



import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput






class 	_a			(      nn.Module  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_=1.0 )	->					Optional[int]:
									super().__init__()

									lowerCAmelCase	:   Tuple					     =					cut_size
									lowerCAmelCase	:   str					     =					cut_power






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					str:
									lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					pixel_values.shape[2:4]
									lowerCAmelCase	:   str					     =					min(lowercase_    ,  lowercase_ )
									lowerCAmelCase	:   Union[str, Any]					     =					min(lowercase_    ,  lowercase_    ,  self.cut_size )
									lowerCAmelCase	:   Union[str, Any]					     =					[]
									for _ in range(lowercase_ ):
															lowerCAmelCase	:   List[Any]					     =					int(torch.rand([] ) ** self.cut_power * (max_size - min_size) + min_size )
															lowerCAmelCase	:   List[Any]					     =					torch.randint(0    ,  sideX - size + 1    ,  () )
															lowerCAmelCase	:   List[Any]					     =					torch.randint(0    ,  sideY - size + 1    ,  () )
															lowerCAmelCase	:   Any					     =					pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
															cutouts.append(F.adaptive_avg_poolad(lowercase_    ,  self.cut_size ) )
									return torch.cat(lowercase_ )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						lowerCAmelCase	:   str					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						lowerCAmelCase	:   Tuple					     =					F.normalize(SCREAMING_SNAKE_CASE__			,dim=-1  )
						return (x - y).norm(dim=-1  ).div(2  ).arcsin().pow(2  ).mul(2  )







def  _UpperCAmelCase  (				SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  ):


						'''simple docstring'''




						for param in model.parameters():
												lowerCAmelCase	:   Optional[int]					     =					value






class 	_a			(      snake_case_  ):






			def __init__(    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )	->					Optional[Any]:
									super().__init__()
									self.register_modules(
									    vae=lowercase_    ,  text_encoder=lowercase_    ,  clip_model=lowercase_    ,  tokenizer=lowercase_    ,  unet=lowercase_    ,  scheduler=lowercase_    ,  feature_extractor=lowercase_    ,  )

									lowerCAmelCase	:   Optional[Any]					     =					transforms.Normalize(mean=feature_extractor.image_mean    ,  std=feature_extractor.image_std )
									lowerCAmelCase	:   Dict					     =					(
									    feature_extractor.size
									    if isinstance(feature_extractor.size    ,  lowercase_ )
									    else feature_extractor.size["""shortest_edge"""]
									)
									lowerCAmelCase	:   str					     =					MakeCutouts(self.cut_out_size )

									set_requires_grad(self.text_encoder    ,  lowercase_ )
									set_requires_grad(self.clip_model    ,  lowercase_ )






			def 							_snake_case   (    self    ,  lowercase_ = "auto" )	->					Any:
									if slice_size == "auto":
															# half the attention head size is usually a good trade-off between
															# speed and memory
															lowerCAmelCase	:   Any					     =					self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(lowercase_ )






			def 							_snake_case   (    self )	->					str:
									self.enable_attention_slicing(lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									set_requires_grad(self.vae    ,  lowercase_ )






			def 							_snake_case   (    self )	->					List[Any]:
									set_requires_grad(self.vae    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Union[str, Any]:
									set_requires_grad(self.unet    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Dict:
									set_requires_grad(self.unet    ,  lowercase_ )






			@torch.enable_grad()
			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_=True    ,  )	->					Dict:
									lowerCAmelCase	:   Optional[int]					     =					latents.detach().requires_grad_()

									lowerCAmelCase	:   Optional[int]					     =					self.scheduler.scale_model_input(lowercase_    ,  lowercase_ )

									# predict the noise residual
									lowerCAmelCase	:   Dict					     =					self.unet(lowercase_    ,  lowercase_    ,  encoder_hidden_states=lowercase_ ).sample

									if isinstance(self.scheduler    ,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
															lowerCAmelCase	:   Any					     =					self.scheduler.alphas_cumprod[timestep]
															lowerCAmelCase	:   Union[str, Any]					     =					1 - alpha_prod_t
															# compute predicted original sample from predicted noise also called
															# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
															lowerCAmelCase	:   Tuple					     =					(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

															lowerCAmelCase	:   Any					     =					torch.sqrt(lowercase_ )
															lowerCAmelCase	:   Tuple					     =					pred_original_sample * (fac) + latents * (1 - fac)
									elif isinstance(self.scheduler    ,  lowercase_ ):
															lowerCAmelCase	:   Optional[int]					     =					self.scheduler.sigmas[index]
															lowerCAmelCase	:   str					     =					latents - sigma * noise_pred
									else:
															raise ValueError(f"""scheduler type {type(self.scheduler )} not supported""" )

