
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 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __call__(				self     ,						UpperCamelCase=None     ,						UpperCamelCase=None     ,						**UpperCamelCase) ->	Optional[int]:
										# 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:
																UpperCamelCase__    :		Optional[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:
																			UpperCamelCase__    :		str     =				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:
													UpperCamelCase__    :		Tuple     =				[{'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.')

																UpperCamelCase__    :		str     =				pd.DataFrame(tqa_pipeline_input['table'])

										return tqa_pipeline_inputs



@add_end_docstrings(__UpperCamelCase   )
class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							UpperCamelCase_					   =							"""table,query"""
							def __init__(				self     ,						UpperCamelCase=TableQuestionAnsweringArgumentHandler()     ,						*UpperCamelCase     ,						**UpperCamelCase) ->	Optional[int]:
										super().__init__(*UpperCamelCase     ,						**UpperCamelCase)
										UpperCamelCase__    :		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()))

										UpperCamelCase__    :		List[str]     =				bool(getattr(self.model.config     ,						'aggregation_labels'     ,						UpperCamelCase)) and bool(
										    getattr(self.model.config     ,						'num_aggregation_labels'     ,						UpperCamelCase))
										UpperCamelCase__    :		Union[str, Any]     =				'tapas' if hasattr(self.model.config     ,						'aggregation_labels') else None
							def        lowerCAmelCase__ (				self     ,						**UpperCamelCase) ->	int:
										return self.model(**UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						**UpperCamelCase) ->	List[Any]:
										if self.framework == "pt":
													UpperCamelCase__    :		Any     =				[]
													UpperCamelCase__    :		Union[str, Any]     =				[]
													UpperCamelCase__    :		Tuple     =				None
													UpperCamelCase__    :		Tuple     =				inputs['input_ids'].shape[0]

													UpperCamelCase__    :		Tuple     =				inputs['input_ids'].to(self.device)
													UpperCamelCase__    :		int     =				inputs['attention_mask'].to(self.device)
													UpperCamelCase__    :		List[str]     =				inputs['token_type_ids'].to(self.device)
													UpperCamelCase__    :		int     =				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:
																			UpperCamelCase__    :		List[Any]     =				token_type_ids_example[:, 3]  # shape (seq_len,)
																			UpperCamelCase__    :		List[Any]     =				np.zeros_like(prev_labels_example.cpu().numpy())  # shape (seq_len,)

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

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

																			UpperCamelCase__    :		Optional[Any]     =				torch.from_numpy(UpperCamelCase).type(torch.long).to(self.device)

																UpperCamelCase__    :		Tuple     =				input_ids[index]
																UpperCamelCase__    :		List[Any]     =				attention_mask[index]  # shape (seq_len,)
																UpperCamelCase__    :		Dict     =				token_type_ids[index]  # shape (seq_len, 7)
																UpperCamelCase__    :		Union[str, Any]     =				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)     ,						)
																UpperCamelCase__    :		List[str]     =				outputs.logits

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

																all_logits.append(UpperCamelCase)

																UpperCamelCase__    :		List[Any]     =				torch.distributions.Bernoulli(logits=UpperCamelCase)
																UpperCamelCase__    :		Tuple     =				dist_per_token.probs * attention_mask_example.type(torch.floataa).to(
																    dist_per_token.probs.device)

																UpperCamelCase__    :		int     =				collections.defaultdict(UpperCamelCase)
																for i, p in enumerate(probabilities.squeeze().tolist()):
																			UpperCamelCase__    :		Optional[int]     =				token_type_ids_example[:, 0].tolist()[i]
																			UpperCamelCase__    :		Any     =				token_type_ids_example[:, 1].tolist()[i] - 1
																			UpperCamelCase__    :		Optional[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)

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

													UpperCamelCase__    :		Any     =				torch.cat(tuple(UpperCamelCase)     ,						0)

