict            =       bool(getattr(self.model.config						,					'aggregation_labels'						,					UpperCAmelCase_   )   ) and bool(
			    getattr(self.model.config						,					'num_aggregation_labels'						,					UpperCAmelCase_   )   )
			lowerCamelCase	:     str            =       'tapas' if hasattr(self.model.config						,					'aggregation_labels'   ) else None




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Any:
			return self.model(**UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					**UpperCAmelCase_   )						->				Dict:
			if self.framework == "pt":
					lowerCamelCase	:     List[Any]            =       []
					lowerCamelCase	:     Optional[Any]            =       []
					lowerCamelCase	:     List[str]            =       None
					lowerCamelCase	:     List[str]            =       inputs['input_ids'].shape[0]

					lowerCamelCase	:     Any            =       inputs['input_ids'].to(self.device   )
					lowerCamelCase	:     Optional[Any]            =       inputs['attention_mask'].to(self.device   )
					lowerCamelCase	:     Optional[int]            =       inputs['token_type_ids'].to(self.device   )
					lowerCamelCase	:     str            =       None

					for index in range(UpperCAmelCase_   ):
							# If sequences have already been processed, the token type IDs will be created according to the previous
							# answer.
							if prev_answers is not None:
									lowerCamelCase	:     List[str]            =       token_type_ids_example[:, 3]  # shape (seq_len,)
									lowerCamelCase	:     Dict            =       np.zeros_like(prev_labels_example.cpu().numpy()   )  # shape (seq_len,)

									lowerCamelCase	:     Optional[Any]            =       token_type_ids[index]  # shape (seq_len, 7)
									for i in range(model_labels.shape[0]   ):
											lowerCamelCase	:     str            =       token_type_ids_example[:, 0].tolist()[i]
											lowerCamelCase	:     str            =       token_type_ids_example[:, 1].tolist()[i] - 1
											lowerCamelCase	:     str            =       token_type_ids_example[:, 2].tolist()[i] - 1

											if row_id >= 0 and col_id >= 0 and segment_id == 1:
													lowerCamelCase	:     List[str]            =       int(prev_answers[(col_id, row_id)]   )

									lowerCamelCase	:     Optional[int]            =       torch.from_numpy(UpperCAmelCase_   ).type(torch.long   ).to(self.device   )

							lowerCamelCase	:     List[Any]            =       input_ids[index]
							lowerCamelCase	:     Optional[int]            =       attention_mask[index]  # shape (seq_len,)
							lowerCamelCase	:     List[str]            =       token_type_ids[index]  # shape (seq_len, 7)
							lowerCamelCase	:     Tuple            =       self.model(
							    input_ids=input_ids_example.unsqueeze(0   )						,					attention_mask=attention_mask_example.unsqueeze(0   )						,					token_type_ids=token_type_ids_example.unsqueeze(0   )						,					)
							lowerCamelCase	:     str            =       outputs.logits

							if self.aggregate:
									all_aggregations.append(outputs.logits_aggregation   )

							all_logits.append(UpperCAmelCase_   )

							lowerCamelCase	:     Dict            =       torch.distributions.Bernoulli(logits=UpperCAmelCase_   )
							lowerCamelCase	:     Tuple            =       dist_per_token.probs * attention_mask_example.type(torch.floataa   ).to(
							    dist_per_token.probs.device   )

							lowerCamelCase	:     str            =       collections.defaultdict(UpperCAmelCase_   )
							for i, p in enumerate(probabilities.squeeze().tolist()   ):
									lowerCamelCase	:     Tuple            =       token_type_ids_example[:, 0].tolist()[i]
									lowerCamelCase	:     Dict            =       token_type_ids_example[:, 1].tolist()[i] - 1
									lowerCamelCase	:     List[Any]            =       token_type_ids_example[:, 2].tolist()[i] - 1
									if col >= 0 and row >= 0 and segment_id == 1:
											coords_to_probs[(col, row)].append(UpperCAmelCase_   )

