





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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						torch.FloatTensor
   _UpperCamelCase :						Optional[torch.FloatTensor]     		=				None







def       _A  (      lowerCamelCase							, lowerCamelCase=0.999							, lowerCamelCase="cosine"							, ):
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(lowerCamelCase   ):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(lowerCamelCase   ):
         return math.exp(t * -12.0   )

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""   )

   a__	:						List[Any]      		=  []
   for i in range(lowerCamelCase   ):
      a__	:						Tuple      		=  i / num_diffusion_timesteps
      a__	:						Optional[int]      		=  (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(lowerCamelCase   ) / alpha_bar_fn(lowerCamelCase   )							, lowerCamelCase   )   )
   return torch.tensor(lowerCamelCase							, dtype=torch.floataa   )







class   __lowerCAmelCase			(					_UpperCamelCase		,_UpperCamelCase  ):





   @register_to_config
   def __init__( self					,			snake_case = 1_000					,			snake_case = "fixed_small_log"					,			snake_case = True					,			snake_case = 1.0					,			snake_case = "epsilon"					,			snake_case = "squaredcos_cap_v2"					,			)				->  str:
      """simple docstring"""





      if beta_schedule != "squaredcos_cap_v2":
         raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"      )

      a__	:						Union[str, Any]      		=  betas_for_alpha_bar(snake_case      )

      a__	:						int      		=  1.0 - self.betas
      a__	:						Any      		=  torch.cumprod(self.alphas					,			dim=0      )
      a__	:						Optional[int]      		=  torch.tensor(1.0      )

      # standard deviation of the initial noise distribution
      a__	:						List[Any]      		=  1.0

      # setable values
      a__	:						Tuple      		=  None
      a__	:						int      		=  torch.from_numpy(np.arange(0					,			snake_case      )[::-1].copy()      )

      a__	:						Optional[int]      		=  variance_type





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  torch.FloatTensor:
      """simple docstring"""





      return sample





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  Tuple:
      """simple docstring"""





      a__	:						List[str]      		=  num_inference_steps
      a__	:						Union[str, Any]      		=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
      a__	:						Optional[int]      		=  (np.arange(0					,			snake_case      ) * step_ratio).round()[::-1].copy().astype(np.intaa      )
      a__	:						Tuple      		=  torch.from_numpy(snake_case      ).to(snake_case      )





   def 	_snake_case ( self					,			snake_case					,			snake_case=None					,			snake_case=None					,			snake_case=None      )				->  List[Any]:
      """simple docstring"""





      if prev_timestep is None:
         a__	:						Union[str, Any]      		=  t - 1

      a__	:						int      		=  self.alphas_cumprod[t]
      a__	:						Union[str, Any]      		=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      a__	:						str      		=  1 - alpha_prod_t
      a__	:						Tuple      		=  1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
         a__	:						Union[str, Any]      		=  self.betas[t]
      else:
         a__	:						Union[str, Any]      		=  1 - alpha_prod_t / alpha_prod_t_prev

      # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
      # and sample from it to get previous sample
      # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
      a__	:						Dict      		=  beta_prod_t_prev / beta_prod_t * beta

      if variance_type is None:
         a__	:						str      		=  self.config.variance_type

      # hacks - were probably added for training stability
      if variance_type == "fixed_small_log":
         a__	:						List[Any]      		=  torch.log(torch.clamp(snake_case					,			min=1E-20      )      )
         a__	:						Optional[int]      		=  torch.exp(0.5 * variance      )
      elif variance_type == "learned_range":
         # NOTE difference with DDPM scheduler
         a__	:						str      		=  variance.log()
         a__	:						Optional[int]      		=  beta.log()

         a__	:						int      		=  (predicted_variance + 1) / 2
         a__	:						Tuple      		=  frac * max_log + (1 - frac) * min_log

      return variance





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			snake_case = None					,			snake_case=None					,			snake_case = True					,			)				->  Union[UnCLIPSchedulerOutput, Tuple]:
      """simple docstring"""





      a__	:						Optional[int]      		=  timestep

      if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
         a__		,     a__	:						Dict      		=  torch.split(snake_case					,			sample.shape[1]					,			dim=1      )
      else:
         a__	:						Union[str, Any]      		=  None

