

'''simple docstring'''
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 					_snake_case		(__SCREAMING_SNAKE_CASE):
			__A  :	torch.FloatTensor
			__A  :	Optional[torch.FloatTensor]        =None




def  a__       (    _SCREAMING_SNAKE_CASE   :       int	,				_SCREAMING_SNAKE_CASE   :       Optional[int]=0.999	,				_SCREAMING_SNAKE_CASE   :       List[Any]="cosine"	,				)     ->					Union[str, Any]:


	"""simple docstring"""





	if alpha_transform_type == "cosine":

		def alpha_bar_fn(_SCREAMING_SNAKE_CASE   :       Union[str, Any]				):
			return math.cos((t + 0.008) / 1.008 * math.pi / 2				) ** 2

	elif alpha_transform_type == "exp":

		def alpha_bar_fn(_SCREAMING_SNAKE_CASE   :       Optional[Any]				):
			return math.exp(t * -12.0				)

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

	UpperCAmelCase_     :      List[str]						=  []
	for i in range(_SCREAMING_SNAKE_CASE				):
		UpperCAmelCase_     :      Tuple						=  i / num_diffusion_timesteps
		UpperCAmelCase_     :      int						=  (i + 1) / num_diffusion_timesteps
		betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE				) / alpha_bar_fn(_SCREAMING_SNAKE_CASE				)	,				_SCREAMING_SNAKE_CASE				)				)
	return torch.tensor(_SCREAMING_SNAKE_CASE	,				dtype=torch.floataa				)





class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE):






			@register_to_config
			def __init__(  self			,_snake_case = 10_00			,_snake_case = "fixed_small_log"			,_snake_case = True			,_snake_case = 1.0			,_snake_case = "epsilon"			,_snake_case = "squaredcos_cap_v2"			,):
				if beta_schedule != "squaredcos_cap_v2":
					raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"     )

				UpperCAmelCase_     :      Optional[Any]						=  betas_for_alpha_bar(_snake_case     )

				UpperCAmelCase_     :      Union[str, Any]						=  1.0 - self.betas
				UpperCAmelCase_     :      int						=  torch.cumprod(self.alphas			,dim=0     )
				UpperCAmelCase_     :      List[str]						=  torch.tensor(1.0     )

				# standard deviation of the initial noise distribution
				UpperCAmelCase_     :      int						=  1.0

				# setable values
				UpperCAmelCase_     :      Any						=  None
				UpperCAmelCase_     :      Union[str, Any]						=  torch.from_numpy(np.arange(0			,_snake_case     )[::-1].copy()     )

				UpperCAmelCase_     :      Optional[Any]						=  variance_type






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				return sample






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case = None     ):
				UpperCAmelCase_     :      Optional[Any]						=  num_inference_steps
				UpperCAmelCase_     :      Optional[Any]						=  (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
				UpperCAmelCase_     :      Tuple						=  (np.arange(0			,_snake_case     ) * step_ratio).round()[::-1].copy().astype(np.intaa     )
				UpperCAmelCase_     :      Tuple						=  torch.from_numpy(_snake_case     ).to(_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=None			,_snake_case=None			,_snake_case=None     ):
				if prev_timestep is None:
					UpperCAmelCase_     :      Any						=  t - 1

				UpperCAmelCase_     :      Tuple						=  self.alphas_cumprod[t]
				UpperCAmelCase_     :      List[str]						=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				UpperCAmelCase_     :      Tuple						=  1 - alpha_prod_t
				UpperCAmelCase_     :      Optional[Any]						=  1 - alpha_prod_t_prev

				if prev_timestep == t - 1:
					UpperCAmelCase_     :      Any						=  self.betas[t]
				else:
					UpperCAmelCase_     :      Optional[int]						=  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
				UpperCAmelCase_     :      List[str]						=  beta_prod_t_prev / beta_prod_t * beta

				if variance_type is None:
					UpperCAmelCase_     :      Dict						=  self.config.variance_type

				# hacks - were probably added for training stability
				if variance_type == "fixed_small_log":
					UpperCAmelCase_     :      int						=  torch.log(torch.clamp(_snake_case			,min=1E-20     )     )
					UpperCAmelCase_     :      List[str]						=  torch.exp(0.5 * variance     )
				elif variance_type == "learned_range":
					# NOTE difference with DDPM scheduler
					UpperCAmelCase_     :      Optional[Any]						=  variance.log()
					UpperCAmelCase_     :      Union[str, Any]						=  beta.log()

					UpperCAmelCase_     :      Dict						=  (predicted_variance + 1) / 2
					UpperCAmelCase_     :      List[str]						=  frac * max_log + (1 - frac) * min_log

				return variance






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,_snake_case = None			,_snake_case=None			,_snake_case = True			,):
				UpperCAmelCase_     :      int						=  timestep

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

				# 1. compute alphas, betas
				if prev_timestep is None:
					UpperCAmelCase_     :      Optional[int]						=  t - 1

				UpperCAmelCase_     :      int						=  self.alphas_cumprod[t]
				UpperCAmelCase_     :      Optional[int]						=  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
				UpperCAmelCase_     :      Dict						=  1 - alpha_prod_t
				UpperCAmelCase_     :      Dict						=  1 - alpha_prod_t_prev

