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

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


UpperCAmelCase__						:	List[str]	     = logging.get_logger(__name__)  # pylint: disable=invalid-name






def     lowerCamelCase__				(   self						,						a						,						a						,						a						,						a ) ->  Dict:
   _A:    Optional[Any]						     =			image.to(device=a )
   _A:    List[Any]						     =			image  # take image as image_embeddings
   _A:    List[str]						     =			image_embeddings.unsqueeze(1 )

   # duplicate image embeddings for each generation per prompt, using mps friendly method
   _A     ,    _A     ,    _A:    Any						     =			image_embeddings.shape
   _A:    Optional[int]						     =			image_embeddings.repeat(1						,						a						,						1 )
   _A:    str						     =			image_embeddings.view(bs_embed * num_images_per_prompt						,						a						,						-1 )

   if do_classifier_free_guidance:
      _A:    List[str]						     =			torch.zeros_like(a )

      # 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:    Any						     =			torch.cat([uncond_embeddings, image_embeddings] )

   return image_embeddings






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				PriorTransformer     ,     lowerCAmelCase_	:				CLIPTokenizer     ,     lowerCAmelCase_	:				CLIPTextModelWithProjection     ,     lowerCAmelCase_	:				UnCLIPScheduler     ,     lowerCAmelCase_	:				Optional[dict] = None     ,     ):

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

      _A:    Any						     =			{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

      _A:    str						     =			decoder_pipe_kwargs.get('''torch_dtype'''     ,     lowerCAmelCase_	) or prior.dtype

      _A:    Optional[Any]						     =			StableDiffusionImageVariationPipeline.from_pretrained(
          '''lambdalabs/sd-image-variations-diffusers'''     ,     **lowerCAmelCase_	)

      # replace `_encode_image` method
      _A:    List[str]						     =			types.MethodType(_encode_image     ,     self.decoder_pipe	)

      self.register_modules(
          prior=lowerCAmelCase_     ,     tokenizer=lowerCAmelCase_     ,     text_encoder=lowerCAmelCase_     ,     prior_scheduler=lowerCAmelCase_     ,     )


   def 					__magic_name__							(   self	:				Tuple     ,     lowerCAmelCase_	:				Optional[int]     ,     lowerCAmelCase_	:				Optional[Any]     ,     lowerCAmelCase_	:				str     ,     lowerCAmelCase_	:				Optional[int]     ,     lowerCAmelCase_	:				Optional[Union[CLIPTextModelOutput, Tuple]] = None     ,     lowerCAmelCase_	:				Optional[torch.Tensor] = None     ,     ):

      """simple docstring"""
      if text_model_output is None:
         _A:    int						     =			len(lowerCAmelCase_	) if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	) else 1
         # get prompt text embeddings
         _A:    Tuple						     =			self.tokenizer(
             lowerCAmelCase_     ,     padding='''max_length'''     ,     max_length=self.tokenizer.model_max_length     ,     return_tensors='''pt'''     ,     )
         _A:    int						     =			text_inputs.input_ids
         _A:    Any						     =			text_inputs.attention_mask.bool().to(lowerCAmelCase_	)

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            _A:    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}"""	)
            _A:    str						     =			text_input_ids[:, : self.tokenizer.model_max_length]

         _A:    Tuple						     =			self.text_encoder(text_input_ids.to(lowerCAmelCase_	)	)

         _A:    List[Any]						     =			text_encoder_output.text_embeds
         _A:    str						     =			text_encoder_output.last_hidden_state

      else:
         _A:    Optional[int]						     =			text_model_output[0].shape[0]
         _A     ,    _A:    Any						     =			text_model_output[0], text_model_output[1]
         _A:    int						     =			text_attention_mask

      _A:    Any						     =			text_embeddings.repeat_interleave(lowerCAmelCase_     ,     dim=0	)
      _A:    Tuple						     =			text_encoder_hidden_states.repeat_interleave(lowerCAmelCase_     ,     dim=0	)
      _A:    Any						     =			text_mask.repeat_interleave(lowerCAmelCase_     ,     dim=0	)

      if do_classifier_free_guidance:
         _A:    Tuple						     =			[''''''] * batch_size

         _A:    Any						     =			self.tokenizer(
             lowerCAmelCase_     ,     padding='''max_length'''     ,     max_length=self.tokenizer.model_max_length     ,     truncation=lowerCAmelCase_     ,     return_tensors='''pt'''     ,     )
         _A:    int						     =			uncond_input.attention_mask.bool().to(lowerCAmelCase_	)
         _A:    List[str]						     =			self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase_	)	)

         _A:    Optional[Any]						     =			uncond_embeddings_text_encoder_output.text_embeds
         _A:    Any						     =			uncond_embeddings_text_encoder_output.last_hidden_state