							lowerCamelCase	:     List[str]            =       {key: np.array(coords_to_probs[key]   ).mean() > 0.5 for key in coords_to_probs}

					lowerCamelCase	:     int            =       torch.cat(tuple(UpperCAmelCase_   )						,					0   )

					return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(UpperCAmelCase_   )						,					0   ))
			else:
					lowerCamelCase	:     Union[str, Any]            =       []
					lowerCamelCase	:     Union[str, Any]            =       []
					lowerCamelCase	:     Union[str, Any]            =       None
					lowerCamelCase	:     str            =       inputs['input_ids'].shape[0]

					lowerCamelCase	:     List[Any]            =       inputs['input_ids']
					lowerCamelCase	:     str            =       inputs['attention_mask']
					lowerCamelCase	:     Optional[Any]            =       inputs['token_type_ids'].numpy()
					lowerCamelCase	:     Optional[Any]            =       None

					for index in range(UpperCAmelCase_   ):
							# If sequences have already been processed, the token type IDs will be created according to the previous
							# answer.
							if prev_answers is not None:
									lowerCamelCase	:     Union[str, Any]            =       token_type_ids_example[:, 3]  # shape (seq_len,)
									lowerCamelCase	:     List[Any]            =       np.zeros_like(UpperCAmelCase_						,					dtype=np.intaa   )  # shape (seq_len,)

									lowerCamelCase	:     str            =       token_type_ids[index]  # shape (seq_len, 7)
									for i in range(model_labels.shape[0]   ):
											lowerCamelCase	:     int            =       token_type_ids_example[:, 0].tolist()[i]
											lowerCamelCase	:     Optional[int]            =       token_type_ids_example[:, 1].tolist()[i] - 1
											lowerCamelCase	:     Tuple            =       token_type_ids_example[:, 2].tolist()[i] - 1

											if row_id >= 0 and col_id >= 0 and segment_id == 1:
													lowerCamelCase	:     Optional[int]            =       int(prev_answers[(col_id, row_id)]   )

									lowerCamelCase	:     Optional[int]            =       model_labels

							lowerCamelCase	:     str            =       input_ids[index]
							lowerCamelCase	:     Tuple            =       attention_mask[index]  # shape (seq_len,)
							lowerCamelCase	:     Tuple            =       token_type_ids[index]  # shape (seq_len, 7)
							lowerCamelCase	:     Union[str, Any]            =       self.model(
							    input_ids=np.expand_dims(UpperCAmelCase_						,					axis=0   )						,					attention_mask=np.expand_dims(UpperCAmelCase_						,					axis=0   )						,					token_type_ids=np.expand_dims(UpperCAmelCase_						,					axis=0   )						,					)
							lowerCamelCase	:     int            =       outputs.logits

							if self.aggregate:
									all_aggregations.append(outputs.logits_aggregation   )

							all_logits.append(UpperCAmelCase_   )

							lowerCamelCase	:     int            =       tfp.distributions.Bernoulli(logits=UpperCAmelCase_   )
							lowerCamelCase	:     Dict            =       dist_per_token.probs_parameter() * tf.cast(UpperCAmelCase_						,					tf.floataa   )

							lowerCamelCase	:     Union[str, Any]            =       collections.defaultdict(UpperCAmelCase_   )
							lowerCamelCase	:     List[str]            =       token_type_ids_example
							for i, p in enumerate(tf.squeeze(UpperCAmelCase_   ).numpy().tolist()   ):
									lowerCamelCase	:     List[str]            =       token_type_ids_example[:, 0].tolist()[i]
									lowerCamelCase	:     Optional[int]            =       token_type_ids_example[:, 1].tolist()[i] - 1
									lowerCamelCase	:     int            =       token_type_ids_example[:, 2].tolist()[i] - 1
									if col >= 0 and row >= 0 and segment_id == 1:
											coords_to_probs[(col, row)].append(UpperCAmelCase_   )

							lowerCamelCase	:     Any            =       {key: np.array(coords_to_probs[key]   ).mean() > 0.5 for key in coords_to_probs}

					lowerCamelCase	:     Union[str, Any]            =       tf.concat(tuple(UpperCAmelCase_   )						,					0   )

					return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(UpperCAmelCase_   )						,					0   ))




	def __call__(					self						,					*UpperCAmelCase_						,					**UpperCAmelCase_   )						->				Optional[int]:
			lowerCamelCase	:     Optional[Any]            =       self._args_parser(*UpperCAmelCase_						,					**UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       super().__call__(UpperCAmelCase_						,					**UpperCAmelCase_   )
			if len(UpperCAmelCase_   ) == 1:
					return results[0]
			return results