									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * sample
									lowerCAmelCase	:   List[str]					     =					self.vae.decode(lowercase_ ).sample
									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )

									if use_cutouts:
															lowerCAmelCase	:   Union[str, Any]					     =					self.make_cutouts(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   Union[str, Any]					     =					transforms.Resize(self.cut_out_size )(lowercase_ )
									lowerCAmelCase	:   Any					     =					self.normalize(lowercase_ ).to(latents.dtype )

									lowerCAmelCase	:   Union[str, Any]					     =					self.clip_model.get_image_features(lowercase_ )
									lowerCAmelCase	:   Dict					     =					image_embeddings_clip / image_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )

									if use_cutouts:
															lowerCAmelCase	:   Optional[int]					     =					spherical_dist_loss(lowercase_    ,  lowercase_ )
															lowerCAmelCase	:   Optional[int]					     =					dists.view([num_cutouts, sample.shape[0], -1] )
															lowerCAmelCase	:   int					     =					dists.sum(2 ).mean(0 ).sum() * clip_guidance_scale
									else:
															lowerCAmelCase	:   List[Any]					     =					spherical_dist_loss(lowercase_    ,  lowercase_ ).mean() * clip_guidance_scale

									lowerCAmelCase	:   Optional[Any]					     =					-torch.autograd.grad(lowercase_    ,  lowercase_ )[0]

									if isinstance(self.scheduler    ,  lowercase_ ):
															lowerCAmelCase	:   int					     =					latents.detach() + grads * (sigma**2)
															lowerCAmelCase	:   Any					     =					noise_pred_original
									else:
															lowerCAmelCase	:   Any					     =					noise_pred_original - torch.sqrt(lowercase_ ) * grads
									return noise_pred, latents






			@torch.no_grad()
			def __call__(    self    ,  lowercase_    ,  lowercase_ = 512    ,  lowercase_ = 512    ,  lowercase_ = 50    ,  lowercase_ = 7.5    ,  lowercase_ = 1    ,  lowercase_ = 0.0    ,  lowercase_ = 100    ,  lowercase_ = None    ,  lowercase_ = 4    ,  lowercase_ = True    ,  lowercase_ = None    ,  lowercase_ = None    ,  lowercase_ = "pil"    ,  lowercase_ = True    ,  )	->					str:
									if isinstance(lowercase_    ,  lowercase_ ):
															lowerCAmelCase	:   str					     =					1
									elif isinstance(lowercase_    ,  lowercase_ ):
															lowerCAmelCase	:   Optional[Any]					     =					len(lowercase_ )
									else:
															raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(lowercase_ )}""" )

									if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}.""" )

									# get prompt text embeddings
									lowerCAmelCase	:   Tuple					     =					self.tokenizer(
									    lowercase_    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  truncation=lowercase_    ,  return_tensors="""pt"""    ,  )
									lowerCAmelCase	:   str					     =					self.text_encoder(text_input.input_ids.to(self.device ) )[0]
									# duplicate text embeddings for each generation per prompt
									lowerCAmelCase	:   Dict					     =					text_embeddings.repeat_interleave(lowercase_    ,  dim=0 )

									if clip_guidance_scale > 0:
															if clip_prompt is not None:
																					lowerCAmelCase	:   int					     =					self.tokenizer(
																					    lowercase_    ,  padding="""max_length"""    ,  max_length=self.tokenizer.model_max_length    ,  truncation=lowercase_    ,  return_tensors="""pt"""    ,  ).input_ids.to(self.device )
															else:
																					lowerCAmelCase	:   Tuple					     =					text_input.input_ids.to(self.device )
															lowerCAmelCase	:   Dict					     =					self.clip_model.get_text_features(lowercase_ )
															lowerCAmelCase	:   Tuple					     =					text_embeddings_clip / text_embeddings_clip.norm(p=2    ,  dim=-1    ,  keepdim=lowercase_ )
															# duplicate text embeddings clip for each generation per prompt
															lowerCAmelCase	:   Any					     =					text_embeddings_clip.repeat_interleave(lowercase_    ,  dim=0 )

