


'''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__      :




			def __init__(		self	:  Optional[int]						,	lowerCamelCase__	:  List[Any]						,	lowerCamelCase__	:  Any=14						,	lowerCamelCase__	:  int=7						,	lowerCamelCase__	:  Tuple=True						,	lowerCamelCase__	:  Tuple=True						,	lowerCamelCase__	:  Optional[int]=True						,	lowerCamelCase__	:  int=99						,	lowerCamelCase__	:  str=32						,	lowerCamelCase__	:  Union[str, Any]=5						,	lowerCamelCase__	:  Any=4						,	lowerCamelCase__	:  List[str]=37						,	lowerCamelCase__	:  str="gelu"						,	lowerCamelCase__	:  Optional[int]=0.1						,	lowerCamelCase__	:  Any=0.1						,	lowerCamelCase__	:  List[str]=5_12						,	lowerCamelCase__	:  Union[str, Any]=0.0_2						,	lowerCamelCase__	:  List[str]=None						,	) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase		:							Tuple             =   parent
					_UpperCAmelCase		:							Optional[Any]             =   batch_size
					_UpperCAmelCase		:							Any             =   seq_length
					_UpperCAmelCase		:							Tuple             =   is_training
					_UpperCAmelCase		:							List[Any]             =   use_input_mask
					_UpperCAmelCase		:							List[str]             =   use_labels
					_UpperCAmelCase		:							List[Any]             =   vocab_size
					_UpperCAmelCase		:							Union[str, Any]             =   d_model
					_UpperCAmelCase		:							Tuple             =   num_hidden_layers
					_UpperCAmelCase		:							Any             =   num_attention_heads
					_UpperCAmelCase		:							int             =   ffn_dim
					_UpperCAmelCase		:							int             =   activation_function
					_UpperCAmelCase		:							Any             =   activation_dropout
					_UpperCAmelCase		:							int             =   attention_dropout
					_UpperCAmelCase		:							List[Any]             =   max_position_embeddings
					_UpperCAmelCase		:							List[Any]             =   initializer_range
					_UpperCAmelCase		:							Any             =   None
					_UpperCAmelCase		:							Dict             =   0
					_UpperCAmelCase		:							Union[str, Any]             =   2
					_UpperCAmelCase		:							List[str]             =   1




			def 				lowerCAmelCase__     (		self	:  Optional[Any]			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   np.clip(ids_tensor([self.batch_size, self.seq_length]						,	self.vocab_size			)						,	3						,	self.vocab_size			)

					_UpperCAmelCase		:							List[Any]             =   None
					if self.use_input_mask:
							_UpperCAmelCase		:							Union[str, Any]             =   random_attention_mask([self.batch_size, self.seq_length]			)

					_UpperCAmelCase		:							Dict             =   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 				lowerCAmelCase__     (		self	:  Union[str, Any]			) ->Optional[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[Any]             =   self.prepare_config_and_inputs()
					_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   config_and_inputs
					_UpperCAmelCase		:							Any             =   {"input_ids": input_ids, "attention_mask": attention_mask}
					return config, inputs_dict




			def 				lowerCAmelCase__     (		self	:  Any			) ->str:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							int             =   self.prepare_config_and_inputs()

					_UpperCAmelCase		:							Optional[int]             =   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 				lowerCAmelCase__     (		self	:  List[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  List[str]						,	lowerCamelCase__	:  List[str]						,	lowerCamelCase__	:  List[str]			) ->int:



					'''simple docstring'''







					_UpperCAmelCase		:							Union[str, Any]             =   20
					_UpperCAmelCase		:							Union[str, Any]             =   model_class_name(lowercase__			)

					_UpperCAmelCase		:							Optional[Any]             =   model.init_cache(input_ids.shape[0]						,	lowercase__			)
					_UpperCAmelCase		:							str             =   jnp.ones((input_ids.shape[0], max_decoder_length)						,	dtype="i4"			)

					_UpperCAmelCase		:							Any             =   jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1			)[None, :]						,	(input_ids.shape[0], input_ids.shape[-1] - 1)			)
					_UpperCAmelCase		:							Optional[Any]             =   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		:							Dict             =   model(
					    input_ids[:, -1:]						,	attention_mask=lowercase__						,	past_key_values=outputs_cache.past_key_values						,	position_ids=lowercase__						,	)

					_UpperCAmelCase		:							Dict             =   model(lowercase__			)

					_UpperCAmelCase		:							List[str]             =   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 				lowerCAmelCase__     (		self	:  Any						,	lowerCamelCase__	:  Optional[Any]						,	lowerCamelCase__	:  Optional[int]						,	lowerCamelCase__	:  Tuple						,	lowerCamelCase__	:  Tuple			) ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							Any             =   20
					_UpperCAmelCase		:							Any             =   model_class_name(lowercase__			)

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

					_UpperCAmelCase		:							Any             =   model.init_cache(input_ids.shape[0]						,	lowercase__			)
					_UpperCAmelCase		:							Dict             =   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		:							Any             =   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,	dtype="i4"			)
					_UpperCAmelCase		:							Optional[Any]             =   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		:							Optional[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 						lowerCAmelCase__      (      _snake_case     ,							_snake_case     ,							unittest.TestCase						):
			lowerCAmelCase			:						Dict							 =  (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
			lowerCAmelCase			:						Optional[Any]							 =  (FlaxXGLMForCausalLM,) if is_flax_available() else ()




			def 				lowerCAmelCase__     (		self	:  List[Any]			) ->List[Any]:



					'''simple docstring'''







