
'''simple docstring'''

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


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


class  _UpperCAmelCase ( lowerCAmelCase__	,lowerCAmelCase__    ):

     """simple docstring"""

     @register_to_config
     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None ):

       '''simple docstring'''

       super().__init__()

       a_      :	Dict      =							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"

         a_      :	int      =							torch.zeros(lowerCAmelCase_							,		lowerCAmelCase_ )
       else:
         a_      :	Any      =							None

       a_      :	Tuple      =							torch.nn.Parameter(lowerCAmelCase_ )







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					42
     a_    	=					42
     a_    	=					42
     a_    	=					42
     a_    	=					42
     a_    	=					42


     def __init__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_							,		):

       '''simple docstring'''

       super().__init__()

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


     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      :	Optional[Any]      =							len(lowerCAmelCase_ ) if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ) else 1

       # get prompt text embeddings
       a_      :	Any      =							self.tokenizer(
           lowerCAmelCase_							,		padding="""max_length"""							,		max_length=self.tokenizer.model_max_length							,		return_tensors="""pt"""							,		)
       a_      :	List[str]      =							text_inputs.input_ids

       if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
         a_      :	List[Any]      =							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}''' )
         a_      :	Tuple      =							text_input_ids[:, : self.tokenizer.model_max_length]
       a_      :	Optional[Any]      =							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
       a_      :	Tuple      =							prompt_embeds / prompt_embeds.norm(dim=-1							,		keepdim=lowerCAmelCase_ )

       # duplicate text embeddings for each generation per prompt
       a_      :	Optional[int]      =							prompt_embeds.repeat_interleave(lowerCAmelCase_							,		dim=0 )

       if do_classifier_free_guidance:
         if self.learned_classifier_free_sampling_embeddings.learnable:
           a_      :	str      =							self.learned_classifier_free_sampling_embeddings.embeddings
           a_      :	Optional[int]      =							negative_prompt_embeds.unsqueeze(0 ).repeat(lowerCAmelCase_							,		1							,		1 )
         else:
           a_      :	Tuple      =							[""""""] * batch_size

           a_      :	Optional[int]      =							text_input_ids.shape[-1]
           a_      :	Optional[int]      =							self.tokenizer(
               lowerCAmelCase_							,		padding="""max_length"""							,		max_length=lowerCAmelCase_							,		truncation=lowerCAmelCase_							,		return_tensors="""pt"""							,		)
           a_      :	Optional[Any]      =							self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
           # See comment for normalizing text embeddings
           a_      :	Union[str, Any]      =							negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1							,		keepdim=lowerCAmelCase_ )

         # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
         a_      :	Dict      =							negative_prompt_embeds.shape[1]
         a_      :	Tuple      =							negative_prompt_embeds.repeat(1							,		lowerCAmelCase_							,		1 )
         a_      :	List[Any]      =							negative_prompt_embeds.view(batch_size * num_images_per_prompt							,		lowerCAmelCase_							,		-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
         a_      :	Tuple      =							torch.cat([negative_prompt_embeds, prompt_embeds] )

       return prompt_embeds


     @torch.no_grad()
     def __call__(      self							,		lowerCAmelCase_							,		lowerCAmelCase_ = 1_00							,		lowerCAmelCase_ = 5.0							,		lowerCAmelCase_ = 1.0							,		lowerCAmelCase_ = 1							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = "pil"							,		lowerCAmelCase_ = True							,		lowerCAmelCase_ = None							,		lowerCAmelCase_ = 1							,		):

       '''simple docstring'''

       if isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
         a_      :	str      =							1
       elif isinstance(lowerCAmelCase_							,		lowerCAmelCase_ ):
         a_      :	Optional[int]      =							len(lowerCAmelCase_ )
       else:
         raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_ )}''' )

       a_      :	str      =							batch_size * num_images_per_prompt

       a_      :	Any      =							guidance_scale > 1.0

       a_      :	Any      =							self._encode_prompt(lowerCAmelCase_							,		lowerCAmelCase_							,		lowerCAmelCase_ )

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

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

       a_      :	List[Any]      =							(batch_size, self.transformer.num_latent_pixels)
       if latents is None:
         a_      :	Optional[int]      =							self.transformer.num_vector_embeds - 1
         a_      :	List[str]      =							torch.full(lowerCAmelCase_							,		lowerCAmelCase_ ).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).''' )
         a_      :	str      =							latents.to(self.device )

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

       a_      :	List[Any]      =							self.scheduler.timesteps.to(self.device )

       a_      :	Any      =							latents

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

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

         if do_classifier_free_guidance:
           a_      , a_      :	int      =							model_output.chunk(2 )
           a_      :	Tuple      =							model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
           model_output -= torch.logsumexp(lowerCAmelCase_							,		dim=1							,		keepdim=lowerCAmelCase_ )

         a_      :	Optional[int]      =							self.truncate(lowerCAmelCase_							,		lowerCAmelCase_ )

