code: 
  """simple docstring"""
  
  
  
  
  
  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       ,     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_=0.02       ,     lowercase_=None       ,     ):
  
       """simple docstring"""
       UpperCAmelCase_      :      Dict      =					parent
       UpperCAmelCase_      :      Union[str, Any]      =					batch_size
       UpperCAmelCase_      :      int      =					seq_length
       UpperCAmelCase_      :      Optional[int]      =					is_training
       UpperCAmelCase_      :      Dict      =					use_input_mask
       UpperCAmelCase_      :      Union[str, Any]      =					use_labels
       UpperCAmelCase_      :      Optional[Any]      =					vocab_size
       UpperCAmelCase_      :      Tuple      =					d_model
       UpperCAmelCase_      :      str      =					num_hidden_layers
       UpperCAmelCase_      :      Optional[int]      =					num_attention_heads
       UpperCAmelCase_      :      Tuple      =					ffn_dim
       UpperCAmelCase_      :      int      =					activation_function
       UpperCAmelCase_      :      List[Any]      =					activation_dropout
       UpperCAmelCase_      :      Optional[Any]      =					attention_dropout
       UpperCAmelCase_      :      List[str]      =					max_position_embeddings
       UpperCAmelCase_      :      Tuple      =					initializer_range
       UpperCAmelCase_      :      Tuple      =					None
       UpperCAmelCase_      :      str      =					0
       UpperCAmelCase_      :      Tuple      =					2
       UpperCAmelCase_      :      List[str]      =					1
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[int]      =					np.clip(ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size						)       ,     3       ,     self.vocab_size						)
  
       UpperCAmelCase_      :      Any      =					None
       if self.use_input_mask:
          UpperCAmelCase_      :      Any      =					random_attention_mask([self.batch_size, self.seq_length]						)
  
       UpperCAmelCase_      :      Optional[int]      =					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  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      int      =					self.prepare_config_and_inputs()
       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      Tuple      =					config_and_inputs
       UpperCAmelCase_      :      Optional[int]      =					{"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      str      =					self.prepare_config_and_inputs()
  
       UpperCAmelCase_      :      str      =					floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
       UpperCAmelCase_      :      List[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       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Optional[Any]      =					20
       UpperCAmelCase_      :      Any      =					model_class_name(lowercase_						)
  
       UpperCAmelCase_      :      List[Any]      =					model.init_cache(input_ids.shape[0]       ,     lowercase_						)
       UpperCAmelCase_      :      Dict      =					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_      :      Tuple      =					model(
           input_ids[:, :-1]       ,     attention_mask=lowercase_       ,     past_key_values=lowercase_       ,     position_ids=lowercase_       ,     )
  
       UpperCAmelCase_      :      int      =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="i4"						)
       UpperCAmelCase_      :      int      =					model(
           input_ids[:, -1:]       ,     attention_mask=lowercase_       ,     past_key_values=outputs_cache.past_key_values       ,     position_ids=lowercase_       ,     )
  
       UpperCAmelCase_      :      Dict      =					model(lowercase_						)
  
       UpperCAmelCase_      :      Union[str, 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       ,     lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Any      =					20
       UpperCAmelCase_      :      Optional[int]      =					model_class_name(lowercase_						)
  
       UpperCAmelCase_      :      Optional[Any]      =					jnp.concatenate(
           [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])						)]       ,     axis=-1       ,     )
  
       UpperCAmelCase_      :      int      =					model.init_cache(input_ids.shape[0]       ,     lowercase_						)
       UpperCAmelCase_      :      Union[str, Any]      =					jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1						)[None, :]       ,     (input_ids.shape[0], input_ids.shape[-1] - 1)						)
  
       UpperCAmelCase_      :      int      =					model(
           input_ids[:, :-1]       ,     attention_mask=lowercase_       ,     past_key_values=lowercase_       ,     position_ids=lowercase_       ,     )
       UpperCAmelCase_      :      Optional[int]      =					jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,     dtype="i4"						)
       UpperCAmelCase_      :      List[str]      =					model(
           input_ids[:, -1:]       ,     past_key_values=outputs_cache.past_key_values       ,     attention_mask=lowercase_       ,     position_ids=lowercase_       ,     )
  
