
'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)





@flax.struct.dataclass
class 							A__	:
      _UpperCAmelCase       :					CommonSchedulerState

      # setable values
      _UpperCAmelCase       :					jnp.ndarray
      _UpperCAmelCase       :					jnp.ndarray
      _UpperCAmelCase       :					jnp.ndarray
      _UpperCAmelCase       :					Optional[int]					 =    None

      # running values
      _UpperCAmelCase       :					Optional[jnp.ndarray]					 =    None
      @classmethod
      def 		UpperCamelCase__							(   cls					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       return cls(common=__magic_name__					,						init_noise_sigma=__magic_name__					,						timesteps=__magic_name__					,						sigmas=__magic_name__   )





@dataclass
class 							A__	(			__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					LMSDiscreteSchedulerState





class 							A__	(			__SCREAMING_SNAKE_CASE						,     __SCREAMING_SNAKE_CASE):
      _UpperCAmelCase       :					Union[str, Any]					 =    [e.name for e in FlaxKarrasDiffusionSchedulers]

      _UpperCAmelCase       :					jnp.dtype
      @property
      def 		UpperCamelCase__							(   self   ):
       return True
      @register_to_config
      def __init__(   self					,						__magic_name__ = 1_0_0_0					,						__magic_name__ = 0.0_001					,						__magic_name__ = 0.02					,						__magic_name__ = "linear"					,						__magic_name__ = None					,						__magic_name__ = "epsilon"					,						__magic_name__ = jnp.floataa					,						):
       lowerCamelCase	:			Tuple           =   dtype
      def 		UpperCamelCase__							(   self					,						__magic_name__ = None   ):
       if common is None:
        lowerCamelCase	:			int           =   CommonSchedulerState.create(self   )

       lowerCamelCase	:			Dict           =   jnp.arange(0					,						self.config.num_train_timesteps   ).round()[::-1]
       lowerCamelCase	:			Union[str, Any]           =   ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

       # standard deviation of the initial noise distribution
       lowerCamelCase	:			List[Any]           =   sigmas.max()

       return LMSDiscreteSchedulerState.create(
           common=__magic_name__					,						init_noise_sigma=__magic_name__					,						timesteps=__magic_name__					,						sigmas=__magic_name__					,						)
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       (lowerCamelCase					)	:			Union[str, Any]           =   jnp.where(state.timesteps == timestep					,						size=1   )
       lowerCamelCase	:			Dict           =   step_index[0]

       lowerCamelCase	:			List[Any]           =   state.sigmas[step_index]
       lowerCamelCase	:			Optional[Any]           =   sample / ((sigma**2 + 1) ** 0.5)
       return sample
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):

       def lms_derivative(__magic_name__   ):
        lowerCamelCase	:			Union[str, Any]           =   1.0
        for k in range(__magic_name__   ):
         if current_order == k:
          continue
         prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

       lowerCamelCase	:			str           =   integrate.quad(__magic_name__					,						state.sigmas[t]					,						state.sigmas[t + 1]					,						epsrel=1e-4   )[0]

       return integrated_coeff
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__ = ()   ):
       lowerCamelCase	:			int           =   jnp.linspace(self.config.num_train_timesteps - 1					,						0					,						__magic_name__					,						dtype=self.dtype   )

       lowerCamelCase	:			Optional[Any]           =   jnp.floor(__magic_name__   ).astype(jnp.intaa   )
       lowerCamelCase	:			Any           =   jnp.ceil(__magic_name__   ).astype(jnp.intaa   )

       lowerCamelCase	:			int           =   jnp.mod(__magic_name__					,						1.0   )

       lowerCamelCase	:			Tuple           =   ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
       lowerCamelCase	:			int           =   (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
       lowerCamelCase	:			Optional[int]           =   jnp.concatenate([sigmas, jnp.array([0.0]					,						dtype=self.dtype   )]   )

       lowerCamelCase	:			Any           =   timesteps.astype(jnp.intaa   )

       # initial running values
       lowerCamelCase	:			List[Any]           =   jnp.zeros((0,) + shape					,						dtype=self.dtype   )

       return state.replace(
           timesteps=__magic_name__					,						sigmas=__magic_name__					,						num_inference_steps=__magic_name__					,						derivatives=__magic_name__					,						)
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__ = 4					,						__magic_name__ = True					,						):
       if state.num_inference_steps is None:
        raise ValueError(
            """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""   )

       lowerCamelCase	:			Dict           =   state.sigmas[timestep]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
        lowerCamelCase	:			List[Any]           =   sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        lowerCamelCase	:			str           =   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       else:
        raise ValueError(
            F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

       # 2. Convert to an ODE derivative
       lowerCamelCase	:			Dict           =   (sample - pred_original_sample) / sigma
       lowerCamelCase	:			List[str]           =   state.replace(derivatives=jnp.append(state.derivatives					,						__magic_name__   )   )
       if len(state.derivatives   ) > order:
        lowerCamelCase	:			Any           =   state.replace(derivatives=jnp.delete(state.derivatives					,						0   )   )

       # 3. Compute linear multistep coefficients
       lowerCamelCase	:			int           =   min(timestep + 1					,						__magic_name__   )
       lowerCamelCase	:			Tuple           =   [self.get_lms_coefficient(__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__   ) for curr_order in range(__magic_name__   )]

