






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


lowercase  							=					"scheduler_config.json"




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= 1
	lowerCAmelCase													= 2
	lowerCAmelCase													= 3
	lowerCAmelCase													= 4
	lowerCAmelCase													= 5






@dataclass
class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= 42






class  UpperCamelCase_      :



	'''simple docstring'''




	lowerCAmelCase													= SCHEDULER_CONFIG_NAME
	lowerCAmelCase													= ['''dtype''']
	lowerCAmelCase													= []
	lowerCAmelCase													= True




	@classmethod
	def 							_UpperCamelCase			(       cls       ,			a = None       ,			a = None       ,			a=False       ,			**a       ,			)       ->    Any:
								snake_case_			,		snake_case_				=							cls.load_config(
								    pretrained_model_name_or_path=a       ,			subfolder=a       ,			return_unused_kwargs=a       ,			**a       ,			)
								snake_case_			,		snake_case_				=							cls.from_config(a       ,			return_unused_kwargs=a       ,			**a					)

								if hasattr(a       ,			'create_state'					) and getattr(a       ,			'has_state'       ,			a					):
															snake_case_				=							scheduler.create_state()

								if return_unused_kwargs:
															return scheduler, state, unused_kwargs

								return scheduler, state




	def 							_UpperCamelCase			(       self       ,			a       ,			a = False       ,			**a					)       ->    str:
								self.save_config(save_directory=a       ,			push_to_hub=a       ,			**a					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    int:
								return self._get_compatibles()






	@classmethod
	def 							_UpperCamelCase			(       cls					)       ->    Any:
								snake_case_				=							list(set([cls.__name__] + cls._compatibles					)					)
								snake_case_				=							importlib.import_module(__name__.split('.'					)[0]					)
								snake_case_				=							[
								    getattr(a       ,			a					) for c in compatible_classes_str if hasattr(a       ,			a					)
								]
								return compatible_classes






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





def 						__UpperCAmelCase			(     a_							,     a_=0.9_99							,     a_=jnp.floataa):

							def alpha_bar(a_):
														return math.cos((time_step + 0.0_08) / 1.0_08 * math.pi / 2) ** 2

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




@flax.struct.dataclass
class  UpperCamelCase_      :



	'''simple docstring'''




	lowerCAmelCase													= 42
	lowerCAmelCase													= 42
	lowerCAmelCase													= 42




	@classmethod
	def 							_UpperCamelCase			(       cls       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							scheduler.config

								if config.trained_betas is not None:
															snake_case_				=							jnp.asarray(config.trained_betas       ,			dtype=scheduler.dtype					)
								elif config.beta_schedule == "linear":
															snake_case_				=							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.
															snake_case_				=							(
															    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
															snake_case_				=							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__}'''					)

								snake_case_				=							1.0 - betas

								snake_case_				=							jnp.cumprod(a       ,			axis=0					)

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






def 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_):
							snake_case_				=							state.alphas_cumprod

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

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

							return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





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





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













import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel




class  UpperCamelCase_      :



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a=13       ,			a=7       ,			a=True       ,			a=True       ,			a=True       ,			a=True       ,			a=99       ,			a=32       ,			a=5       ,			a=4       ,			a=4       ,			a="gelu"       ,			a=0.0       ,			a=0.1       ,			a=True       ,			a=5_12       ,			a=16       ,			a=2       ,			a=0.02       ,			a=3       ,			a=4       ,			a=None       ,			)       ->    Optional[Any]:
								snake_case_				=							parent
								snake_case_				=							batch_size
								snake_case_				=							seq_length
								snake_case_				=							is_training
								snake_case_				=							use_input_mask
								snake_case_				=							use_token_type_ids
								snake_case_				=							use_labels
								snake_case_				=							vocab_size
								snake_case_				=							hidden_size
								snake_case_				=							num_hidden_layers
								snake_case_				=							num_attention_heads
								snake_case_				=							intermediate_multiple_size
								snake_case_				=							hidden_act
								snake_case_				=							hidden_dropout
								snake_case_				=							attention_dropout
								snake_case_				=							weight_tying
								snake_case_				=							max_position_embeddings
								snake_case_				=							type_vocab_size
								snake_case_				=							type_sequence_label_size
								snake_case_				=							initializer_range
								snake_case_				=							num_labels
								snake_case_				=							num_choices
								snake_case_				=							scope




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size					)

								snake_case_				=							None
								if self.use_input_mask:
															snake_case_				=							random_attention_mask([self.batch_size, self.seq_length]					)

								snake_case_				=							None
								if self.use_labels:
															snake_case_				=							ids_tensor([self.batch_size, self.seq_length]       ,			self.num_labels					)

								snake_case_				=							self.get_config()

