

import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


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


__A    	=			'''lfs-multipart-upload'''



class 						lowercase_					(						__lowercase  ):



      @staticmethod
      def 		UpperCamelCase_	(    A__   :       ArgumentParser	)					->  Optional[Any]:
        _snake_case		=							parser.add_parser(
            '''lfs-enable-largefiles'''    ,      help=(
                '''Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB.'''
            )    ,      )
        enable_parser.add_argument('''path'''    ,      type=A__    ,      help='''Local path to repository you want to configure.'''	)
        enable_parser.set_defaults(func=lambda A__	: LfsEnableCommand(A__	)	)

        _snake_case		=							parser.add_parser(
            A__    ,      help=(
                '''Deprecated: use `huggingface-cli` instead. '''
                '''Command will get called by git-lfs, do not call it directly.'''
            )    ,      )
        upload_parser.set_defaults(func=lambda A__	: LfsUploadCommand(A__	)	)



class 						lowercase_					:



      def __init__(    self   :       Any    ,      A__   :       Any	)					->  Dict:
        _snake_case		=							args







      def 		UpperCamelCase_	(    self   :       Dict	)					->  List[str]:
        warnings.warn(
            '''Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead.'''	)
        _snake_case		=							os.path.abspath(self.args.path	)
        if not os.path.isdir(A__	):
          print('''This does not look like a valid git repo.'''	)
          exit(1	)
        subprocess.run(
            '''git config lfs.customtransfer.multipart.path transformers-cli'''.split()    ,      check=A__    ,      cwd=A__	)
        subprocess.run(
            f"""git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}""".split()    ,      check=A__    ,      cwd=A__    ,      )
        print('''Local repo set up for largefiles'''	)



def    snake_case_(_UpperCamelCase  )							->			Union[str, Any]:



  """simple docstring"""






  _snake_case		=							json.dumps(_UpperCamelCase  ) + '''\n'''
  sys.stdout.write(_UpperCamelCase  )
  sys.stdout.flush()



def    snake_case_()							->			Optional[Dict]:



  """simple docstring"""






  _snake_case		=							json.loads(sys.stdin.readline().strip()  )

  if "terminate" in (msg.get('''type'''  ), msg.get('''event'''  )):
    # terminate message received
    return None

  if msg.get('''event'''  ) not in ("download", "upload"):
    logger.critical('''Received unexpected message'''  )
    sys.exit(1  )

  return msg



class 						lowercase_					(						__lowercase  ):



      def __init__(    self   :       List[str]    ,      A__   :       str    ,      A__   :       int    ,      A__   :       int	)					->  Union[str, Any]:
        _snake_case		=							filepath
        _snake_case		=							seek_from
        _snake_case		=							read_limit
        _snake_case		=							0



      def __enter__(    self   :       Tuple	)					->  List[Any]:
        _snake_case		=							open(self.filepath    ,      '''rb'''	)
        self.f.seek(self.seek_from	)
        return self



      def __len__(    self   :       List[Any]	)					->  Union[str, Any]:
        _snake_case		=							os.fstat(self.f.fileno()	).st_size
        return min(self.read_limit    ,      total_length - self.seek_from	)



      def 		UpperCamelCase_	(    self   :       Dict    ,      A__   :       Dict=-1	)					->  Dict:
        if self.n_seen >= self.read_limit:
          return b""
        _snake_case		=							self.read_limit - self.n_seen
        _snake_case		=							self.f.read(remaining_amount if n < 0 else min(A__    ,      A__	)	)
        self.n_seen += len(A__	)
        return data



      def __iter__(    self   :       Any	)					->  Optional[Any]:
        yield self.read(n=4 * 1024 * 1024	)







      def __exit__(    self   :       Dict    ,      *A__   :       Dict	)					->  Union[str, Any]:
        self.f.close()



class 						lowercase_					:



      def __init__(    self   :       Dict    ,      A__   :       Tuple	)					->  Optional[Any]:
        _snake_case		=							args







      def 		UpperCamelCase_	(    self   :       Tuple	)					->  Union[str, Any]:
        # Immediately after invoking a custom transfer process, git-lfs
        # sends initiation data to the process over stdin.
        # This tells the process useful information about the configuration.
        _snake_case		=							json.loads(sys.stdin.readline().strip()	)
        if not (init_msg.get('''event'''	) == "init" and init_msg.get('''operation'''	) == "upload"):
          write_msg({'''error''': {'''code''': 32, '''message''': '''Wrong lfs init operation'''}}	)
          sys.exit(1	)

        # The transfer process should use the information it needs from the
        # initiation structure, and also perform any one-off setup tasks it
        # needs to do. It should then respond on stdout with a simple empty
        # confirmation structure, as follows:
        write_msg({}	)

        # After the initiation exchange, git-lfs will send any number of
        # transfer requests to the stdin of the transfer process, in a serial sequence.
        while True:
          _snake_case		=							read_msg()
          if msg is None:
            # When all transfers have been processed, git-lfs will send
            # a terminate event to the stdin of the transfer process.
            # On receiving this message the transfer process should
            # clean up and terminate. No response is expected.
            sys.exit(0	)

