
'''simple docstring'''





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase=0.999   ,	UpperCAmelCase="cosine"   ,	)->      str:







      """simple docstring"""
      if alpha_transform_type == "cosine":

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

      elif alpha_transform_type == "exp":

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

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

      __A													=      []
      for i in range(UpperCAmelCase						):
            __A													=      i / num_diffusion_timesteps
            __A													=      (i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(UpperCAmelCase						) / alpha_bar_fn(UpperCAmelCase						)   ,	UpperCAmelCase						)						)
      return torch.tensor(UpperCAmelCase   ,	dtype=torch.floataa						)


class 			UpperCamelCase__	(     SCREAMING_SNAKE_CASE   ,  SCREAMING_SNAKE_CASE):
 UpperCAmelCase__     :     Optional[Any]    					=       [e.name for e in KarrasDiffusionSchedulers]
 UpperCAmelCase__     :     Tuple    					=       2






 @register_to_config
 def __init__(      self	:Any						,					_A	:int = 1_000						,					_A	:float = 0.00_085						,					_A	:float = 0.012						,					_A	:str = "linear"						,					_A	:Optional[Union[np.ndarray, List[float]]] = None						,					_A	:str = "epsilon"						,					_A	:str = "linspace"						,					_A	:int = 0						,					)					->      List[Any]:







       '''simple docstring'''



       if trained_betas is not None:
             __A													=      torch.tensor(_A						,					dtype=torch.floataa      )
       elif beta_schedule == "linear":
             __A													=      torch.linspace(_A						,					_A						,					_A						,					dtype=torch.floataa      )
       elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             __A													=      (
                 torch.linspace(beta_start**0.5						,					beta_end**0.5						,					_A						,					dtype=torch.floataa      ) ** 2
             )
       elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __A													=      betas_for_alpha_bar(_A      )
       else:
             raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'      )

       __A													=      1.0 - self.betas
       __A													=      torch.cumprod(self.alphas						,					dim=0      )

       #  set all values
       self.set_timesteps(_A						,					_A						,					_A      )






 def 						lowercase_    (      self	:Any						,					_A	:List[str]						,					_A	:Optional[Any]=None      )					->      Tuple:







       '''simple docstring'''



       if schedule_timesteps is None:
             __A													=      self.timesteps

       __A													=      (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter      ) == 0:
             __A													=      1 if len(_A      ) > 1 else 0
       else:
             __A													=      timestep.cpu().item() if torch.is_tensor(_A      ) else timestep
             __A													=      self._index_counter[timestep_int]

       return indices[pos].item()






 @property
 def 						lowercase_    (      self	:List[str]      )					->      Any:







       '''simple docstring'''



       if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5






 def 						lowercase_    (      self	:Tuple						,					_A	:torch.FloatTensor						,					_A	:Union[float, torch.FloatTensor]						,					)					->      torch.FloatTensor:







       '''simple docstring'''



       __A													=      self.index_for_timestep(_A      )

       if self.state_in_first_order:
             __A													=      self.sigmas[step_index]
       else:
             __A													=      self.sigmas_interpol[step_index - 1]

       __A													=      sample / ((sigma**2 + 1) ** 0.5)
       return sample






 def 						lowercase_    (      self	:str						,					_A	:int						,					_A	:Union[str, torch.device] = None						,					_A	:Optional[int] = None						,					)					->      Union[str, Any]:







       '''simple docstring'''



       __A													=      num_inference_steps

       __A													=      num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
             __A													=      np.linspace(0						,					num_train_timesteps - 1						,					_A						,					dtype=_A      )[::-1].copy()
       elif self.config.timestep_spacing == "leading":
             __A													=      num_train_timesteps // self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             __A													=      (np.arange(0						,					_A      ) * step_ratio).round()[::-1].copy().astype(_A      )
             timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
             __A													=      num_train_timesteps / self.num_inference_steps
             # creates integer timesteps by multiplying by ratio
             # casting to int to avoid issues when num_inference_step is power of 3
             __A													=      (np.arange(_A						,					0						,					-step_ratio      )).round().copy().astype(_A      )
             timesteps -= 1
       else:
             raise ValueError(
                 F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'      )

       __A													=      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5      )
       __A													=      torch.from_numpy(np.log(_A      )      ).to(_A      )

       __A													=      np.interp(_A						,					np.arange(0						,					len(_A      )      )						,					_A      )
       __A													=      np.concatenate([sigmas, [0.0]]      ).astype(np.floataa      )
       __A													=      torch.from_numpy(_A      ).to(device=_A      )

       # compute up and down sigmas
       __A													=      sigmas.roll(-1      )
       __A													=      0.0
       __A													=      (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       __A													=      (sigmas_next**2 - sigmas_up**2) ** 0.5
       __A													=      0.0

       # compute interpolated sigmas
       __A													=      sigmas.log().lerp(sigmas_down.log()						,					0.5      ).exp()
       __A													=      0.0

       # set sigmas
       __A													=      torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2      ), sigmas[-1:]]      )
       __A													=      torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2      ), sigmas_interpol[-1:]]      )
       __A													=      torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2      ), sigmas_up[-1:]]      )
       __A													=      torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2      ), sigmas_down[-1:]]      )

       if str(_A      ).startswith('mps'      ):
             # mps does not support float64
             __A													=      torch.from_numpy(_A      ).to(_A						,					dtype=torch.floataa      )
       else:
             __A													=      torch.from_numpy(_A      ).to(_A      )

