from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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

class    snake_case       (   _a ,    _a    ):




  @register_to_config
  def __init__(   self  :						Union[str, Any]  ,			a_  :						List[str]  ,			a_  :						List[Any] = None  ,			a_  :						Tuple = None					)->							str:




       """simple docstring"""
       super().__init__()

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		learnable

       if self.learnable:
            assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
            assert length is not None, "learnable=True requires `length` to be set"

            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.zeros(__UpperCamelCase  ,			__UpperCamelCase					)
       else:
            SCREAMING_SNAKE_CASE__					:					List[str]      =		None

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.nn.Parameter(__UpperCamelCase					)




class    snake_case       (   _a    ):
  lowercase_								=  42
  lowercase_								=  42
  lowercase_								=  42
  lowercase_								=  42
  lowercase_								=  42
  lowercase_								=  42




  def __init__(   self  :						Any  ,			a_  :						Tuple  ,			a_  :						Any  ,			a_  :						Optional[int]  ,			a_  :						Optional[int]  ,			a_  :						Optional[Any]  ,			a_  :						List[str]  ,			)->							Optional[int]:




       """simple docstring"""
       super().__init__()

       self.register_modules(
           vqvae=__UpperCamelCase  ,			transformer=__UpperCamelCase  ,			text_encoder=__UpperCamelCase  ,			tokenizer=__UpperCamelCase  ,			scheduler=__UpperCamelCase  ,			learned_classifier_free_sampling_embeddings=__UpperCamelCase  ,			)




  def 						__lowercase(   self  :						List[Any]  ,			a_  :						Any  ,			a_  :						Tuple  ,			a_  :						str					)->							List[str]:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__					:					int      =		len(__UpperCamelCase					) if isinstance(__UpperCamelCase  ,			__UpperCamelCase					) else 1

       # get prompt text embeddings
       SCREAMING_SNAKE_CASE__					:					Optional[int]      =		self.tokenizer(
           __UpperCamelCase  ,			padding='max_length'  ,			max_length=self.tokenizer.model_max_length  ,			return_tensors='pt'  ,			)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            SCREAMING_SNAKE_CASE__					:					Dict      =		self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]					)
            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}'''					)
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		text_input_ids[:, : self.tokenizer.model_max_length]
       SCREAMING_SNAKE_CASE__					:					str      =		self.text_encoder(text_input_ids.to(self.device					)					)[0]

       # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
       # While CLIP does normalize the pooled output of the text transformer when combining
       # the image and text embeddings, CLIP does not directly normalize the last hidden state.
       #
       # CLIP normalizing the pooled output.
       # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
       SCREAMING_SNAKE_CASE__					:					Tuple      =		prompt_embeds / prompt_embeds.norm(dim=-1  ,			keepdim=__UpperCamelCase					)

       # duplicate text embeddings for each generation per prompt
       SCREAMING_SNAKE_CASE__					:					Tuple      =		prompt_embeds.repeat_interleave(__UpperCamelCase  ,			dim=0					)

       if do_classifier_free_guidance:
            if self.learned_classifier_free_sampling_embeddings.learnable:
                 SCREAMING_SNAKE_CASE__					:					Tuple      =		self.learned_classifier_free_sampling_embeddings.embeddings
                 SCREAMING_SNAKE_CASE__					:					int      =		negative_prompt_embeds.unsqueeze(0					).repeat(__UpperCamelCase  ,			1  ,			1					)
            else:
                 SCREAMING_SNAKE_CASE__					:					Any      =		[''] * batch_size

                 SCREAMING_SNAKE_CASE__					:					Dict      =		text_input_ids.shape[-1]
                 SCREAMING_SNAKE_CASE__					:					Tuple      =		self.tokenizer(
                     __UpperCamelCase  ,			padding='max_length'  ,			max_length=__UpperCamelCase  ,			truncation=__UpperCamelCase  ,			return_tensors='pt'  ,			)
                 SCREAMING_SNAKE_CASE__					:					List[Any]      =		self.text_encoder(uncond_input.input_ids.to(self.device					)					)[0]
                 # See comment for normalizing text embeddings
                 SCREAMING_SNAKE_CASE__					:					Tuple      =		negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1  ,			keepdim=__UpperCamelCase					)

