





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

import flax
import jax
import jax.numpy as jnp

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



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

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





      @classmethod
      def        __UpperCamelCase( cls       ,							A_       ,							A_       ,							A_   ):
       '''simple docstring'''




       return cls(common=_UpperCAmelCase       ,							init_noise_sigma=_UpperCAmelCase       ,							timesteps=_UpperCAmelCase   )



@dataclass
class 					A__   (       _UpperCamelCase			):
      _UpperCAmelCase	:DDPMSchedulerState



class 					A__   (       _UpperCamelCase       ,   _UpperCamelCase			):
      _UpperCAmelCase	:Dict						    =   [e.name for e in FlaxKarrasDiffusionSchedulers]

      _UpperCAmelCase	:jnp.dtype





      @property
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       return True





      @register_to_config
      def __init__( self       ,							A_ = 1000       ,							A_ = 0.00_01       ,							A_ = 0.02       ,							A_ = "linear"       ,							A_ = None       ,							A_ = "fixed_small"       ,							A_ = True       ,							A_ = "epsilon"       ,							A_ = jnp.floataa       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   dtype





      def        __UpperCamelCase( self       ,							A_ = None   ):
       '''simple docstring'''




       if common is None:
        UpperCamelCase	:					Optional[int]       	=   CommonSchedulerState.create(self   )

       # standard deviation of the initial noise distribution
       UpperCamelCase	:					Union[str, Any]       	=   jnp.array(1.0       ,							dtype=self.dtype   )

       UpperCamelCase	:					Dict       	=   jnp.arange(0       ,							self.config.num_train_timesteps   ).round()[::-1]

       return DDPMSchedulerState.create(
           common=_UpperCAmelCase       ,							init_noise_sigma=_UpperCAmelCase       ,							timesteps=_UpperCAmelCase       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       return sample





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_ = ()   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   self.config.num_train_timesteps // num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # rounding to avoid issues when num_inference_step is power of 3
       UpperCamelCase	:					Optional[Any]       	=   (jnp.arange(0       ,							_UpperCAmelCase   ) * step_ratio).round()[::-1]

       return state.replace(
           num_inference_steps=_UpperCAmelCase       ,							timesteps=_UpperCAmelCase       ,							)





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_=None       ,							A_=None   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   state.common.alphas_cumprod[t]
       UpperCamelCase	:					Optional[int]       	=   jnp.where(t > 0       ,							state.common.alphas_cumprod[t - 1]       ,							jnp.array(1.0       ,							dtype=self.dtype   )   )

       # 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
       UpperCamelCase	:					Optional[Any]       	=   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

       if variance_type is None:
        UpperCamelCase	:					Tuple       	=   self.config.variance_type

       # hacks - were probably added for training stability
       if variance_type == "fixed_small":
        UpperCamelCase	:					Dict       	=   jnp.clip(_UpperCAmelCase       ,							a_min=1e-20   )
       # for rl-diffuser https://arxiv.org/abs/2205.09991
       elif variance_type == "fixed_small_log":
        UpperCamelCase	:					int       	=   jnp.log(jnp.clip(_UpperCAmelCase       ,							a_min=1e-20   )   )
       elif variance_type == "fixed_large":
        UpperCamelCase	:					Dict       	=   state.common.betas[t]
       elif variance_type == "fixed_large_log":
        # Glide max_log
        UpperCamelCase	:					Tuple       	=   jnp.log(state.common.betas[t]   )
       elif variance_type == "learned":
        return predicted_variance
       elif variance_type == "learned_range":
        UpperCamelCase	:					Optional[Any]       	=   variance
        UpperCamelCase	:					Any       	=   state.common.betas[t]
        UpperCamelCase	:					str       	=   (predicted_variance + 1) / 2
        UpperCamelCase	:					Dict       	=   frac * max_log + (1 - frac) * min_log

       return variance





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   timestep

       if key is None:
        UpperCamelCase	:					Union[str, Any]       	=   jax.random.PRNGKey(0   )

       if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
        UpperCamelCase	:					List[str]       	=   jnp.split(_UpperCAmelCase       ,							sample.shape[1]       ,							axis=1   )
       else:
        UpperCamelCase	:					Any       	=   None

       # 1. compute alphas, betas
       UpperCamelCase	:					List[Any]       	=   state.common.alphas_cumprod[t]
       UpperCamelCase	:					int       	=   jnp.where(t > 0       ,							state.common.alphas_cumprod[t - 1]       ,							jnp.array(1.0       ,							dtype=self.dtype   )   )
       UpperCamelCase	:					Tuple       	=   1 - alpha_prod_t
       UpperCamelCase	:					List[Any]       	=   1 - alpha_prod_t_prev

       # 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":
        UpperCamelCase	:					int       	=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif self.config.prediction_type == "sample":
        UpperCamelCase	:					Any       	=   model_output
       elif self.config.prediction_type == "v_prediction":
        UpperCamelCase	:					Union[str, Any]       	=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
       else:
        raise ValueError(
            F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
            " for the FlaxDDPMScheduler."   )

