
from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

SCREAMING_SNAKE_CASE							:int										=    '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")\n        >>> pipe.to("cuda")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save("cat.png")\n        ```\n'


def 						UpperCAmelCase      (     a_		,       a_		,       a_=8						)		->     Any:







				"""simple docstring"""



				__A             =			h // scale_factor**2
				if h % scale_factor**2 != 0:
								new_h += 1
				__A             =			w // scale_factor**2
				if w % scale_factor**2 != 0:
								new_w += 1
				return new_h * scale_factor, new_w * scale_factor





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			Dict							,A :			MultilingualCLIP							,A :			XLMRobertaTokenizer							,A :			UNetaDConditionModel							,A :			Union[DDIMScheduler, DDPMScheduler]							,A :			VQModel							,):
					super().__init__()

					self.register_modules(
					    text_encoder=A							,tokenizer=A							,unet=A							,scheduler=A							,movq=A							,)
					__A             =			2 ** (len(self.movq.config.block_out_channels			) - 1)






	def     UpperCamelCase_  (	self :			int							,A :			Dict							,A :			Any							,A :			Union[str, Any]							,A :			Any							,A :			Optional[Any]							,A :			Any			):
					if latents is None:
									__A             =			randn_tensor(A							,generator=A							,device=A							,dtype=A			)
					else:
									if latents.shape != shape:
													raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
									__A             =			latents.to(A			)

					__A             =			latents * scheduler.init_noise_sigma
					return latents






	def     UpperCamelCase_  (	self :			List[str]							,A :			int							,A :			Optional[Any]							,A :			str							,A :			str							,A :			Tuple=None							,):
					__A             =			len(A			) if isinstance(A							,A			) else 1
					# get prompt text embeddings
					__A             =			self.tokenizer(
					    A							,padding="max_length"							,truncation=A							,max_length=77							,return_attention_mask=A							,add_special_tokens=A							,return_tensors="pt"							,)

					__A             =			text_inputs.input_ids
					__A             =			self.tokenizer(A							,padding="longest"							,return_tensors="pt"			).input_ids

					if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(A							,A			):
									__A             =			self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]			)
									logger.warning(
									    "The following part of your input was truncated because CLIP can only handle sequences up to"
									    f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''			)

					__A             =			text_input_ids.to(A			)
					__A             =			text_inputs.attention_mask.to(A			)

					__A      ,							__A             =			self.text_encoder(
					    input_ids=A							,attention_mask=A			)

					__A             =			prompt_embeds.repeat_interleave(A							,dim=0			)
					__A             =			text_encoder_hidden_states.repeat_interleave(A							,dim=0			)
					__A             =			text_mask.repeat_interleave(A							,dim=0			)

					if do_classifier_free_guidance:
									__A             =			42
									if negative_prompt is None:
													__A             =			[""] * batch_size
									elif type(A			) is not type(A			):
													raise TypeError(
													    f'''`negative_prompt` should be the same type to `prompt`, but got {type(A			)} !='''
													    f''' {type(A			)}.'''			)
									elif isinstance(A							,A			):
													__A             =			[negative_prompt]
									elif batch_size != len(A			):
													raise ValueError(
													    f'''`negative_prompt`: {negative_prompt} has batch size {len(A			)}, but `prompt`:'''
													    f''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
													    " the batch size of `prompt`."			)
									else:
													__A             =			negative_prompt

									__A             =			self.tokenizer(
									    A							,padding="max_length"							,max_length=77							,truncation=A							,return_attention_mask=A							,add_special_tokens=A							,return_tensors="pt"							,)
									__A             =			uncond_input.input_ids.to(A			)
									__A             =			uncond_input.attention_mask.to(A			)

									__A      ,							__A             =			self.text_encoder(
									    input_ids=A							,attention_mask=A			)

									# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

									__A             =			negative_prompt_embeds.shape[1]
									__A             =			negative_prompt_embeds.repeat(1							,A			)
									__A             =			negative_prompt_embeds.view(batch_size * num_images_per_prompt							,A			)

									__A             =			uncond_text_encoder_hidden_states.shape[1]
									__A             =			uncond_text_encoder_hidden_states.repeat(1							,A							,1			)
									__A             =			uncond_text_encoder_hidden_states.view(
									    batch_size * num_images_per_prompt							,A							,-1			)
									__A             =			uncond_text_mask.repeat_interleave(A							,dim=0			)

									# done duplicates

									# For classifier free guidance, we need to do two forward passes.
									# Here we concatenate the unconditional and text embeddings into a single batch
									# to avoid doing two forward passes
									__A             =			torch.cat([negative_prompt_embeds, prompt_embeds]			)
									__A             =			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]			)

									__A             =			torch.cat([uncond_text_mask, text_mask]			)

					return prompt_embeds, text_encoder_hidden_states, text_mask






	def     UpperCamelCase_  (	self :			Dict							,A :			Union[str, Any]=0			):
					if is_accelerate_available():
									from accelerate import cpu_offload
					else:
									raise ImportError("Please install accelerate via `pip install accelerate`"			)