       __A													=      self.sigma_to_t(_A      ).to(_A						,					dtype=timesteps.dtype      )
       __A													=      torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])						,					dim=-1      ).flatten()

       __A													=      torch.cat([timesteps[:1], interleaved_timesteps]      )

       __A													=      None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       __A													=      defaultdict(_A      )






 def 						lowercase_    (      self	:str						,					_A	:Any      )					->      Tuple:







       '''simple docstring'''



       __A													=      sigma.log()

       # get distribution
       __A													=      log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       __A													=      dists.ge(0      ).cumsum(dim=0      ).argmax(dim=0      ).clamp(max=self.log_sigmas.shape[0] - 2      )
       __A													=      low_idx + 1

       __A													=      self.log_sigmas[low_idx]
       __A													=      self.log_sigmas[high_idx]

       # interpolate sigmas
       __A													=      (low - log_sigma) / (low - high)
       __A													=      w.clamp(0						,					1      )

       # transform interpolation to time range
       __A													=      (1 - w) * low_idx + w * high_idx
       __A													=      t.view(sigma.shape      )
       return t






 @property
 def 						lowercase_    (      self	:List[Any]      )					->      Optional[int]:







       '''simple docstring'''



       return self.sample is None






 def 						lowercase_    (      self	:Any						,					_A	:Union[torch.FloatTensor, np.ndarray]						,					_A	:Union[float, torch.FloatTensor]						,					_A	:Union[torch.FloatTensor, np.ndarray]						,					_A	:Optional[torch.Generator] = None						,					_A	:bool = True						,					)					->      Union[SchedulerOutput, Tuple]:







       '''simple docstring'''



       __A													=      self.index_for_timestep(_A      )

       # advance index counter by 1
       __A													=      timestep.cpu().item() if torch.is_tensor(_A      ) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
             __A													=      self.sigmas[step_index]
             __A													=      self.sigmas_interpol[step_index]
             __A													=      self.sigmas_up[step_index]
             __A													=      self.sigmas_down[step_index - 1]
       else:
             # 2nd order / KPDM2's method
             __A													=      self.sigmas[step_index - 1]
             __A													=      self.sigmas_interpol[step_index - 1]
             __A													=      self.sigmas_up[step_index - 1]
             __A													=      self.sigmas_down[step_index - 1]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       __A													=      0
       __A													=      sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

       __A													=      model_output.device
       __A													=      randn_tensor(model_output.shape						,					dtype=model_output.dtype						,					device=_A						,					generator=_A      )

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
             __A													=      sigma_hat if self.state_in_first_order else sigma_interpol
             __A													=      sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
             __A													=      sigma_hat if self.state_in_first_order else sigma_interpol
             __A													=      model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
                 sample / (sigma_input**2 + 1)
             )
       elif self.config.prediction_type == "sample":
             raise NotImplementedError('prediction_type not implemented yet: sample'      )
       else:
             raise ValueError(
                 F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'      )

       if self.state_in_first_order:
             # 2. Convert to an ODE derivative for 1st order
             __A													=      (sample - pred_original_sample) / sigma_hat
             # 3. delta timestep
             __A													=      sigma_interpol - sigma_hat

             # store for 2nd order step
             __A													=      sample
             __A													=      dt
             __A													=      sample + derivative * dt
       else:
             # DPM-Solver-2
             # 2. Convert to an ODE derivative for 2nd order
             __A													=      (sample - pred_original_sample) / sigma_interpol
             # 3. delta timestep
             __A													=      sigma_down - sigma_hat

             __A													=      self.sample
             __A													=      None

             __A													=      sample + derivative * dt
             __A													=      prev_sample + noise * sigma_up

       if not return_dict:
             return (prev_sample,)

       return SchedulerOutput(prev_sample=_A      )






 def 						lowercase_    (      self	:List[str]						,					_A	:torch.FloatTensor						,					_A	:torch.FloatTensor						,					_A	:torch.FloatTensor						,					)					->      torch.FloatTensor:







       '''simple docstring'''



       __A													=      self.sigmas.to(device=original_samples.device						,					dtype=original_samples.dtype      )
       if original_samples.device.type == "mps" and torch.is_floating_point(_A      ):
             # mps does not support float64
             __A													=      self.timesteps.to(original_samples.device						,					dtype=torch.floataa      )
             __A													=      timesteps.to(original_samples.device						,					dtype=torch.floataa      )
       else:
             __A													=      self.timesteps.to(original_samples.device      )
             __A													=      timesteps.to(original_samples.device      )

       __A													=      [self.index_for_timestep(_A						,					_A      ) for t in timesteps]

       __A													=      sigmas[step_indices].flatten()
       while len(sigma.shape      ) < len(original_samples.shape      ):
             __A													=      sigma.unsqueeze(-1      )

       __A													=      original_samples + noise * sigma
       return noisy_samples






 def __len__(      self	:Union[str, Any]      )					->      Tuple:







       '''simple docstring'''



       return self.config.num_train_timesteps











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_ )

