






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 	snake_case__ :
	lowercase__						:					Dict											=   XGLMConfig
	lowercase__						:					str											=   {}
	lowercase__						:					List[str]											=   '''gelu'''
	def __init__(    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=14      ,       lowerCAmelCase__=7      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=True      ,       lowerCAmelCase__=99      ,       lowerCAmelCase__=32      ,       lowerCAmelCase__=2      ,       lowerCAmelCase__=4      ,       lowerCAmelCase__=37      ,       lowerCAmelCase__="gelu"      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=0.1      ,       lowerCAmelCase__=5_12      ,       lowerCAmelCase__=0.0_2      ,       )							->				Optional[int]:
							__magic_name__      :       List[str]       						= parent
							__magic_name__      :       Dict       						= batch_size
							__magic_name__      :       Any       						= seq_length
							__magic_name__      :       Dict       						= is_training
							__magic_name__      :       Dict       						= use_input_mask
							__magic_name__      :       Dict       						= use_labels
							__magic_name__      :       int       						= vocab_size
							__magic_name__      :       List[Any]       						= d_model
							__magic_name__      :       List[str]       						= num_hidden_layers
							__magic_name__      :       int       						= num_attention_heads
							__magic_name__      :       Optional[int]       						= ffn_dim
							__magic_name__      :       Dict       						= activation_function
							__magic_name__      :       str       						= activation_dropout
							__magic_name__      :       Tuple       						= attention_dropout
							__magic_name__      :       Union[str, Any]       						= max_position_embeddings
							__magic_name__      :       Union[str, Any]       						= initializer_range
							__magic_name__      :       Any       						= None
							__magic_name__      :       Tuple       						= 0
							__magic_name__      :       str       						= 2
							__magic_name__      :       Any       						= 1
	def       __magic_name__    (    self			)							->				Any:
							return XGLMConfig.from_pretrained("""facebook/xglm-564M"""			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       List[Any]       						= tf.clip_by_value(
							    ids_tensor([self.batch_size, self.seq_length]      ,       self.vocab_size			)      ,       clip_value_min=0      ,       clip_value_max=3			)

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

							__magic_name__      :       List[str]       						= self.get_config()

							__magic_name__      :       Dict       						= floats_tensor([self.num_hidden_layers, self.num_attention_heads]      ,       2			)

							return (
							    config,
							    input_ids,
							    input_mask,
							    head_mask,
							)
	def       __magic_name__    (    self			)							->				Optional[Any]:
							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=lowerCAmelCase__      ,       bos_token_id=self.bos_token_id      ,       eos_token_id=self.eos_token_id      ,       pad_token_id=self.pad_token_id      ,       return_dict=lowerCAmelCase__      ,       )







	def       __magic_name__    (    self			)							->				Optional[Any]:
							__magic_name__      :       int       						= self.prepare_config_and_inputs()

							(
							    __magic_name__
							)      :       List[Any]       						= config_and_inputs

							__magic_name__      :       Tuple       						= {
							    """input_ids""": input_ids,
							    """head_mask""": head_mask,
							}

							return config, inputs_dict





@require_tf
class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase						,  unittest.TestCase				):
	lowercase__						:					int											=   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
	lowercase__						:					Any											=   (TFXGLMForCausalLM,) if is_tf_available() else ()
	lowercase__						:					Optional[Any]											=   (
	    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
	)
	lowercase__						:					List[Any]											=   False
	lowercase__						:					Any											=   False
	lowercase__						:					Optional[int]											=   False
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       List[str]       						= TFXGLMModelTester(self			)
							__magic_name__      :       List[Any]       						= ConfigTester(self      ,       config_class=lowerCAmelCase__      ,       n_embd=37			)
	def       __magic_name__    (    self			)							->				str:
							self.config_tester.run_common_tests()
	@slow
	def       __magic_name__    (    self			)							->				List[str]:
							for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__      :       Dict       						= TFXGLMModel.from_pretrained(lowerCAmelCase__			)
													self.assertIsNotNone(lowerCAmelCase__			)







	@unittest.skip(reason="""Currently, model embeddings are going to undergo a major refactor."""			)
	def       __magic_name__    (    self			)							->				int:
							super().test_resize_token_embeddings()