          _snake_case		=							msg['''oid''']
          _snake_case		=							msg['''path''']
          _snake_case		=							msg['''action''']['''href''']
          _snake_case		=							msg['''action''']['''header''']
          _snake_case		=							int(header.pop('''chunk_size'''	)	)
          _snake_case		=							list(header.values()	)

          _snake_case		=							[]
          for i, presigned_url in enumerate(A__	):
            with FileSlice(A__    ,      seek_from=i * chunk_size    ,      read_limit=A__	) as data:
              _snake_case		=							requests.put(A__    ,      data=A__	)
              r.raise_for_status()
              parts.append(
                  {
                      '''etag''': r.headers.get('''etag'''	),
                      '''partNumber''': i + 1,
                  }	)
              # In order to support progress reporting while data is uploading / downloading,
              # the transfer process should post messages to stdout
              write_msg(
                  {
                      '''event''': '''progress''',
                      '''oid''': oid,
                      '''bytesSoFar''': (i + 1) * chunk_size,
                      '''bytesSinceLast''': chunk_size,
                  }	)
              # Not precise but that's ok.

          _snake_case		=							requests.post(
              A__    ,      json={
                  '''oid''': oid,
                  '''parts''': parts,
              }    ,      )
          r.raise_for_status()

          write_msg({'''event''': '''complete''', '''oid''': oid}	)




from typing import Callable, List, Optional, Union

import torch

from ...models import UNetaDModel
from ...schedulers import CMStochasticIterativeScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


__A    	=			'''
    Examples:
        ```py
        >>> import torch

        >>> from diffusers import ConsistencyModelPipeline

        >>> device = "cuda"
        >>> # Load the cd_imagenet64_l2 checkpoint.
        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"
        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
        >>> pipe.to(device)

        >>> # Onestep Sampling
        >>> image = pipe(num_inference_steps=1).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample.png")

        >>> # Onestep sampling, class-conditional image generation
        >>> # ImageNet-64 class label 145 corresponds to king penguins
        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")

        >>> # Multistep sampling, class-conditional image generation
        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:
        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77
        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]
        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")
        ```
'''



class 						lowercase_					(						__lowercase  ):



      def __init__(    self   :       str    ,      A__   :       UNetaDModel    ,      A__   :       CMStochasticIterativeScheduler	)					->  None:
        super().__init__()

        self.register_modules(
            unet=A__    ,      scheduler=A__    ,      )

        _snake_case		=							None



      def 		UpperCamelCase_	(    self   :       Union[str, Any]    ,      A__   :       Optional[int]=0	)					->  List[str]:
        if is_accelerate_available() and is_accelerate_version('''>='''    ,      '''0.14.0'''	):
          from accelerate import cpu_offload
        else:
          raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''	)

        _snake_case		=							torch.device(f"""cuda:{gpu_id}"""	)

        if self.device.type != "cpu":
          self.to('''cpu'''    ,      silence_dtype_warnings=A__	)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        for cpu_offloaded_model in [self.unet]:
          cpu_offload(A__    ,      A__	)

        if self.safety_checker is not None:
          cpu_offload(self.safety_checker    ,      execution_device=A__    ,      offload_buffers=A__	)



      def 		UpperCamelCase_	(    self   :       Optional[int]    ,      A__   :       Optional[Any]=0	)					->  Union[str, Any]:
        if is_accelerate_available() and is_accelerate_version('''>='''    ,      '''0.17.0.dev0'''	):
          from accelerate import cpu_offload_with_hook
        else:
          raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''	)

        _snake_case		=							torch.device(f"""cuda:{gpu_id}"""	)

        if self.device.type != "cpu":
          self.to('''cpu'''    ,      silence_dtype_warnings=A__	)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

        _snake_case		=							None
        for cpu_offloaded_model in [self.unet]:
          _snake_case,  _snake_case		=							cpu_offload_with_hook(A__    ,      A__    ,      prev_module_hook=A__	)

        if self.safety_checker is not None:
          _snake_case,  _snake_case		=							cpu_offload_with_hook(self.safety_checker    ,      A__    ,      prev_module_hook=A__	)

        # We'll offload the last model manually.
        _snake_case		=							hook



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 		UpperCamelCase_	(    self   :       int	)					->  List[str]:
        if not hasattr(self.unet    ,      '''_hf_hook'''	):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(A__    ,      '''_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 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       Tuple    ,      A__   :       str    ,      A__   :       Tuple    ,      A__   :       Union[str, Any]    ,      A__   :       Union[str, Any]    ,      A__   :       Union[str, Any]    ,      A__   :       Tuple    ,      A__   :       Union[str, Any]=None	)					->  Optional[int]:
        _snake_case		=							(batch_size, num_channels, height, width)
        if isinstance(A__    ,      A__	) and len(A__	) != batch_size:
          raise ValueError(
              f"""You have passed a list of generators of length {len(A__	)}, but requested an effective batch"""
              f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""	)

        if latents is None:
          _snake_case		=							randn_tensor(A__    ,      generator=A__    ,      device=A__    ,      dtype=A__	)
        else:
          _snake_case		=							latents.to(device=A__    ,      dtype=A__	)

