






'''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 				snake_case__     :




					def __init__(       self       :						List[Any]							,				_A       :						List[str]							,				_A       :						Tuple=14							,				_A       :						Any=7							,				_A       :						List[str]=True							,				_A       :						Tuple=True							,				_A       :						Dict=True							,				_A       :						Union[str, Any]=99							,				_A       :						str=32							,				_A       :						Optional[Any]=5							,				_A       :						Optional[Any]=4							,				_A       :						Tuple=37							,				_A       :						Tuple="gelu"							,				_A       :						Union[str, Any]=0.1							,				_A       :						Optional[Any]=0.1							,				_A       :						Union[str, Any]=5_12							,				_A       :						Optional[Any]=0.02							,				_A       :						Any=None							,				)  ->    int:
								UpperCAmelCase_     :   List[Any]       =   parent
								UpperCAmelCase_     :   int       =   batch_size
								UpperCAmelCase_     :   int       =   seq_length
								UpperCAmelCase_     :   List[str]       =   is_training
								UpperCAmelCase_     :   int       =   use_input_mask
								UpperCAmelCase_     :   List[Any]       =   use_labels
								UpperCAmelCase_     :   Optional[Any]       =   vocab_size
								UpperCAmelCase_     :   Optional[Any]       =   d_model
								UpperCAmelCase_     :   Optional[Any]       =   num_hidden_layers
								UpperCAmelCase_     :   List[str]       =   num_attention_heads
								UpperCAmelCase_     :   Union[str, Any]       =   ffn_dim
								UpperCAmelCase_     :   Optional[int]       =   activation_function
								UpperCAmelCase_     :   Optional[int]       =   activation_dropout
								UpperCAmelCase_     :   str       =   attention_dropout
								UpperCAmelCase_     :   Union[str, Any]       =   max_position_embeddings
								UpperCAmelCase_     :   List[Any]       =   initializer_range
								UpperCAmelCase_     :   Tuple       =   None
								UpperCAmelCase_     :   List[Any]       =   0
								UpperCAmelCase_     :   Union[str, Any]       =   2
								UpperCAmelCase_     :   str       =   1




					def 						A     (       self       :						Optional[int]     )  ->    Union[str, Any]:
								UpperCAmelCase_     :   Dict       =   np.clip(ids_tensor([self.batch_size, self.seq_length]							,				self.vocab_size     )							,				3							,				self.vocab_size     )

								UpperCAmelCase_     :   Tuple       =   None
								if self.use_input_mask:
											UpperCAmelCase_     :   str       =   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=_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       :						Dict     )  ->    Dict:
								UpperCAmelCase_     :   Dict       =   self.prepare_config_and_inputs()
								UpperCAmelCase_     :   Tuple       =   config_and_inputs
								UpperCAmelCase_     :   Any       =   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
								return config, inputs_dict




					def 						A     (       self       :						str     )  ->    str:
								UpperCAmelCase_     :   Union[str, Any]       =   self.prepare_config_and_inputs()

								UpperCAmelCase_     :   Dict       =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
								UpperCAmelCase_     :   Dict       =   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       :						Optional[int]							,				_A       :						List[Any]							,				_A       :						Optional[Any]							,				_A       :						Optional[int]							,				_A       :						Any     )  ->    str:
								UpperCAmelCase_     :   List[Any]       =   20
								UpperCAmelCase_     :   Tuple       =   model_class_name(_A     )

								UpperCAmelCase_     :   Tuple       =   model.init_cache(input_ids.shape[0]							,				_A     )
								UpperCAmelCase_     :   str       =   jnp.ones((input_ids.shape[0], max_decoder_length)							,				dtype='''i4'''     )

								UpperCAmelCase_     :   Dict       =   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=_A							,				past_key_values=_A							,				position_ids=_A							,				)

								UpperCAmelCase_     :   Any       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,				dtype='''i4'''     )
								UpperCAmelCase_     :   Optional[Any]       =   model(
								    input_ids[:, -1:]							,				attention_mask=_A							,				past_key_values=outputs_cache.past_key_values							,				position_ids=_A							,				)

								UpperCAmelCase_     :   List[Any]       =   model(_A     )

								UpperCAmelCase_     :   Dict       =   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       :						Tuple							,				_A       :						Tuple							,				_A       :						List[Any]							,				_A       :						Optional[Any]							,				_A       :						Any     )  ->    int:
								UpperCAmelCase_     :   Any       =   20
								UpperCAmelCase_     :   List[str]       =   model_class_name(_A     )