@require_tf
class 	snake_case__ (							unittest.TestCase				):
	@slow
	def       __magic_name__    (    self      ,       lowerCAmelCase__=True			)							->				int:
							__magic_name__      :       str       						= TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
							__magic_name__      :       int       						= tf.convert_to_tensor([[2, 2_68, 98_65]]      ,       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
							__magic_name__      :       Optional[int]       						= [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
							# fmt: on
							__magic_name__      :       Optional[int]       						= model.generate(lowerCAmelCase__      ,       do_sample=lowerCAmelCase__      ,       num_beams=1			)
							if verify_outputs:
													self.assertListEqual(output_ids[0].numpy().tolist()      ,       lowerCAmelCase__			)
	@slow
	def       __magic_name__    (    self			)							->				Optional[int]:
							__magic_name__      :       Dict       						= XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""			)
							__magic_name__      :       Dict       						= TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)

							tf.random.set_seed(0			)
							__magic_name__      :       List[Any]       						= tokenizer("""Today is a nice day and"""      ,       return_tensors="""tf"""			)
							__magic_name__      :       Optional[int]       						= 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"""			):
													__magic_name__      :       int       						= model.generate(lowerCAmelCase__      ,       do_sample=lowerCAmelCase__      ,       seed=[7, 0]			)
							__magic_name__      :       Tuple       						= tokenizer.decode(output_ids[0]      ,       skip_special_tokens=lowerCAmelCase__			)

							__magic_name__      :       Union[str, Any]       						= (
							    """Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due"""
							)
							self.assertEqual(lowerCAmelCase__      ,       lowerCAmelCase__			)







	@slow
	def       __magic_name__    (    self			)							->				Any:
							__magic_name__      :       Dict       						= TFXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""			)
							__magic_name__      :       str       						= XGLMTokenizer.from_pretrained("""facebook/xglm-564M"""			)

							__magic_name__      :       Tuple       						= """left"""

							# use different length sentences to test batching
							__magic_name__      :       Optional[int]       						= [
							    """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""",
							]

							__magic_name__      :       int       						= tokenizer(lowerCAmelCase__      ,       return_tensors="""tf"""      ,       padding=lowerCAmelCase__			)
							__magic_name__      :       Dict       						= inputs["""input_ids"""]

							__magic_name__      :       List[str]       						= model.generate(input_ids=lowerCAmelCase__      ,       attention_mask=inputs["""attention_mask"""]      ,       max_new_tokens=12			)

							__magic_name__      :       Any       						= tokenizer(sentences[0]      ,       return_tensors="""tf"""			).input_ids
							__magic_name__      :       List[Any]       						= model.generate(input_ids=lowerCAmelCase__      ,       max_new_tokens=12			)

							__magic_name__      :       Dict       						= tokenizer(sentences[1]      ,       return_tensors="""tf"""			).input_ids
							__magic_name__      :       str       						= model.generate(input_ids=lowerCAmelCase__      ,       max_new_tokens=12			)

							__magic_name__      :       Any       						= tokenizer.batch_decode(lowerCAmelCase__      ,       skip_special_tokens=lowerCAmelCase__			)
							__magic_name__      :       str       						= tokenizer.decode(output_non_padded[0]      ,       skip_special_tokens=lowerCAmelCase__			)
							__magic_name__      :       str       						= tokenizer.decode(output_padded[0]      ,       skip_special_tokens=lowerCAmelCase__			)

							__magic_name__      :       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 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(lowerCAmelCase__      ,       lowerCAmelCase__			)
							self.assertListEqual(lowerCAmelCase__      ,       [non_padded_sentence, padded_sentence]			)








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__ (							_lowerCAmelCase				):
	lowercase__						:					torch.FloatTensor
	lowercase__						:					Optional[torch.FloatTensor]											=   None



def    UpperCamelCase						(	_A,   _A=0.999,   _A="cosine",   ):






						"""simple docstring"""







						if alpha_transform_type == "cosine":

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

						elif alpha_transform_type == "exp":

