



import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


snake_case__					:     str    		=   """scheduler_config.json"""

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Union[str, Any]      							=      1
						_snake_case			:     int      							=      2
						_snake_case			:     Optional[int]      							=      3
						_snake_case			:     Optional[int]      							=      4
						_snake_case			:     int      							=      5

@dataclass
class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     jnp.ndarray

class   _A   :


						'''simple docstring'''





						_snake_case			:     Optional[int]      							=      SCHEDULER_CONFIG_NAME
						_snake_case			:     Dict      							=      ["""dtype"""]
						_snake_case			:     Dict      							=      []
						_snake_case			:     Union[str, Any]      							=      True



						@classmethod
						def     _snake_case	(	cls	:							Dict							,      lowerCamelCase	:							Dict[str, Any] = None							,      lowerCamelCase	:							Optional[str] = None							,      lowerCamelCase	:							Optional[Any]=False							,      **lowerCamelCase	:							Union[str, Any]							,      ):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			cls.load_config(
							    pretrained_model_name_or_path=lowerCamelCase							,      subfolder=lowerCamelCase							,      return_unused_kwargs=lowerCamelCase							,      **lowerCamelCase							,      )
							__lowercase       ,    __lowercase		       =			cls.from_config(lowerCamelCase							,      return_unused_kwargs=lowerCamelCase							,      **lowerCamelCase	)

							if hasattr(lowerCamelCase							,      "create_state"	) and getattr(lowerCamelCase							,      "has_state"							,      lowerCamelCase	):
								__lowercase		       =			scheduler.create_state()

							if return_unused_kwargs:
								return scheduler, state, unused_kwargs

							return scheduler, state



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Union[str, os.PathLike]							,      lowerCamelCase	:							bool = False							,      **lowerCamelCase	:							List[str]	):






							'''simple docstring'''



							self.save_config(save_directory=lowerCamelCase							,      push_to_hub=lowerCamelCase							,      **lowerCamelCase	)



						@property
						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							return self._get_compatibles()



						@classmethod
						def     _snake_case	(	cls	:							Union[str, Any]	):






							'''simple docstring'''



							__lowercase		       =			list(set([cls.__name__] + cls._compatibles	)	)
							__lowercase		       =			importlib.import_module(__name__.split("."	)[0]	)
							__lowercase		       =			[
							    getattr(lowerCamelCase							,      lowerCamelCase	) for c in compatible_classes_str if hasattr(lowerCamelCase							,      lowerCamelCase	)
							]
							return compatible_classes



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	assert len(_SCREAMING_SNAKE_CASE   ) >= x.ndim
	return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(_SCREAMING_SNAKE_CASE   ) - x.ndim)   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=0.9_9_9  ,	_SCREAMING_SNAKE_CASE=jnp.floataa   ):

	def alpha_bar(_SCREAMING_SNAKE_CASE   ):
		return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

	__lowercase		       =			[]
	for i in range(_SCREAMING_SNAKE_CASE   ):
		__lowercase		       =			i / num_diffusion_timesteps
		__lowercase		       =			(i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar(_SCREAMING_SNAKE_CASE   ) / alpha_bar(_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )   )
	return jnp.array(_SCREAMING_SNAKE_CASE  ,	dtype=_SCREAMING_SNAKE_CASE   )

@flax.struct.dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     jnp.ndarray
						_snake_case			:     jnp.ndarray
						_snake_case			:     jnp.ndarray



						@classmethod
						def     _snake_case	(	cls	:							str							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			scheduler.config

							if config.trained_betas is not None:
								__lowercase		       =			jnp.asarray(config.trained_betas							,      dtype=scheduler.dtype	)
							elif config.beta_schedule == "linear":
								__lowercase		       =			jnp.linspace(config.beta_start							,      config.beta_end							,      config.num_train_timesteps							,      dtype=scheduler.dtype	)
							elif config.beta_schedule == "scaled_linear":
								# this schedule is very specific to the latent diffusion model.
								__lowercase		       =			(
								    jnp.linspace(
								        config.beta_start**0.5							,      config.beta_end**0.5							,      config.num_train_timesteps							,      dtype=scheduler.dtype	)
								    ** 2
								)
							elif config.beta_schedule == "squaredcos_cap_v2":
								# Glide cosine schedule
								__lowercase		       =			betas_for_alpha_bar(config.num_train_timesteps							,      dtype=scheduler.dtype	)
							else:
								raise NotImplementedError(
								    f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""	)