       # 3. Clip "predicted x_0"
       if self.config.clip_sample:
        UpperCamelCase	:					Union[str, Any]       	=   jnp.clip(_UpperCAmelCase       ,							-1       ,							1   )

       # 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
       UpperCamelCase	:					Dict       	=   (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
       UpperCamelCase	:					Any       	=   state.common.alphas[t] ** 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
       UpperCamelCase	:					List[Any]       	=   pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       def random_variance():
        UpperCamelCase	:					List[Any]       	=   jax.random.split(_UpperCAmelCase       ,							num=1   )
        UpperCamelCase	:					int       	=   jax.random.normal(_UpperCAmelCase       ,							shape=model_output.shape       ,							dtype=self.dtype   )
        return (self._get_variance(_UpperCAmelCase       ,							_UpperCAmelCase       ,							predicted_variance=_UpperCAmelCase   ) ** 0.5) * noise

       UpperCamelCase	:					List[str]       	=   jnp.where(t > 0       ,							random_variance()       ,							jnp.zeros(model_output.shape       ,							dtype=self.dtype   )   )

       UpperCamelCase	:					Tuple       	=   pred_prev_sample + variance

       if not return_dict:
        return (pred_prev_sample, state)

       return FlaxDDPMSchedulerOutput(prev_sample=_UpperCAmelCase       ,							state=_UpperCAmelCase   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       return add_noise_common(state.common       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase   )





      def        __UpperCamelCase( self       ,							A_       ,							A_       ,							A_       ,							A_       ,							):
       '''simple docstring'''




       return get_velocity_common(state.common       ,							_UpperCAmelCase       ,							_UpperCAmelCase       ,							_UpperCAmelCase   )





      def __len__( self   ):
       '''simple docstring'''




       return self.config.num_train_timesteps





'''simple docstring'''

import subprocess
import sys

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

class 	__magic_name__						(    _UpperCamelCase   ):







							@require_torch
							def 							__lowercase			(							self					:       Tuple		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Optional[int]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      List[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     :      Tuple        =							'\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     :      List[Any]        =							'hf-internal-testing/tiny-random-bert'
									BertConfig.from_pretrained(_UpperCAmelCase		)
									BertModel.from_pretrained(_UpperCAmelCase		)
									BertTokenizer.from_pretrained(_UpperCAmelCase		)
									pipeline(task='fill-mask'	,model=_UpperCAmelCase		)

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

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







							@require_torch
							def 							__lowercase			(							self					:       Any		):
									# python one-liner segments
									# this must be loaded before socket.socket is monkey-patched
									_a     :      Dict        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer, pipeline\n        '

									_a     :      Optional[int]        =							'\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 socket.error("Faking flaky internet")\nsocket.socket = offline_socket\n        '

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

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

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







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									# this test is a bit tricky since TRANSFORMERS_OFFLINE can only be changed before
									# `transformers` is loaded, and it's too late for inside pytest - so we are changing it
									# while running an external program

									# python one-liner segments

									# this must be loaded before socket.socket is monkey-patched
									_a     :      Union[str, Any]        =							'\nfrom transformers import BertConfig, BertModel, BertTokenizer\n        '

									_a     :      Optional[Any]        =							'\nmname = "hf-internal-testing/tiny-random-bert-sharded"\nBertConfig.from_pretrained(mname)\nBertModel.from_pretrained(mname)\nprint("success")\n        '

									_a     :      str        =							'\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     :      Optional[Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Dict        =							self.get_env()
									_a     :      int        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									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     :      int        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									_a     :      Optional[Any]        =							'\nfrom transformers import pipeline\n        '
									_a     :      str        =							'\nmname = "hf-internal-testing/tiny-random-bert"\npipe = pipeline(model=mname)\n        '

									_a     :      List[str]        =							'\nimport socket\ndef offline_socket(*args, **kwargs): raise socket.error("Offline mode is enabled")\nsocket.socket = offline_socket\n        '
									_a     :      List[Any]        =							self.get_env()
									_a     :      Dict        =							'1'
									_a     :      Dict        =							[sys.executable, '-c', '\n'.join([load, mock, run]		)]
									_a     :      str        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									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 							__lowercase			(							self					:       int		):
									_a     :      Optional[int]        =							'\nfrom transformers import AutoModel\n        '
									_a     :      List[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     :      Union[str, Any]        =							[sys.executable, '-c', '\n'.join([load, run]		)]

									# should succeed
									_a     :      Tuple        =							self.get_env()
									_a     :      List[str]        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									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     :      Optional[Any]        =							'1'
									_a     :      Any        =							subprocess.run(_UpperCAmelCase	,env=_UpperCAmelCase	,check=_UpperCAmelCase	,capture_output=_UpperCAmelCase		)
									self.assertEqual(result.returncode	,0	,result.stderr		)
									self.assertIn('success'	,result.stdout.decode()		)




