"""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        UpperCamelCase		:
   lowercase							=  42

   # setable values
   lowercase							=  42
   lowercase							=  42
   lowercase							=  None
   @classmethod
   def 			_UpperCAmelCase   (			cls     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


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







@dataclass
class        UpperCamelCase		(			_a  ):
   lowercase							=  42







class        UpperCamelCase		(			_a						,      _a  ):
   lowercase							=  [e.name for e in FlaxKarrasDiffusionSchedulers]

   lowercase							=  42
   @property
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        return True
   @register_to_config
   def __init__(			self     ,__UpperCamelCase = 1000     ,__UpperCamelCase = 0.0001     ,__UpperCamelCase = 0.02     ,__UpperCamelCase = "linear"     ,__UpperCamelCase = None     ,__UpperCamelCase = "fixed_small"     ,__UpperCamelCase = True     ,__UpperCamelCase = "epsilon"     ,__UpperCamelCase = jnp.floataa     ,)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	dtype
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase = None			)	->      DDPMSchedulerState:


        '''simple docstring'''


        if common is None:
             lowercase_	:				str													=	CommonSchedulerState.create(self			)

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

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

        return DDPMSchedulerState.create(
            common=__UpperCamelCase     ,init_noise_sigma=__UpperCamelCase     ,timesteps=__UpperCamelCase     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      jnp.ndarray:


        '''simple docstring'''


        return sample
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = ()			)	->      DDPMSchedulerState:


        '''simple docstring'''


        lowercase_	:				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
        lowercase_	:				int													=	(jnp.arange(0     ,__UpperCamelCase			) * step_ratio).round()[::-1]

        return state.replace(
            num_inference_steps=__UpperCamelCase     ,timesteps=__UpperCamelCase     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase=None     ,__UpperCamelCase=None			)	->      Any:


        '''simple docstring'''


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

        if variance_type is None:
             lowercase_	:				int													=	self.config.variance_type

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

        return variance
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase = None     ,__UpperCamelCase = True     ,)	->      Union[FlaxDDPMSchedulerOutput, Tuple]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	timestep

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

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

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

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

        lowercase_	:				Tuple													=	jnp.where(t > 0     ,random_variance()     ,jnp.zeros(model_output.shape     ,dtype=self.dtype			)			)

        lowercase_	:				int													=	pred_prev_sample + variance

        if not return_dict:
             return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase     ,state=__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,)	->      jnp.ndarray:


        '''simple docstring'''


        return add_noise_common(state.common     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,)	->      jnp.ndarray:


        '''simple docstring'''


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







   def __len__(			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.config.num_train_timesteps








import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory

from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
    convert,
    ensure_valid_input,
    generate_identified_filename,
    infer_shapes,
    quantize,
)
from transformers.testing_utils import require_tf, require_tokenizers, require_torch, slow



class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   Dict:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       :






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   List[str]:


							'''simple docstring'''



							return None




class 					__lowerCAmelCase       (			unittest.TestCase     ):
			lowerCamelCase_     :     Dict       			=      [
			    # (model_name, model_kwargs)
			    ('''bert-base-cased''', {}),
			    ('''gpt2''', {'''use_cache''': False}),  # We don't support exporting GPT2 past keys anymore
			]






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Union[str, Any]:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   int:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    str				=  ['''[UNK]''', '''[SEP]''', '''[CLS]''', '''[PAD]''', '''[MASK]''', '''some''', '''other''', '''words''']
							with NamedTemporaryFile(mode='''w+t'''    ) as vocab_file:
											vocab_file.write('''\n'''.join(__magic_name__    )    )
											vocab_file.flush()
											snake_case_						:    Optional[Any]				=  BertTokenizerFast(vocab_file.name    )

							with TemporaryDirectory() as bert_save_dir:
											snake_case_						:    str				=  BertModel(BertConfig(vocab_size=len(__magic_name__    )    )    )
											model.save_pretrained(__magic_name__    )
											self._test_export(__magic_name__					,							'''pt'''					,							12					,							__magic_name__    )






			@require_tf
			@slow
			def 			lowerCamelCase  (self    )     ->   Tuple:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Tuple				=  self._test_export(__magic_name__					,							'''tf'''					,							12					,							**__magic_name__    )
											snake_case_						:    List[str]				=  quantize(Path(__magic_name__    )    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			@require_torch
			@slow
			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							for model, model_kwargs in OnnxExportTestCase.MODEL_TO_TEST:
											snake_case_						:    Any				=  self._test_export(__magic_name__					,							'''pt'''					,							12					,							**__magic_name__    )
											snake_case_						:    Any				=  quantize(__magic_name__    )