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

													UpperCamelCase__    :		int     =				inputs['input_ids']
													UpperCamelCase__    :		Optional[Any]     =				inputs['attention_mask']
													UpperCamelCase__    :		int     =				inputs['token_type_ids'].numpy()
													UpperCamelCase__    :		Dict     =				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:
																			UpperCamelCase__    :		List[Any]     =				token_type_ids_example[:, 3]  # shape (seq_len,)
																			UpperCamelCase__    :		Union[str, Any]     =				np.zeros_like(UpperCamelCase     ,						dtype=np.intaa)  # shape (seq_len,)

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

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

																			UpperCamelCase__    :		str     =				model_labels

																UpperCamelCase__    :		Tuple     =				input_ids[index]
																UpperCamelCase__    :		List[Any]     =				attention_mask[index]  # shape (seq_len,)
																UpperCamelCase__    :		Tuple     =				token_type_ids[index]  # shape (seq_len, 7)
																UpperCamelCase__    :		int     =				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)     ,						)
																UpperCamelCase__    :		List[Any]     =				outputs.logits

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

																all_logits.append(UpperCamelCase)

																UpperCamelCase__    :		str     =				tfp.distributions.Bernoulli(logits=UpperCamelCase)
																UpperCamelCase__    :		Optional[Any]     =				dist_per_token.probs_parameter() * tf.cast(UpperCamelCase     ,						tf.floataa)

																UpperCamelCase__    :		List[str]     =				collections.defaultdict(UpperCamelCase)
																UpperCamelCase__    :		Optional[Any]     =				token_type_ids_example
																for i, p in enumerate(tf.squeeze(UpperCamelCase).numpy().tolist()):
																			UpperCamelCase__    :		List[Any]     =				token_type_ids_example[:, 0].tolist()[i]
																			UpperCamelCase__    :		Dict     =				token_type_ids_example[:, 1].tolist()[i] - 1
																			UpperCamelCase__    :		Optional[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)

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

													UpperCamelCase__    :		Optional[int]     =				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) ->	Union[str, Any]:
										UpperCamelCase__    :		Optional[int]     =				self._args_parser(*UpperCamelCase     ,						**UpperCamelCase)

										UpperCamelCase__    :		Any     =				super().__call__(UpperCamelCase     ,						**UpperCamelCase)
										if len(UpperCamelCase) == 1:
													return results[0]
										return results
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=None     ,						UpperCamelCase=None     ,						UpperCamelCase=None     ,						**UpperCamelCase) ->	List[Any]:
										UpperCamelCase__    :		Optional[int]     =				{}
										if padding is not None:
													UpperCamelCase__    :		Any     =				padding
										if truncation is not None:
													UpperCamelCase__    :		Dict     =				truncation

										UpperCamelCase__    :		Dict     =				{}
										if sequential is not None:
													UpperCamelCase__    :		Optional[int]     =				sequential
										return preprocess_params, forward_params, {}
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase=None     ,						UpperCamelCase=True     ,						UpperCamelCase=None) ->	Tuple:
										if truncation is None:
													if self.type == "tapas":
																UpperCamelCase__    :		str     =				'drop_rows_to_fit'
													else:
																UpperCamelCase__    :		str     =				'do_not_truncate'

										UpperCamelCase__   ,			UpperCamelCase__    :		Union[str, Any]     =				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')
										UpperCamelCase__    :		Tuple     =				self.tokenizer(UpperCamelCase     ,						UpperCamelCase     ,						return_tensors=self.framework     ,						truncation=UpperCamelCase     ,						padding=UpperCamelCase)
										UpperCamelCase__    :		int     =				table
										return inputs
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase=False) ->	List[Any]:
										UpperCamelCase__    :		List[str]     =				model_inputs.pop('table')

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






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

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

																answers.append(UpperCamelCase)
													if len(UpperCamelCase) == 0:
																raise PipelineException('Empty answer')
										else:
													UpperCamelCase__    :		int     =				[{'answer': answer} for answer in self.tokenizer.batch_decode(UpperCamelCase     ,						skip_special_tokens=UpperCamelCase)]

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





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,
)