					__A             =			torch.device(f'''cuda:{gpu_id}'''			)

					__A             =			[
					    self.unet,
					    self.text_encoder,
					    self.movq,
					]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(A							,A			)






	def     UpperCamelCase_  (	self :			Any							,A :			List[str]=0			):
					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."			)

					__A             =			torch.device(f'''cuda:{gpu_id}'''			)

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

					__A             =			None
					for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
									__A      ,							__A             =			cpu_offload_with_hook(A							,A							,prev_module_hook=A			)

					if self.safety_checker is not None:
									__A      ,							__A             =			cpu_offload_with_hook(self.safety_checker							,A							,prev_module_hook=A			)

					# We'll offload the last model manually.
					__A             =			hook






	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def     UpperCamelCase_  (	self :			Any			):
					if not hasattr(self.unet							,"_hf_hook"			):
									return self.device
					for module in self.unet.modules():
									if (
									    hasattr(A							,"_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(A			)
	def __call__(	self :			Dict							,A :			Union[str, List[str]]							,A :			Union[torch.FloatTensor, List[torch.FloatTensor]]							,A :			Union[torch.FloatTensor, List[torch.FloatTensor]]							,A :			Optional[Union[str, List[str]]] = None							,A :			int = 5_12							,A :			int = 5_12							,A :			int = 1_00							,A :			float = 4.0							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			Optional[torch.FloatTensor] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					if isinstance(A							,A			):
									__A             =			1
					elif isinstance(A							,A			):
									__A             =			len(A			)
					else:
									raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(A			)}'''			)

					__A             =			self._execution_device

					__A             =			batch_size * num_images_per_prompt
					__A             =			guidance_scale > 1.0

					__A      ,							__A      ,							__A             =			self._encode_prompt(
					    A							,A							,A							,A							,A			)

					if isinstance(A							,A			):
									__A             =			torch.cat(A							,dim=0			)
					if isinstance(A							,A			):
									__A             =			torch.cat(A							,dim=0			)

					if do_classifier_free_guidance:
									__A             =			image_embeds.repeat_interleave(A							,dim=0			)
									__A             =			negative_image_embeds.repeat_interleave(A							,dim=0			)

					__A             =			torch.cat([negative_image_embeds, image_embeds]							,dim=0			).to(
					    dtype=prompt_embeds.dtype							,device=A			)

					self.scheduler.set_timesteps(A							,device=A			)
					__A             =			self.scheduler.timesteps

					__A             =			self.unet.config.in_channels

					__A      ,							__A             =			get_new_h_w(A							,A							,self.movq_scale_factor			)

					# create initial latent
					__A             =			self.prepare_latents(
					    (batch_size, num_channels_latents, height, width)							,text_encoder_hidden_states.dtype							,A							,A							,A							,self.scheduler							,)

					for i, t in enumerate(self.progress_bar(A			)			):
									# expand the latents if we are doing classifier free guidance
									__A             =			torch.cat([latents] * 2			) if do_classifier_free_guidance else latents

									__A             =			{"text_embeds": prompt_embeds, "image_embeds": image_embeds}
									__A             =			self.unet(
									    sample=A							,timestep=A							,encoder_hidden_states=A							,added_cond_kwargs=A							,return_dict=A							,)[0]

									if do_classifier_free_guidance:
													__A      ,							__A             =			noise_pred.split(latents.shape[1]							,dim=1			)
													__A      ,							__A             =			noise_pred.chunk(2			)
													__A      ,							__A             =			variance_pred.chunk(2			)
													__A             =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
													__A             =			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"]
									):
													__A      ,							__A             =			noise_pred.split(latents.shape[1]							,dim=1			)

									# compute the previous noisy sample x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,generator=A							,).prev_sample
					# post-processing
					__A             =			self.movq.decode(A							,force_not_quantize=A			)["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"]:
									__A             =			image * 0.5 + 0.5
									__A             =			image.clamp(0							,1			)
									__A             =			image.cpu().permute(0							,2							,3							,1			).float().numpy()

					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)


# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"facebook/bart-large-mnli"
	snake_case_    =					(
	    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
	    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
	    "It returns the most likely label in the list of provided `labels` for the input text."
	)
	snake_case_    =					"text_classifier"
	snake_case_    =					AutoTokenizer
	snake_case_    =					AutoModelForSequenceClassification

	snake_case_    =					["text", ["text"]]
	snake_case_    =					["text"]






	def     UpperCamelCase_  (	self :			str			):
					super().setup()
					__A             =			self.model.config
					__A             =			-1
					for idx, label in config.idalabel.items():
									if label.lower().startswith("entail"			):
													__A             =			int(A			)
					if self.entailment_id == -1:
									raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."			)






	def     UpperCamelCase_  (	self :			int							,A :			Optional[Any]							,A :			Dict			):
					__A             =			labels
					return self.pre_processor(
					    [text] * len(A			)							,[f'''This example is {label}''' for label in labels]							,return_tensors="pt"							,padding="max_length"							,)







	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple			):
					__A             =			outputs.logits
					__A             =			torch.argmax(logits[:, 2]			).item()
					return self._labels[label_id]