								UpperCAmelCase_     :   Optional[int]       =   jnp.concatenate(
								    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])     )]							,				axis=-1							,				)

								UpperCAmelCase_     :   Tuple       =   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_     :   int       =   model(
								    input_ids[:, :-1]							,				attention_mask=_A							,				past_key_values=_A							,				position_ids=_A							,				)
								UpperCAmelCase_     :   Dict       =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,				dtype='''i4'''     )
								UpperCAmelCase_     :   Optional[int]       =   model(
								    input_ids[:, -1:]							,				past_key_values=outputs_cache.past_key_values							,				attention_mask=_A							,				position_ids=_A							,				)

								UpperCAmelCase_     :   Optional[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 				snake_case__     (			UpperCamelCase      ,							UpperCamelCase      ,							unittest.TestCase):
					a_       =			(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					a_       =			(FlaxXGLMForCausalLM,) if is_flax_available() else ()




					def 						A     (       self       :						int     )  ->    int:
								UpperCAmelCase_     :   str       =   FlaxXGLMModelTester(self     )




					def 						A     (       self       :						List[str]     )  ->    int:
								for model_class_name in self.all_model_classes:
											UpperCAmelCase_     :   str       =   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(_A							,				_A							,				_A							,				_A     )




					def 						A     (       self       :						Tuple     )  ->    List[str]:
								for model_class_name in self.all_model_classes:
											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 						A     (       self       :						Optional[int]     )  ->    Tuple:
								UpperCAmelCase_     :   Union[str, Any]       =   XGLMTokenizer.from_pretrained('''XGLM'''							,				padding_side='''left'''     )
								UpperCAmelCase_     :   Optional[Any]       =   tokenizer(['''Hello this is a long string''', '''Hey''']							,				return_tensors='''np'''							,				padding=_A							,				truncation=_A     )

								UpperCAmelCase_     :   str       =   FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''     )
								UpperCAmelCase_     :   List[str]       =   1
								UpperCAmelCase_     :   Dict       =   False

								UpperCAmelCase_     :   Optional[int]       =   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_     :   Tuple       =   [
								    '''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       :						int     )  ->    Dict:
								UpperCAmelCase_     :   Union[str, 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_     :   Dict       =   self._prepare_for_class(_A							,				_A     )
														UpperCAmelCase_     :   Dict       =   {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_     :   Dict       =   getattr(_A							,				_A     )

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

														with torch.no_grad():
																	UpperCAmelCase_     :   Optional[Any]       =   pt_model(**_A     ).to_tuple()

														UpperCAmelCase_     :   Union[str, 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:
																	pt_model.save_pretrained(_A     )
																	UpperCAmelCase_     :   int       =   model_class.from_pretrained(_A							,				from_pt=_A     )

														UpperCAmelCase_     :   int       =   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       :						int     )  ->    Optional[int]:
								UpperCAmelCase_     :   Union[str, 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_     :   Optional[Any]       =   self._prepare_for_class(_A							,				_A     )
														UpperCAmelCase_     :   Union[str, 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_     :   Any       =   getattr(_A							,				_A     )

														UpperCAmelCase_     :   List[Any]       =   pt_model_class(_A     ).eval()
														UpperCAmelCase_     :   Union[str, Any]       =   False
														UpperCAmelCase_     :   Union[str, Any]       =   model_class(_A							,				dtype=jnp.floataa     )

														UpperCAmelCase_     :   List[Any]       =   load_flax_weights_in_pytorch_model(_A							,				fx_model.params     )
														UpperCAmelCase_     :   str       =   pt_inputs['''input_ids'''].shape
														UpperCAmelCase_     :   List[Any]       =   np.random.randint(0							,				seq_length - 1							,				size=(batch_size,)     )
														for batch_idx, start_index in enumerate(_A     ):
																	UpperCAmelCase_     :   Optional[Any]       =   0
																	UpperCAmelCase_     :   str       =   1
																	UpperCAmelCase_     :   Union[str, Any]       =   0
																	UpperCAmelCase_     :   Optional[Any]       =   1

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

														with torch.no_grad():
																	UpperCAmelCase_     :   Optional[int]       =   pt_model(**_A     ).to_tuple()