							__lowercase		       =			1.0 - betas

							__lowercase		       =			jnp.cumprod(lowerCamelCase							,      axis=0	)

							return cls(
							    alphas=lowerCamelCase							,      betas=lowerCamelCase							,      alphas_cumprod=lowerCamelCase							,      )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			state.alphas_cumprod

	__lowercase		       =			alphas_cumprod[timesteps] ** 0.5
	__lowercase		       =			sqrt_alpha_prod.flatten()
	__lowercase		       =			broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE  ,	original_samples.shape   )

	__lowercase		       =			(1 - alphas_cumprod[timesteps]) ** 0.5
	__lowercase		       =			sqrt_one_minus_alpha_prod.flatten()
	__lowercase		       =			broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE  ,	original_samples.shape   )

	return sqrt_alpha_prod, sqrt_one_minus_alpha_prod



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase       ,    __lowercase		       =			get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
	return noisy_samples



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase       ,    __lowercase		       =			get_sqrt_alpha_prod(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
	return velocity








import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
snake_case__					:     Optional[Any]    		=   logging.getLogger()



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			"\n".join(_SCREAMING_SNAKE_CASE   )
	Path(_SCREAMING_SNAKE_CASE   ).open("w"   ).writelines(_SCREAMING_SNAKE_CASE   )


snake_case__					:     List[str]    		=   """patrickvonplaten/t5-tiny-random"""
snake_case__					:     int    		=   """sshleifer/bart-tiny-random"""
snake_case__					:     Union[str, Any]    		=   """sshleifer/tiny-mbart"""

snake_case__					:     List[str]    		=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks

class   _A   ( _lowercase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()
							__lowercase		       =			[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
							_dump_articles(lowerCamelCase							,      lowerCamelCase	)

							__lowercase		       =			str(Path(self.get_auto_remove_tmp_dir()	) / "scores.json"	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {input_file_name}
            {output_file_name}
            --score_path {score_path}
            --task {task}
            --num_beams 2
            --length_penalty 2.0
            """.split()

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								run_generate()
								assert Path(lowerCamelCase	).exists()
								# os.remove(Path(output_file_name))



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([BART_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							str	):






							'''simple docstring'''



							self.run_eval_tester(lowerCamelCase	)



						@parameterized.expand([T5_TINY, MBART_TINY]	)
						@slow
						def     _snake_case	(	self	:							Optional[Any]							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
							__lowercase		       =			input_file_name.parent / "utest_output.txt"
							assert not output_file_name.exists()

							__lowercase		       =			{
							    "en": ["Machine learning is great, isn't it?", "I like to eat bananas", "Tomorrow is another great day!"],
							    "de": [
							        "Maschinelles Lernen ist großartig, oder?",
							        "Ich esse gerne Bananen",
							        "Morgen ist wieder ein toller Tag!",
							    ],
							}

							__lowercase		       =			Path(self.get_auto_remove_tmp_dir()	)
							__lowercase		       =			str(tmp_dir / "scores.json"	)
							__lowercase		       =			str(tmp_dir / "val.target"	)
							_dump_articles(lowerCamelCase							,      text["en"]	)
							_dump_articles(lowerCamelCase							,      text["de"]	)
							__lowercase		       =			"translation_en_to_de" if model == T5_TINY else "summarization"
							__lowercase		       =			f"""
            run_eval_search.py
            {model}
            {str(lowerCamelCase	)}
            {str(lowerCamelCase	)}
            --score_path {score_path}
            --reference_path {reference_path}
            --task {task}
            """.split()
							testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]	)

							with patch.object(lowerCamelCase							,      "argv"							,      lowerCamelCase	):
								with CaptureStdout() as cs:
									run_search()
								__lowercase		       =			[" num_beams | length_penalty", model, "Best score args"]
								__lowercase		       =			["Info"]
								if "translation" in task:
									expected_strings.append("bleu"	)
								else:
									expected_strings.extend(lowerCamelCase	)
								for w in expected_strings:
									assert w in cs.out
								for w in un_expected_strings:
									assert w not in cs.out
								assert Path(lowerCamelCase	).exists()
								os.remove(Path(lowerCamelCase	)	)