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								return GPTNeoXJapaneseConfig(
								    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			intermediate_multiple_size=self.intermediate_multiple_size       ,			hidden_act=self.hidden_act       ,			hidden_dropout=self.hidden_dropout       ,			attention_dropout=self.attention_dropout       ,			weight_tying=self.weight_tying       ,			max_position_embeddings=self.max_position_embeddings       ,			type_vocab_size=self.type_vocab_size       ,			is_decoder=a       ,			initializer_range=self.initializer_range       ,			)




	def 							_UpperCamelCase			(       self					)       ->    int:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.prepare_config_and_inputs()

								snake_case_				=							True

								return config, input_ids, input_mask, token_labels




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Any:
								snake_case_				=							GPTNeoXJapaneseModel(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								snake_case_				=							model(a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Union[str, Any]:
								snake_case_				=							True
								snake_case_				=							GPTNeoXJapaneseModel(a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a					)
								self.parent.assertEqual(result.last_hidden_state.shape       ,			(self.batch_size, self.seq_length, self.hidden_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a       ,			a					)       ->    int:
								snake_case_				=							GPTNeoXJapaneseForCausalLM(config=a					)
								model.to(a					)
								model.eval()
								snake_case_				=							model(a       ,			attention_mask=a       ,			labels=a					)
								self.parent.assertEqual(result.logits.shape       ,			(self.batch_size, self.seq_length, self.vocab_size)					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							True
								snake_case_				=							GPTNeoXJapaneseForCausalLM(config=a					)
								model.to(a					)
								model.eval()

								# first forward pass
								snake_case_				=							model(a       ,			attention_mask=a       ,			use_cache=a					)
								snake_case_				=							outputs.past_key_values

								# create hypothetical multiple next token and extent to next_input_ids
								snake_case_				=							ids_tensor((self.batch_size, 3)       ,			config.vocab_size					)
								snake_case_				=							ids_tensor((self.batch_size, 3)       ,			vocab_size=2					)

								# append to next input_ids and
								snake_case_				=							torch.cat([input_ids, next_tokens]       ,			dim=-1					)
								snake_case_				=							torch.cat([input_mask, next_mask]       ,			dim=-1					)

								snake_case_				=							model(a       ,			attention_mask=a       ,			output_hidden_states=a					)
								snake_case_				=							output_from_no_past['hidden_states'][0]
								snake_case_				=							model(
								    a       ,			attention_mask=a       ,			past_key_values=a       ,			output_hidden_states=a       ,			)['hidden_states'][0]

								# select random slice
								snake_case_				=							ids_tensor((1,)       ,			output_from_past.shape[-1]					).item()
								snake_case_				=							output_from_no_past[:, -3:, random_slice_idx].detach()
								snake_case_				=							output_from_past[:, :, random_slice_idx].detach()

								self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

								# test that outputs are equal for slice
								self.parent.assertTrue(torch.allclose(a       ,			a       ,			atol=1E-3					)					)






	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_				=							self.prepare_config_and_inputs()
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							config_and_inputs
								snake_case_				=							{'input_ids': input_ids, 'attention_mask': input_mask}
								return config, inputs_dict






@require_torch
class  UpperCamelCase_      (			snake_case_       ,					snake_case_       ,					unittest.TestCase				):



	'''simple docstring'''




	lowerCAmelCase													= (GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
	lowerCAmelCase													= (GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
	lowerCAmelCase													= (
	    {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
	    if is_torch_available()
	    else {}
	)
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False
	lowerCAmelCase													= False




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							GPTNeoXJapaneseModelTester(self					)
								snake_case_				=							ConfigTester(self       ,			config_class=a       ,			hidden_size=37					)




	def 							_UpperCamelCase			(       self					)       ->    str:
								self.config_tester.run_common_tests()




	def 							_UpperCamelCase			(       self					)       ->    Optional[Any]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Union[str, Any]:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()
								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Optional[int]:
								# This regression test was failing with PyTorch < 1.3
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs_for_decoder()

								snake_case_				=							None

								self.model_tester.create_and_check_model_as_decoder(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    Dict:
								snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_decoder_model_past_large_inputs(a       ,			a       ,			a					)




	def 							_UpperCamelCase			(       self					)       ->    List[Any]:
								snake_case_				=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_causal_lm(*a					)






	@slow
	def 							_UpperCamelCase			(       self					)       ->    Any:
								snake_case_				=							'abeja/gpt-neox-japanese-2.7b'

								snake_case_				=							['データサイエンティストとは、', '100年後に必要とされる会社は、', 'フルリモートの環境で働くために必要なことは、', '国境の長いトンネルを抜けると', '美味しい日本食といえば、']

								snake_case_				=							[
								    'データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。',
								    '100年後に必要とされる会社は、「人」が中心の会社です。',
								    'フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。',
								    '国境の長いトンネルを抜けると、そこは雪国だった。',
								    '美味しい日本食といえば、やっぱりお寿司ですよね。',
								]

								snake_case_				=							GPTNeoXJapaneseTokenizer.from_pretrained(a					)
								snake_case_				=							GPTNeoXJapaneseForCausalLM.from_pretrained(a					)

								snake_case_				=							[]
								for prompt in prompts:
															snake_case_				=							tokenizer(a       ,			return_tensors='pt'					).input_ids
															snake_case_				=							model.generate(a       ,			max_length=50					)
															snake_case_				=							tokenizer.batch_decode(a       ,			skip_special_tokens=a					)
															predicted_outputs += generated_string
								self.assertListEqual(a       ,			a					)





