




'''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				)











'''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()





