

"""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


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





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE     ,   SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    @register_to_config
    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ = None ,lowerCamelCase_ = None    )    ->      Dict:
          super().__init__()

          A				  =		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				  =		torch.zeros(lowerCamelCase_ ,lowerCamelCase_    )
          else:
                A				  =		None

          A				  =		torch.nn.Parameter(lowerCamelCase_    )





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   42
    _lowerCamelCase					=   42
    _lowerCamelCase					=   42
    _lowerCamelCase					=   42
    _lowerCamelCase					=   42
    _lowerCamelCase					=   42




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,)    ->      List[str]:
          super().__init__()

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




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )    ->      int:
          A				  =		len(lowerCamelCase_    ) if isinstance(lowerCamelCase_ ,lowerCamelCase_    ) else 1

          # get prompt text embeddings
          A				  =		self.tokenizer(
              lowerCamelCase_ ,padding="""max_length""" ,max_length=self.tokenizer.model_max_length ,return_tensors="""pt""" ,)
          A				  =		text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                A				  =		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				  =		text_input_ids[:, : self.tokenizer.model_max_length]
          A				  =		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				  =		prompt_embeds / prompt_embeds.norm(dim=-1 ,keepdim=lowerCamelCase_    )

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

          if do_classifier_free_guidance:
                if self.learned_classifier_free_sampling_embeddings.learnable:
                      A				  =		self.learned_classifier_free_sampling_embeddings.embeddings
                      A				  =		negative_prompt_embeds.unsqueeze(0    ).repeat(lowerCamelCase_ ,1 ,1    )
                else:
                      A				  =		[""""""] * batch_size

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

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                A				  =		negative_prompt_embeds.shape[1]
                A				  =		negative_prompt_embeds.repeat(1 ,lowerCamelCase_ ,1    )
                A				  =		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				  =		torch.cat([negative_prompt_embeds, prompt_embeds]    )

          return prompt_embeds




    @torch.no_grad()
    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ = 1_0_0 ,lowerCamelCase_ = 5.0 ,lowerCamelCase_ = 1.0 ,lowerCamelCase_ = 1 ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = "pil" ,lowerCamelCase_ = True ,lowerCamelCase_ = None ,lowerCamelCase_ = 1 ,)    ->      Union[ImagePipelineOutput, Tuple]:
          if isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                A				  =		1
          elif isinstance(lowerCamelCase_ ,lowerCamelCase_    ):
                A				  =		len(lowerCamelCase_    )
          else:
                raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase_    )}'    )

          A				  =		batch_size * num_images_per_prompt

          A				  =		guidance_scale > 1.0

          A				  =		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				  =		(batch_size, self.transformer.num_latent_pixels)
          if latents is None:
                A				  =		self.transformer.num_vector_embeds - 1
                A				  =		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				  =		latents.to(self.device    )

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

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

          A				  =		latents

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

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

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

                A				  =		self.truncate(lowerCamelCase_ ,lowerCamelCase_    )

                # remove `log(0)`'s (`-inf`s)
                A				  =		model_output.clamp(-7_0    )

                # compute the previous noisy sample x_t -> x_t-1
                A				  =		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				  =		self.vqvae.config.vq_embed_dim
          A				  =		(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
          A				  =		self.vqvae.quantize.get_codebook_entry(lowerCamelCase_ ,shape=lowerCamelCase_    )
          A				  =		self.vqvae.decode(lowerCamelCase_ ,force_not_quantize=lowerCamelCase_    ).sample

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

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

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=lowerCamelCase_    )




    def UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      torch.FloatTensor:
          A  ,			A				  =		torch.sort(lowerCamelCase_ ,1 ,descending=lowerCamelCase_    )
          A				  =		torch.exp(lowerCamelCase_    )
          A				  =		sorted_p_x_0.cumsum(dim=1    ) < truncation_rate

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

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

          A				  =		log_p_x_0.clone()

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

          return rv







"""simple docstring"""


import qiskit



def   _A					(	_a	:						int     ,   _a	:						int				):



      """simple docstring"""






      A				  =		qiskit.Aer.get_backend("""aer_simulator"""				)

      # Create a Quantum Circuit acting on the q register
      A				  =		qiskit.QuantumCircuit(_a     ,   _a				)

      # Apply X (NOT) Gate to Qubits 0 & 1
      circuit.x(0				)
      circuit.x(1				)

      # Map the quantum measurement to the classical bits
      circuit.measure([0, 1]     ,   [0, 1]				)

      # Execute the circuit on the qasm simulator
      A				  =		qiskit.execute(_a     ,   _a     ,   shots=1_0_0_0				)

      # Return the histogram data of the results of the experiment.
      return job.result().get_counts(_a				)


if __name__ == "__main__":
  UpperCAmelCase													=single_qubit_measure(2, 2)
  print(f"""Total count for various states are: {counts}""")




