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   lowerCamelCase_							:
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase,						__UpperCamelCase=14,						__UpperCamelCase=7,						__UpperCamelCase=True,						__UpperCamelCase=True,						__UpperCamelCase=True,						__UpperCamelCase=99,						__UpperCamelCase=32,						__UpperCamelCase=5,						__UpperCamelCase=4,						__UpperCamelCase=37,						__UpperCamelCase="gelu",						__UpperCamelCase=0.1,						__UpperCamelCase=0.1,						__UpperCamelCase=512,						__UpperCamelCase=0.02,						__UpperCamelCase=None,						):
  							_snake_case			 = parent
  							_snake_case			 = batch_size
  							_snake_case			 = seq_length
  							_snake_case			 = is_training
  							_snake_case			 = use_input_mask
  							_snake_case			 = use_labels
  							_snake_case			 = vocab_size
  							_snake_case			 = d_model
  							_snake_case			 = num_hidden_layers
  							_snake_case			 = num_attention_heads
  							_snake_case			 = ffn_dim
  							_snake_case			 = activation_function
  							_snake_case			 = activation_dropout
  							_snake_case			 = attention_dropout
  							_snake_case			 = max_position_embeddings
  							_snake_case			 = initializer_range
  							_snake_case			 = None
  							_snake_case			 = 0
  							_snake_case			 = 2
  							_snake_case			 = 1
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = np.clip(ids_tensor([self.batch_size, self.seq_length],						self.vocab_size				),						3,						self.vocab_size				)
  
  							_snake_case			 = None
  							if self.use_input_mask:
  									_snake_case			 = random_attention_mask([self.batch_size, self.seq_length]				)
  
  							_snake_case			 = 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=__UpperCamelCase,						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        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.prepare_config_and_inputs()
  							_snake_case ,					_snake_case ,					_snake_case			 = config_and_inputs
  							_snake_case			 = {"input_ids": input_ids, "attention_mask": attention_mask}
  							return config, inputs_dict
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case ,					_snake_case ,					_snake_case			 = self.prepare_config_and_inputs()
  
  							_snake_case			 = floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
  							_snake_case			 = ids_tensor([self.batch_size, self.seq_length],						vocab_size=2				)
  
  							return (
  							    config,
  							    input_ids,
  							    attention_mask,
  							    encoder_hidden_states,
  							    encoder_attention_mask,
  							)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = 20
  							_snake_case			 = model_class_name(__UpperCamelCase				)
  
  							_snake_case			 = model.init_cache(input_ids.shape[0],						__UpperCamelCase				)
  							_snake_case			 = jnp.ones((input_ids.shape[0], max_decoder_length),						dtype="i4"				)
  
  							_snake_case			 = jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1				)[None, :],						(input_ids.shape[0], input_ids.shape[-1] - 1)				)
  							_snake_case			 = model(
  							    input_ids[:, :-1],						attention_mask=__UpperCamelCase,						past_key_values=__UpperCamelCase,						position_ids=__UpperCamelCase,						)
  
  							_snake_case			 = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],						dtype="i4"				)
  							_snake_case			 = model(
  							    input_ids[:, -1:],						attention_mask=__UpperCamelCase,						past_key_values=outputs_cache.past_key_values,						position_ids=__UpperCamelCase,						)
  
  							_snake_case			 = model(__UpperCamelCase				)
  
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				):
  							_snake_case			 = 20
  							_snake_case			 = model_class_name(__UpperCamelCase				)
  
  							_snake_case			 = jnp.concatenate(
  							    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])				)],						axis=-1,						)
  
  							_snake_case			 = model.init_cache(input_ids.shape[0],						__UpperCamelCase				)
  							_snake_case			 = jnp.broadcast_to(
  							    jnp.arange(input_ids.shape[-1] - 1				)[None, :],						(input_ids.shape[0], input_ids.shape[-1] - 1)				)
  
  							_snake_case			 = model(
  							    input_ids[:, :-1],						attention_mask=__UpperCamelCase,						past_key_values=__UpperCamelCase,						position_ids=__UpperCamelCase,						)
  							_snake_case			 = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],						dtype="i4"				)
  							_snake_case			 = model(
  							    input_ids[:, -1:],						past_key_values=outputs_cache.past_key_values,						attention_mask=__UpperCamelCase,						position_ids=__UpperCamelCase,						)
  