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            SCREAMING_SNAKE_CASE__					:					Any      =		negative_prompt_embeds.shape[1]
            SCREAMING_SNAKE_CASE__					:					int      =		negative_prompt_embeds.repeat(1  ,			__UpperCamelCase  ,			1					)
            SCREAMING_SNAKE_CASE__					:					List[Any]      =		negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,			__UpperCamelCase  ,			-1					)

            # 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
            SCREAMING_SNAKE_CASE__					:					Tuple      =		torch.cat([negative_prompt_embeds, prompt_embeds]					)

       return prompt_embeds




  @torch.no_grad()
  def __call__(   self  :						Union[str, Any]  ,			a_  :						int  ,			a_  :						Union[str, Any] = 100  ,			a_  :						Union[str, Any] = 5.0  ,			a_  :						Any = 1.0  ,			a_  :						Tuple = 1  ,			a_  :						Tuple = None  ,			a_  :						Optional[int] = None  ,			a_  :						Optional[Any] = "pil"  ,			a_  :						Optional[Any] = True  ,			a_  :						List[Any] = None  ,			a_  :						Tuple = 1  ,			)->							Tuple:




       """simple docstring"""
       if isinstance(__UpperCamelCase  ,			__UpperCamelCase					):
            SCREAMING_SNAKE_CASE__					:					List[str]      =		1
       elif isinstance(__UpperCamelCase  ,			__UpperCamelCase					):
            SCREAMING_SNAKE_CASE__					:					str      =		len(__UpperCamelCase					)
       else:
            raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__UpperCamelCase					)}'''					)

       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		batch_size * num_images_per_prompt

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		guidance_scale > 1.0

       SCREAMING_SNAKE_CASE__					:					int      =		self._encode_prompt(__UpperCamelCase  ,			__UpperCamelCase  ,			__UpperCamelCase					)

       if (callback_steps is None) or (
           callback_steps is not None and (not isinstance(__UpperCamelCase  ,			__UpperCamelCase					) or callback_steps <= 0)
       ):
            raise ValueError(
                F'''`callback_steps` has to be a positive integer but is {callback_steps} of type'''
                F''' {type(__UpperCamelCase					)}.'''					)

       # get the initial completely masked latents unless the user supplied it

       SCREAMING_SNAKE_CASE__					:					str      =		(batch_size, self.transformer.num_latent_pixels)
       if latents is None:
            SCREAMING_SNAKE_CASE__					:					str      =		self.transformer.num_vector_embeds - 1
            SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.full(__UpperCamelCase  ,			__UpperCamelCase					).to(self.device					)
       else:
            if latents.shape != latents_shape:
                 raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''					)
            if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                 raise ValueError(
                     'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
                     F''' {self.transformer.num_vector_embeds - 1} (inclusive).'''					)
            SCREAMING_SNAKE_CASE__					:					str      =		latents.to(self.device					)

       # set timesteps
       self.scheduler.set_timesteps(__UpperCamelCase  ,			device=self.device					)

       SCREAMING_SNAKE_CASE__					:					Dict      =		self.scheduler.timesteps.to(self.device					)

       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		latents

       for i, t in enumerate(self.progress_bar(__UpperCamelCase					)					):
            # expand the sample if we are doing classifier free guidance
            SCREAMING_SNAKE_CASE__					:					List[str]      =		torch.cat([sample] * 2					) if do_classifier_free_guidance else sample

            # predict the un-noised image
            # model_output == `log_p_x_0`
            SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.transformer(__UpperCamelCase  ,			encoder_hidden_states=__UpperCamelCase  ,			timestep=__UpperCamelCase					).sample

            if do_classifier_free_guidance:
                 SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					List[Any]      =		model_output.chunk(2					)
                 SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                 model_output -= torch.logsumexp(__UpperCamelCase  ,			dim=1  ,			keepdim=__UpperCamelCase					)

            SCREAMING_SNAKE_CASE__					:					Any      =		self.truncate(__UpperCamelCase  ,			__UpperCamelCase					)

            # remove `log(0)`'s (`-inf`s)
            SCREAMING_SNAKE_CASE__					:					Tuple      =		model_output.clamp(-70					)

            # compute the previous noisy sample x_t -> x_t-1
            SCREAMING_SNAKE_CASE__					:					str      =		self.scheduler.step(__UpperCamelCase  ,			timestep=__UpperCamelCase  ,			sample=__UpperCamelCase  ,			generator=__UpperCamelCase					).prev_sample