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

				# 6. Add noise
				UpperCAmelCase_     :      Union[str, Any]						=  0
				if t > 0:
					UpperCAmelCase_     :      Optional[Any]						=  randn_tensor(
					    model_output.shape			,dtype=model_output.dtype			,generator=_snake_case			,device=model_output.device     )

					UpperCAmelCase_     :      Any						=  self._get_variance(
					    _snake_case			,predicted_variance=_snake_case			,prev_timestep=_snake_case			,)

					if self.variance_type == "fixed_small_log":
						UpperCAmelCase_     :      Union[str, Any]						=  variance
					elif self.variance_type == "learned_range":
						UpperCAmelCase_     :      List[str]						=  (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."     )

					UpperCAmelCase_     :      List[Any]						=  variance * variance_noise

				UpperCAmelCase_     :      Tuple						=  pred_prev_sample + variance

				if not return_dict:
					return (pred_prev_sample,)

				return UnCLIPSchedulerOutput(prev_sample=_snake_case			,pred_original_sample=_snake_case     )






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case			,):
				# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
				UpperCAmelCase_     :      int						=  self.alphas_cumprod.to(device=original_samples.device			,dtype=original_samples.dtype     )
				UpperCAmelCase_     :      str						=  timesteps.to(original_samples.device     )

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

				UpperCAmelCase_     :      List[str]						=  (1 - alphas_cumprod[timesteps]) ** 0.5
				UpperCAmelCase_     :      Optional[int]						=  sqrt_one_minus_alpha_prod.flatten()
				while len(sqrt_one_minus_alpha_prod.shape     ) < len(original_samples.shape     ):
					UpperCAmelCase_     :      Union[str, Any]						=  sqrt_one_minus_alpha_prod.unsqueeze(-1     )

				UpperCAmelCase_     :      Optional[Any]						=  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
				return noisy_samples








"""simple docstring"""
from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.xglm.modeling_tf_xglm import (
     TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXGLMForCausalLM,
     TFXGLMModel,
 )




@require_tf
class 	lowercase__				:
   __UpperCAmelCase							       =					XGLMConfig
   __UpperCAmelCase							       =					{}
   __UpperCAmelCase							       =					'''gelu'''





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=14      ,   SCREAMING_SNAKE_CASE=7      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=99      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=4      ,   SCREAMING_SNAKE_CASE=37      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE=512      ,   SCREAMING_SNAKE_CASE=0.02      ,   )  ->  List[str]:
      _lowerCamelCase	:					Optional[int]   =		parent
      _lowerCamelCase	:					int   =		batch_size
      _lowerCamelCase	:					str   =		seq_length
      _lowerCamelCase	:					Any   =		is_training
      _lowerCamelCase	:					int   =		use_input_mask
      _lowerCamelCase	:					Union[str, Any]   =		use_labels
      _lowerCamelCase	:					str   =		vocab_size
      _lowerCamelCase	:					List[str]   =		d_model
      _lowerCamelCase	:					List[Any]   =		num_hidden_layers
      _lowerCamelCase	:					Dict   =		num_attention_heads
      _lowerCamelCase	:					int   =		ffn_dim
      _lowerCamelCase	:					str   =		activation_function
      _lowerCamelCase	:					Optional[int]   =		activation_dropout
      _lowerCamelCase	:					Tuple   =		attention_dropout
      _lowerCamelCase	:					Tuple   =		max_position_embeddings
      _lowerCamelCase	:					Dict   =		initializer_range
      _lowerCamelCase	:					Optional[Any]   =		None
      _lowerCamelCase	:					Union[str, Any]   =		0
      _lowerCamelCase	:					List[Any]   =		2
      _lowerCamelCase	:					str   =		1





   def 		UpperCamelCase_  (							self)  ->  int:
      return XGLMConfig.from_pretrained("""facebook/xglm-564M""")





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Union[str, Any]   =		tf.clip_by_value(
          ids_tensor([self.batch_size, self.seq_length]      ,   self.vocab_size)      ,   clip_value_min=0      ,   clip_value_max=3)

      _lowerCamelCase	:					str   =		None
      if self.use_input_mask:
         _lowerCamelCase	:					Union[str, Any]   =		random_attention_mask([self.batch_size, self.seq_length])

      _lowerCamelCase	:					Tuple   =		self.get_config()

      _lowerCamelCase	:					Optional[int]   =		floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,   2)

      return (
          config,
          input_ids,
          input_mask,
          head_mask,
      )





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      return XGLMConfig(
          vocab_size=self.vocab_size      ,   d_model=self.hidden_size      ,   num_layers=self.num_hidden_layers      ,   attention_heads=self.num_attention_heads      ,   ffn_dim=self.ffn_dim      ,   activation_function=self.activation_function      ,   activation_dropout=self.activation_dropout      ,   attention_dropout=self.attention_dropout      ,   max_position_embeddings=self.max_position_embeddings      ,   initializer_range=self.initializer_range      ,   use_cache=SCREAMING_SNAKE_CASE      ,   bos_token_id=self.bos_token_id      ,   eos_token_id=self.eos_token_id      ,   pad_token_id=self.pad_token_id      ,   return_dict=SCREAMING_SNAKE_CASE      ,   )