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									lowerCAmelCase	:   Optional[Any]					     =					guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															lowerCAmelCase	:   Optional[Any]					     =					text_input.input_ids.shape[-1]
															lowerCAmelCase	:   Dict					     =					self.tokenizer([""""""]    ,  padding="""max_length"""    ,  max_length=lowercase_    ,  return_tensors="""pt""" )
															lowerCAmelCase	:   str					     =					self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
															# duplicate unconditional embeddings for each generation per prompt
															lowerCAmelCase	:   Optional[int]					     =					uncond_embeddings.repeat_interleave(lowercase_    ,  dim=0 )

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															lowerCAmelCase	:   str					     =					torch.cat([uncond_embeddings, text_embeddings] )

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									lowerCAmelCase	:   Optional[int]					     =					(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									lowerCAmelCase	:   int					     =					text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not work reproducibly on mps
																					lowerCAmelCase	:   str					     =					torch.randn(lowercase_    ,  generator=lowercase_    ,  device="""cpu"""    ,  dtype=lowercase_ ).to(
																					    self.device )
															else:
																					lowerCAmelCase	:   Optional[int]					     =					torch.randn(lowercase_    ,  generator=lowercase_    ,  device=self.device    ,  dtype=lowercase_ )
									else:
															if latents.shape != latents_shape:
																					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}""" )
															lowerCAmelCase	:   Any					     =					latents.to(self.device )

									# set timesteps
									lowerCAmelCase	:   List[str]					     =					"""offset""" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_offset:
															lowerCAmelCase	:   List[str]					     =					1

									self.scheduler.set_timesteps(lowercase_    ,  **lowercase_ )

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									lowerCAmelCase	:   Union[str, Any]					     =					self.scheduler.timesteps.to(self.device )

									# scale the initial noise by the standard deviation required by the scheduler
									lowerCAmelCase	:   List[Any]					     =					latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									lowerCAmelCase	:   List[str]					     =					"""eta""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									lowerCAmelCase	:   int					     =					{}
									if accepts_eta:
															lowerCAmelCase	:   Any					     =					eta

									# check if the scheduler accepts generator
									lowerCAmelCase	:   Union[str, Any]					     =					"""generator""" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
									if accepts_generator:
															lowerCAmelCase	:   Dict					     =					generator

									for i, t in enumerate(self.progress_bar(lowercase_ ) ):
															# expand the latents if we are doing classifier free guidance
															lowerCAmelCase	:   int					     =					torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
															lowerCAmelCase	:   Dict					     =					self.scheduler.scale_model_input(lowercase_    ,  lowercase_ )

															# predict the noise residual
															lowerCAmelCase	:   Optional[Any]					     =					self.unet(lowercase_    ,  lowercase_    ,  encoder_hidden_states=lowercase_ ).sample

															# perform classifier free guidance
															if do_classifier_free_guidance:
																					lowerCAmelCase     ,	lowerCAmelCase	:   int					     =					noise_pred.chunk(2 )
																					lowerCAmelCase	:   str					     =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# perform clip guidance
															if clip_guidance_scale > 0:
																					lowerCAmelCase	:   Any					     =					(
																					    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
																					)
																					lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					self.cond_fn(
																					    lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  )

															# compute the previous noisy sample x_t -> x_t-1
															lowerCAmelCase	:   List[Any]					     =					self.scheduler.step(lowercase_    ,  lowercase_    ,  lowercase_    ,  **lowercase_ ).prev_sample