            # call the callback, if provided
            if callback is not None and i % callback_steps == 0:
                 callback(__UpperCamelCase  ,			__UpperCamelCase  ,			__UpperCamelCase					)

       SCREAMING_SNAKE_CASE__					:					Any      =		self.vqvae.config.vq_embed_dim
       SCREAMING_SNAKE_CASE__					:					Dict      =		(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
       SCREAMING_SNAKE_CASE__					:					int      =		self.vqvae.quantize.get_codebook_entry(__UpperCamelCase  ,			shape=__UpperCamelCase					)
       SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		self.vqvae.decode(__UpperCamelCase  ,			force_not_quantize=__UpperCamelCase					).sample

       SCREAMING_SNAKE_CASE__					:					Tuple      =		(image / 2 + 0.5).clamp(0  ,			1					)
       SCREAMING_SNAKE_CASE__					:					Dict      =		image.cpu().permute(0  ,			2  ,			3  ,			1					).numpy()

       if output_type == "pil":
            SCREAMING_SNAKE_CASE__					:					Tuple      =		self.numpy_to_pil(__UpperCamelCase					)

       if not return_dict:
            return (image,)

       return ImagePipelineOutput(images=__UpperCamelCase					)




  def 						__lowercase(   self  :						Optional[int]  ,			a_  :						Optional[Any]  ,			a_  :						List[Any]					)->							Dict:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__      ,				SCREAMING_SNAKE_CASE__					:					Optional[Any]      =		torch.sort(__UpperCamelCase  ,			1  ,			descending=__UpperCamelCase					)
       SCREAMING_SNAKE_CASE__					:					Union[str, Any]      =		torch.exp(__UpperCamelCase					)
       SCREAMING_SNAKE_CASE__					:					int      =		sorted_p_x_0.cumsum(dim=1					) < truncation_rate

       # Ensure that at least the largest probability is not zeroed out
       SCREAMING_SNAKE_CASE__					:					int      =		torch.full_like(keep_mask[:, 0:1, :]  ,			__UpperCamelCase					)
       SCREAMING_SNAKE_CASE__					:					str      =		torch.cat((all_true, keep_mask)  ,			dim=1					)
       SCREAMING_SNAKE_CASE__					:					Any      =		keep_mask[:, :-1, :]

       SCREAMING_SNAKE_CASE__					:					Any      =		keep_mask.gather(1  ,			indices.argsort(1					)					)

       SCREAMING_SNAKE_CASE__					:					Tuple      =		log_p_x_0.clone()

       SCREAMING_SNAKE_CASE__					:					List[Any]      =		-torch.inf  # -inf = log(0)

       return rv





from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching




def 	UpperCamelCase__  (     )  ->     List[str]:
						import os as original_os
						from os import path as original_path
						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join

						_lowercase					       =					"""__test_patch_submodule_mock__"""
						with patch_submodule(_test_patching		,   """os.path.join"""		,   SCREAMING_SNAKE_CASE_       ):
												# Every way to access os.path.join must be patched, and the rest must stay untouched

												# check os.path.join
												assert isinstance(_test_patching.os		,   _PatchedModuleObj       )
												assert isinstance(_test_patching.os.path		,   _PatchedModuleObj       )
												assert _test_patching.os.path.join is mock

												# check path.join
												assert isinstance(_test_patching.path		,   _PatchedModuleObj       )
												assert _test_patching.path.join is mock

												# check join
												assert _test_patching.join is mock

												# check that the other attributes are untouched
												assert _test_patching.os.rename is original_rename
												assert _test_patching.path.dirname is original_dirname
												assert _test_patching.os.path.dirname is original_dirname

												# Even renamed modules or objects must be patched

												# check renamed_os.path.join
												assert isinstance(_test_patching.renamed_os		,   _PatchedModuleObj       )
												assert isinstance(_test_patching.renamed_os.path		,   _PatchedModuleObj       )
												assert _test_patching.renamed_os.path.join is mock

												# check renamed_path.join
												assert isinstance(_test_patching.renamed_path		,   _PatchedModuleObj       )
												assert _test_patching.renamed_path.join is mock