       # 4. Compute previous sample based on the derivatives path
       lowerCamelCase	:			Optional[Any]           =   sample + sum(
           coeff * derivative for coeff, derivative in zip(__magic_name__					,						reversed(state.derivatives   )   )   )

       if not return_dict:
        return (prev_sample, state)

       return FlaxLMSSchedulerOutput(prev_sample=__magic_name__					,						state=__magic_name__   )
      def 		UpperCamelCase__							(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						__magic_name__					,						):
       lowerCamelCase	:			int           =   state.sigmas[timesteps].flatten()
       lowerCamelCase	:			str           =   broadcast_to_shape_from_left(__magic_name__					,						noise.shape   )

       lowerCamelCase	:			Union[str, Any]           =   original_samples + noise * sigma

       return noisy_samples






      def __len__(   self   ):
       return self.config.num_train_timesteps






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


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


_lowerCamelCase           ="""lfs-multipart-upload"""





class 							A__	(			__SCREAMING_SNAKE_CASE):
      @staticmethod
      def 		UpperCamelCase__							(   __magic_name__   ):
       lowerCamelCase	:			List[Any]           =   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=__magic_name__					,						help="""Local path to repository you want to configure."""   )
       enable_parser.set_defaults(func=lambda __magic_name__   : LfsEnableCommand(__magic_name__   )   )

       lowerCamelCase	:			Union[str, Any]           =   parser.add_parser(
           __magic_name__					,						help=(
               """Deprecated: use `huggingface-cli` instead. """
               """Command will get called by git-lfs, do not call it directly."""
           )					,						)
       upload_parser.set_defaults(func=lambda __magic_name__   : LfsUploadCommand(__magic_name__   )   )





class 							A__	:
      def __init__(   self					,						__magic_name__   ):
       lowerCamelCase	:			int           =   args






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





def 					_a      (		lowerCamelCase							):
 lowerCamelCase	:			Dict           =   json.dumps(lowerCamelCase							) + """\n"""
 sys.stdout.write(lowerCamelCase							)
 sys.stdout.flush()





def 					_a      (		):
 lowerCamelCase	:			Tuple           =   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 							A__	(			__SCREAMING_SNAKE_CASE):
      def __init__(   self					,						__magic_name__					,						__magic_name__					,						__magic_name__   ):
       lowerCamelCase	:			List[str]           =   filepath
       lowerCamelCase	:			Dict           =   seek_from
       lowerCamelCase	:			str           =   read_limit
       lowerCamelCase	:			Tuple           =   0
      def __enter__(   self   ):
       lowerCamelCase	:			str           =   open(self.filepath					,						"""rb"""   )
       self.f.seek(self.seek_from   )
       return self
      def __len__(   self   ):
       lowerCamelCase	:			Any           =   os.fstat(self.f.fileno()   ).st_size
       return min(self.read_limit					,						total_length - self.seek_from   )
      def 		UpperCamelCase__							(   self					,						__magic_name__=-1   ):
       if self.n_seen >= self.read_limit:
        return b""
       lowerCamelCase	:			Tuple           =   self.read_limit - self.n_seen
       lowerCamelCase	:			Dict           =   self.f.read(remaining_amount if n < 0 else min(__magic_name__					,						__magic_name__   )   )
       self.n_seen += len(__magic_name__   )
       return data
      def __iter__(   self   ):
       yield self.read(n=4 * 1_0_2_4 * 1_0_2_4   )






      def __exit__(   self					,						*__magic_name__   ):
       self.f.close()





class 							A__	:
      def __init__(   self					,						__magic_name__   ):
       lowerCamelCase	:			Dict           =   args






      def 		UpperCamelCase__							(   self   ):
       # 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.
       lowerCamelCase	:			Dict           =   json.loads(sys.stdin.readline().strip()   )
       if not (init_msg.get("""event"""   ) == "init" and init_msg.get("""operation"""   ) == "upload"):
        write_msg({"""error""": {"""code""": 3_2, """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:
        lowerCamelCase	:			Dict           =   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   )

        lowerCamelCase	:			Optional[int]           =   msg["""oid"""]
        lowerCamelCase	:			Union[str, Any]           =   msg["""path"""]
        lowerCamelCase	:			Any           =   msg["""action"""]["""href"""]
        lowerCamelCase	:			Optional[int]           =   msg["""action"""]["""header"""]
        lowerCamelCase	:			Dict           =   int(header.pop("""chunk_size"""   )   )
        lowerCamelCase	:			List[str]           =   list(header.values()   )

        lowerCamelCase	:			str           =   []
        for i, presigned_url in enumerate(__magic_name__   ):
         with FileSlice(__magic_name__					,						seek_from=i * chunk_size					,						read_limit=__magic_name__   ) as data:
          lowerCamelCase	:			Dict           =   requests.put(__magic_name__					,						data=__magic_name__   )
          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.

        lowerCamelCase	:			Optional[Any]           =   requests.post(
            __magic_name__					,						json={
                """oid""": oid,
                """parts""": parts,
            }					,						)
        r.raise_for_status()

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