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					UpperCAmelCase_=None						,					**UpperCAmelCase_   )						->				Union[str, Any]:
			lowerCamelCase	:     List[Any]            =       {}
			if padding is not None:
					lowerCamelCase	:     Union[str, Any]            =       padding
			if truncation is not None:
					lowerCamelCase	:     Dict            =       truncation

			lowerCamelCase	:     Optional[Any]            =       {}
			if sequential is not None:
					lowerCamelCase	:     Any            =       sequential
			return preprocess_params, forward_params, {}




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=None						,					UpperCAmelCase_=True						,					UpperCAmelCase_=None   )						->				str:
			if truncation is None:
					if self.type == "tapas":
							lowerCamelCase	:     Any            =       'drop_rows_to_fit'
					else:
							lowerCamelCase	:     str            =       'do_not_truncate'

			lowerCamelCase      ,       lowerCamelCase	:     List[str]            =       pipeline_input['table'], pipeline_input['query']
			if table.empty:
					raise ValueError('table is empty'   )
			if query is None or query == "":
					raise ValueError('query is empty'   )
			lowerCamelCase	:     Union[str, Any]            =       self.tokenizer(UpperCAmelCase_						,					UpperCAmelCase_						,					return_tensors=self.framework						,					truncation=UpperCAmelCase_						,					padding=UpperCAmelCase_   )
			lowerCamelCase	:     int            =       table
			return inputs




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_=False   )						->				Optional[Any]:
			lowerCamelCase	:     Optional[Any]            =       model_inputs.pop('table'   )

			if self.type == "tapas":
					if sequential:
							lowerCamelCase	:     List[Any]            =       self.sequential_inference(**UpperCAmelCase_   )
					else:
							lowerCamelCase	:     int            =       self.batch_inference(**UpperCAmelCase_   )
			else:
					lowerCamelCase	:     Tuple            =       self.model.generate(**UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       {'model_inputs': model_inputs, 'table': table, 'outputs': outputs}
			return model_outputs




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_   )						->				str:
			lowerCamelCase	:     str            =       model_outputs['model_inputs']
			lowerCamelCase	:     int            =       model_outputs['table']
			lowerCamelCase	:     Optional[Any]            =       model_outputs['outputs']
			if self.type == "tapas":
					if self.aggregate:
							lowerCamelCase      ,       lowerCamelCase	:     Optional[int]            =       outputs[:2]
							lowerCamelCase	:     List[str]            =       self.tokenizer.convert_logits_to_predictions(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       predictions
							lowerCamelCase	:     Dict            =       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(UpperCAmelCase_   )}

							lowerCamelCase	:     Union[str, Any]            =       self.model.config.no_aggregation_label_index
							lowerCamelCase	:     int            =       {
							    i: aggregators[i] + ' > ' for i, pred in enumerate(UpperCAmelCase_   ) if pred != no_agg_label_index
							}
					else:
							lowerCamelCase	:     int            =       outputs[0]
							lowerCamelCase	:     Any            =       self.tokenizer.convert_logits_to_predictions(UpperCAmelCase_						,					UpperCAmelCase_   )
							lowerCamelCase	:     List[str]            =       predictions[0]
							lowerCamelCase	:     Optional[int]            =       {}
							lowerCamelCase	:     List[str]            =       {}
					lowerCamelCase	:     Any            =       []
					for index, coordinates in enumerate(UpperCAmelCase_   ):
							lowerCamelCase	:     Optional[int]            =       [table.iat[coordinate] for coordinate in coordinates]
							lowerCamelCase	:     Dict            =       aggregators.get(UpperCAmelCase_						,					''   )
							lowerCamelCase	:     Optional[Any]            =       aggregators_prefix.get(UpperCAmelCase_						,					''   )
							lowerCamelCase	:     int            =       {
							    'answer': aggregator_prefix + ', '.join(UpperCAmelCase_   ),
							    'coordinates': coordinates,
							    'cells': [table.iat[coordinate] for coordinate in coordinates],
							}
							if aggregator:
									lowerCamelCase	:     Optional[Any]            =       aggregator

