






'''simple docstring'''





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
						import os

						# The slow tests are often failing with OOM error on GPU
						# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
						# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
						_UpperCamelCase		:		List[Any]								=			'platform'

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer
def __UpperCAmelCase (			A			:	List[Any]	,   A			:	List[str]	,   A			:	Union[str, Any]=None	,   A			:	Optional[int]=None						)  -> Tuple:
			if attention_mask is None:
						UpperCAmelCase_     :   Dict       =   np.where(input_ids != config.pad_token_id	,   1	,   0						)
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}


@require_flax
class 				snake_case__     :




					def __init__(       self       :						int							,				_A       :						Union[str, Any]							,				_A       :						Tuple=13							,				_A       :						Any=7							,				_A       :						Union[str, Any]=True							,				_A       :						Dict=False							,				_A       :						Union[str, Any]=99							,				_A       :						int=16							,				_A       :						Union[str, Any]=2							,				_A       :						Any=4							,				_A       :						int=4							,				_A       :						Optional[int]="gelu"							,				_A       :						Optional[int]=0.1							,				_A       :						Optional[int]=0.1							,				_A       :						Union[str, Any]=20							,				_A       :						Dict=2							,				_A       :						List[str]=1							,				_A       :						Any=0							,				_A       :						Union[str, Any]=16							,				_A       :						Optional[Any]=16							,				_A       :						Optional[Any]=0.02							,				)  ->    Dict:
								UpperCAmelCase_     :   Tuple       =   parent
								UpperCAmelCase_     :   Optional[Any]       =   batch_size
								UpperCAmelCase_     :   Any       =   seq_length
								UpperCAmelCase_     :   List[Any]       =   is_training
								UpperCAmelCase_     :   Union[str, Any]       =   use_labels
								UpperCAmelCase_     :   Optional[Any]       =   vocab_size
								UpperCAmelCase_     :   str       =   hidden_size
								UpperCAmelCase_     :   Union[str, Any]       =   num_hidden_layers
								UpperCAmelCase_     :   Any       =   num_attention_heads
								UpperCAmelCase_     :   Dict       =   intermediate_size
								UpperCAmelCase_     :   List[Any]       =   hidden_act
								UpperCAmelCase_     :   Any       =   hidden_dropout_prob
								UpperCAmelCase_     :   int       =   attention_probs_dropout_prob
								UpperCAmelCase_     :   int       =   max_position_embeddings
								UpperCAmelCase_     :   Optional[Any]       =   eos_token_id
								UpperCAmelCase_     :   str       =   pad_token_id
								UpperCAmelCase_     :   str       =   bos_token_id
								UpperCAmelCase_     :   List[Any]       =   embed_dim
								UpperCAmelCase_     :   Optional[int]       =   word_embed_proj_dim
								UpperCAmelCase_     :   str       =   initializer_range
								UpperCAmelCase_     :   Tuple       =   False




					def 						A     (       self       :						int     )  ->    Tuple:
								UpperCAmelCase_     :   Tuple       =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,				self.vocab_size     )							,				3							,				self.vocab_size     )
								UpperCAmelCase_     :   Any       =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,				dtype=np.intaa     ))							,				-1     )

								UpperCAmelCase_     :   Optional[Any]       =   OPTConfig(
								    vocab_size=self.vocab_size							,				hidden_size=self.hidden_size							,				num_hidden_layers=self.num_hidden_layers							,				num_attention_heads=self.num_attention_heads							,				ffn_dim=self.intermediate_size							,				dropout=self.hidden_dropout_prob							,				attention_dropout=self.attention_probs_dropout_prob							,				max_position_embeddings=self.max_position_embeddings							,				eos_token_id=self.eos_token_id							,				bos_token_id=self.bos_token_id							,				pad_token_id=self.pad_token_id							,				embed_dim=self.embed_dim							,				is_encoder_decoder=_A							,				word_embed_proj_dim=self.word_embed_proj_dim							,				initializer_range=self.initializer_range							,				use_cache=_A							,				)
								UpperCAmelCase_     :   List[str]       =   prepare_opt_inputs_dict(_A							,				_A     )
								return config, inputs_dict




					def 						A     (       self       :						str     )  ->    List[str]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Tuple       =   self.prepare_config_and_inputs()
								return config, inputs_dict




					def 						A     (       self       :						Dict							,				_A       :						Dict							,				_A       :						Tuple							,				_A       :						Optional[int]     )  ->    str:
								UpperCAmelCase_     :   str       =   20
								UpperCAmelCase_     :   Tuple       =   model_class_name(_A     )