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

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

						__magic_name__      :       Optional[Any]       						= []
						for i in range(_A			):
												__magic_name__      :       Dict       						= i / num_diffusion_timesteps
												__magic_name__      :       Any       						= (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(_A			) / alpha_bar_fn(_A			),   _A			)			)
						return torch.tensor(_A,   dtype=torch.floataa			)





class 	snake_case__ (							_lowerCAmelCase						,  _lowerCAmelCase				):
	@register_to_config
	def __init__(    self      ,       lowerCAmelCase__ = 10_00      ,       lowerCAmelCase__ = "fixed_small_log"      ,       lowerCAmelCase__ = True      ,       lowerCAmelCase__ = 1.0      ,       lowerCAmelCase__ = "epsilon"      ,       lowerCAmelCase__ = "squaredcos_cap_v2"      ,       )							->				Union[str, Any]:
							if beta_schedule != "squaredcos_cap_v2":
													raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""			)

							__magic_name__      :       Tuple       						= betas_for_alpha_bar(lowerCAmelCase__			)

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

							# standard deviation of the initial noise distribution
							__magic_name__      :       Tuple       						= 1.0

							# setable values
							__magic_name__      :       List[Any]       						= None
							__magic_name__      :       int       						= torch.from_numpy(np.arange(0      ,       lowerCAmelCase__			)[::-1].copy()			)

							__magic_name__      :       List[Any]       						= variance_type
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				torch.FloatTensor:
							return sample
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None			)							->				str:
							__magic_name__      :       List[Any]       						= num_inference_steps
							__magic_name__      :       Union[str, Any]       						= (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
							__magic_name__      :       List[Any]       						= (np.arange(0      ,       lowerCAmelCase__			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
							__magic_name__      :       Dict       						= torch.from_numpy(lowerCAmelCase__			).to(lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__=None			)							->				Tuple:
							if prev_timestep is None:
													__magic_name__      :       int       						= t - 1

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

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

							if variance_type is None:
													__magic_name__      :       str       						= self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small_log":
													__magic_name__      :       str       						= torch.log(torch.clamp(lowerCAmelCase__      ,       min=1e-2_0			)			)
													__magic_name__      :       Optional[Any]       						= torch.exp(0.5 * variance			)
							elif variance_type == "learned_range":
													# NOTE difference with DDPM scheduler
													__magic_name__      :       List[str]       						= variance.log()
													__magic_name__      :       Optional[int]       						= beta.log()

													__magic_name__      :       Any       						= (predicted_variance + 1) / 2
													__magic_name__      :       Any       						= frac * max_log + (1 - frac) * min_log

							return variance
	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__ = None      ,       lowerCAmelCase__=None      ,       lowerCAmelCase__ = True      ,       )							->				Union[UnCLIPSchedulerOutput, Tuple]:
							__magic_name__      :       List[Any]       						= timestep

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

							# 1. compute alphas, betas
							if prev_timestep is None:
													__magic_name__      :       Union[str, Any]       						= t - 1

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

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

							# 6. Add noise
							__magic_name__      :       Tuple       						= 0
							if t > 0:
													__magic_name__      :       Any       						= randn_tensor(
													    model_output.shape      ,       dtype=model_output.dtype      ,       generator=lowerCAmelCase__      ,       device=model_output.device			)

													__magic_name__      :       Tuple       						= self._get_variance(
													    lowerCAmelCase__      ,       predicted_variance=lowerCAmelCase__      ,       prev_timestep=lowerCAmelCase__      ,       )

													if self.variance_type == "fixed_small_log":
																			__magic_name__      :       Tuple       						= variance
													elif self.variance_type == "learned_range":
																			__magic_name__      :       int       						= (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."""			)

													__magic_name__      :       Tuple       						= variance * variance_noise

							__magic_name__      :       List[str]       						= pred_prev_sample + variance

							if not return_dict:
													return (pred_prev_sample,)

							return UnCLIPSchedulerOutput(prev_sample=lowerCAmelCase__      ,       pred_original_sample=lowerCAmelCase__			)







	def       __magic_name__    (    self      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       lowerCAmelCase__      ,       )							->				torch.FloatTensor:
							# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
							__magic_name__      :       List[str]       						= self.alphas_cumprod.to(device=original_samples.device      ,       dtype=original_samples.dtype			)
							__magic_name__      :       Any       						= timesteps.to(original_samples.device			)

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

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

							__magic_name__      :       Any       						= sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
							return noisy_samples
