





"""simple docstring"""



import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


a_ =			"""hf-internal-testing/tiny-random-bert"""
a_ =			os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
a_ =			"""9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""






class 		__snake_case					(		unittest.TestCase     ):





 """simple docstring"""


 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     __A    :						List[str]						 =     cached_file(__lowerCamelCase					,						__lowerCamelCase  )
     # Should have downloaded the file in here
     self.assertTrue(os.path.isdir(__lowerCamelCase  )  )
     # Cache should contain at least those three subfolders:
     for subfolder in ["blobs", "refs", "snapshots"]:
         self.assertTrue(os.path.isdir(os.path.join(__lowerCamelCase					,						__lowerCamelCase  )  )  )
     with open(os.path.join(__lowerCamelCase					,						'''refs'''					,						'''main'''  )  ) as f:
         __A    :						List[Any]						 =     f.read()
     self.assertEqual(__lowerCamelCase					,						os.path.join(__lowerCamelCase					,						'''snapshots'''					,						__lowerCamelCase					,						__lowerCamelCase  )  )
     self.assertTrue(os.path.isfile(__lowerCamelCase  )  )

     # File is cached at the same place the second time.
     __A    :						List[Any]						 =     cached_file(__lowerCamelCase					,						__lowerCamelCase  )
     self.assertEqual(__lowerCamelCase					,						__lowerCamelCase  )

     # Using a specific revision to test the full commit hash.
     __A    :						int						 =     cached_file(__lowerCamelCase					,						__lowerCamelCase					,						revision='''9b8c223'''  )
     self.assertEqual(__lowerCamelCase					,						os.path.join(__lowerCamelCase					,						'''snapshots'''					,						__lowerCamelCase					,						__lowerCamelCase  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with self.assertRaisesRegex(__lowerCamelCase					,						'''is not a valid model identifier'''  ):
         __A    :						List[Any]						 =     cached_file('''tiny-random-bert'''					,						__lowerCamelCase  )

     with self.assertRaisesRegex(__lowerCamelCase					,						'''is not a valid git identifier'''  ):
         __A    :						Optional[int]						 =     cached_file(__lowerCamelCase					,						__lowerCamelCase					,						revision='''aaaa'''  )

     with self.assertRaisesRegex(__lowerCamelCase					,						'''does not appear to have a file named'''  ):
         __A    :						Optional[Any]						 =     cached_file(__lowerCamelCase					,						'''conf'''  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with self.assertRaisesRegex(__lowerCamelCase					,						'''does not appear to have a file named'''  ):
         __A    :						Any						 =     cached_file(__lowerCamelCase					,						'''conf'''  )

     with open(os.path.join(__lowerCamelCase					,						'''refs'''					,						'''main'''  )  ) as f:
         __A    :						Union[str, Any]						 =     f.read()
     self.assertTrue(os.path.isfile(os.path.join(__lowerCamelCase					,						'''.no_exist'''					,						__lowerCamelCase					,						'''conf'''  )  )  )

     __A    :						Any						 =     cached_file(__lowerCamelCase					,						'''conf'''					,						_raise_exceptions_for_missing_entries=__lowerCamelCase  )
     self.assertIsNone(__lowerCamelCase  )

     __A    :						Optional[Any]						 =     cached_file(__lowerCamelCase					,						'''conf'''					,						local_files_only=__lowerCamelCase					,						_raise_exceptions_for_missing_entries=__lowerCamelCase  )
     self.assertIsNone(__lowerCamelCase  )

     __A    :						List[Any]						 =     mock.Mock()
     __A    :						List[str]						 =     500
     __A    :						str						 =     {}
     __A    :						Any						 =     HTTPError
     __A    :						Optional[int]						 =     {}

     # Under the mock environment we get a 500 error when trying to reach the tokenizer.
     with mock.patch('''requests.Session.request'''					,						return_value=__lowerCamelCase  ) as mock_head:
         __A    :						List[str]						 =     cached_file(__lowerCamelCase					,						'''conf'''					,						_raise_exceptions_for_connection_errors=__lowerCamelCase  )
         self.assertIsNone(__lowerCamelCase  )
         # This check we did call the fake head request
         mock_head.assert_called()

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only'''					,						__lowerCamelCase  )  )
     self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only'''					,						__lowerCamelCase  )  )
     self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only'''					,						__lowerCamelCase  )  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self.assertIsNone(get_file_from_repo('''bert-base-cased'''					,						'''ahah.txt'''  )  )

     # The function raises if the repository does not exist.
     with self.assertRaisesRegex(__lowerCamelCase					,						'''is not a valid model identifier'''  ):
         get_file_from_repo('''bert-base-case'''					,						__lowerCamelCase  )

     # The function raises if the revision does not exist.
     with self.assertRaisesRegex(__lowerCamelCase					,						'''is not a valid git identifier'''  ):
         get_file_from_repo('''bert-base-cased'''					,						__lowerCamelCase					,						revision='''ahaha'''  )

     __A    :						List[str]						 =     get_file_from_repo('''bert-base-cased'''					,						__lowerCamelCase  )
     # The name is the cached name which is not very easy to test, so instead we load the content.
     __A    :						List[str]						 =     json.loads(open(__lowerCamelCase					,						'''r'''  ).read()  )
     self.assertEqual(config['''hidden_size''']					,						768  )