  							_snake_case			 = model(__UpperCamelCase,						attention_mask=__UpperCamelCase				)
  							_snake_case			 = 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   lowerCamelCase_							( UpperCAmelCase_, UpperCAmelCase_, unittest.TestCase							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  					lowercase__					=				(FlaxXGLMForCausalLM,) if is_flax_available() else ()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = FlaxXGLMModelTester(self				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							for model_class_name in self.all_model_classes:
  									_snake_case ,					_snake_case ,					_snake_case			 = self.model_tester.prepare_config_and_inputs()
  									self.model_tester.check_use_cache_forward(__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							for model_class_name in self.all_model_classes:
  									_snake_case ,					_snake_case ,					_snake_case			 = self.model_tester.prepare_config_and_inputs()
  									self.model_tester.check_use_cache_forward_with_attn_mask(
  									    __UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase,						__UpperCamelCase				)
  
  
  
  
  
  
  					@slow
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = XGLMTokenizer.from_pretrained("XGLM",						padding_side="left"				)
  							_snake_case			 = tokenizer(["Hello this is a long string", "Hey"],						return_tensors="np",						padding=__UpperCamelCase,						truncation=__UpperCamelCase				)
  
  							_snake_case			 = FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"				)
  							_snake_case			 = 1
  							_snake_case			 = False
  
  							_snake_case			 = jax.jit(model.generate				)
  
  							_snake_case			 = jit_generate(inputs["input_ids"],						attention_mask=inputs["attention_mask"]				).sequences
  
  							_snake_case			 = tokenizer.batch_decode(__UpperCamelCase,						skip_special_tokens=__UpperCamelCase				)
  
  							_snake_case			 = [
  							    "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(__UpperCamelCase,						__UpperCamelCase				)
  
  
  
  
  
  
  					@is_pt_flax_cross_test
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case ,					_snake_case			 = 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
  											_snake_case			 = self._prepare_for_class(__UpperCamelCase,						__UpperCamelCase				)
  											_snake_case			 = {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}
  
  											# load corresponding PyTorch class
  											_snake_case			 = model_class.__name__[4:]  # Skip the "Flax" at the beginning
  											_snake_case			 = getattr(__UpperCamelCase,						__UpperCamelCase				)
  
  											_snake_case ,					_snake_case			 = pt_inputs["input_ids"].shape
  											_snake_case			 = np.random.randint(0,						seq_length - 1,						size=(batch_size,)				)
  											for batch_idx, start_index in enumerate(__UpperCamelCase				):
  													_snake_case			 = 0
  													_snake_case			 = 1
  													_snake_case			 = 0
  													_snake_case			 = 1
  											_snake_case			 = pt_model_class(__UpperCamelCase				).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.
  											_snake_case			 = False
  											_snake_case			 = model_class(__UpperCamelCase,						dtype=jnp.floataa				)
  											_snake_case			 = convert_pytorch_state_dict_to_flax(pt_model.state_dict(),						__UpperCamelCase				)
  											_snake_case			 = fx_state
  
  											with torch.no_grad():
  													_snake_case			 = pt_model(**__UpperCamelCase				).to_tuple()
  
  											_snake_case			 = fx_model(**__UpperCamelCase				).to_tuple()
  											self.assertEqual(len(__UpperCamelCase				),						len(__UpperCamelCase				),						"Output lengths differ between Flax and PyTorch"				)
  											for fx_output, pt_output in zip(__UpperCamelCase,						__UpperCamelCase				):
  													self.assert_almost_equals(fx_output[:, -1],						pt_output[:, -1].numpy(),						4E-2				)
  
  											with tempfile.TemporaryDirectory() as tmpdirname:
  													pt_model.save_pretrained(__UpperCamelCase				)
  													_snake_case			 = model_class.from_pretrained(__UpperCamelCase,						from_pt=__UpperCamelCase				)
  
  											_snake_case			 = fx_model_loaded(**__UpperCamelCase				).to_tuple()
  											self.assertEqual(
  											    len(__UpperCamelCase				),						len(__UpperCamelCase				),						"Output lengths differ between Flax and PyTorch"				)
  											for fx_output_loaded, pt_output in zip(__UpperCamelCase,						__UpperCamelCase				):
  													self.assert_almost_equals(fx_output_loaded[:, -1],						pt_output[:, -1].numpy(),						4E-2				)
  
  
  
  
  
  
  					@is_pt_flax_cross_test
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case ,					_snake_case			 = 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
  											_snake_case			 = self._prepare_for_class(__UpperCamelCase,						__UpperCamelCase				)
  											_snake_case			 = {k: torch.tensor(v.tolist()				) for k, v in prepared_inputs_dict.items()}
  
  											# load corresponding PyTorch class
  											_snake_case			 = model_class.__name__[4:]  # Skip the "Flax" at the beginning
  											_snake_case			 = getattr(__UpperCamelCase,						__UpperCamelCase				)
  
  											_snake_case			 = pt_model_class(__UpperCamelCase				).eval()
  											_snake_case			 = False
  											_snake_case			 = model_class(__UpperCamelCase,						dtype=jnp.floataa				)
  
  											_snake_case			 = load_flax_weights_in_pytorch_model(__UpperCamelCase,						fx_model.params				)
  											_snake_case ,					_snake_case			 = pt_inputs["input_ids"].shape
  											_snake_case			 = np.random.randint(0,						seq_length - 1,						size=(batch_size,)				)
  											for batch_idx, start_index in enumerate(__UpperCamelCase				):
  													_snake_case			 = 0
  													_snake_case			 = 1
  													_snake_case			 = 0
  													_snake_case			 = 1
  
  											# make sure weights are tied in PyTorch
  											pt_model.tie_weights()
  
  											with torch.no_grad():
  													_snake_case			 = pt_model(**__UpperCamelCase				).to_tuple()
  
  											_snake_case			 = fx_model(**__UpperCamelCase				).to_tuple()
  											self.assertEqual(len(__UpperCamelCase				),						len(__UpperCamelCase				),						"Output lengths differ between Flax and PyTorch"				)
  											for fx_output, pt_output in zip(__UpperCamelCase,						__UpperCamelCase				):
  													self.assert_almost_equals(fx_output[:, -1],						pt_output[:, -1].numpy(),						4E-2				)
  
  											with tempfile.TemporaryDirectory() as tmpdirname:
  													fx_model.save_pretrained(__UpperCamelCase				)
  													_snake_case			 = pt_model_class.from_pretrained(__UpperCamelCase,						from_flax=__UpperCamelCase				)
  
  											with torch.no_grad():
  													_snake_case			 = pt_model_loaded(**__UpperCamelCase				).to_tuple()
  
  											self.assertEqual(
  											    len(__UpperCamelCase				),						len(__UpperCamelCase				),						"Output lengths differ between Flax and PyTorch"				)
  											for fx_output, pt_output in zip(__UpperCamelCase,						__UpperCamelCase				):
  													self.assert_almost_equals(fx_output[:, -1],						pt_output[:, -1].numpy(),						4E-2				)
  
  
  
  
  
  
  					@slow
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							for model_class_name in self.all_model_classes:
  									_snake_case			 = model_class_name.from_pretrained("facebook/xglm-564M"				)
  									_snake_case			 = model(np.ones((1, 1)				)				)
  									self.assertIsNotNone(__UpperCamelCase				)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  from __future__ import annotations
  
  from collections.abc import Iterable, Iterator
  from typing import Any, Generic, TypeVar
  
  _lowerCamelCase							:		List[str]										=						TypeVar('T', bound=bool)
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase				):
  							_snake_case			 = value
  							_snake_case			 = None
  							_snake_case			 = None
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self._value
  
  
  
  
  
  
  					@staticmethod
  					def        __SCREAMING_SNAKE_CASE			(					__UpperCamelCase,						__UpperCamelCase				):
  							if not roota:
  									return roota
  
  							if not roota:
  									return roota
  
  							if roota.value > roota.value:
  									_snake_case ,					_snake_case			 = roota, roota
  
  							_snake_case			 = roota
  							_snake_case			 = roota.right
  							_snake_case			 = roota.left
  							_snake_case			 = SkewNode.merge(__UpperCamelCase,						__UpperCamelCase				)
  
  							return result
  
  
  
  
  
  class   lowerCamelCase_							( Generic[T]							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase = ()				):
  							_snake_case			 = None
  							if data:
  									for item in data:
  											self.insert(__UpperCamelCase				)
  
  
  
  
  
  
  					def __bool__(					self				):
  							return self._root is not None
  
  
  
  
  
  
  					def __iter__(					self				):
  							_snake_case			 = []
  							while self:
  									result.append(self.pop()				)
  
  							# Pushing items back to the heap not to clear it.
  							for item in result:
  									self.insert(__UpperCamelCase				)
  
  							return iter(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = SkewNode.merge(self._root,						SkewNode(__UpperCamelCase				)				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.top()
  							_snake_case			 = (
  							    SkewNode.merge(self._root.left,						self._root.right				) if self._root else None
  							)
  
  							return result
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if not self._root:
  									raise IndexError("Can't get top element for the empty heap."				)
  							return self._root.value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = None
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  
  
  
  
  
  