





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


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

class 		SCREAMING_SNAKE_CASE__    (	lowerCamelCase__						,				lowerCamelCase__  ):


      @register_to_config
      def __init__(self	:				List[Any]    ,      a__	:				Union[str, Any]    ,      a__	:				Optional[int] = None    ,      a__	:				int = None				):





         """simple docstring"""



         super().__init__()

         __snake_case								=       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"

            __snake_case								=       torch.zeros(snake_case__    ,      snake_case__				)
         else:
            __snake_case								=       None

         __snake_case								=       torch.nn.Parameter(snake_case__				)






class 		SCREAMING_SNAKE_CASE__    (	lowerCamelCase__  ):
      A_						:    VQModel
      A_						:    CLIPTextModel
      A_						:    CLIPTokenizer
      A_						:    TransformeraDModel
      A_						:    LearnedClassifierFreeSamplingEmbeddings
      A_						:    VQDiffusionScheduler


      def __init__(self	:				List[Any]    ,      a__	:				Union[str, Any]    ,      a__	:				str    ,      a__	:				Union[str, Any]    ,      a__	:				List[str]    ,      a__	:				Any    ,      a__	:				int    ,      ):





         """simple docstring"""



         super().__init__()

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


      def        a			(self	:				Any    ,      a__	:				Tuple    ,      a__	:				Dict    ,      a__	:				Dict				):





         """simple docstring"""



         __snake_case								=       len(snake_case__				) if isinstance(snake_case__    ,      snake_case__				) else 1

         # get prompt text embeddings
         __snake_case								=       self.tokenizer(
             snake_case__    ,      padding='''max_length'''    ,      max_length=self.tokenizer.model_max_length    ,      return_tensors='''pt'''    ,      )
         __snake_case								=       text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            __snake_case								=       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}"""				)
            __snake_case								=       text_input_ids[:, : self.tokenizer.model_max_length]
         __snake_case								=       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
         __snake_case								=       prompt_embeds / prompt_embeds.norm(dim=-1    ,      keepdim=snake_case__				)

         # duplicate text embeddings for each generation per prompt
         __snake_case								=       prompt_embeds.repeat_interleave(snake_case__    ,      dim=0				)

         if do_classifier_free_guidance:
            if self.learned_classifier_free_sampling_embeddings.learnable:
               __snake_case								=       self.learned_classifier_free_sampling_embeddings.embeddings
               __snake_case								=       negative_prompt_embeds.unsqueeze(0				).repeat(snake_case__    ,      1    ,      1				)
            else:
               __snake_case								=       [''''''] * batch_size

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

            # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
            __snake_case								=       negative_prompt_embeds.shape[1]
            __snake_case								=       negative_prompt_embeds.repeat(1    ,      snake_case__    ,      1				)
            __snake_case								=       negative_prompt_embeds.view(batch_size * num_images_per_prompt    ,      snake_case__    ,      -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
            __snake_case								=       torch.cat([negative_prompt_embeds, prompt_embeds]				)

         return prompt_embeds


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





         """simple docstring"""



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

         __snake_case								=       batch_size * num_images_per_prompt

         __snake_case								=       guidance_scale > 1.0

         __snake_case								=       self._encode_prompt(snake_case__    ,      snake_case__    ,      snake_case__				)

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

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

         __snake_case								=       (batch_size, self.transformer.num_latent_pixels)
         if latents is None:
            __snake_case								=       self.transformer.num_vector_embeds - 1
            __snake_case								=       torch.full(snake_case__    ,      snake_case__				).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)."""				)
            __snake_case								=       latents.to(self.device				)

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

         __snake_case								=       self.scheduler.timesteps.to(self.device				)

         __snake_case								=       latents

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

            # predict the un-noised image
            # model_output == `log_p_x_0`
            __snake_case								=       self.transformer(snake_case__    ,      encoder_hidden_states=snake_case__    ,      timestep=snake_case__				).sample

            if do_classifier_free_guidance:
               __snake_case								=       model_output.chunk(2				)
               __snake_case								=       model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
               model_output -= torch.logsumexp(snake_case__    ,      dim=1    ,      keepdim=snake_case__				)

            __snake_case								=       self.truncate(snake_case__    ,      snake_case__				)

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

            # compute the previous noisy sample x_t -> x_t-1
            __snake_case								=       self.scheduler.step(snake_case__    ,      timestep=snake_case__    ,      sample=snake_case__    ,      generator=snake_case__				).prev_sample

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

         __snake_case								=       self.vqvae.config.vq_embed_dim
         __snake_case								=       (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
         __snake_case								=       self.vqvae.quantize.get_codebook_entry(snake_case__    ,      shape=snake_case__				)
         __snake_case								=       self.vqvae.decode(snake_case__    ,      force_not_quantize=snake_case__				).sample

         __snake_case								=       (image / 2 + 0.5).clamp(0    ,      1				)
         __snake_case								=       image.cpu().permute(0    ,      2    ,      3    ,      1				).numpy()

         if output_type == "pil":
            __snake_case								=       self.numpy_to_pil(snake_case__				)

         if not return_dict:
            return (image,)

         return ImagePipelineOutput(images=snake_case__				)


      def        a			(self	:				List[Any]    ,      a__	:				Tuple    ,      a__	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       torch.sort(snake_case__    ,      1    ,      descending=snake_case__				)
         __snake_case								=       torch.exp(snake_case__				)
         __snake_case								=       sorted_p_x_0.cumsum(dim=1				) < truncation_rate

