


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_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					42
					__UpperCAmelCase							  =					None




def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase=0.999							,      _UpperCamelCase="cosine"							,      ):


			'''simple docstring'''







			if alpha_transform_type == "cosine":

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

			elif alpha_transform_type == "exp":

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

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

			__lowercase			       = []
			for i in range(_UpperCamelCase	):
						__lowercase			       = i / num_diffusion_timesteps
						__lowercase			       = (i + 1) / num_diffusion_timesteps
						betas.append(min(1 - alpha_bar_fn(_UpperCamelCase	) / alpha_bar_fn(_UpperCamelCase	)							,      _UpperCamelCase	)	)
			return torch.tensor(_UpperCamelCase							,      dtype=torch.floataa	)





class 						lowerCamelCase_							(					lowerCAmelCase__  ,  lowerCAmelCase__					):



					'''simple docstring'''







					@register_to_config
					def __init__(					self			,							snake_case_ = 1_0_0_0			,							snake_case_ = "fixed_small_log"			,							snake_case_ = True			,							snake_case_ = 1.0			,							snake_case_ = "epsilon"			,							snake_case_ = "squaredcos_cap_v2"			,							) ->      int:







								'''simple docstring'''

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

								__lowercase			       = betas_for_alpha_bar(snake_case_    )

								__lowercase			       = 1.0 - self.betas
								__lowercase			       = torch.cumprod(self.alphas			,							dim=0    )
								__lowercase			       = torch.tensor(1.0    )

								# standard deviation of the initial noise distribution
								__lowercase			       = 1.0

								# setable values
								__lowercase			       = None
								__lowercase			       = torch.from_numpy(np.arange(0			,							snake_case_    )[::-1].copy()    )

								__lowercase			       = variance_type






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      torch.FloatTensor:







								'''simple docstring'''

								return sample






					def A   (					self			,							snake_case_			,							snake_case_ = None    ) ->      Optional[Any]:







								'''simple docstring'''

								__lowercase			       = num_inference_steps
								__lowercase			       = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
								__lowercase			       = (np.arange(0			,							snake_case_    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
								__lowercase			       = torch.from_numpy(snake_case_    ).to(snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_=None			,							snake_case_=None			,							snake_case_=None    ) ->      int:







								'''simple docstring'''

								if prev_timestep is None:
											__lowercase			       = t - 1

								__lowercase			       = self.alphas_cumprod[t]
								__lowercase			       = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								__lowercase			       = 1 - alpha_prod_t
								__lowercase			       = 1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
											__lowercase			       = self.betas[t]
								else:
											__lowercase			       = 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
								__lowercase			       = beta_prod_t_prev / beta_prod_t * beta

								if variance_type is None:
											__lowercase			       = self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small_log":
											__lowercase			       = torch.log(torch.clamp(snake_case_			,							min=1e-2_0    )    )
											__lowercase			       = torch.exp(0.5 * variance    )
								elif variance_type == "learned_range":
											# NOTE difference with DDPM scheduler
											__lowercase			       = variance.log()
											__lowercase			       = beta.log()

											__lowercase			       = (predicted_variance + 1) / 2
											__lowercase			       = frac * max_log + (1 - frac) * min_log

								return variance






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							snake_case_=None			,							snake_case_ = True			,							) ->      Union[UnCLIPSchedulerOutput, Tuple]:







								'''simple docstring'''

								__lowercase			       = timestep

								if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
											__lowercase       ,			__lowercase			       = torch.split(snake_case_			,							sample.shape[1]			,							dim=1    )
								else:
											__lowercase			       = None

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

								__lowercase			       = self.alphas_cumprod[t]
								__lowercase			       = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
								__lowercase			       = 1 - alpha_prod_t
								__lowercase			       = 1 - alpha_prod_t_prev

								if prev_timestep == t - 1:
											__lowercase			       = self.betas[t]
											__lowercase			       = self.alphas[t]
								else:
											__lowercase			       = 1 - alpha_prod_t / alpha_prod_t_prev
											__lowercase			       = 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":
											__lowercase			       = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
											__lowercase			       = 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:
											__lowercase			       = 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
								__lowercase			       = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
								__lowercase			       = 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
								__lowercase			       = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								__lowercase			       = 0
								if t > 0:
											__lowercase			       = randn_tensor(
											    model_output.shape			,							dtype=model_output.dtype			,							generator=snake_case_			,							device=model_output.device    )

											__lowercase			       = self._get_variance(
											    snake_case_			,							predicted_variance=snake_case_			,							prev_timestep=snake_case_			,							)

											if self.variance_type == "fixed_small_log":
														__lowercase			       = variance
											elif self.variance_type == "learned_range":
														__lowercase			       = (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.'''    )

											__lowercase			       = variance * variance_noise

								__lowercase			       = pred_prev_sample + variance

								if not return_dict:
											return (pred_prev_sample,)

								return UnCLIPSchedulerOutput(prev_sample=snake_case_			,							pred_original_sample=snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							) ->      torch.FloatTensor:







								'''simple docstring'''

								__lowercase			       = self.alphas_cumprod.to(device=original_samples.device			,							dtype=original_samples.dtype    )
								__lowercase			       = timesteps.to(original_samples.device    )

								__lowercase			       = alphas_cumprod[timesteps] ** 0.5
								__lowercase			       = sqrt_alpha_prod.flatten()
								while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
											__lowercase			       = sqrt_alpha_prod.unsqueeze(-1    )

								__lowercase			       = (1 - alphas_cumprod[timesteps]) ** 0.5
								__lowercase			       = sqrt_one_minus_alpha_prod.flatten()
								while len(sqrt_one_minus_alpha_prod.shape    ) < len(original_samples.shape    ):
											__lowercase			       = sqrt_one_minus_alpha_prod.unsqueeze(-1    )

								__lowercase			       = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
								return noisy_samples






# 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			:			Union[str, Any]												=					'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
a			:			Tuple												=					[
    '''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			:			List[Any]												=					dict(zip(vocab, range(len(vocab))))
a			:			Optional[int]												=					['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
	a			:			Union[str, Any]												=					Path(tmpdirname)
	a			:			Dict												=					build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
	a			:			Optional[int]												=					build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
	a			:			Tuple												=					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			:			Optional[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=1000,
    tgt_vocab_size=1000,
    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			:			Dict												=					tokenizer(['''Making tiny model'''], return_tensors='''pt''')
a			:			Union[str, Any]												=					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