								UpperCAmelCase_     :   Optional[Any]       =   inputs_dict['''input_ids''']
								UpperCAmelCase_     :   List[str]       =   inputs_dict['''attention_mask''']

								UpperCAmelCase_     :   Optional[int]       =   model.init_cache(input_ids.shape[0]							,				_A     )
								UpperCAmelCase_     :   str       =   jnp.ones((input_ids.shape[0], max_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=_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:]							,				attention_mask=_A							,				past_key_values=outputs_cache.past_key_values							,				position_ids=_A							,				)

								UpperCAmelCase_     :   List[Any]       =   model(_A     )

								UpperCAmelCase_     :   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 						A     (       self       :						List[str]							,				_A       :						str							,				_A       :						Any							,				_A       :						List[str]     )  ->    Union[str, Any]:
								UpperCAmelCase_     :   Optional[int]       =   20
								UpperCAmelCase_     :   List[Any]       =   model_class_name(_A     )

								UpperCAmelCase_  ,     UpperCAmelCase_     :   Union[str, Any]       =   (
								    inputs_dict['''input_ids'''],
								    inputs_dict['''attention_mask'''],
								)

								UpperCAmelCase_     :   Union[str, Any]       =   jnp.concatenate(
								    [
								        attention_mask,
								        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])     ),
								    ]							,				axis=-1							,				)

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

								UpperCAmelCase_     :   List[Any]       =   model(_A							,				attention_mask=_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}"     )






@require_flax
class 				snake_case__     (			UpperCamelCase      ,							unittest.TestCase      ,							UpperCamelCase):
					a_       =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
					a_       =			() if is_flax_available() else ()




					def 						A     (       self       :						Optional[int]     )  ->    Tuple:
								UpperCAmelCase_     :   Optional[Any]       =   FlaxOPTModelTester(self     )




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




					def 						A     (       self       :						str     )  ->    Optional[Any]:
								UpperCAmelCase_  ,     UpperCAmelCase_     :   Optional[Any]       =   self.model_tester.prepare_config_and_inputs()
								for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward_with_attn_mask(_A							,				_A							,				_A     )





					@slow
					def 						A     (       self       :						List[Any]     )  ->    Dict:
								for model_class_name in self.all_model_classes:
											UpperCAmelCase_     :   Any       =   model_class_name.from_pretrained('''facebook/opt-125m'''     )
											UpperCAmelCase_     :   List[Any]       =   np.ones((1, 1)     ) * model.config.eos_token_id
											UpperCAmelCase_     :   Tuple       =   model(_A     )
											self.assertIsNotNone(_A     )






@require_sentencepiece
@require_flax
class 				snake_case__     (			unittest.TestCase):




					@slow
					def 						A     (       self       :						Optional[int]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Optional[int]       =   FlaxOPTModel.from_pretrained('''facebook/opt-350m'''     )
								UpperCAmelCase_     :   Tuple       =   jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]     )
								UpperCAmelCase_     :   str       =   model(input_ids=_A     ).last_hidden_state
								UpperCAmelCase_     :   int       =   (1, 11, 5_12)
								self.assertEqual(output.shape							,				_A     )
								UpperCAmelCase_     :   Any       =   jnp.array(
								    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]     )
								self.assertTrue(jnp.allclose(output[:, :3, :3]							,				_A							,				atol=4e-2     )     )






@require_flax
@slow
class 				snake_case__     (			unittest.TestCase):




					def 						A     (       self       :						int     )  ->    Union[str, Any]:
								super().setUp()
								UpperCAmelCase_     :   str       =   '''facebook/opt-350m'''





					def 						A     (       self       :						List[Any]     )  ->    Optional[Any]:
								UpperCAmelCase_     :   Optional[Any]       =   FlaxOPTForCausalLM.from_pretrained(self.path_model     )
								UpperCAmelCase_     :   Tuple       =   GPTaTokenizer.from_pretrained(self.path_model     )