							answers.append(UpperCAmelCase_   )
					if len(UpperCAmelCase_   ) == 0:
							raise PipelineException('Empty answer'   )
			else:
					lowerCamelCase	:     List[Any]            =       [{'answer': answer} for answer in self.tokenizer.batch_decode(UpperCAmelCase_						,					skip_special_tokens=UpperCAmelCase_   )]

			return answers if len(UpperCAmelCase_   ) > 1 else answers[0]








"""simple docstring"""

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_A				=					logging.get_logger(__name__)  # pylint: disable=invalid-name

_A				=					'\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'
def 						UpperCAmelCase   (						a_,		a_,		a_=8     ):





		'''simple docstring'''


		lowerCamelCase	:     List[str]            =       height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		lowerCamelCase	:     List[str]            =       width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor
def 						UpperCAmelCase   (						a_,		a_=512,		a_=512     ):





		'''simple docstring'''


		lowerCamelCase	:     Union[str, Any]            =       pil_image.resize((w, h),		resample=Image.BICUBIC,		reducing_gap=1     )
		lowerCamelCase	:     Tuple            =       np.array(pil_image.convert('RGB'     )     )
		lowerCamelCase	:     List[str]            =       arr.astype(np.floataa     ) / 1_2_7.5 - 1
		lowerCamelCase	:     Optional[int]            =       np.transpose(a_,		[2, 0, 1]     )
		lowerCamelCase	:     int            =       torch.from_numpy(a_     ).unsqueeze(0     )
		return image






class 			_lowercase  (							__UpperCAmelCase						):




	def __init__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					)						->				Optional[Any]:
			super().__init__()

			self.register_modules(
			    unet=UpperCAmelCase_						,					scheduler=UpperCAmelCase_						,					movq=UpperCAmelCase_						,					)
			lowerCamelCase	:     Any            =       2 ** (len(self.movq.config.block_out_channels   ) - 1)




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )						->				str:
			# get the original timestep using init_timestep
			lowerCamelCase	:     List[str]            =       min(int(num_inference_steps * strength   )						,					UpperCAmelCase_   )

			lowerCamelCase	:     Dict            =       max(num_inference_steps - init_timestep						,					0   )
			lowerCamelCase	:     Optional[Any]            =       self.scheduler.timesteps[t_start:]

			return timesteps, num_inference_steps - t_start




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_=None   )						->				Union[str, Any]:
			if not isinstance(UpperCAmelCase_						,					(torch.Tensor, PIL.Image.Image, list)   ):
					raise ValueError(
					    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(UpperCAmelCase_   )}"""   )

			lowerCamelCase	:     Optional[Any]            =       image.to(device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			lowerCamelCase	:     int            =       batch_size * num_images_per_prompt

			if image.shape[1] == 4:
					lowerCamelCase	:     Any            =       image

			else:
					if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ) and len(UpperCAmelCase_   ) != batch_size:
							raise ValueError(
							    F"""You have passed a list of generators of length {len(UpperCAmelCase_   )}, but requested an effective batch"""
							    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""   )

					elif isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
							lowerCamelCase	:     List[Any]            =       [
							    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(UpperCAmelCase_   )
							]
							lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
					else:
							lowerCamelCase	:     Any            =       self.movq.encode(UpperCAmelCase_   ).latent_dist.sample(UpperCAmelCase_   )

					lowerCamelCase	:     List[Any]            =       self.movq.config.scaling_factor * init_latents

			lowerCamelCase	:     List[str]            =       torch.cat([init_latents]						,					dim=0   )

			lowerCamelCase	:     List[Any]            =       init_latents.shape
			lowerCamelCase	:     str            =       randn_tensor(UpperCAmelCase_						,					generator=UpperCAmelCase_						,					device=UpperCAmelCase_						,					dtype=UpperCAmelCase_   )

