


import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# This example also demonstrates the checkpointing and sharding capabilities
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_snake_case				=			1_6
_snake_case				=			3_2







def 	A (		A__		,		A__			):
     # Initialize accelerator
     if args.with_tracking:
          UpperCAmelCase    :					Any			=      Accelerator(
              cpu=args.cpu		,		mixed_precision=args.mixed_precision		,		log_with="all"		,		project_dir=args.project_dir			)
     else:
          UpperCAmelCase    :					List[Any]			=      Accelerator(cpu=args.cpu		,		mixed_precision=args.mixed_precision			)

     if hasattr(args.checkpointing_steps		,		"isdigit"			):
          if args.checkpointing_steps == "epoch":
               UpperCAmelCase    :					str			=      args.checkpointing_steps
          elif args.checkpointing_steps.isdigit():
               UpperCAmelCase    :					List[Any]			=      int(args.checkpointing_steps			)
          else:
               raise ValueError(
                   f'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'			)
     else:
          UpperCAmelCase    :					Dict			=      None
     # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
     UpperCAmelCase    :					Tuple			=      config["lr"]
     UpperCAmelCase    :					Any			=      int(config["num_epochs"]			)
     UpperCAmelCase    :					Tuple			=      int(config["seed"]			)
     UpperCAmelCase    :					Optional[int]			=      int(config["batch_size"]			)

     # We need to initialize the trackers we use, and also store our configuration
     if args.with_tracking:
          UpperCAmelCase    :					List[Any]			=      os.path.split(A__			)[-1].split("."			)[0]
          accelerator.init_trackers(A__		,		A__			)

     UpperCAmelCase    :					List[str]			=      AutoTokenizer.from_pretrained("bert-base-cased"			)
     UpperCAmelCase    :					Dict			=      load_dataset("glue"		,		"mrpc"			)
     UpperCAmelCase    :					List[str]			=      evaluate.load("glue"		,		"mrpc"			)

     def tokenize_function(A__			):
          # max_length=None => use the model max length (it's actually the default)
          UpperCAmelCase    :					int			=      tokenizer(examples["sentence1"]		,		examples["sentence2"]		,		truncation=A__		,		max_length=A__			)
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     # starting with the main process first:
     with accelerator.main_process_first():
          UpperCAmelCase    :					int			=      datasets.map(
              A__		,		batched=A__		,		remove_columns=["idx", "sentence1", "sentence2"]		,		)

     # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
     # transformers library
     UpperCAmelCase    :					Tuple			=      tokenized_datasets.rename_column("label"		,		"labels"			)

     # If the batch size is too big we use gradient accumulation
     UpperCAmelCase    :					Any			=      1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          UpperCAmelCase    :					Tuple			=      batch_size // MAX_GPU_BATCH_SIZE
          UpperCAmelCase    :					Dict			=      MAX_GPU_BATCH_SIZE

     def collate_fn(A__			):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          UpperCAmelCase    :					Any			=      1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
          # When using mixed precision we want round multiples of 8/16
          if accelerator.mixed_precision == "fp8":
               UpperCAmelCase    :					Optional[Any]			=      1_6
          elif accelerator.mixed_precision != "no":
               UpperCAmelCase    :					Union[str, Any]			=      8
          else:
               UpperCAmelCase    :					Union[str, Any]			=      None

          return tokenizer.pad(
              A__		,		padding="longest"		,		max_length=A__		,		pad_to_multiple_of=A__		,		return_tensors="pt"		,		)

     # Instantiate dataloaders.
     UpperCAmelCase    :					Tuple			=      DataLoader(
         tokenized_datasets["train"]		,		shuffle=A__		,		collate_fn=A__		,		batch_size=A__			)
     UpperCAmelCase    :					List[str]			=      DataLoader(
         tokenized_datasets["validation"]		,		shuffle=A__		,		collate_fn=A__		,		batch_size=A__			)

     set_seed(A__			)

     # Instantiate the model (we build the model here so that the seed also control new weights initialization)
     UpperCAmelCase    :					int			=      AutoModelForSequenceClassification.from_pretrained("bert-base-cased"		,		return_dict=A__			)

     # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
     # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
     # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
     UpperCAmelCase    :					Optional[int]			=      model.to(accelerator.device			)

     # Instantiate optimizer
     UpperCAmelCase    :					Dict			=      AdamW(params=model.parameters()		,		lr=A__			)

     # Instantiate scheduler
     UpperCAmelCase    :					List[Any]			=      get_linear_schedule_with_warmup(
         optimizer=A__		,		num_warmup_steps=1_0_0		,		num_training_steps=(len(A__			) * num_epochs) // gradient_accumulation_steps		,		)

     # Prepare everything
     # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
     # prepare method.
     UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      accelerator.prepare(
         A__		,		A__		,		A__		,		A__		,		A__			)