        # scale the initial noise by the standard deviation required by the scheduler
        _snake_case		=							latents * self.scheduler.init_noise_sigma
        return latents



      def 		UpperCamelCase_	(    self   :       Optional[Any]    ,      A__   :       torch.FloatTensor    ,      A__   :       str = "pil"	)					->  Optional[int]:
        if output_type not in ["pt", "np", "pil"]:
          raise ValueError(
              f"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""	)

        # Equivalent to diffusers.VaeImageProcessor.denormalize
        _snake_case		=							(sample / 2 + 0.5).clamp(0    ,      1	)
        if output_type == "pt":
          return sample

        # Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
        _snake_case		=							sample.cpu().permute(0    ,      2    ,      3    ,      1	).numpy()
        if output_type == "np":
          return sample

        # Output_type must be 'pil'
        _snake_case		=							self.numpy_to_pil(A__	)
        return sample



      def 		UpperCamelCase_	(    self   :       Any    ,      A__   :       Optional[Any]    ,      A__   :       Dict    ,      A__   :       int=None	)					->  Dict:
        if self.unet.config.num_class_embeds is not None:
          if isinstance(A__    ,      A__	):
            _snake_case		=							torch.tensor(A__    ,      dtype=torch.int	)
          elif isinstance(A__    ,      A__	):
            assert batch_size == 1, "Batch size must be 1 if classes is an int"
            _snake_case		=							torch.tensor([class_labels]    ,      dtype=torch.int	)
          elif class_labels is None:
            # Randomly generate batch_size class labels
            # TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
            _snake_case		=							torch.randint(0    ,      self.unet.config.num_class_embeds    ,      size=(batch_size,)	)
          _snake_case		=							class_labels.to(A__	)
        else:
          _snake_case		=							None
        return class_labels



      def 		UpperCamelCase_	(    self   :       List[str]    ,      A__   :       List[str]    ,      A__   :       List[str]    ,      A__   :       str    ,      A__   :       List[Any]    ,      A__   :       Optional[Any]    ,      A__   :       Dict	)					->  Optional[Any]:
        if num_inference_steps is None and timesteps is None:
          raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''	)

        if num_inference_steps is not None and timesteps is not None:
          logger.warning(
              f"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
              ''' `timesteps` will be used over `num_inference_steps`.'''	)

        if latents is not None:
          _snake_case		=							(batch_size, 3, img_size, img_size)
          if latents.shape != expected_shape:
            raise ValueError(f"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""	)

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







      @torch.no_grad()
      @replace_example_docstring(A__	)
      def __call__(    self   :       Any    ,      A__   :       int = 1    ,      A__   :       Optional[Union[torch.Tensor, List[int], int]] = None    ,      A__   :       int = 1    ,      A__   :       List[int] = None    ,      A__   :       Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,      A__   :       Optional[torch.FloatTensor] = None    ,      A__   :       Optional[str] = "pil"    ,      A__   :       bool = True    ,      A__   :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None    ,      A__   :       int = 1    ,      )					->  Dict:
        _snake_case		=							self.unet.config.sample_size
        _snake_case		=							self._execution_device

        # 1. Check inputs
        self.check_inputs(A__    ,      A__    ,      A__    ,      A__    ,      A__    ,      A__	)

        # 2. Prepare image latents
        # Sample image latents x_0 ~ N(0, sigma_0^2 * I)
        _snake_case		=							self.prepare_latents(
            batch_size=A__    ,      num_channels=self.unet.config.in_channels    ,      height=A__    ,      width=A__    ,      dtype=self.unet.dtype    ,      device=A__    ,      generator=A__    ,      latents=A__    ,      )

        # 3. Handle class_labels for class-conditional models
        _snake_case		=							self.prepare_class_labels(A__    ,      A__    ,      class_labels=A__	)

        # 4. Prepare timesteps
        if timesteps is not None:
          self.scheduler.set_timesteps(timesteps=A__    ,      device=A__	)
          _snake_case		=							self.scheduler.timesteps
          _snake_case		=							len(A__	)
        else:
          self.scheduler.set_timesteps(A__	)
          _snake_case		=							self.scheduler.timesteps

        # 5. Denoising loop
        # Multistep sampling: implements Algorithm 1 in the paper
        with self.progress_bar(total=A__	) as progress_bar:
          for i, t in enumerate(A__	):
            _snake_case		=							self.scheduler.scale_model_input(A__    ,      A__	)
            _snake_case		=							self.unet(A__    ,      A__    ,      class_labels=A__    ,      return_dict=A__	)[0]

            _snake_case		=							self.scheduler.step(A__    ,      A__    ,      A__    ,      generator=A__	)[0]

            # call the callback, if provided
            progress_bar.update()
            if callback is not None and i % callback_steps == 0:
              callback(A__    ,      A__    ,      A__	)

        # 6. Post-process image sample
        _snake_case		=							self.postprocess_image(A__    ,      output_type=A__	)

        # Offload last model to CPU
        if hasattr(self    ,      '''final_offload_hook'''	) and self.final_offload_hook is not None:
          self.final_offload_hook.offload()

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=A__	)