UpperCAmelCase__				:  Optional[int]       					=				logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase__				:  Optional[int]       					=				'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))


        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=8 )	->     List[Any]:
			UpperCamelCase__    :		Union[str, Any]     =				height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			UpperCamelCase__    :		Tuple     =				width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor



def       _lowercase					(      __SCREAMING_SNAKE_CASE				,    __SCREAMING_SNAKE_CASE=512				,    __SCREAMING_SNAKE_CASE=512 )	->     Union[str, Any]:
			UpperCamelCase__    :		List[str]     =				pil_image.resize((w, h)				,    resample=Image.BICUBIC				,    reducing_gap=1 )
			UpperCamelCase__    :		List[str]     =				np.array(pil_image.convert('RGB' ) )
			UpperCamelCase__    :		Tuple     =				arr.astype(np.floataa ) / 127.5 - 1
			UpperCamelCase__    :		Tuple     =				np.transpose(__SCREAMING_SNAKE_CASE				,    [2, 0, 1] )
			UpperCamelCase__    :		int     =				torch.from_numpy(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
			return image



class 			UpperCamelCase_			(						__UpperCamelCase   ):




							'''simple docstring'''

							def __init__(				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						) ->	Any:
										super().__init__()

										self.register_modules(
										    unet=UpperCamelCase     ,						scheduler=UpperCamelCase     ,						movq=UpperCamelCase     ,						)
										UpperCamelCase__    :		str     =				2 ** (len(self.movq.config.block_out_channels) - 1)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) ->	Tuple:
										# get the original timestep using init_timestep
										UpperCamelCase__    :		str     =				min(int(num_inference_steps * strength)     ,						UpperCamelCase)

										UpperCamelCase__    :		int     =				max(num_inference_steps - init_timestep     ,						0)
										UpperCamelCase__    :		str     =				self.scheduler.timesteps[t_start:]

										return timesteps, num_inference_steps - t_start
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase=None) ->	Optional[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)}""")

										UpperCamelCase__    :		int     =				image.to(device=UpperCamelCase     ,						dtype=UpperCamelCase)

										UpperCamelCase__    :		int     =				batch_size * num_images_per_prompt

										if image.shape[1] == 4:
													UpperCamelCase__    :		Tuple     =				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):
																UpperCamelCase__    :		Union[str, Any]     =				[
																    self.movq.encode(image[i : i + 1]).latent_dist.sample(generator[i]) for i in range(UpperCamelCase)
																]
																UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)
													else:
																UpperCamelCase__    :		Any     =				self.movq.encode(UpperCamelCase).latent_dist.sample(UpperCamelCase)

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

										UpperCamelCase__    :		List[Any]     =				torch.cat([init_latents]     ,						dim=0)

										UpperCamelCase__    :		Union[str, Any]     =				init_latents.shape
										UpperCamelCase__    :		Union[str, Any]     =				randn_tensor(UpperCamelCase     ,						generator=UpperCamelCase     ,						device=UpperCamelCase     ,						dtype=UpperCamelCase)

										# get latents
										UpperCamelCase__    :		Optional[int]     =				self.scheduler.add_noise(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				init_latents

										return latents
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	str:
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError('Please install accelerate via `pip install accelerate`')

										UpperCamelCase__    :		Any     =				torch.device(F"""cuda:{gpu_id}""")

										UpperCamelCase__    :		int     =				[
										    self.unet,
										    self.movq,
										]
										for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																cpu_offload(UpperCamelCase     ,						UpperCamelCase)
							def        lowerCAmelCase__ (				self     ,						UpperCamelCase=0) ->	Tuple:
										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.')