       UpperCAmelCase_      :      Optional[Any]      =					model(lowercase_       ,     attention_mask=lowercase_						)
       UpperCAmelCase_      :      Union[str, 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}"""						)
  
  
  
  
  @require_sentencepiece
  @require_flax
  class A_			(lowercase__       ,lowercase__       ,unittest.TestCase   ):
  
  
  
  
    '''simple docstring'''
    SCREAMING_SNAKE_CASE__    :		int						  =	(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
    SCREAMING_SNAKE_CASE__    :		Any						  =	(FlaxXGLMForCausalLM,) if is_flax_available() else ()
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      List[str]      =					FlaxXGLMModelTester(self						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       for model_class_name in self.all_model_classes:
          UpperCAmelCase_  ,     UpperCAmelCase_  ,     UpperCAmelCase_      :      List[Any]      =					self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  
  
  
  
  
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       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_with_attn_mask(
              lowercase_       ,     lowercase_       ,     lowercase_       ,     lowercase_						)
  
  
  
  
  
  
  
    @slow
    def  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_      :      Union[str, Any]      =					XGLMTokenizer.from_pretrained("XGLM"       ,     padding_side="left"						)
       UpperCAmelCase_      :      Any      =					tokenizer(["Hello this is a long string", "Hey"]       ,     return_tensors="np"       ,     padding=lowercase_       ,     truncation=lowercase_						)
  
       UpperCAmelCase_      :      Any      =					FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"						)
       UpperCAmelCase_      :      List[Any]      =					1
       UpperCAmelCase_      :      Optional[Any]      =					False
  
       UpperCAmelCase_      :      Dict      =					jax.jit(model.generate						)
  
       UpperCAmelCase_      :      Optional[int]      =					jit_generate(inputs["input_ids"]       ,     attention_mask=inputs["attention_mask"]						).sequences
  
       UpperCAmelCase_      :      Any      =					tokenizer.batch_decode(lowercase_       ,     skip_special_tokens=lowercase_						)
  
       UpperCAmelCase_      :      List[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  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       UpperCAmelCase_  ,     UpperCAmelCase_      :      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_      :      Any      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
             UpperCAmelCase_      :      int      =					{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}
  
             # load corresponding PyTorch class
             UpperCAmelCase_      :      List[Any]      =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
             UpperCAmelCase_      :      List[Any]      =					getattr(lowercase_       ,     lowercase_						)
  
             UpperCAmelCase_  ,     UpperCAmelCase_      :      Dict      =					pt_inputs["input_ids"].shape
             UpperCAmelCase_      :      Optional[int]      =					np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)						)
             for batch_idx, start_index in enumerate(lowercase_						):
                UpperCAmelCase_      :      List[Any]      =					0
                UpperCAmelCase_      :      int      =					1
                UpperCAmelCase_      :      List[str]      =					0
                UpperCAmelCase_      :      List[str]      =					1
             UpperCAmelCase_      :      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.
             UpperCAmelCase_      :      int      =					False
             UpperCAmelCase_      :      List[Any]      =					model_class(lowercase_       ,     dtype=jnp.floataa						)
             UpperCAmelCase_      :      Any      =					convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,     lowercase_						)
             UpperCAmelCase_      :      Union[str, Any]      =					fx_state
  
             with torch.no_grad():
                UpperCAmelCase_      :      Tuple      =					pt_model(**lowercase_						).to_tuple()
  
             UpperCAmelCase_      :      Union[str, 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:
                pt_model.save_pretrained(lowercase_						)
                UpperCAmelCase_      :      Tuple      =					model_class.from_pretrained(lowercase_       ,     from_pt=lowercase_						)
  
             UpperCAmelCase_      :      List[Any]      =					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  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       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_      :      List[str]      =					self._prepare_for_class(lowercase_       ,     lowercase_						)
             UpperCAmelCase_      :      Optional[Any]      =					{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}
  