									# scale and decode the image latents with vae
									lowerCAmelCase	:   Dict					     =					1 / self.vae.config.scaling_factor * latents
									lowerCAmelCase	:   Optional[int]					     =					self.vae.decode(lowercase_ ).sample

									lowerCAmelCase	:   Any					     =					(image / 2 + 0.5).clamp(0    ,  1 )
									lowerCAmelCase	:   str					     =					image.cpu().permute(0    ,  2    ,  3    ,  1 ).numpy()

									if output_type == "pil":
															lowerCAmelCase	:   Tuple					     =					self.numpy_to_pil(lowercase_ )

									if not return_dict:
															return (image, None)

									return StableDiffusionPipelineOutput(images=lowercase_    ,  nsfw_content_detected=lowercase_ )






import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
		import torch






@require_flax
class 	_a			:






			def __init__(    self    ,  lowercase_    ,  lowercase_=14    ,  lowercase_=7    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=True    ,  lowercase_=99    ,  lowercase_=32    ,  lowercase_=5    ,  lowercase_=4    ,  lowercase_=37    ,  lowercase_="gelu"    ,  lowercase_=0.1    ,  lowercase_=0.1    ,  lowercase_=512    ,  lowercase_=0.0_2    ,  lowercase_=None    ,  )	->					Tuple:
									lowerCAmelCase	:   Union[str, Any]					     =					parent
									lowerCAmelCase	:   Optional[Any]					     =					batch_size
									lowerCAmelCase	:   Tuple					     =					seq_length
									lowerCAmelCase	:   Optional[Any]					     =					is_training
									lowerCAmelCase	:   Union[str, Any]					     =					use_input_mask
									lowerCAmelCase	:   int					     =					use_labels
									lowerCAmelCase	:   List[str]					     =					vocab_size
									lowerCAmelCase	:   List[Any]					     =					d_model
									lowerCAmelCase	:   List[Any]					     =					num_hidden_layers
									lowerCAmelCase	:   int					     =					num_attention_heads
									lowerCAmelCase	:   Optional[Any]					     =					ffn_dim
									lowerCAmelCase	:   str					     =					activation_function
									lowerCAmelCase	:   Any					     =					activation_dropout
									lowerCAmelCase	:   List[str]					     =					attention_dropout
									lowerCAmelCase	:   str					     =					max_position_embeddings
									lowerCAmelCase	:   Dict					     =					initializer_range
									lowerCAmelCase	:   str					     =					None
									lowerCAmelCase	:   Any					     =					0
									lowerCAmelCase	:   Tuple					     =					2
									lowerCAmelCase	:   Dict					     =					1






			def 							_snake_case   (    self )	->					Optional[Any]:
									lowerCAmelCase	:   Tuple					     =					np.clip(ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size )    ,  3    ,  self.vocab_size )

									lowerCAmelCase	:   List[Any]					     =					None
									if self.use_input_mask:
															lowerCAmelCase	:   Dict					     =					random_attention_mask([self.batch_size, self.seq_length] )

									lowerCAmelCase	:   Tuple					     =					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=lowercase_    ,  bos_token_id=self.bos_token_id    ,  eos_token_id=self.eos_token_id    ,  pad_token_id=self.pad_token_id    ,  )

									return (config, input_ids, input_mask)






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Dict					     =					self.prepare_config_and_inputs()
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					config_and_inputs
									lowerCAmelCase	:   int					     =					{"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict






			def 							_snake_case   (    self )	->					Optional[int]:
									lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   str					     =					self.prepare_config_and_inputs()

									lowerCAmelCase	:   Union[str, Any]					     =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
									lowerCAmelCase	:   List[str]					     =					ids_tensor([self.batch_size, self.seq_length]    ,  vocab_size=2 )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    encoder_hidden_states,
									    encoder_attention_mask,
									)






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   str					     =					20
									lowerCAmelCase	:   List[str]					     =					model_class_name(lowercase_ )

									lowerCAmelCase	:   Any					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   Optional[int]					     =					jnp.ones((input_ids.shape[0], max_decoder_length)    ,  dtype="""i4""" )

									lowerCAmelCase	:   List[str]					     =					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )
									lowerCAmelCase	:   Optional[int]					     =					model(
									    input_ids[:, :-1]    ,  attention_mask=lowercase_    ,  past_key_values=lowercase_    ,  position_ids=lowercase_    ,  )

									lowerCAmelCase	:   List[Any]					     =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,  dtype="""i4""" )
									lowerCAmelCase	:   str					     =					model(
									    input_ids[:, -1:]    ,  attention_mask=lowercase_    ,  past_key_values=outputs_cache.past_key_values    ,  position_ids=lowercase_    ,  )

									lowerCAmelCase	:   List[str]					     =					model(lowercase_ )

									lowerCAmelCase	:   Optional[int]					     =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
									self.parent.assertTrue(diff < 1e-3    ,  msg=f"""Max diff is {diff}""" )