     # We need to keep track of how many total steps we have iterated over
     UpperCAmelCase    :					Tuple			=      0
     # We also need to keep track of the stating epoch so files are named properly
     UpperCAmelCase    :					Optional[Any]			=      0

     # Potentially load in the weights and states from a previous save
     if args.resume_from_checkpoint:
          if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
               accelerator.print(f'Resumed from checkpoint: {args.resume_from_checkpoint}'			)
               accelerator.load_state(args.resume_from_checkpoint			)
               UpperCAmelCase    :					Any			=      os.path.basename(args.resume_from_checkpoint			)
          else:
               # Get the most recent checkpoint
               UpperCAmelCase    :					List[Any]			=      [f.name for f in os.scandir(os.getcwd()			) if f.is_dir()]
               dirs.sort(key=os.path.getctime			)
               UpperCAmelCase    :					str			=      dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
          # Extract `epoch_{i}` or `step_{i}`
          UpperCAmelCase    :					Tuple			=      os.path.splitext(A__			)[0]

          if "epoch" in training_difference:
               UpperCAmelCase    :					Tuple			=      int(training_difference.replace("epoch_"		,		""			)			) + 1
               UpperCAmelCase    :					List[Any]			=      None
          else:
               UpperCAmelCase    :					Any			=      int(training_difference.replace("step_"		,		""			)			)
               UpperCAmelCase    :					List[str]			=      resume_step // len(A__			)
               resume_step -= starting_epoch * len(A__			)

    # Now we train the model
     for epoch in range(A__		,		A__			):
          model.train()
          if args.with_tracking:
               UpperCAmelCase    :					Any			=      0
          if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
               # We need to skip steps until we reach the resumed step
               UpperCAmelCase    :					Optional[Any]			=      accelerator.skip_first_batches(A__		,		A__			)
               overall_step += resume_step
          else:
               # After the first iteration though, we need to go back to the original dataloader
               UpperCAmelCase    :					List[str]			=      train_dataloader
          for step, batch in enumerate(A__			):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device			)
               UpperCAmelCase    :					Optional[int]			=      model(**A__			)
               UpperCAmelCase    :					List[str]			=      outputs.loss
               UpperCAmelCase    :					Optional[int]			=      loss / gradient_accumulation_steps
               # We keep track of the loss at each epoch
               if args.with_tracking:
                    total_loss += loss.detach().float()
               accelerator.backward(A__			)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

               overall_step += 1

               if isinstance(A__		,		A__			):
                    UpperCAmelCase    :					Dict			=      f'step_{overall_step}'
                    if overall_step % checkpointing_steps == 0:
                         if args.output_dir is not None:
                              UpperCAmelCase    :					List[Any]			=      os.path.join(args.output_dir		,		A__			)
                         accelerator.save_state(A__			)

          model.eval()
          for step, batch in enumerate(A__			):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device			)
               with torch.no_grad():
                    UpperCAmelCase    :					int			=      model(**A__			)
               UpperCAmelCase    :					Dict			=      outputs.logits.argmax(dim=-1			)
               UpperCAmelCase	,			UpperCAmelCase    :					Optional[int]			=      accelerator.gather_for_metrics((predictions, batch["labels"])			)
               metric.add_batch(
                   predictions=A__		,		references=A__		,		)

          UpperCAmelCase    :					Dict			=      metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f'epoch {epoch}:'		,		A__			)
          if args.with_tracking:
               accelerator.log(
                   {
                       "accuracy": eval_metric["accuracy"],
                       "f1": eval_metric["f1"],
                       "train_loss": total_loss.item() / len(A__			),
                       "epoch": epoch,
                   }		,		step=A__		,		)

          if checkpointing_steps == "epoch":
               UpperCAmelCase    :					Union[str, Any]			=      f'epoch_{epoch}'
               if args.output_dir is not None:
                    UpperCAmelCase    :					Optional[Any]			=      os.path.join(args.output_dir		,		A__			)
               accelerator.save_state(A__			)

     if args.with_tracking:
          accelerator.end_training()