					_UpperCAmelCase		:							int             =   FlaxXGLMModelTester(self			)




			def 				lowerCAmelCase__     (		self	:  str			) ->List[Any]:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Dict             =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(lowercase__						,	lowercase__						,	lowercase__						,	lowercase__			)




			def 				lowerCAmelCase__     (		self	:  List[str]			) ->Optional[int]:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase       ,     _UpperCAmelCase       ,     _UpperCAmelCase		:							Optional[int]             =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward_with_attn_mask(
							    lowercase__						,	lowercase__						,	lowercase__						,	lowercase__			)




			@slow
			def 				lowerCAmelCase__     (		self	:  Any			) ->Dict:



					'''simple docstring'''







					_UpperCAmelCase		:							Optional[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		:							int             =   FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"			)
					_UpperCAmelCase		:							Union[str, Any]             =   1
					_UpperCAmelCase		:							List[str]             =   False

					_UpperCAmelCase		:							Optional[int]             =   jax.jit(model.generate			)

					_UpperCAmelCase		:							int             =   jit_generate(inputs["input_ids"]						,	attention_mask=inputs["attention_mask"]			).sequences

					_UpperCAmelCase		:							str             =   tokenizer.batch_decode(lowercase__						,	skip_special_tokens=lowercase__			)

					_UpperCAmelCase		:							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 				lowerCAmelCase__     (		self	:  Optional[int]			) ->Union[str, Any]:



					'''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		:							Optional[int]             =   self._prepare_for_class(lowercase__						,	lowercase__			)
									_UpperCAmelCase		:							str             =   {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		:							Optional[int]             =   getattr(lowercase__						,	lowercase__			)

									_UpperCAmelCase       ,     _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(lowercase__			):
											_UpperCAmelCase		:							Union[str, Any]             =   0
											_UpperCAmelCase		:							Dict             =   1
											_UpperCAmelCase		:							List[str]             =   0
											_UpperCAmelCase		:							Union[str, Any]             =   1
									_UpperCAmelCase		:							int             =   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		:							Dict             =   False
									_UpperCAmelCase		:							str             =   model_class(lowercase__						,	dtype=jnp.floataa			)
									_UpperCAmelCase		:							Tuple             =   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,	lowercase__			)
									_UpperCAmelCase		:							Any             =   fx_state

									with torch.no_grad():
											_UpperCAmelCase		:							str             =   pt_model(**lowercase__			).to_tuple()

									_UpperCAmelCase		:							int             =   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		:							str             =   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 				lowerCAmelCase__     (		self	:  Tuple			) ->Optional[int]:



					'''simple docstring'''







					_UpperCAmelCase       ,     _UpperCAmelCase		:							Dict             =   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(lowercase__						,	lowercase__			)
									_UpperCAmelCase		:							Any             =   {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

									# load corresponding PyTorch class
									_UpperCAmelCase		:							Optional[int]             =   model_class.__name__[4:]  # Skip the "Flax" at the beginning
									_UpperCAmelCase		:							int             =   getattr(lowercase__						,	lowercase__			)

									_UpperCAmelCase		:							Optional[Any]             =   pt_model_class(lowercase__			).eval()
									_UpperCAmelCase		:							int             =   False
									_UpperCAmelCase		:							Dict             =   model_class(lowercase__						,	dtype=jnp.floataa			)

									_UpperCAmelCase		:							Dict             =   load_flax_weights_in_pytorch_model(lowercase__						,	fx_model.params			)
									_UpperCAmelCase       ,     _UpperCAmelCase		:							List[str]             =   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		:							str             =   0
											_UpperCAmelCase		:							Dict             =   1
											_UpperCAmelCase		:							List[str]             =   0
											_UpperCAmelCase		:							int             =   1

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

									with torch.no_grad():
											_UpperCAmelCase		:							List[Any]             =   pt_model(**lowercase__			).to_tuple()

									_UpperCAmelCase		:							Dict             =   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		:							Union[str, Any]             =   pt_model_class.from_pretrained(lowercase__						,	from_flax=lowercase__			)

									with torch.no_grad():
											_UpperCAmelCase		:							List[str]             =   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 				lowerCAmelCase__     (		self	:  Tuple			) ->List[str]:



					'''simple docstring'''







					for model_class_name in self.all_model_classes:
							_UpperCAmelCase		:							int             =   model_class_name.from_pretrained("facebook/xglm-564M"			)
							_UpperCAmelCase		:							Tuple             =   model(np.ones((1, 1)			)			)
							self.assertIsNotNone(lowercase__			)






'''simple docstring'''







from __future__ import annotations

from pathlib import Path






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_	):
	return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def 					_SCREAMING_SNAKE_CASE		(						snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_							,       snake_case_	):
	_lowercase							=     (xa, ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     (xa - xa, ya - ya)
	_lowercase							=     -vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)
	_lowercase							=     +vector_product(snake_case_							,       snake_case_	) / vector_product(
	    snake_case_							,       snake_case_	)

	return a > 0 and b > 0 and a + b < 1






def 					_SCREAMING_SNAKE_CASE		(						snake_case_ = "p102_triangles.txt"	):
	_lowercase							=     Path(snake_case_	).parent.joinpath(snake_case_	).read_text(encoding="""utf-8"""	)

	_lowercase							=     []
	for line in data.strip().split("""\n"""	):
		triangles.append([int(snake_case_	) for number in line.split(""","""	)]	)

	_lowercase							=     0
	_lowercase							=     42

	for triangle in triangles:
		ret += contains_origin(*snake_case_	)

	return ret


if __name__ == "__main__":
							print(F"""{solution() = }""")