														UpperCAmelCase_     :   Union[str, 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_     :   Dict       =   pt_model_class.from_pretrained(_A							,				from_flax=_A     )

														with torch.no_grad():
																	UpperCAmelCase_     :   Optional[int]       =   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       :						str     )  ->    Dict:
								for model_class_name in self.all_model_classes:
											UpperCAmelCase_     :   Optional[int]       =   model_class_name.from_pretrained('''facebook/xglm-564M'''     )
											UpperCAmelCase_     :   int       =   model(np.ones((1, 1)     )     )
											self.assertIsNotNone(_A     )










'''simple docstring'''





from __future__ import annotations

from collections import deque
from enum import Enum
from math import atana, degrees
from sys import maxsize


class 				snake_case__     (			UpperCamelCase):
					a_       =			1
					a_       =			2
					a_       =			3




					def __repr__(       self       :						str     )  ->    Optional[int]:
								return F"{self.__class__.__name__}.{self.name}"






def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	int	,   A			:	int						)  -> float:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Any       =   point
			return degrees(atana(y - miny	,   x - minx						)						)
def __UpperCAmelCase (			A			:	tuple[int, int]	,   A			:	tuple[int, int]	,   A			:	tuple[int, int]						)  -> Direction:
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   starting
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[Any]       =   via
			UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[int]       =   target
			UpperCAmelCase_     :   Optional[int]       =   degrees(atana(ya - ya	,   xa - xa						)						)
			via_angle %= 3_6_0
			UpperCAmelCase_     :   Dict       =   degrees(atana(ya - ya	,   xa - xa						)						)
			target_angle %= 3_6_0
			# t-
			#  \ \
			#   \ v
			#    \|
			#     s
			# via_angle is always lower than target_angle, if direction is left.
			# If they are same, it means they are on a same line of convex hull.
			if target_angle > via_angle:
						return Direction.left
			elif target_angle == via_angle:
						return Direction.straight
			else:
						return Direction.right







def __UpperCAmelCase (			A			:	list[tuple[int, int]]						)  -> list[tuple[int, int]]:

			if len(A						) <= 2:
						# There is no convex hull
						raise ValueError('''graham_scan: argument must contain more than 3 points.'''						)
			if len(A						) == 3:
						return points
			# find the lowest and the most left point
			UpperCAmelCase_     :   List[str]       =   0
			UpperCAmelCase_  ,     UpperCAmelCase_     :   List[Any]       =   maxsize, maxsize
			for i, point in enumerate(A						):
						UpperCAmelCase_     :   str       =   point[0]
						UpperCAmelCase_     :   List[Any]       =   point[1]
						if y < miny:
									UpperCAmelCase_     :   int       =   y
									UpperCAmelCase_     :   Dict       =   x
									UpperCAmelCase_     :   Any       =   i
						if y == miny and x < minx:
									UpperCAmelCase_     :   str       =   x
									UpperCAmelCase_     :   Optional[int]       =   i

    # remove the lowest and the most left point from points for preparing for sort
			points.pop(A						)

			UpperCAmelCase_     :   Optional[Any]       =   sorted(A	,   key=lambda A						: angle_comparer(A	,   A	,   A						)						)
			# This insert actually costs complexity,
			# and you should instead add (minx, miny) into stack later.
			# I'm using insert just for easy understanding.
			sorted_points.insert(0	,   (minx, miny)						)

			UpperCAmelCase_     :   deque[tuple[int, int]]       =   deque()
			stack.append(sorted_points[0]						)
			stack.append(sorted_points[1]						)
			stack.append(sorted_points[2]						)
			# In any ways, the first 3 points line are towards left.
			# Because we sort them the angle from minx, miny.
			UpperCAmelCase_     :   Any       =   Direction.left

			for i in range(3	,   len(A						)						):
						while True:
									UpperCAmelCase_     :   Any       =   stack[-2]
									UpperCAmelCase_     :   List[Any]       =   stack[-1]
									UpperCAmelCase_     :   Any       =   sorted_points[i]
									UpperCAmelCase_     :   Tuple       =   check_direction(A	,   A	,   A						)

									if next_direction == Direction.left:
												UpperCAmelCase_     :   Optional[Any]       =   Direction.left
												break
									if next_direction == Direction.straight:
												if current_direction == Direction.left:
															# We keep current_direction as left.
															# Because if the straight line keeps as straight,
															# we want to know if this straight line is towards left.
															break
												elif current_direction == Direction.right:
															# If the straight line is towards right,
															# every previous points on those straigh line is not convex hull.
															stack.pop()
									if next_direction == Direction.right:
												stack.pop()
						stack.append(sorted_points[i]						)
			return list(A						)