											# Ensure the actual quantized model is not bigger than the original one
											if quantized_path.stat().st_size >= Path(__magic_name__    ).stat().st_size:
															self.fail('''Quantized model is bigger than initial ONNX model'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__=None					,							**__magic_name__    )     ->   Tuple:


							'''simple docstring'''



							try:
											# Compute path
											with TemporaryDirectory() as tempdir:
															snake_case_						:    List[str]				=  Path(__magic_name__    ).joinpath('''model.onnx'''    )

											# Remove folder if exists
											if path.parent.exists():
															path.parent.rmdir()

											# Export
											convert(__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							__magic_name__					,							**__magic_name__    )

											return path
							except Exception as e:
											self.fail(__magic_name__    )






			@require_torch
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   Optional[Any]:


							'''simple docstring'''



							from transformers import BertModel

							snake_case_						:    Optional[Any]				=  BertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    int				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''pt'''    )






			@require_tf
			@require_tokenizers
			@slow
			def 			lowerCamelCase  (self    )     ->   List[str]:


							'''simple docstring'''



							from transformers import TFBertModel

							snake_case_						:    Any				=  TFBertModel(BertConfig.from_pretrained('''lysandre/tiny-bert-random'''    )    )
							snake_case_						:    str				=  BertTokenizerFast.from_pretrained('''lysandre/tiny-bert-random'''    )
							self._test_infer_dynamic_axis(__magic_name__					,							__magic_name__					,							'''tf'''    )






			def 			lowerCamelCase  (self					,							__magic_name__					,							__magic_name__					,							__magic_name__    )     ->   int:


							'''simple docstring'''



							snake_case_						:    Tuple				=  FeatureExtractionPipeline(__magic_name__					,							__magic_name__    )

							snake_case_						:    Optional[int]				=  ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''output_0''', '''output_1''']
							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_						:    Optional[int]				=  infer_shapes(__magic_name__					,							__magic_name__    )

							# Assert all variables are present
							self.assertEqual(len(__magic_name__    )					,							len(__magic_name__    )    )
							self.assertTrue(all(var_name in shapes for var_name in variable_names    )    )
							self.assertSequenceEqual(variable_names[:3]					,							__magic_name__    )
							self.assertSequenceEqual(variable_names[3:]					,							__magic_name__    )

							# Assert inputs are {0: batch, 1: sequence}
							for var_name in ["input_ids", "token_type_ids", "attention_mask"]:
											self.assertDictEqual(shapes[var_name]					,							{0: '''batch''', 1: '''sequence'''}    )

							# Assert outputs are {0: batch, 1: sequence} and {0: batch}
							self.assertDictEqual(shapes['''output_0''']					,							{0: '''batch''', 1: '''sequence'''}    )
							self.assertDictEqual(shapes['''output_1''']					,							{0: '''batch'''}    )






			def 			lowerCamelCase  (self    )     ->   Optional[int]:


							'''simple docstring'''



							snake_case_						:    Tuple				=  ['''input_ids''', '''attention_mask''', '''token_type_ids''']
							snake_case_						:    List[str]				=  {'''input_ids''': [1, 2, 3, 4], '''attention_mask''': [0, 0, 0, 0], '''token_type_ids''': [1, 1, 1, 1]}
							snake_case_			,							snake_case_						:    Tuple				=  ensure_valid_input(FuncContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the same number of args (all are valid)
							self.assertEqual(len(__magic_name__    )					,							3    )

							# Should have exactly the same input names
							self.assertEqual(set(__magic_name__    )					,							set(__magic_name__    )    )

							# Parameter should be reordered according to their respective place in the function:
							# (input_ids, token_type_ids, attention_mask)
							self.assertEqual(__magic_name__					,							(tokens['''input_ids'''], tokens['''token_type_ids'''], tokens['''attention_mask'''])    )

							# Generated args are interleaved with another args (for instance parameter "past" in GPT2)
							snake_case_			,							snake_case_						:    Dict				=  ensure_valid_input(FuncNonContiguousArgs()					,							__magic_name__					,							__magic_name__    )

							# Should have exactly the one arg (all before the one not provided "some_other_args")
							self.assertEqual(len(__magic_name__    )					,							1    )
							self.assertEqual(len(__magic_name__    )					,							1    )

							# Should have only "input_ids"
							self.assertEqual(inputs_args[0]					,							tokens['''input_ids''']    )
							self.assertEqual(ordered_input_names[0]					,							'''input_ids'''    )






			def 			lowerCamelCase  (self    )     ->   Any:


							'''simple docstring'''



							snake_case_						:    Optional[int]				=  generate_identified_filename(Path('''/home/something/my_fake_model.onnx'''    )					,							'''-test'''    )
							self.assertEqual('''/home/something/my_fake_model-test.onnx'''					,							generated.as_posix()    )