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )	->					List[str]:
									lowerCAmelCase	:   Union[str, Any]					     =					20
									lowerCAmelCase	:   List[Any]					     =					model_class_name(lowercase_ )

									lowerCAmelCase	:   int					     =					jnp.concatenate(
									    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )]    ,  axis=-1    ,  )

									lowerCAmelCase	:   int					     =					model.init_cache(input_ids.shape[0]    ,  lowercase_ )
									lowerCAmelCase	:   int					     =					jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1 )[None, :]    ,  (input_ids.shape[0], input_ids.shape[-1] - 1) )

									lowerCAmelCase	:   Optional[int]					     =					model(
									    input_ids[:, :-1]    ,  attention_mask=lowercase_    ,  past_key_values=lowercase_    ,  position_ids=lowercase_    ,  )
									lowerCAmelCase	:   Optional[int]					     =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]    ,  dtype="""i4""" )
									lowerCAmelCase	:   List[Any]					     =					model(
									    input_ids[:, -1:]    ,  past_key_values=outputs_cache.past_key_values    ,  attention_mask=lowercase_    ,  position_ids=lowercase_    ,  )

									lowerCAmelCase	:   Union[str, Any]					     =					model(lowercase_    ,  attention_mask=lowercase_ )
									lowerCAmelCase	:   str					     =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
									self.parent.assertTrue(diff < 1e-3    ,  msg=f"""Max diff is {diff}""" )






@require_sentencepiece
@require_flax
class 	_a			(      snake_case_     , snake_case_     , unittest.TestCase  ):
			_UpperCamelCase:   List[str]	     =		(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			_UpperCamelCase:   str	     =		(FlaxXGLMForCausalLM,) if is_flax_available() else ()






			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase	:   Any					     =					FlaxXGLMModelTester(self )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			def 							_snake_case   (    self )	->					Tuple:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase     ,	lowerCAmelCase     ,	lowerCAmelCase	:   Optional[int]					     =					self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    lowercase_    ,  lowercase_    ,  lowercase_    ,  lowercase_ )






			@slow
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase	:   List[str]					     =					XGLMTokenizer.from_pretrained("""XGLM"""    ,  padding_side="""left""" )
									lowerCAmelCase	:   int					     =					tokenizer(["""Hello this is a long string""", """Hey"""]    ,  return_tensors="""np"""    ,  padding=lowercase_    ,  truncation=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M""" )
									lowerCAmelCase	:   Union[str, Any]					     =					1
									lowerCAmelCase	:   Optional[Any]					     =					False

									lowerCAmelCase	:   int					     =					jax.jit(model.generate )

									lowerCAmelCase	:   Optional[Any]					     =					jit_generate(inputs["""input_ids"""]    ,  attention_mask=inputs["""attention_mask"""] ).sequences

									lowerCAmelCase	:   List[str]					     =					tokenizer.batch_decode(lowercase_    ,  skip_special_tokens=lowercase_ )

									lowerCAmelCase	:   Optional[Any]					     =					[
									    """Hello this is a long string of questions, but I'm not sure if I'm""",
									    """Hey, I'm a newbie to the forum and I'""",
									]

									self.assertListEqual(lowercase_    ,  lowercase_ )






			@is_pt_flax_cross_test
			def 							_snake_case   (    self )	->					Tuple:
									lowerCAmelCase     ,	lowerCAmelCase	:   Any					     =					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__ ):
																					# prepare inputs
																					lowerCAmelCase	:   Union[str, Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   List[Any]					     =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					lowerCAmelCase	:   Optional[int]					     =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					lowerCAmelCase	:   Dict					     =					getattr(lowercase_    ,  lowercase_ )

																					lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					pt_inputs["""input_ids"""].shape
																					lowerCAmelCase	:   Tuple					     =					np.random.randint(0    ,  seq_length - 1    ,  size=(batch_size,) )
																					for batch_idx, start_index in enumerate(lowercase_ ):
																											lowerCAmelCase	:   List[str]					     =					0
																											lowerCAmelCase	:   List[Any]					     =					1
																											lowerCAmelCase	:   str					     =					0
																											lowerCAmelCase	:   Union[str, Any]					     =					1
																					lowerCAmelCase	:   List[Any]					     =					pt_model_class(lowercase_ ).eval()
																					# Flax models don't use the `use_cache` option and cache is not returned as a default.
																					# So we disable `use_cache` here for PyTorch model.
																					lowerCAmelCase	:   Optional[int]					     =					False
																					lowerCAmelCase	:   Optional[Any]					     =					model_class(lowercase_    ,  dtype=jnp.floataa )
																					lowerCAmelCase	:   Union[str, Any]					     =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,  lowercase_ )
																					lowerCAmelCase	:   List[str]					     =					fx_state