												# check renamed_join
												assert _test_patching.renamed_join is mock

												# check that the other attributes are untouched
												assert _test_patching.renamed_os.rename is original_rename
												assert _test_patching.renamed_path.dirname is original_dirname
												assert _test_patching.renamed_os.path.dirname is original_dirname

						# check that everthing is back to normal when the patch is over

						assert _test_patching.os is original_os
						assert _test_patching.path is original_path
						assert _test_patching.join is original_join

						assert _test_patching.renamed_os is original_os
						assert _test_patching.renamed_path is original_path
						assert _test_patching.renamed_join is original_join




def 	UpperCamelCase__  (     )  ->     int:
						assert _test_patching.open is open

						_lowercase					       =					"""__test_patch_submodule_builtin_mock__"""
						# _test_patching has "open" in its globals
						assert _test_patching.open is open
						with patch_submodule(_test_patching		,   """open"""		,   SCREAMING_SNAKE_CASE_       ):
												assert _test_patching.open is mock

						# check that everthing is back to normal when the patch is over

						assert _test_patching.open is open




def 	UpperCamelCase__  (     )  ->     List[Any]:
						# pandas.read_csv is not present in _test_patching
						_lowercase					       =					"""__test_patch_submodule_missing_mock__"""
						with patch_submodule(_test_patching		,   """pandas.read_csv"""		,   SCREAMING_SNAKE_CASE_       ):
												pass




def 	UpperCamelCase__  (     )  ->     Tuple:
						# builtin should always be mocked even if they're not in the globals
						# in case they're loaded at one point
						_lowercase					       =					"""__test_patch_submodule_missing_builtin_mock__"""
						# _test_patching doesn't have "len" in its globals
						assert getattr(_test_patching		,   """len"""		,   SCREAMING_SNAKE_CASE_       ) is None
						with patch_submodule(_test_patching		,   """len"""		,   SCREAMING_SNAKE_CASE_       ):
												assert _test_patching.len is mock
						assert _test_patching.len is len




def 	UpperCamelCase__  (     )  ->     List[str]:
						_lowercase					       =					"""__test_patch_submodule_start_and_stop_mock__"""
						_lowercase					       =					patch_submodule(_test_patching		,   """open"""		,   SCREAMING_SNAKE_CASE_       )
						assert _test_patching.open is open
						patch.start()
						assert _test_patching.open is mock
						patch.stop()
						assert _test_patching.open is open




def 	UpperCamelCase__  (     )  ->     Union[str, Any]:
						from os import rename as original_rename
						from os.path import dirname as original_dirname
						from os.path import join as original_join

						_lowercase					       =					"""__test_patch_submodule_successive_join__"""
						_lowercase					       =					"""__test_patch_submodule_successive_dirname__"""
						_lowercase					       =					"""__test_patch_submodule_successive_rename__"""
						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename

						with patch_submodule(_test_patching		,   """os.path.join"""		,   SCREAMING_SNAKE_CASE_       ):
												with patch_submodule(_test_patching		,   """os.rename"""		,   SCREAMING_SNAKE_CASE_       ):
																		with patch_submodule(_test_patching		,   """os.path.dirname"""		,   SCREAMING_SNAKE_CASE_       ):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

    # try another order
						with patch_submodule(_test_patching		,   """os.rename"""		,   SCREAMING_SNAKE_CASE_       ):
												with patch_submodule(_test_patching		,   """os.path.join"""		,   SCREAMING_SNAKE_CASE_       ):
																		with patch_submodule(_test_patching		,   """os.path.dirname"""		,   SCREAMING_SNAKE_CASE_       ):
																								assert _test_patching.os.path.join is mock_join
																								assert _test_patching.os.path.dirname is mock_dirname
																								assert _test_patching.os.rename is mock_rename

						assert _test_patching.os.path.join is original_join
						assert _test_patching.os.path.dirname is original_dirname
						assert _test_patching.os.rename is original_rename




def 	UpperCamelCase__  (     )  ->     List[Any]:
						_lowercase					       =					"""__test_patch_submodule_doesnt_exist_mock__"""
						with patch_submodule(_test_patching		,   """__module_that_doesn_exist__.__attribute_that_doesn_exist__"""		,   SCREAMING_SNAKE_CASE_       ):
												pass
						with patch_submodule(_test_patching		,   """os.__attribute_that_doesn_exist__"""		,   SCREAMING_SNAKE_CASE_       ):
												pass