
import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
					import torch
					from torch import nn

					from transformers import (
					    Adafactor,
					    AdamW,
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_inverse_sqrt_schedule,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					)


def 						UpperCAmelCase      (     a_		,       a_=1_0						)		->     List[Any]:







				"""simple docstring"""



				__A             =			[]
				for _ in range(_lowerCamelCase						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
				return lrs


def 						UpperCAmelCase      (     a_		,       a_=1_0						)		->     Union[str, Any]:







				"""simple docstring"""



				__A             =			[]
				for step in range(_lowerCamelCase						):
								lrs.append(scheduler.get_lr()[0]						)
								scheduler.step()
								if step == num_steps // 2:
												with tempfile.TemporaryDirectory() as tmpdirname:
																__A             =			os.path.join(_lowerCamelCase		,       "schedule.bin"						)
																torch.save(scheduler.state_dict()		,       _lowerCamelCase						)

																__A             =			torch.load(_lowerCamelCase						)
																scheduler.load_state_dict(_lowerCamelCase						)
				return lrs





@require_torch
class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Optional[Any]							,A :			str							,A :			Any			):
					self.assertEqual(len(A			)							,len(A			)			)
					for a, b in zip(A							,A			):
									self.assertAlmostEqual(A							,A							,delta=A			)






	def     UpperCamelCase_  (	self :			int			):
					__A             =			torch.tensor([0.1, -0.2, -0.1]							,requires_grad=A			)
					__A             =			torch.tensor([0.4, 0.2, -0.5]			)
					__A             =			nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					__A             =			AdamW(params=[w]							,lr=2E-1							,weight_decay=0.0			)
					for _ in range(1_00			):
									__A             =			criterion(A							,A			)
									loss.backward()
									optimizer.step()
									w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
									w.grad.zero_()
					self.assertListAlmostEqual(w.tolist()							,[0.4, 0.2, -0.5]							,tol=1E-2			)







	def     UpperCamelCase_  (	self :			List[Any]			):
					__A             =			torch.tensor([0.1, -0.2, -0.1]							,requires_grad=A			)
					__A             =			torch.tensor([0.4, 0.2, -0.5]			)
					__A             =			nn.MSELoss()
					# No warmup, constant schedule, no gradient clipping
					__A             =			Adafactor(
					    params=[w]							,lr=1E-2							,eps=(1E-30, 1E-3)							,clip_threshold=1.0							,decay_rate=-0.8							,betaa=A							,weight_decay=0.0							,relative_step=A							,scale_parameter=A							,warmup_init=A							,)
					for _ in range(10_00			):
									__A             =			criterion(A							,A			)
									loss.backward()
									optimizer.step()
									w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
									w.grad.zero_()
					self.assertListAlmostEqual(w.tolist()							,[0.4, 0.2, -0.5]							,tol=1E-2			)





@require_torch
class 							UpperCAmelCase				(  unittest.TestCase  ):




	'''simple docstring'''





	snake_case_    =					nn.Linear(50      ,      50  ) if is_torch_available() else None
	snake_case_    =					AdamW(m.parameters()      ,      lr=10.0  ) if is_torch_available() else None
	snake_case_    =					10






	def     UpperCamelCase_  (	self :			int							,A :			Dict							,A :			Any							,A :			Tuple							,A :			Any=None			):
					self.assertEqual(len(A			)							,len(A			)			)
					for a, b in zip(A							,A			):
									self.assertAlmostEqual(A							,A							,delta=A							,msg=A			)







	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			{"""num_warmup_steps""": 2, """num_training_steps""": 10}
					# schedulers doct format
					# function: (sched_args_dict, expected_learning_rates)
					__A             =			{
					    get_constant_schedule: ({}, [10.0] * self.num_steps),
					    get_constant_schedule_with_warmup: (
					        {"""num_warmup_steps""": 4},
					        [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
					    ),
					    get_linear_schedule_with_warmup: (
					        {**common_kwargs},
					        [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
					    ),
					    get_cosine_schedule_with_warmup: (
					        {**common_kwargs},
					        [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
					    ),
					    get_cosine_with_hard_restarts_schedule_with_warmup: (
					        {**common_kwargs, """num_cycles""": 2},
					        [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
					    ),
					    get_polynomial_decay_schedule_with_warmup: (
					        {**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
					        [0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
					    ),
					    get_inverse_sqrt_schedule: (
					        {"""num_warmup_steps""": 2},
					        [0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
					    ),
					}

					for scheduler_func, data in scheds.items():
									__A             =			data

									__A             =			scheduler_func(self.optimizer							,**A			)
									self.assertEqual(len([scheduler.get_lr()[0]]			)							,1			)
									__A             =			unwrap_schedule(A							,self.num_steps			)
									self.assertListAlmostEqual(
									    A							,A							,tol=1E-2							,msg=f'''failed for {scheduler_func} in normal scheduler'''							,)