         # remove `log(0)`'s (`-inf`s)
         a_      :	List[Any]      =							model_output.clamp(-70 )

         # compute the previous noisy sample x_t -> x_t-1
         a_      :	int      =							self.scheduler.step(lowerCAmelCase_							,		timestep=lowerCAmelCase_							,		sample=lowerCAmelCase_							,		generator=lowerCAmelCase_ ).prev_sample

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

       a_      :	Optional[int]      =							self.vqvae.config.vq_embed_dim
       a_      :	List[Any]      =							(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
       a_      :	Union[str, Any]      =							self.vqvae.quantize.get_codebook_entry(lowerCAmelCase_							,		shape=lowerCAmelCase_ )
       a_      :	Tuple      =							self.vqvae.decode(lowerCAmelCase_							,		force_not_quantize=lowerCAmelCase_ ).sample

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

       if output_type == "pil":
         a_      :	int      =							self.numpy_to_pil(lowerCAmelCase_ )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=lowerCAmelCase_ )






     def 							_lowerCAmelCase  (      self							,		lowerCAmelCase_							,		lowerCAmelCase_ ):

       '''simple docstring'''

       a_      , a_      :	Tuple      =							torch.sort(lowerCAmelCase_							,		1							,		descending=lowerCAmelCase_ )
       a_      :	int      =							torch.exp(lowerCAmelCase_ )
       a_      :	Optional[int]      =							sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

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

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

       a_      :	Optional[int]      =							log_p_x_0.clone()

       a_      :	Tuple      =							-torch.inf  # -inf = log(0)

       return rv




'''simple docstring'''

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__snake_case:  Tuple								=			logging.get_logger(__name__)

__snake_case:  Tuple								=			{
    "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json",
    "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json",
    "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json",
    "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json",
    "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json",
}


class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     a_    	=					"t5"
     a_    	=					["past_key_values"]
     a_    	=					{"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


     def __init__(      self							,		lowerCAmelCase_=3_21_28							,		lowerCAmelCase_=5_12							,		lowerCAmelCase_=64							,		lowerCAmelCase_=20_48							,		lowerCAmelCase_=6							,		lowerCAmelCase_=None							,		lowerCAmelCase_=8							,		lowerCAmelCase_=32							,		lowerCAmelCase_=1_28							,		lowerCAmelCase_=0.1							,		lowerCAmelCase_=1E-6							,		lowerCAmelCase_=1.0							,		lowerCAmelCase_="relu"							,		lowerCAmelCase_=True							,		lowerCAmelCase_=True							,		lowerCAmelCase_=0							,		lowerCAmelCase_=1							,		**lowerCAmelCase_							,		):

       '''simple docstring'''

       a_      :	List[Any]      =							vocab_size
       a_      :	List[str]      =							d_model
       a_      :	Union[str, Any]      =							d_kv
       a_      :	List[Any]      =							d_ff
       a_      :	int      =							num_layers
       a_      :	Any      =							(
           num_decoder_layers if num_decoder_layers is not None else self.num_layers
       )  # default = symmetry
       a_      :	Union[str, Any]      =							num_heads
       a_      :	int      =							relative_attention_num_buckets
       a_      :	Dict      =							relative_attention_max_distance
       a_      :	Union[str, Any]      =							dropout_rate
       a_      :	Union[str, Any]      =							layer_norm_epsilon
       a_      :	List[Any]      =							initializer_factor
       a_      :	Any      =							feed_forward_proj
       a_      :	List[Any]      =							use_cache

       a_      :	Dict      =							self.feed_forward_proj.split("""-""" )
       a_      :	Union[str, Any]      =							act_info[-1]
       a_      :	str      =							act_info[0] == """gated"""

       if len(lowerCAmelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_ ) > 2:
         raise ValueError(
             f'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
             """Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
             """'gated-gelu' or 'relu'""" )

       # for backwards compatibility
       if feed_forward_proj == "gated-gelu":
         a_      :	List[str]      =							"""gelu_new"""

       super().__init__(
           pad_token_id=lowerCAmelCase_							,		eos_token_id=lowerCAmelCase_							,		is_encoder_decoder=lowerCAmelCase_							,		**lowerCAmelCase_							,		)







class  _UpperCAmelCase ( lowerCAmelCase__    ):

     """simple docstring"""

     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							{
           """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
           """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
       }
       if self.use_past:
         a_      :	Tuple      =							"""past_encoder_sequence + sequence"""
         a_      :	str      =							{0: """batch"""}
         a_      :	Any      =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
       else:
         a_      :	List[Any]      =							{0: """batch""", 1: """decoder_sequence"""}
         a_      :	Optional[int]      =							{0: """batch""", 1: """decoder_sequence"""}

       if self.use_past:
         self.fill_with_past_key_values_(lowerCAmelCase_							,		direction="""inputs""" )

       return common_inputs






     @property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return 13