def 	A (		):
     UpperCAmelCase    :					int			=      argparse.ArgumentParser(description="Simple example of training script."			)
     parser.add_argument(
         "--mixed_precision"		,		type=A__		,		default=A__		,		choices=["no", "fp16", "bf16", "fp8"]		,		help="Whether to use mixed precision. Choose"
         "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
         "and an Nvidia Ampere GPU."		,		)
     parser.add_argument("--cpu"		,		action="store_true"		,		help="If passed, will train on the CPU."			)
     parser.add_argument(
         "--checkpointing_steps"		,		type=A__		,		default=A__		,		help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."		,		)
     parser.add_argument(
         "--resume_from_checkpoint"		,		type=A__		,		default=A__		,		help="If the training should continue from a checkpoint folder."		,		)
     parser.add_argument(
         "--with_tracking"		,		action="store_true"		,		help="Whether to load in all available experiment trackers from the environment and use them for logging."		,		)
     parser.add_argument(
         "--output_dir"		,		type=A__		,		default="."		,		help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."		,		)
     parser.add_argument(
         "--project_dir"		,		type=A__		,		default="logs"		,		help="Location on where to store experiment tracking logs` and relevent project information"		,		)
     UpperCAmelCase    :					Optional[int]			=      parser.parse_args()
     UpperCAmelCase    :					str			=      {"lr": 2E-5, "num_epochs": 3, "seed": 4_2, "batch_size": 1_6}
     training_function(A__		,		A__			)


if __name__ == "__main__":
      main()







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_ :







 """simple docstring"""





 def __init__(      self       :			Tuple							,     _a       :			Optional[int]							,     _a       :			Tuple=1_4							,     _a       :			List[Any]=7							,     _a       :			List[str]=True							,     _a       :			Tuple=True							,     _a       :			Any=True							,     _a       :			str=9_9							,     _a       :			str=3_2							,     _a       :			Optional[Any]=5							,     _a       :			int=4							,     _a       :			Union[str, Any]=3_7							,     _a       :			List[Any]="gelu"							,     _a       :			List[Any]=0.1							,     _a       :			Tuple=0.1							,     _a       :			int=5_1_2							,     _a       :			Tuple=0.0_2							,     _a       :			Dict=None							,     )     ->      Tuple:
      UpperCAmelCase    :					List[Any]			=      parent
      UpperCAmelCase    :					str			=      batch_size
      UpperCAmelCase    :					Dict			=      seq_length
      UpperCAmelCase    :					Any			=      is_training
      UpperCAmelCase    :					Union[str, Any]			=      use_input_mask
      UpperCAmelCase    :					int			=      use_labels
      UpperCAmelCase    :					Optional[Any]			=      vocab_size
      UpperCAmelCase    :					Union[str, Any]			=      d_model
      UpperCAmelCase    :					Optional[Any]			=      num_hidden_layers
      UpperCAmelCase    :					Tuple			=      num_attention_heads
      UpperCAmelCase    :					str			=      ffn_dim
      UpperCAmelCase    :					Dict			=      activation_function
      UpperCAmelCase    :					int			=      activation_dropout
      UpperCAmelCase    :					Union[str, Any]			=      attention_dropout
      UpperCAmelCase    :					Optional[int]			=      max_position_embeddings
      UpperCAmelCase    :					str			=      initializer_range
      UpperCAmelCase    :					Tuple			=      None
      UpperCAmelCase    :					List[Any]			=      0
      UpperCAmelCase    :					List[str]			=      2
      UpperCAmelCase    :					Dict			=      1
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      List[str]:
      UpperCAmelCase    :					str			=      np.clip(ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size					)							,     3							,     self.vocab_size					)

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

      UpperCAmelCase    :					Union[str, Any]			=      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=_a							,     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 	_UpperCamelCase     (      self       :			str					)     ->      List[str]:
      UpperCAmelCase    :					Tuple			=      self.prepare_config_and_inputs()
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      config_and_inputs
      UpperCAmelCase    :					str			=      {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict
 def 	_UpperCamelCase     (      self       :			Dict					)     ->      List[str]:
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      self.prepare_config_and_inputs()

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

      return (
          config,
          input_ids,
          attention_mask,
          encoder_hidden_states,
          encoder_attention_mask,
      )
 def 	_UpperCamelCase     (      self       :			int							,     _a       :			Optional[int]							,     _a       :			Dict							,     _a       :			Tuple							,     _a       :			Dict					)     ->      str:
      UpperCAmelCase    :					List[str]			=      2_0
      UpperCAmelCase    :					Optional[Any]			=      model_class_name(_a					)

      UpperCAmelCase    :					Dict			=      model.init_cache(input_ids.shape[0]							,     _a					)
      UpperCAmelCase    :					List[Any]			=      jnp.ones((input_ids.shape[0], max_decoder_length)							,     dtype="i4"					)

      UpperCAmelCase    :					str			=      jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)					)
      UpperCAmelCase    :					List[str]			=      model(
          input_ids[:, :-1]							,     attention_mask=_a							,     past_key_values=_a							,     position_ids=_a							,     )

      UpperCAmelCase    :					Any			=      jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,     dtype="i4"					)
      UpperCAmelCase    :					Dict			=      model(
          input_ids[:, -1:]							,     attention_mask=_a							,     past_key_values=outputs_cache.past_key_values							,     position_ids=_a							,     )

      UpperCAmelCase    :					int			=      model(_a					)