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

         _A:    List[Any]						     =			uncond_embeddings.shape[1]
         _A:    Union[str, Any]						     =			uncond_embeddings.repeat(1     ,     lowerCAmelCase_	)
         _A:    Optional[int]						     =			uncond_embeddings.view(batch_size * num_images_per_prompt     ,     lowerCAmelCase_	)

         _A:    Tuple						     =			uncond_text_encoder_hidden_states.shape[1]
         _A:    Optional[int]						     =			uncond_text_encoder_hidden_states.repeat(1     ,     lowerCAmelCase_     ,     1	)
         _A:    List[str]						     =			uncond_text_encoder_hidden_states.view(
             batch_size * num_images_per_prompt     ,     lowerCAmelCase_     ,     -1	)
         _A:    Union[str, Any]						     =			uncond_text_mask.repeat_interleave(lowerCAmelCase_     ,     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:    List[Any]						     =			torch.cat([uncond_embeddings, text_embeddings]	)
         _A:    Optional[int]						     =			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)

         _A:    Union[str, Any]						     =			torch.cat([uncond_text_mask, text_mask]	)

      return text_embeddings, text_encoder_hidden_states, text_mask


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

      """simple docstring"""
      if self.device != torch.device('''meta'''	) or not hasattr(self.prior     ,     '''_hf_hook'''	):
         return self.device
      for module in self.prior.modules():
         if (
             hasattr(lowerCAmelCase_     ,     '''_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


   def 					__magic_name__							(   self	:				str     ,     lowerCAmelCase_	:				List[str]     ,     lowerCAmelCase_	:				List[str]     ,     lowerCAmelCase_	:				List[Any]     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				Any     ,     lowerCAmelCase_	:				str	):

      """simple docstring"""
      if latents is None:
         _A:    Any						     =			randn_tensor(lowerCAmelCase_     ,     generator=lowerCAmelCase_     ,     device=lowerCAmelCase_     ,     dtype=lowerCAmelCase_	)
      else:
         if latents.shape != shape:
            raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""	)
         _A:    int						     =			latents.to(lowerCAmelCase_	)

      _A:    Any						     =			latents * scheduler.init_noise_sigma
      return latents


   def 					__magic_name__							(   self	:				List[Any]     ,     lowerCAmelCase_	:				Optional[Union[str, torch.device]] = None	):

      """simple docstring"""
      self.decoder_pipe.to(lowerCAmelCase_	)
      super().to(lowerCAmelCase_	)


   @torch.no_grad()
   def __call__(   self	:				Optional[int]     ,     lowerCAmelCase_	:				Optional[Union[str, List[str]]] = None     ,     lowerCAmelCase_	:				Optional[int] = None     ,     lowerCAmelCase_	:				Optional[int] = None     ,     lowerCAmelCase_	:				int = 1     ,     lowerCAmelCase_	:				int = 2_5     ,     lowerCAmelCase_	:				Optional[torch.Generator] = None     ,     lowerCAmelCase_	:				Optional[torch.FloatTensor] = None     ,     lowerCAmelCase_	:				Optional[Union[CLIPTextModelOutput, Tuple]] = None     ,     lowerCAmelCase_	:				Optional[torch.Tensor] = None     ,     lowerCAmelCase_	:				float = 4.0     ,     lowerCAmelCase_	:				float = 8.0     ,     lowerCAmelCase_	:				int = 5_0     ,     lowerCAmelCase_	:				Optional[int] = 1     ,     lowerCAmelCase_	:				float = 0.0     ,     lowerCAmelCase_	:				Optional[str] = "pil"     ,     lowerCAmelCase_	:				bool = True     ,     ):

      """simple docstring"""
      if prompt is not None:
         if isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	):
            _A:    Dict						     =			1
         elif isinstance(lowerCAmelCase_     ,     lowerCAmelCase_	):
            _A:    Tuple						     =			len(lowerCAmelCase_	)
         else:
            raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_	)}"""	)
      else:
         _A:    List[str]						     =			text_model_output[0].shape[0]

      _A:    int						     =			self._execution_device

      _A:    str						     =			batch_size * num_images_per_prompt

      _A:    int						     =			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

      _A     ,    _A     ,    _A:    Tuple						     =			self._encode_prompt(
          lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_	)

      # prior

      self.prior_scheduler.set_timesteps(lowerCAmelCase_     ,     device=lowerCAmelCase_	)
      _A:    str						     =			self.prior_scheduler.timesteps

      _A:    int						     =			self.prior.config.embedding_dim

      _A:    List[str]						     =			self.prepare_latents(
          (batch_size, embedding_dim)     ,     text_embeddings.dtype     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     self.prior_scheduler     ,     )

