






'''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 _lowercase	:
							a				      =       42

							# setable values
							a				      =       42
							a				      =       42
							a				      =       None




							@classmethod
							def 		lowerCamelCase_				(							cls:				Optional[int]			,							UpperCamelCase__:				CommonSchedulerState			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				jnp.ndarray     ):
														return cls(common=UpperCamelCase__			,							init_noise_sigma=UpperCamelCase__			,							timesteps=UpperCamelCase__     )



@dataclass
class _lowercase	(				_lowercase		):
							a				      =       42



class _lowercase	(				_lowercase				,      _lowercase		):
							a				      =       [e.name for e in FlaxKarrasDiffusionSchedulers]

							a				      =       42




							@property
							def 		lowerCamelCase_				(							self:				str     ):
														return True




							@register_to_config
							def __init__(							self:				List[str]			,							UpperCamelCase__:				int = 1_000			,							UpperCamelCase__:				float = 0.0_001			,							UpperCamelCase__:				float = 0.02			,							UpperCamelCase__:				str = "linear"			,							UpperCamelCase__:				Optional[jnp.ndarray] = None			,							UpperCamelCase__:				str = "fixed_small"			,							UpperCamelCase__:				bool = True			,							UpperCamelCase__:				str = "epsilon"			,							UpperCamelCase__:				jnp.dtype = jnp.floataa			,							):
														lowerCamelCase__					:	Any         =       dtype




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				Optional[CommonSchedulerState] = None     ):
														if common is None:
																					lowerCamelCase__					:	Any         =       CommonSchedulerState.create(self     )

														# standard deviation of the initial noise distribution
														lowerCamelCase__					:	Optional[int]         =       jnp.array(1.0			,							dtype=self.dtype     )

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

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




							def 		lowerCamelCase_				(							self:				List[Any]			,							UpperCamelCase__:				DDPMSchedulerState			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				Optional[int] = None     ):
														return sample




							def 		lowerCamelCase_				(							self:				List[str]			,							UpperCamelCase__:				DDPMSchedulerState			,							UpperCamelCase__:				int			,							UpperCamelCase__:				Tuple = ()     ):
														lowerCamelCase__					:	Union[str, 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
														lowerCamelCase__					:	Optional[Any]         =       (jnp.arange(0			,							UpperCamelCase__     ) * step_ratio).round()[::-1]

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




							def 		lowerCamelCase_				(							self:				Dict			,							UpperCamelCase__:				DDPMSchedulerState			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Optional[int]=None			,							UpperCamelCase__:				Any=None     ):
														lowerCamelCase__					:	Optional[int]         =       state.common.alphas_cumprod[t]
														lowerCamelCase__					:	Union[str, Any]         =       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
														lowerCamelCase__					:	List[Any]         =       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

														if variance_type is None:
																					lowerCamelCase__					:	Optional[int]         =       self.config.variance_type

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

														return variance




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				DDPMSchedulerState			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				int			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				Optional[jax.random.KeyArray] = None			,							UpperCamelCase__:				bool = True			,							):
														lowerCamelCase__					:	int         =       timestep

														if key is None:
																					lowerCamelCase__					:	Optional[int]         =       jax.random.PRNGKey(0     )

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

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

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

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

														lowerCamelCase__					:	Any         =       pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample, state)

														return FlaxDDPMSchedulerOutput(prev_sample=UpperCamelCase__			,							state=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]			,							UpperCamelCase__:				DDPMSchedulerState			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				jnp.ndarray			,							):
														return add_noise_common(state.common			,							UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]			,							UpperCamelCase__:				DDPMSchedulerState			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				jnp.ndarray			,							UpperCamelCase__:				jnp.ndarray			,							):
														return get_velocity_common(state.common			,							UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




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









'''simple docstring'''







# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

_A	:			Dict         ='''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
_A	:			List[str]         =[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
_A	:			str         =dict(zip(vocab, range(len(vocab))))
_A	:			List[str]         =['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			_A	:			Union[str, Any]         =Path(tmpdirname)
			_A	:			str         =build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			_A	:			int         =build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			_A	:			List[Any]         =build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			_A	:			int         =FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

_A	:			List[str]         =FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1_000,
    tgt_vocab_size=1_000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

_A	:			Union[str, Any]         =FSMTForConditionalGeneration(config)
print(F'num of params {tiny_model.num_parameters()}')

# Test
_A	:			List[str]         =tokenizer(['''Making tiny model'''], return_tensors='''pt''')
_A	:			Tuple         =tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru

