






"""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 					_UpperCamelCase		:






				def __init__(self						,      lowerCamelCase__						,      lowerCamelCase__=1_4						,      lowerCamelCase__=7						,      lowerCamelCase__=True						,      lowerCamelCase__=True						,      lowerCamelCase__=True						,      lowerCamelCase__=9_9						,      lowerCamelCase__=3_2						,      lowerCamelCase__=5						,      lowerCamelCase__=4						,      lowerCamelCase__=3_7						,      lowerCamelCase__="gelu"						,      lowerCamelCase__=0.1						,      lowerCamelCase__=0.1						,      lowerCamelCase__=5_1_2						,      lowerCamelCase__=0.0_2						,      lowerCamelCase__=None						,      ):


						"""simple docstring"""
						A__											=							parent
						A__											=							batch_size
						A__											=							seq_length
						A__											=							is_training
						A__											=							use_input_mask
						A__											=							use_labels
						A__											=							vocab_size
						A__											=							d_model
						A__											=							num_hidden_layers
						A__											=							num_attention_heads
						A__											=							ffn_dim
						A__											=							activation_function
						A__											=							activation_dropout
						A__											=							attention_dropout
						A__											=							max_position_embeddings
						A__											=							initializer_range
						A__											=							None
						A__											=							0
						A__											=							2
						A__											=							1






				def  A    (self   ):


						"""simple docstring"""
						A__											=							np.clip(ids_tensor([self.batch_size, self.seq_length]						,      self.vocab_size   )						,      3						,      self.vocab_size   )

						A__											=							None
						if self.use_input_mask:
								A__											=							random_attention_mask([self.batch_size, self.seq_length]   )

						A__											=							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  A    (self   ):


						"""simple docstring"""
						A__											=							self.prepare_config_and_inputs()
						A__						,A__						,A__											=							config_and_inputs
						A__											=							{"""input_ids""": input_ids, """attention_mask""": attention_mask}
						return config, inputs_dict






				def  A    (self   ):


						"""simple docstring"""
						A__						,A__						,A__											=							self.prepare_config_and_inputs()

						A__											=							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
						A__											=							ids_tensor([self.batch_size, self.seq_length]						,      vocab_size=2   )

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






				def  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


						"""simple docstring"""
						A__											=							2_0
						A__											=							model_class_name(a_   )

						A__											=							model.init_cache(input_ids.shape[0]						,      a_   )
						A__											=							jnp.ones((input_ids.shape[0], max_decoder_length)						,      dtype="""i4"""   )

						A__											=							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1   )[None, :]						,      (input_ids.shape[0], input_ids.shape[-1] - 1)   )
						A__											=							model(
						    input_ids[:, :-1]						,      attention_mask=a_						,      past_key_values=a_						,      position_ids=a_						,      )

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

						A__											=							model(a_   )

						A__											=							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  A    (self						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__						,      lowerCamelCase__   ):


						"""simple docstring"""
						A__											=							2_0
						A__											=							model_class_name(a_   )

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

						A__											=							model.init_cache(input_ids.shape[0]						,      a_   )
						A__											=							jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1   )[None, :]						,      (input_ids.shape[0], input_ids.shape[-1] - 1)   )

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

						A__											=							model(a_						,      attention_mask=a_   )
						A__											=							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 					_UpperCamelCase		( __snake_case						, __snake_case						, unittest.TestCase):
				__lowerCamelCase         =  (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				__lowerCamelCase         =  (FlaxXGLMForCausalLM,) if is_flax_available() else ()






				def  A    (self   ):


						"""simple docstring"""
						A__											=							FlaxXGLMModelTester(self   )






				def  A    (self   ):


						"""simple docstring"""
						for model_class_name in self.all_model_classes:
								A__						,A__						,A__											=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward(a_						,      a_						,      a_						,      a_   )






				def  A    (self   ):


						"""simple docstring"""
						for model_class_name in self.all_model_classes:
								A__						,A__						,A__											=							self.model_tester.prepare_config_and_inputs()
								self.model_tester.check_use_cache_forward_with_attn_mask(
								    a_						,      a_						,      a_						,      a_   )






				@slow
				def  A    (self   ):