      # 1. compute alphas, betas
      if prev_timestep is None:
         a__	:						Dict      		=  t - 1

      a__	:						Dict      		=  self.alphas_cumprod[t]
      a__	:						Union[str, Any]      		=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
      a__	:						int      		=  1 - alpha_prod_t
      a__	:						List[str]      		=  1 - alpha_prod_t_prev

      if prev_timestep == t - 1:
         a__	:						Any      		=  self.betas[t]
         a__	:						Tuple      		=  self.alphas[t]
      else:
         a__	:						Dict      		=  1 - alpha_prod_t / alpha_prod_t_prev
         a__	:						Optional[int]      		=  1 - beta

      # 2. compute predicted original sample from predicted noise also called
      # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
      if self.config.prediction_type == "epsilon":
         a__	:						int      		=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
      elif self.config.prediction_type == "sample":
         a__	:						Dict      		=  model_output
      else:
         raise ValueError(
             F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
             " for the UnCLIPScheduler."      )

      # 3. Clip "predicted x_0"
      if self.config.clip_sample:
         a__	:						Tuple      		=  torch.clamp(
             snake_case					,			-self.config.clip_sample_range					,			self.config.clip_sample_range      )

      # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      a__	:						Optional[int]      		=  (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
      a__	:						Any      		=  alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

      # 5. Compute predicted previous sample µ_t
      # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
      a__	:						Optional[int]      		=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

      # 6. Add noise
      a__	:						Dict      		=  0
      if t > 0:
         a__	:						int      		=  randn_tensor(
             model_output.shape					,			dtype=model_output.dtype					,			generator=snake_case					,			device=model_output.device      )

         a__	:						Tuple      		=  self._get_variance(
             snake_case					,			predicted_variance=snake_case					,			prev_timestep=snake_case					,			)

         if self.variance_type == "fixed_small_log":
            a__	:						Any      		=  variance
         elif self.variance_type == "learned_range":
            a__	:						Union[str, Any]      		=  (0.5 * variance).exp()
         else:
            raise ValueError(
                F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                " for the UnCLIPScheduler."      )

         a__	:						List[Any]      		=  variance * variance_noise

      a__	:						List[str]      		=  pred_prev_sample + variance

      if not return_dict:
         return (pred_prev_sample,)

      return UnCLIPSchedulerOutput(prev_sample=snake_case					,			pred_original_sample=snake_case      )





   def 	_snake_case ( self					,			snake_case					,			snake_case					,			snake_case					,			)				->  torch.FloatTensor:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.alphas_cumprod.to(device=original_samples.device					,			dtype=original_samples.dtype      )
      a__	:						Optional[Any]      		=  timesteps.to(original_samples.device      )

      a__	:						Optional[int]      		=  alphas_cumprod[timesteps] ** 0.5
      a__	:						Optional[int]      		=  sqrt_alpha_prod.flatten()
      while len(sqrt_alpha_prod.shape      ) < len(original_samples.shape      ):
         a__	:						List[Any]      		=  sqrt_alpha_prod.unsqueeze(-1      )

      a__	:						List[str]      		=  (1 - alphas_cumprod[timesteps]) ** 0.5
      a__	:						Union[str, Any]      		=  sqrt_one_minus_alpha_prod.flatten()
      while len(sqrt_one_minus_alpha_prod.shape      ) < len(original_samples.shape      ):
         a__	:						Tuple      		=  sqrt_one_minus_alpha_prod.unsqueeze(-1      )

      a__	:						Optional[Any]      		=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
      return noisy_samples








import subprocess
import sys

from transformers import BertConfig, BertModel, BertTokenizer, pipeline
from transformers.testing_utils import TestCasePlus, require_torch







class   __lowerCAmelCase			(					_UpperCamelCase  ):





   @require_torch
   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Tuple      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      a__	:						str      		=  "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      a__	:						Optional[Any]      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise RuntimeError(\"Offline mode is enabled, we shouldn't access internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      a__	:						Tuple      		=  "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case      )
      BertModel.from_pretrained(snake_case      )
      BertTokenizer.from_pretrained(snake_case      )
      pipeline(task="fill-mask"					,			model=snake_case      )