			# get latents
			lowerCamelCase	:     Union[str, Any]            =       self.scheduler.add_noise(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )

			lowerCamelCase	:     Union[str, Any]            =       init_latents

			return latents




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Tuple:
			if is_accelerate_available():
					from accelerate import cpu_offload
			else:
					raise ImportError('Please install accelerate via `pip install accelerate`'   )

			lowerCamelCase	:     Dict            =       torch.device(F"""cuda:{gpu_id}"""   )

			lowerCamelCase	:     Optional[int]            =       [
			    self.unet,
			    self.movq,
			]
			for cpu_offloaded_model in models:
					if cpu_offloaded_model is not None:
							cpu_offload(UpperCAmelCase_						,					UpperCAmelCase_   )




	def      _UpperCamelCase       (					self						,					UpperCAmelCase_=0   )						->				Optional[Any]:
			if is_accelerate_available() and is_accelerate_version('>='						,					'0.17.0.dev0'   ):
					from accelerate import cpu_offload_with_hook
			else:
					raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'   )

			lowerCamelCase	:     str            =       torch.device(F"""cuda:{gpu_id}"""   )

			if self.device.type != "cpu":
					self.to('cpu'						,					silence_dtype_warnings=UpperCAmelCase_   )
					torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

			lowerCamelCase	:     Optional[int]            =       None
			for cpu_offloaded_model in [self.unet, self.movq]:
					lowerCamelCase      ,       lowerCamelCase	:     Any            =       cpu_offload_with_hook(UpperCAmelCase_						,					UpperCAmelCase_						,					prev_module_hook=UpperCAmelCase_   )

			# We'll offload the last model manually.
			lowerCamelCase	:     Optional[Any]            =       hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def      _UpperCamelCase       (					self   )						->				List[Any]:
			if not hasattr(self.unet						,					'_hf_hook'   ):
					return self.device
			for module in self.unet.modules():
					if (
					    hasattr(UpperCAmelCase_						,					'_hf_hook'   )
					    and hasattr(module._hf_hook						,					'execution_device'   )
					    and module._hf_hook.execution_device is not None
					):
							return torch.device(module._hf_hook.execution_device   )
			return self.device




	@torch.no_grad()
	@replace_example_docstring(UpperCAmelCase_   )
	def __call__(					self						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 512						,					UpperCAmelCase_ = 100						,					UpperCAmelCase_ = 4.0						,					UpperCAmelCase_ = 0.3						,					UpperCAmelCase_ = 1						,					UpperCAmelCase_ = None						,					UpperCAmelCase_ = "pil"						,					UpperCAmelCase_ = True						,					)						->				Any:
			lowerCamelCase	:     List[Any]            =       self._execution_device

			lowerCamelCase	:     int            =       guidance_scale > 1.0

			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[Any]            =       torch.cat(UpperCAmelCase_						,					dim=0   )
			if isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     List[str]            =       torch.cat(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       image_embeds.shape[0]

			if do_classifier_free_guidance:
					lowerCamelCase	:     str            =       image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Tuple            =       negative_image_embeds.repeat_interleave(UpperCAmelCase_						,					dim=0   )
					lowerCamelCase	:     Optional[Any]            =       hint.repeat_interleave(UpperCAmelCase_						,					dim=0   )

			lowerCamelCase	:     Optional[Any]            =       torch.cat([negative_image_embeds, image_embeds]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )
			lowerCamelCase	:     Optional[Any]            =       torch.cat([hint, hint]						,					dim=0   ).to(dtype=self.unet.dtype						,					device=UpperCAmelCase_   )