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

         _A:    Tuple						     =			self.prior(
             lowerCAmelCase_     ,     timestep=lowerCAmelCase_     ,     proj_embedding=lowerCAmelCase_     ,     encoder_hidden_states=lowerCAmelCase_     ,     attention_mask=lowerCAmelCase_     ,     ).predicted_image_embedding

         if do_classifier_free_guidance:
            _A     ,    _A:    Optional[Any]						     =			predicted_image_embedding.chunk(2	)
            _A:    int						     =			predicted_image_embedding_uncond + prior_guidance_scale * (
                predicted_image_embedding_text - predicted_image_embedding_uncond
            )

         if i + 1 == prior_timesteps_tensor.shape[0]:
            _A:    Tuple						     =			None
         else:
            _A:    str						     =			prior_timesteps_tensor[i + 1]

         _A:    List[str]						     =			self.prior_scheduler.step(
             lowerCAmelCase_     ,     timestep=lowerCAmelCase_     ,     sample=lowerCAmelCase_     ,     generator=lowerCAmelCase_     ,     prev_timestep=lowerCAmelCase_     ,     ).prev_sample

      _A:    Union[str, Any]						     =			self.prior.post_process_latents(lowerCAmelCase_	)

      _A:    Tuple						     =			prior_latents

      _A:    List[Any]						     =			self.decoder_pipe(
          image=lowerCAmelCase_     ,     height=lowerCAmelCase_     ,     width=lowerCAmelCase_     ,     num_inference_steps=lowerCAmelCase_     ,     guidance_scale=lowerCAmelCase_     ,     generator=lowerCAmelCase_     ,     output_type=lowerCAmelCase_     ,     return_dict=lowerCAmelCase_     ,     num_images_per_prompt=lowerCAmelCase_     ,     eta=lowerCAmelCase_     ,     )
      return output





import numpy as np
import qiskit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    List[Any]						     =			qiskit.QuantumCircuit(num_qubits + 1 )

   # First, let's deal with the case in which oracle is balanced
   if case == "balanced":
      # First generate a random number that tells us which CNOTs to
      # wrap in X-gates:
      _A:    Any						     =			np.random.randint(1						,						2**num_qubits )
      # Next, format 'b' as a binary string of length 'n', padded with zeros:
      _A:    int						     =			format(a						,						f"""0{num_qubits}b""" )
      # Next, we place the first X-gates. Each digit in our binary string
      # corresponds to a qubit, if the digit is 0, we do nothing, if it's 1
      # we apply an X-gate to that qubit:
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )
        # Do the controlled-NOT gates for each qubit, using the output qubit
        # as the target:
      for index in range(a ):
         oracle_qc.cx(a						,						a )
      # Next, place the final X-gates
      for index, bit in enumerate(a ):
         if bit == "1":
            oracle_qc.x(a )

    # Case in which oracle is constant
   if case == "constant":
      # First decide what the fixed output of the oracle will be
      # (either always 0 or always 1)
      _A:    int						     =			np.random.randint(2 )
      if output == 1:
         oracle_qc.x(a )

   _A:    int						     =			oracle_qc.to_gate()
   _A:    str						     =			'''Oracle'''  # To show when we display the circuit
   return oracle_gate






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.QuantumCircuit:
   _A:    Optional[int]						     =			qiskit.QuantumCircuit(num_qubits + 1						,						a )
   # Set up the output qubit:
   dj_circuit.x(a )
   dj_circuit.h(a )
   # And set up the input register:
   for qubit in range(a ):
      dj_circuit.h(a )
   # Let's append the oracle gate to our circuit:
   dj_circuit.append(a						,						range(num_qubits + 1 ) )
   # Finally, perform the H-gates again and measure:
   for qubit in range(a ):
      dj_circuit.h(a )

   for i in range(a ):
      dj_circuit.measure(a						,						a )

   return dj_circuit






def     lowerCamelCase__				(   a						,						a ) ->  qiskit.result.counts.Counts:
   _A:    int						     =			qiskit.Aer.get_backend('''aer_simulator''' )

   _A:    Optional[Any]						     =			dj_oracle(a						,						a )
   _A:    int						     =			dj_algorithm(a						,						a )

   # Execute the circuit on the simulator
   _A:    int						     =			qiskit.execute(a						,						a						,						shots=10_00 )

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


if __name__ == "__main__":
   print(F"""Deutsch Jozsa - Constant Oracle: {deutsch_jozsa("constant", 3)}""")
   print(F"""Deutsch Jozsa - Balanced Oracle: {deutsch_jozsa("balanced", 3)}""")