      # baseline - just load from_pretrained with normal network
      a__	:						Optional[int]      		=  [sys.executable, "-c", "\n".join([load, run, mock]      )]

      # should succeed
      a__	:						Dict      		=  self.get_env()
      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      a__	:						Tuple      		=  "1"
      a__	:						List[Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      a__	:						Tuple      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        "

      a__	:						Optional[Any]      		=  "\nmname = \"hf-internal-testing/tiny-random-bert\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nBertTokenizer.from_pretrained(mname)\npipe = pipeline(task=\"fill-mask\", model=mname)\nprint(\"success\")\n        "

      a__	:						Tuple      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Faking flaky internet\")\nsocket.socket = offline_socket\n        "

      # Force fetching the files so that we can use the cache
      a__	:						List[Any]      		=  "hf-internal-testing/tiny-random-bert"
      BertConfig.from_pretrained(snake_case      )
      BertModel.from_pretrained(snake_case      )
      BertTokenizer.from_pretrained(snake_case      )
      pipeline(task="fill-mask"					,			model=snake_case      )

      # baseline - just load from_pretrained with normal network
      a__	:						Tuple      		=  [sys.executable, "-c", "\n".join([load, run, mock]      )]

      # should succeed
      a__	:						Any      		=  self.get_env()
      a__	:						int      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						List[Any]      		=  "\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        "

      a__	:						Dict      		=  "\nmname = \"hf-internal-testing/tiny-random-bert-sharded\"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint(\"success\")\n        "

      a__	:						int      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise ValueError(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "

      # baseline - just load from_pretrained with normal network
      a__	:						List[Any]      		=  [sys.executable, "-c", "\n".join([load, run]      )]

      # should succeed
      a__	:						str      		=  self.get_env()
      a__	:						List[str]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )

      # next emulate no network
      a__	:						List[Any]      		=  [sys.executable, "-c", "\n".join([load, mock, run]      )]

      # Doesn't fail anymore since the model is in the cache due to other tests, so commenting this.
      # env["TRANSFORMERS_OFFLINE"] = "0"
      # result = subprocess.run(cmd, env=env, check=False, capture_output=True)
      # self.assertEqual(result.returncode, 1, result.stderr)

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      a__	:						Union[str, Any]      		=  "1"
      a__	:						Tuple      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )





   @require_torch
   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Optional[Any]      		=  "\nfrom transformers import pipeline\n        "
      a__	:						int      		=  "\nmname = \"hf-internal-testing/tiny-random-bert\"\npipe = pipeline(model=mname)\n        "

      a__	:						Dict      		=  "\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error(\"Offline mode is enabled\")\nsocket.socket = offline_socket\n        "
      a__	:						List[str]      		=  self.get_env()
      a__	:						Union[str, Any]      		=  "1"
      a__	:						List[str]      		=  [sys.executable, "-c", "\n".join([load, mock, run]      )]
      a__	:						Any      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			1					,			result.stderr      )
      self.assertIn(
          "You cannot infer task automatically within `pipeline` when using offline mode"					,			result.stderr.decode().replace("\n"					,			""      )					,			)





   @require_torch
   def 	_snake_case ( self      )				->  Union[str, Any]:
      """simple docstring"""





      a__	:						Any      		=  "\nfrom transformers import AutoModel\n        "
      a__	:						Any      		=  "\nmname = \"hf-internal-testing/test_dynamic_model\"\nAutoModel.from_pretrained(mname, trust_remote_code=True)\nprint(\"success\")\n        "

      # baseline - just load from_pretrained with normal network
      a__	:						List[str]      		=  [sys.executable, "-c", "\n".join([load, run]      )]

      # should succeed
      a__	:						Optional[Any]      		=  self.get_env()
      a__	:						Union[str, Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )

      # should succeed as TRANSFORMERS_OFFLINE=1 tells it to use local files
      a__	:						Dict      		=  "1"
      a__	:						Union[str, Any]      		=  subprocess.run(snake_case					,			env=snake_case					,			check=snake_case					,			capture_output=snake_case      )
      self.assertEqual(result.returncode					,			0					,			result.stderr      )
      self.assertIn("success"					,			result.stdout.decode()      )