 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     with tempfile.TemporaryDirectory() as tmp_dir:
         __A    :						List[Any]						 =     Path(__lowerCamelCase  ) / '''a.txt'''
         filename.touch()
         self.assertEqual(get_file_from_repo(__lowerCamelCase					,						'''a.txt'''  )					,						str(__lowerCamelCase  )  )

         self.assertIsNone(get_file_from_repo(__lowerCamelCase					,						'''b.txt'''  )  )











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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 		__snake_case					:





 """simple docstring"""


 _lowerCamelCase        =     42

 # setable values
 _lowerCamelCase        =     42
 _lowerCamelCase        =     42
 _lowerCamelCase        =     None

 @classmethod
 def 					UpperCamelCase__(    cls					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase  ):



     '''simple docstring'''




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






@dataclass
class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     42






class 		__snake_case					(		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

 _lowerCamelCase        =     42

 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     return True

 @register_to_config
 def __init__(    self					,						__lowerCamelCase = 1000					,						__lowerCamelCase = 0.0_0_0_1					,						__lowerCamelCase = 0.0_2					,						__lowerCamelCase = "linear"					,						__lowerCamelCase = None					,						__lowerCamelCase = "fixed_small"					,						__lowerCamelCase = True					,						__lowerCamelCase = "epsilon"					,						__lowerCamelCase = jnp.floataa					,						):



     '''simple docstring'''




     __A    :						Tuple						 =     dtype

 def 					UpperCamelCase__(    self					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     if common is None:
         __A    :						Tuple						 =     CommonSchedulerState.create(self  )

     # standard deviation of the initial noise distribution
     __A    :						Tuple						 =     jnp.array(1.0					,						dtype=self.dtype  )

     __A    :						Optional[int]						 =     jnp.arange(0					,						self.config.num_train_timesteps  ).round()[::-1]

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None  ):



     '''simple docstring'''




     return sample

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = ()  ):



     '''simple docstring'''




     __A    :						Optional[Any]						 =     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
     __A    :						Optional[Any]						 =     (jnp.arange(0					,						__lowerCamelCase  ) * step_ratio).round()[::-1]

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase=None					,						__lowerCamelCase=None  ):



     '''simple docstring'''




     __A    :						int						 =     state.common.alphas_cumprod[t]
     __A    :						List[str]						 =     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
     __A    :						str						 =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

     if variance_type is None:
         __A    :						Dict						 =     self.config.variance_type

     # hacks - were probably added for training stability
     if variance_type == "fixed_small":
         __A    :						List[Any]						 =     jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )
     # for rl-diffuser https://arxiv.org/abs/2205.09991
     elif variance_type == "fixed_small_log":
         __A    :						Optional[Any]						 =     jnp.log(jnp.clip(__lowerCamelCase					,						a_min=1e-2_0  )  )
     elif variance_type == "fixed_large":
         __A    :						Tuple						 =     state.common.betas[t]
     elif variance_type == "fixed_large_log":
         # Glide max_log
         __A    :						Union[str, Any]						 =     jnp.log(state.common.betas[t]  )
     elif variance_type == "learned":
         return predicted_variance
     elif variance_type == "learned_range":
         __A    :						Optional[Any]						 =     variance
         __A    :						Optional[Any]						 =     state.common.betas[t]
         __A    :						Any						 =     (predicted_variance + 1) / 2
         __A    :						Union[str, Any]						 =     frac * max_log + (1 - frac) * min_log

     return variance

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = None					,						__lowerCamelCase = True					,						):



     '''simple docstring'''




     __A    :						Optional[int]						 =     timestep

     if key is None:
         __A    :						List[Any]						 =     jax.random.PRNGKey(0  )

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

     # 1. compute alphas, betas
     __A    :						Dict						 =     state.common.alphas_cumprod[t]
     __A    :						int						 =     jnp.where(t > 0					,						state.common.alphas_cumprod[t - 1]					,						jnp.array(1.0					,						dtype=self.dtype  )  )
     __A    :						Tuple						 =     1 - alpha_prod_t
     __A    :						Optional[int]						 =     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":
         __A    :						Optional[Any]						 =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
     elif self.config.prediction_type == "sample":
         __A    :						Any						 =     model_output
     elif self.config.prediction_type == "v_prediction":
         __A    :						str						 =     (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:
         __A    :						str						 =     jnp.clip(__lowerCamelCase					,						-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
     __A    :						Optional[Any]						 =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
     __A    :						Union[str, 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
     __A    :						Optional[Any]						 =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

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

     __A    :						Optional[Any]						 =     jnp.where(t > 0					,						random_variance()					,						jnp.zeros(model_output.shape					,						dtype=self.dtype  )  )

     __A    :						Any						 =     pred_prev_sample + variance

     if not return_dict:
         return (pred_prev_sample, state)

     return FlaxDDPMSchedulerOutput(prev_sample=__lowerCamelCase					,						state=__lowerCamelCase  )

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




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







 def __len__(    self  ):



     '''simple docstring'''




     return self.config.num_train_timesteps