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

         __snake_case								=       keep_mask.gather(1    ,      indices.argsort(1				)				)

         __snake_case								=       log_p_x_0.clone()

         __snake_case								=       -torch.inf  # -inf = log(0)

         return rv








import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'asapp/sew-d-tiny-100k': 'https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    str							=   'sew-d'


      def __init__(self	:				str    ,      a__	:				List[Any]=32    ,      a__	:				Optional[int]=768    ,      a__	:				Union[str, Any]=12    ,      a__	:				Tuple=12    ,      a__	:				int=3072    ,      a__	:				List[Any]=2    ,      a__	:				Dict=512    ,      a__	:				Dict=256    ,      a__	:				List[str]=True    ,      a__	:				List[str]=True    ,      a__	:				str=("p2c", "c2p")    ,      a__	:				str="layer_norm"    ,      a__	:				str="gelu_python"    ,      a__	:				List[Any]=0.1    ,      a__	:				Dict=0.1    ,      a__	:				Optional[Any]=0.1    ,      a__	:				Union[str, Any]=0.0    ,      a__	:				Dict=0.1    ,      a__	:				int=0.0_2    ,      a__	:				str=1E-7    ,      a__	:				Union[str, Any]=1E-5    ,      a__	:				Union[str, Any]="group"    ,      a__	:				Optional[Any]="gelu"    ,      a__	:				List[Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)    ,      a__	:				Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,      a__	:				Optional[int]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,      a__	:				List[str]=False    ,      a__	:				Optional[Any]=128    ,      a__	:				Union[str, Any]=16    ,      a__	:				Optional[Any]=True    ,      a__	:				List[Any]=0.0_5    ,      a__	:				Tuple=10    ,      a__	:				str=2    ,      a__	:				int=0.0    ,      a__	:				Optional[Any]=10    ,      a__	:				List[Any]=0    ,      a__	:				Optional[int]="mean"    ,      a__	:				List[str]=False    ,      a__	:				Tuple=False    ,      a__	:				Optional[int]=256    ,      a__	:				str=0    ,      a__	:				List[Any]=1    ,      a__	:				int=2    ,      **a__	:				List[str]    ,      ):





         """simple docstring"""



         super().__init__(**a__    ,      pad_token_id=a__    ,      bos_token_id=a__    ,      eos_token_id=a__				)
         __snake_case								=       hidden_size
         __snake_case								=       feat_extract_norm
         __snake_case								=       feat_extract_activation
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       list(a__				)
         __snake_case								=       conv_bias
         __snake_case								=       num_conv_pos_embeddings
         __snake_case								=       num_conv_pos_embedding_groups
         __snake_case								=       len(self.conv_dim				)
         __snake_case								=       num_hidden_layers
         __snake_case								=       intermediate_size
         __snake_case								=       squeeze_factor
         __snake_case								=       max_position_embeddings
         __snake_case								=       position_buckets
         __snake_case								=       share_att_key
         __snake_case								=       relative_attention
         __snake_case								=       norm_rel_ebd
         __snake_case								=       list(a__				)
         __snake_case								=       hidden_act
         __snake_case								=       num_attention_heads
         __snake_case								=       hidden_dropout
         __snake_case								=       attention_dropout
         __snake_case								=       activation_dropout
         __snake_case								=       feat_proj_dropout
         __snake_case								=       final_dropout
         __snake_case								=       layer_norm_eps
         __snake_case								=       feature_layer_norm_eps
         __snake_case								=       initializer_range
         __snake_case								=       vocab_size

         if (
             (len(self.conv_stride				) != self.num_feat_extract_layers)
             or (len(self.conv_kernel				) != self.num_feat_extract_layers)
             or (len(self.conv_dim				) != self.num_feat_extract_layers)
         ):
            raise ValueError(
                '''Configuration for convolutional layers is incorrect.'''
                '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
                f"""but is `len(config.conv_dim) = {len(self.conv_dim				)}`, `len(config.conv_stride)"""
                f"""= {len(self.conv_stride				)}`, `len(config.conv_kernel) = {len(self.conv_kernel				)}`."""				)

         # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
         __snake_case								=       apply_spec_augment
         __snake_case								=       mask_time_prob
         __snake_case								=       mask_time_length
         __snake_case								=       mask_time_min_masks
         __snake_case								=       mask_feature_prob
         __snake_case								=       mask_feature_length
         __snake_case								=       mask_feature_min_masks

         # ctc loss
         __snake_case								=       ctc_loss_reduction
         __snake_case								=       ctc_zero_infinity

         # sequence classification
         __snake_case								=       use_weighted_layer_sum
         __snake_case								=       classifier_proj_size


      @property
      def        a			(self	:				List[Any]				):





         """simple docstring"""



         return functools.reduce(operator.mul    ,      self.conv_stride    ,      1				)