									__A             =			scheduler_func(self.optimizer							,**A			)
									if scheduler_func.__name__ != "get_constant_schedule":
													LambdaScheduleWrapper.wrap_scheduler(A			)  # wrap to test picklability of the schedule
									__A             =			unwrap_and_save_reload_schedule(A							,self.num_steps			)
									self.assertListEqual(A							,A							,msg=f'''failed for {scheduler_func} in save and reload'''			)





class 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			Optional[int]							,A :			Union[str, Any]			):
					__A             =			fn






	def __call__(	self :			Union[str, Any]							,*A :			Union[str, Any]							,**A :			List[Any]			):
					return self.fn(*A							,**A			)







	@classmethod
	def     UpperCamelCase_  (	self :			Any							,A :			str			):
					__A             =			list(map(self							,scheduler.lr_lambdas			)			)


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE							:Optional[Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"bloom"
	snake_case_    =					["past_key_values"]
	snake_case_    =					{
	    "num_hidden_layers": "n_layer",
	    "num_attention_heads": "n_head",
	}






	def __init__(	self :			Optional[Any]							,A :			List[Any]=25_08_80							,A :			Optional[int]=64							,A :			List[Any]=2							,A :			Optional[int]=8							,A :			str=1E-5							,A :			str=0.02							,A :			int=True							,A :			Optional[Any]=1							,A :			int=2							,A :			str=False							,A :			Dict=0.0							,A :			List[Any]=0.0							,A :			str=1							,A :			List[Any]=False							,**A :			List[Any]							,):
					__A             =			vocab_size
					# Backward compatibility with n_embed kwarg
					__A             =			kwargs.pop("n_embed"							,A			)
					__A             =			hidden_size if n_embed is None else n_embed
					__A             =			n_layer
					__A             =			n_head
					__A             =			layer_norm_epsilon
					__A             =			initializer_range
					__A             =			use_cache
					__A             =			pretraining_tp
					__A             =			apply_residual_connection_post_layernorm
					__A             =			hidden_dropout
					__A             =			attention_dropout

					__A             =			bos_token_id
					__A             =			eos_token_id
					__A             =			slow_but_exact

					super().__init__(bos_token_id=A							,eos_token_id=A							,**A			)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					version.parse("1.12"  )






	def __init__(	self :			str							,A :			PretrainedConfig							,A :			str = "default"							,A :			List[PatchingSpec] = None							,A :			bool = False							,):
					super().__init__(A							,task=A							,patching_specs=A							,use_past=A			)
					if not getattr(self._config							,"pad_token_id"							,A			):
									# TODO: how to do that better?
									__A             =			0






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}			)
					if self.use_past:
									# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
									self.fill_with_past_key_values_(A							,direction="inputs"							,inverted_values_shape=A			)
									__A             =			{0: "batch", 1: "past_sequence + sequence"}
					else:
									__A             =			{0: "batch", 1: "sequence"}

					return common_inputs






	@property
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					return self._config.n_layer






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self._config.n_head






	@property
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return 1E-3






	def     UpperCamelCase_  (	self :			Any							,A :			"PreTrainedTokenizer"							,A :			int = -1							,A :			int = -1							,A :			bool = False							,A :			Optional["TensorType"] = None							,):
					__A             =			super(A							,self			).generate_dummy_inputs(
					    A							,batch_size=A							,seq_length=A							,is_pair=A							,framework=A			)

					# We need to order the input in the way they appears in the forward()
					__A             =			OrderedDict({"input_ids": common_inputs["input_ids"]}			)

					# Need to add the past_keys
					if self.use_past:
									if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
									else:
													import torch

													__A      ,							__A             =			common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													__A             =			seqlen + 2
													__A             =			self._config.hidden_size // self.num_attention_heads
													__A             =			(
													    batch * self.num_attention_heads,
													    head_dim,
													    past_key_values_length,
													)
													__A             =			(
													    batch * self.num_attention_heads,
													    past_key_values_length,
													    head_dim,
													)
													__A             =			[
													    (torch.zeros(A			), torch.zeros(A			)) for _ in range(self.num_layers			)
													]

					__A             =			common_inputs["attention_mask"]
					if self.use_past:
									__A             =			ordered_inputs["attention_mask"].dtype
									__A             =			torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(A							,A							,dtype=A			)]							,dim=1			)

					return ordered_inputs







	@property
	def     UpperCamelCase_  (	self :			int			):
					return 13