      UpperCAmelCase    :					Optional[Any]			=      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 	_UpperCamelCase     (      self       :			str							,     _a       :			List[Any]							,     _a       :			Any							,     _a       :			Optional[Any]							,     _a       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					List[Any]			=      2_0
      UpperCAmelCase    :					Any			=      model_class_name(_a					)

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

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

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

      UpperCAmelCase    :					Union[str, Any]			=      model(_a							,     attention_mask=_a					)
      UpperCAmelCase    :					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}'					)




@require_sentencepiece
@require_flax
class  A_ (			a_   ,    a_   ,    unittest.TestCase  ):







 """simple docstring"""





 _UpperCamelCase					:			Tuple     	=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
 _UpperCamelCase					:			int     	=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()
 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					Any			=      FlaxXGLMModelTester(self					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(_a							,     _a							,     _a							,     _a					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Tuple			=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward_with_attn_mask(
               _a							,     _a							,     _a							,     _a					)
 @slow
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      UpperCAmelCase    :					int			=      XGLMTokenizer.from_pretrained("XGLM"							,     padding_side="left"					)
      UpperCAmelCase    :					Optional[int]			=      tokenizer(["Hello this is a long string", "Hey"]							,     return_tensors="np"							,     padding=_a							,     truncation=_a					)

      UpperCAmelCase    :					int			=      FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"					)
      UpperCAmelCase    :					Optional[Any]			=      1
      UpperCAmelCase    :					Tuple			=      False

      UpperCAmelCase    :					Any			=      jax.jit(model.generate					)

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

      UpperCAmelCase    :					Any			=      tokenizer.batch_decode(_a							,     skip_special_tokens=_a					)

      UpperCAmelCase    :					Union[str, 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(_a							,     _a					)
 @is_pt_flax_cross_test
 def 	_UpperCamelCase     (      self       :			Union[str, Any]					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[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
                UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					int			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                UpperCAmelCase    :					int			=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
                UpperCAmelCase    :					str			=      getattr(_a							,     _a					)

                UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      pt_inputs["input_ids"].shape
                UpperCAmelCase    :					str			=      np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)					)
                for batch_idx, start_index in enumerate(_a					):
                     UpperCAmelCase    :					str			=      0
                     UpperCAmelCase    :					Optional[Any]			=      1
                     UpperCAmelCase    :					int			=      0
                     UpperCAmelCase    :					Union[str, Any]			=      1
                UpperCAmelCase    :					Dict			=      pt_model_class(_a					).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.
                UpperCAmelCase    :					List[Any]			=      False
                UpperCAmelCase    :					Union[str, Any]			=      model_class(_a							,     dtype=jnp.floataa					)
                UpperCAmelCase    :					Union[str, Any]			=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,     _a					)
                UpperCAmelCase    :					List[Any]			=      fx_state

                with torch.no_grad():
                     UpperCAmelCase    :					str			=      pt_model(**_a					).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                     pt_model.save_pretrained(_a					)
                     UpperCAmelCase    :					Optional[Any]			=      model_class.from_pretrained(_a							,     from_pt=_a					)

                UpperCAmelCase    :					Optional[Any]			=      fx_model_loaded(**_a					).to_tuple()
                self.assertEqual(
                    len(_a					)							,     len(_a					)							,     "Output lengths differ between Flax and PyTorch"					)
                for fx_output_loaded, pt_output in zip(_a							,     _a					):
                     self.assert_almost_equals(fx_output_loaded[:, -1]							,     pt_output[:, -1].numpy()							,     4E-2					)
 @is_pt_flax_cross_test
 def 	_UpperCamelCase     (      self       :			int					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[int]			=      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
                UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					Optional[Any]			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

                UpperCAmelCase    :					Optional[int]			=      pt_model_class(_a					).eval()
                UpperCAmelCase    :					int			=      False
                UpperCAmelCase    :					int			=      model_class(_a							,     dtype=jnp.floataa					)

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

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

                with torch.no_grad():
                     UpperCAmelCase    :					int			=      pt_model(**_a					).to_tuple()

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

                with tempfile.TemporaryDirectory() as tmpdirname:
                     fx_model.save_pretrained(_a					)
                     UpperCAmelCase    :					List[Any]			=      pt_model_class.from_pretrained(_a							,     from_flax=_a					)

                with torch.no_grad():
                     UpperCAmelCase    :					List[Any]			=      pt_model_loaded(**_a					).to_tuple()

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

 @slow
 def 	_UpperCamelCase     (      self       :			Any					)     ->      str:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase    :					Dict			=      model_class_name.from_pretrained("facebook/xglm-564M"					)
           UpperCAmelCase    :					List[Any]			=      model(np.ones((1, 1)					)					)
           self.assertIsNotNone(_a					)