			if not isinstance(UpperCAmelCase_						,					UpperCAmelCase_   ):
					lowerCamelCase	:     Dict            =       [image]
			if not all(isinstance(UpperCAmelCase_						,					(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
					raise ValueError(
					    F"""Input is in incorrect format: {[type(UpperCAmelCase_   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""   )

			lowerCamelCase	:     int            =       torch.cat([prepare_image(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   ) for i in image]						,					dim=0   )
			lowerCamelCase	:     Tuple            =       image.to(dtype=image_embeds.dtype						,					device=UpperCAmelCase_   )

			lowerCamelCase	:     List[Any]            =       self.movq.encode(UpperCAmelCase_   )['latents']
			lowerCamelCase	:     Tuple            =       latents.repeat_interleave(UpperCAmelCase_						,					dim=0   )
			self.scheduler.set_timesteps(UpperCAmelCase_						,					device=UpperCAmelCase_   )
			lowerCamelCase      ,       lowerCamelCase	:     str            =       self.get_timesteps(UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			lowerCamelCase	:     Union[str, Any]            =       timesteps[:1].repeat(batch_size * num_images_per_prompt   )
			lowerCamelCase      ,       lowerCamelCase	:     int            =       downscale_height_and_width(UpperCAmelCase_						,					UpperCAmelCase_						,					self.movq_scale_factor   )
			lowerCamelCase	:     List[Any]            =       self.prepare_latents(
			    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					image_embeds.dtype						,					UpperCAmelCase_						,					UpperCAmelCase_   )
			for i, t in enumerate(self.progress_bar(UpperCAmelCase_   )   ):
					# expand the latents if we are doing classifier free guidance
					lowerCamelCase	:     List[Any]            =       torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

					lowerCamelCase	:     Tuple            =       {'image_embeds': image_embeds, 'hint': hint}
					lowerCamelCase	:     List[Any]            =       self.unet(
					    sample=UpperCAmelCase_						,					timestep=UpperCAmelCase_						,					encoder_hidden_states=UpperCAmelCase_						,					added_cond_kwargs=UpperCAmelCase_						,					return_dict=UpperCAmelCase_						,					)[0]

					if do_classifier_free_guidance:
							lowerCamelCase      ,       lowerCamelCase	:     int            =       noise_pred.split(latents.shape[1]						,					dim=1   )
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.chunk(2   )
							lowerCamelCase      ,       lowerCamelCase	:     Any            =       variance_pred.chunk(2   )
							lowerCamelCase	:     Any            =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
							lowerCamelCase	:     Any            =       torch.cat([noise_pred, variance_pred_text]						,					dim=1   )

					if not (
					    hasattr(self.scheduler.config						,					'variance_type'   )
					    and self.scheduler.config.variance_type in ["learned", "learned_range"]
					):
							lowerCamelCase      ,       lowerCamelCase	:     Union[str, Any]            =       noise_pred.split(latents.shape[1]						,					dim=1   )

					# compute the previous noisy sample x_t -> x_t-1

					lowerCamelCase	:     Union[str, Any]            =       self.scheduler.step(
					    UpperCAmelCase_						,					UpperCAmelCase_						,					UpperCAmelCase_						,					generator=UpperCAmelCase_						,					)[0]

			# post-processing
			lowerCamelCase	:     Optional[Any]            =       self.movq.decode(UpperCAmelCase_						,					force_not_quantize=UpperCAmelCase_   )['sample']

			if output_type not in ["pt", "np", "pil"]:
					raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""   )

			if output_type in ["np", "pil"]:
					lowerCamelCase	:     Optional[Any]            =       image * 0.5 + 0.5
					lowerCamelCase	:     Any            =       image.clamp(0						,					1   )
					lowerCamelCase	:     Any            =       image.cpu().permute(0						,					2						,					3						,					1   ).float().numpy()

			if output_type == "pil":
					lowerCamelCase	:     Tuple            =       self.numpy_to_pil(UpperCAmelCase_   )

			if not return_dict:
					return (image,)

			return ImagePipelineOutput(images=UpperCAmelCase_   )