						"""simple docstring"""
						A__											=							XGLMTokenizer.from_pretrained("""XGLM"""						,      padding_side="""left"""   )
						A__											=							tokenizer(["""Hello this is a long string""", """Hey"""]						,      return_tensors="""np"""						,      padding=a_						,      truncation=a_   )

						A__											=							FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""   )
						A__											=							1
						A__											=							False

						A__											=							jax.jit(model.generate   )

						A__											=							jit_generate(inputs["""input_ids"""]						,      attention_mask=inputs["""attention_mask"""]   ).sequences

						A__											=							tokenizer.batch_decode(a_						,      skip_special_tokens=a_   )

						A__											=							[
						    """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  A    (self   ):


						"""simple docstring"""
						A__						,A__											=							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
										A__											=							self._prepare_for_class(a_						,      a_   )
										A__											=							{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										A__											=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
										A__											=							getattr(a_						,      a_   )

										A__						,A__											=							pt_inputs["""input_ids"""].shape
										A__											=							np.random.randint(0						,      seq_length - 1						,      size=(batch_size,)   )
										for batch_idx, start_index in enumerate(a_   ):
												A__											=							0
												A__											=							1
												A__											=							0
												A__											=							1
										A__											=							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.
										A__											=							False
										A__											=							model_class(a_						,      dtype=jnp.floataa   )
										A__											=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,      a_   )
										A__											=							fx_state

										with torch.no_grad():
												A__											=							pt_model(**a_   ).to_tuple()

										A__											=							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_   )
												A__											=							model_class.from_pretrained(a_						,      from_pt=a_   )

										A__											=							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  A    (self   ):


						"""simple docstring"""
						A__						,A__											=							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
										A__											=							self._prepare_for_class(a_						,      a_   )
										A__											=							{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										A__											=							model_class.__name__[4:]  # Skip the "Flax" at the beginning
										A__											=							getattr(a_						,      a_   )

										A__											=							pt_model_class(a_   ).eval()
										A__											=							False
										A__											=							model_class(a_						,      dtype=jnp.floataa   )

										A__											=							load_flax_weights_in_pytorch_model(a_						,      fx_model.params   )
										A__						,A__											=							pt_inputs["""input_ids"""].shape
										A__											=							np.random.randint(0						,      seq_length - 1						,      size=(batch_size,)   )
										for batch_idx, start_index in enumerate(a_   ):
												A__											=							0
												A__											=							1
												A__											=							0
												A__											=							1

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

										with torch.no_grad():
												A__											=							pt_model(**a_   ).to_tuple()

										A__											=							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_   )
												A__											=							pt_model_class.from_pretrained(a_						,      from_flax=a_   )

										with torch.no_grad():
												A__											=							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  A    (self   ):


						"""simple docstring"""
						for model_class_name in self.all_model_classes:
								A__											=							model_class_name.from_pretrained("""facebook/xglm-564M"""   )
								A__											=							model(np.ones((1, 1)   )   )
								self.assertIsNotNone(a_   )












"""simple docstring"""




def 						_SCREAMING_SNAKE_CASE							( UpperCamelCase     :	Dict    ):
  A__											=							len(UpperCamelCase    )
  for _ in range(
      0		,				int(((no_of_elements - 1) / 2) + 1    )    ):  # we don't need to traverse to end of list as
    for j in range(0		,				no_of_elements - 1    ):
      if (
          lst[j + 1] < lst[j]
      ):  # applying bubble sort algorithm from left to right (or forwards)
        A__											=							lst[j + 1]
        A__											=							lst[j]
        A__											=							temp
      if (
          lst[no_of_elements - 1 - j] < lst[no_of_elements - 2 - j]
      ):  # applying bubble sort algorithm from right to left (or backwards)
        A__											=							lst[no_of_elements - 1 - j]
        A__											=							lst[no_of_elements - 2 - j]
        A__											=							temp
  return lst


if __name__ == "__main__":
      print("enter the list to be sorted")
      lowerCamelCase__			=   [int(x) for x in input().split()]  # inputing elements of the list in one line
      lowerCamelCase__			=   double_sort(lst)
      print("the sorted list is")
      print(sorted_lst)