										UpperCamelCase__    :		int     =				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)

										UpperCamelCase__    :		Dict     =				None
										for cpu_offloaded_model in [self.unet, self.movq]:
													UpperCamelCase__   ,			UpperCamelCase__    :		Optional[int]     =				cpu_offload_with_hook(UpperCamelCase     ,						UpperCamelCase     ,						prev_module_hook=UpperCamelCase)

										# We'll offload the last model manually.
										UpperCamelCase__    :		List[Any]     =				hook
							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        lowerCAmelCase__ (				self) ->	Optional[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 = 5_12     ,						UpperCamelCase = 5_12     ,						UpperCamelCase = 1_00     ,						UpperCamelCase = 4.0     ,						UpperCamelCase = 0.3     ,						UpperCamelCase = 1     ,						UpperCamelCase = None     ,						UpperCamelCase = "pil"     ,						UpperCamelCase = True     ,						) ->	Optional[Any]:
										UpperCamelCase__    :		List[Any]     =				self._execution_device

										UpperCamelCase__    :		Union[str, Any]     =				guidance_scale > 1.0

										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Dict     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		List[Any]     =				torch.cat(UpperCamelCase     ,						dim=0)
										if isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Any     =				torch.cat(UpperCamelCase     ,						dim=0)

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

										if do_classifier_free_guidance:
													UpperCamelCase__    :		List[str]     =				image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		Any     =				negative_image_embeds.repeat_interleave(UpperCamelCase     ,						dim=0)
													UpperCamelCase__    :		str     =				hint.repeat_interleave(UpperCamelCase     ,						dim=0)

										UpperCamelCase__    :		int     =				torch.cat([negative_image_embeds, image_embeds]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)
										UpperCamelCase__    :		Tuple     =				torch.cat([hint, hint]     ,						dim=0).to(dtype=self.unet.dtype     ,						device=UpperCamelCase)

										if not isinstance(UpperCamelCase     ,						UpperCamelCase):
													UpperCamelCase__    :		Tuple     =				[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""")

										UpperCamelCase__    :		List[str]     =				torch.cat([prepare_image(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase) for i in image]     ,						dim=0)
										UpperCamelCase__    :		int     =				image.to(dtype=image_embeds.dtype     ,						device=UpperCamelCase)

										UpperCamelCase__    :		List[Any]     =				self.movq.encode(UpperCamelCase)['latents']
										UpperCamelCase__    :		str     =				latents.repeat_interleave(UpperCamelCase     ,						dim=0)
										self.scheduler.set_timesteps(UpperCamelCase     ,						device=UpperCamelCase)
										UpperCamelCase__   ,			UpperCamelCase__    :		List[str]     =				self.get_timesteps(UpperCamelCase     ,						UpperCamelCase     ,						UpperCamelCase)
										UpperCamelCase__    :		List[str]     =				timesteps[:1].repeat(batch_size * num_images_per_prompt)
										UpperCamelCase__   ,			UpperCamelCase__    :		Optional[Any]     =				downscale_height_and_width(UpperCamelCase     ,						UpperCamelCase     ,						self.movq_scale_factor)
										UpperCamelCase__    :		int     =				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
													UpperCamelCase__    :		List[Any]     =				torch.cat([latents] * 2) if do_classifier_free_guidance else latents

													UpperCamelCase__    :		List[str]     =				{'image_embeds': image_embeds, 'hint': hint}
													UpperCamelCase__    :		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:
																UpperCamelCase__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				noise_pred.chunk(2)
																UpperCamelCase__   ,			UpperCamelCase__    :		Tuple     =				variance_pred.chunk(2)
																UpperCamelCase__    :		Tuple     =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																UpperCamelCase__    :		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"]
													):
																UpperCamelCase__   ,			UpperCamelCase__    :		str     =				noise_pred.split(latents.shape[1]     ,						dim=1)

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

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

										# post-processing
										UpperCamelCase__    :		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"]:
													UpperCamelCase__    :		Any     =				image * 0.5 + 0.5
													UpperCamelCase__    :		Tuple     =				image.clamp(0     ,						1)
													UpperCamelCase__    :		Tuple     =				image.cpu().permute(0     ,						2     ,						3     ,						1).float().numpy()

										if output_type == "pil":
													UpperCamelCase__    :		Dict     =				self.numpy_to_pil(UpperCamelCase)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=UpperCamelCase)