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					List[Any]   =		self.prepare_config_and_inputs()

      (
          (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    (
          _lowerCamelCase
      )   ,    
      )	:					str   =		config_and_inputs

      _lowerCamelCase	:					Optional[Any]   =		{
          """input_ids""": input_ids,
          """head_mask""": head_mask,
      }

      return config, inputs_dict







@require_tf
class 	lowercase__				(					A_ ,A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					(TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
   __UpperCAmelCase							       =					(TFXGLMForCausalLM,) if is_tf_available() else ()
   __UpperCAmelCase							       =					(
       {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
   )
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[Any]   =		TFXGLMModelTester(self)
      _lowerCamelCase	:					str   =		ConfigTester(self      ,   config_class=SCREAMING_SNAKE_CASE      ,   n_embd=37)





   def 		UpperCamelCase_  (							self)  ->  Dict:
      self.config_tester.run_common_tests()





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Tuple   =		TFXGLMModel.from_pretrained(SCREAMING_SNAKE_CASE)
         self.assertIsNotNone(SCREAMING_SNAKE_CASE)





   @unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor.""")
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      super().test_resize_token_embeddings()







@require_tf
class 	lowercase__				(					unittest.TestCase				):





   @slow
   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE=True)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Union[str, Any]   =		tf.convert_to_tensor([[2, 268, 9865]]      ,   dtype=tf.intaa)  # The dog
      # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
      # fmt: off
      _lowerCamelCase	:					Dict   =		[2, 268, 9865, 67, 11, 1988, 5_7252, 9865, 5, 984, 67, 1988, 21_3838, 1658, 53, 7_0446, 33, 6657, 278, 1581]
      # fmt: on
      _lowerCamelCase	:					str   =		model.generate(SCREAMING_SNAKE_CASE      ,   do_sample=SCREAMING_SNAKE_CASE      ,   num_beams=1)
      if verify_outputs:
         self.assertListEqual(output_ids[0].numpy().tolist()      ,   SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					int   =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Tuple   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")

      tf.random.set_seed(0)
      _lowerCamelCase	:					Union[str, Any]   =		tokenizer("""Today is a nice day and"""      ,   return_tensors="""tf""")
      _lowerCamelCase	:					Any   =		tokenized.input_ids
      # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
      with tf.device(""":/CPU:0"""):
         _lowerCamelCase	:					Any   =		model.generate(SCREAMING_SNAKE_CASE      ,   do_sample=SCREAMING_SNAKE_CASE      ,   seed=[7, 0])
      _lowerCamelCase	:					List[str]   =		tokenizer.decode(output_ids[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Any   =		(
          """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
      )
      self.assertEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)





   @slow
   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					Optional[Any]   =		TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""")
      _lowerCamelCase	:					Any   =		XGLMTokenizer.from_pretrained("""facebook/xglm-564M""")

      _lowerCamelCase	:					List[Any]   =		"""left"""

      # use different length sentences to test batching
      _lowerCamelCase	:					List[Any]   =		[
          """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
          """left-padding, such as in batched generation. The output for the sequence below should be the same """
          """regardless of whether left padding is applied or not. When""",
          """Hello, my dog is a little""",
      ]

      _lowerCamelCase	:					Union[str, Any]   =		tokenizer(SCREAMING_SNAKE_CASE      ,   return_tensors="""tf"""      ,   padding=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		inputs["""input_ids"""]

      _lowerCamelCase	:					List[Any]   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   attention_mask=inputs["""attention_mask"""]      ,   max_new_tokens=12)

      _lowerCamelCase	:					List[str]   =		tokenizer(sentences[0]      ,   return_tensors="""tf""").input_ids
      _lowerCamelCase	:					Optional[Any]   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   max_new_tokens=12)

      _lowerCamelCase	:					Tuple   =		tokenizer(sentences[1]      ,   return_tensors="""tf""").input_ids
      _lowerCamelCase	:					int   =		model.generate(input_ids=SCREAMING_SNAKE_CASE      ,   max_new_tokens=12)

      _lowerCamelCase	:					Optional[int]   =		tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Optional[Any]   =		tokenizer.decode(output_non_padded[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Union[str, Any]   =		tokenizer.decode(output_padded[0]      ,   skip_special_tokens=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Tuple   =		[
          """This is an extremelly long sentence that only exists to test the ability of the model to cope with """
          """left-padding, such as in batched generation. The output for the sequence below should be the same """
          """regardless of whether left padding is applied or not. When left padding is applied, the sequence will be """
          """a single""",
          """Hello, my dog is a little bit of a shy one, but he is very friendly""",
      ]
      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)
      self.assertListEqual(SCREAMING_SNAKE_CASE      ,   [non_padded_sentence, padded_sentence])