																					with torch.no_grad():
																											lowerCAmelCase	:   Dict					     =					pt_model(**lowercase_ ).to_tuple()

																					lowerCAmelCase	:   Tuple					     =					fx_model(**lowercase_ ).to_tuple()
																					self.assertEqual(len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											pt_model.save_pretrained(lowercase_ )
																											lowerCAmelCase	:   Optional[int]					     =					model_class.from_pretrained(lowercase_    ,  from_pt=lowercase_ )

																					lowerCAmelCase	:   Optional[int]					     =					fx_model_loaded(**lowercase_ ).to_tuple()
																					self.assertEqual(
																					    len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output_loaded, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output_loaded[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )






			@is_pt_flax_cross_test
			def 							_snake_case   (    self )	->					Union[str, Any]:
									lowerCAmelCase     ,	lowerCAmelCase	:   Dict					     =					self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__ ):
																					# prepare inputs
																					lowerCAmelCase	:   List[Any]					     =					self._prepare_for_class(lowercase_    ,  lowercase_ )
																					lowerCAmelCase	:   Tuple					     =					{k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

																					# load corresponding PyTorch class
																					lowerCAmelCase	:   Dict					     =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
																					lowerCAmelCase	:   Optional[int]					     =					getattr(lowercase_    ,  lowercase_ )

																					lowerCAmelCase	:   Optional[Any]					     =					pt_model_class(lowercase_ ).eval()
																					lowerCAmelCase	:   Any					     =					False
																					lowerCAmelCase	:   Optional[int]					     =					model_class(lowercase_    ,  dtype=jnp.floataa )

																					lowerCAmelCase	:   Optional[Any]					     =					load_flax_weights_in_pytorch_model(lowercase_    ,  fx_model.params )
																					lowerCAmelCase     ,	lowerCAmelCase	:   List[Any]					     =					pt_inputs["""input_ids"""].shape
																					lowerCAmelCase	:   int					     =					np.random.randint(0    ,  seq_length - 1    ,  size=(batch_size,) )
																					for batch_idx, start_index in enumerate(lowercase_ ):
																											lowerCAmelCase	:   Any					     =					0
																											lowerCAmelCase	:   Tuple					     =					1
																											lowerCAmelCase	:   Union[str, Any]					     =					0
																											lowerCAmelCase	:   Union[str, Any]					     =					1

																					# make sure weights are tied in PyTorch
																					pt_model.tie_weights()

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					pt_model(**lowercase_ ).to_tuple()

																					lowerCAmelCase	:   Optional[Any]					     =					fx_model(**lowercase_ ).to_tuple()
																					self.assertEqual(len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )

																					with tempfile.TemporaryDirectory() as tmpdirname:
																											fx_model.save_pretrained(lowercase_ )
																											lowerCAmelCase	:   List[str]					     =					pt_model_class.from_pretrained(lowercase_    ,  from_flax=lowercase_ )

																					with torch.no_grad():
																											lowerCAmelCase	:   Tuple					     =					pt_model_loaded(**lowercase_ ).to_tuple()

																					self.assertEqual(
																					    len(lowercase_ )    ,  len(lowercase_ )    ,  """Output lengths differ between Flax and PyTorch""" )
																					for fx_output, pt_output in zip(lowercase_    ,  lowercase_ ):
																											self.assert_almost_equals(fx_output[:, -1]    ,  pt_output[:, -1].numpy()    ,  4e-2 )






			@slow
			def 							_snake_case   (    self )	->					str:
									for model_class_name in self.all_model_classes:
															lowerCAmelCase	:   Optional[Any]					     =					model_class_name.from_pretrained("""facebook/xglm-564M""" )
															lowerCAmelCase	:   int					     =					model(np.ones((1, 1) ) )
															self.assertIsNotNone(lowercase_ )