             # load corresponding PyTorch class
             UpperCAmelCase_      :      str      =					model_class.__name__[4:]  # Skip the "Flax" at the beginning
             UpperCAmelCase_      :      int      =					getattr(lowercase_       ,     lowercase_						)
  
             UpperCAmelCase_      :      Dict      =					pt_model_class(lowercase_						).eval()
             UpperCAmelCase_      :      List[Any]      =					False
             UpperCAmelCase_      :      str      =					model_class(lowercase_       ,     dtype=jnp.floataa						)
  
             UpperCAmelCase_      :      Any      =					load_flax_weights_in_pytorch_model(lowercase_       ,     fx_model.params						)
             UpperCAmelCase_  ,     UpperCAmelCase_      :      Any      =					pt_inputs["input_ids"].shape
             UpperCAmelCase_      :      Tuple      =					np.random.randint(0       ,     seq_length - 1       ,     size=(batch_size,)						)
             for batch_idx, start_index in enumerate(lowercase_						):
                UpperCAmelCase_      :      List[str]      =					0
                UpperCAmelCase_      :      str      =					1
                UpperCAmelCase_      :      str      =					0
                UpperCAmelCase_      :      Dict      =					1
  
             # make sure weights are tied in PyTorch
             pt_model.tie_weights()
  
             with torch.no_grad():
                UpperCAmelCase_      :      Any      =					pt_model(**lowercase_						).to_tuple()
  
             UpperCAmelCase_      :      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_						)
                UpperCAmelCase_      :      Tuple      =					pt_model_class.from_pretrained(lowercase_       ,     from_flax=lowercase_						)
  
             with torch.no_grad():
                UpperCAmelCase_      :      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  UpperCamelCase__      (	self						):
  
       """simple docstring"""
       for model_class_name in self.all_model_classes:
          UpperCAmelCase_      :      Optional[int]      =					model_class_name.from_pretrained("facebook/xglm-564M"						)
          UpperCAmelCase_      :      Any      =					model(np.ones((1, 1)						)						)
          self.assertIsNotNone(lowercase_						)
  
  
  
style_context: 
  """simple docstring"""
  
  
  
  
  
  from __future__ import annotations
  
  from statistics import mean
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase						):
     UpperCAmelCase_      :      Dict      =					2
     UpperCAmelCase_      :      Any      =					list(__lowerCamelCase						)
     UpperCAmelCase_      :      List[Any]      =					[0] * len(__lowerCamelCase						)
     UpperCAmelCase_      :      str      =					0
     while True:
        UpperCAmelCase_      :      int      =					True
        for i, burst_time in enumerate(__lowerCamelCase						):
           if rem_burst_times[i] > 0:
              UpperCAmelCase_      :      List[Any]      =					False
              if rem_burst_times[i] > quantum:
                 t += quantum
                 rem_burst_times[i] -= quantum
              else:
                 t += rem_burst_times[i]
                 UpperCAmelCase_      :      Optional[int]      =					t - burst_time
                 UpperCAmelCase_      :      Union[str, Any]      =					0
        if done is True:
           return waiting_times
  
  
  
  
  
  
  
  def 			__a  (	__lowerCamelCase,	__lowerCamelCase						):
     return [burst + waiting for burst, waiting in zip(__lowerCamelCase,	__lowerCamelCase						)]
  
  
  if __name__ == "__main__":
      _a	 =							[3, 5, 7]
      _a	 =							calculate_waiting_times(burst_times)
      _a	 =							calculate_turn_around_times(burst_times, waiting_times)
      print('Process ID \tBurst Time \tWaiting Time \tTurnaround Time')
      for i, burst_time in enumerate(burst_times):
          print(
              f"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              f"""{turn_around_times[i]}"""
          )
      print(f"""\nAverage waiting time = {mean(waiting_times):.5f}""")
      print(f"""Average turn around time = {mean(turn_around_times):.5f}""")
  
  
  