								UpperCAmelCase_     :   Tuple       =   [
								    '''Today is a beautiful day and I want to''',
								    '''In the city of''',
								    '''Paris is the capital of France and''',
								    '''Computers and mobile phones have taken''',
								]
								# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
								UpperCAmelCase_     :   Dict       =   tokenizer(_A							,				return_tensors='''jax'''							,				padding=_A							,				add_special_tokens=_A     )
								UpperCAmelCase_     :   Tuple       =   model(inputs.input_ids							,				attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
								UpperCAmelCase_     :   List[Any]       =   jnp.array(
								    [
								        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
								        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
								        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
								        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
								    ]     )
								self.assertTrue(jnp.allclose(_A							,				_A							,				atol=4e-2     )     )

								UpperCAmelCase_     :   List[Any]       =   jax.jit(_A     )
								UpperCAmelCase_     :   Union[str, Any]       =   model(inputs.input_ids							,				attention_mask=inputs.attention_mask     )[0].mean(axis=-1     )
								self.assertTrue(jnp.allclose(_A							,				_A							,				atol=4e-2     )     )






@require_flax
@slow
class 				snake_case__     (			unittest.TestCase):




					@property
					def 						A     (       self       :						Dict     )  ->    Any:
								return [
								    "Today is a beautiful day and I want",
								    "In the city of",
								    "Paris is the capital of France and",
								    "Computers and mobile phones have taken",
								]




					def 						A     (       self       :						List[str]     )  ->    Optional[int]:
								UpperCAmelCase_     :   Any       =   '''facebook/opt-125m'''

								UpperCAmelCase_     :   Optional[int]       =   [
								    '''Today is a beautiful day and I want to''',
								    '''In the city of New York, the city''',
								    '''Paris is the capital of France and the capital''',
								    '''Computers and mobile phones have taken over the''',
								]

								UpperCAmelCase_     :   List[Any]       =   []

								UpperCAmelCase_     :   Tuple       =   FlaxOPTForCausalLM.from_pretrained(_A     )
								UpperCAmelCase_     :   Tuple       =   GPTaTokenizer.from_pretrained(_A     )

								for prompt in self.prompts:
											UpperCAmelCase_     :   Dict       =   tokenizer(_A							,				return_tensors='''jax'''     ).input_ids

											UpperCAmelCase_     :   List[Any]       =   model.generate(_A							,				max_length=10     )
											UpperCAmelCase_     :   str       =   generated_ids[0]

											UpperCAmelCase_     :   Any       =   tokenizer.batch_decode(_A							,				skip_special_tokens=_A     )
											predicted_outputs += generated_string

								self.assertListEqual(_A							,				_A     )




					def 						A     (       self       :						Dict     )  ->    str:
								UpperCAmelCase_     :   str       =   '''facebook/opt-350m'''

								UpperCAmelCase_     :   str       =   [
								    '''Today is a beautiful day and I want to''',
								    '''In the city of San Francisco, the city''',
								    '''Paris is the capital of France and the capital''',
								    '''Computers and mobile phones have taken over the''',
								]

								UpperCAmelCase_     :   Union[str, Any]       =   []
								UpperCAmelCase_     :   Any       =   FlaxOPTForCausalLM.from_pretrained(_A     )
								UpperCAmelCase_     :   Dict       =   GPTaTokenizer.from_pretrained(_A     )

								for prompt in self.prompts:
											UpperCAmelCase_     :   str       =   tokenizer(_A							,				return_tensors='''jax'''     ).input_ids

											UpperCAmelCase_     :   Dict       =   model.generate(_A							,				max_length=10     )
											UpperCAmelCase_     :   List[Any]       =   generated_ids[0]

											UpperCAmelCase_     :   Optional[int]       =   tokenizer.batch_decode(_A							,				skip_special_tokens=_A     )
											predicted_outputs += generated_string

								self.assertListEqual(_A							,				_A     )




					def 						A     (       self       :						List[str]     )  ->    str:
								UpperCAmelCase_     :   Union[str, Any]       =   '''facebook/opt-125m'''
								UpperCAmelCase_     :   int       =   [
								    '''Today is a beautiful day and I want to thank''',
								    '''In the city of Rome Canaver Canaver Canaver Canaver''',
								]
								UpperCAmelCase_     :   Union[str, Any]       =   FlaxOPTForCausalLM.from_pretrained(_A     )
								UpperCAmelCase_     :   int       =   GPTaTokenizer.from_pretrained(_A     )
								UpperCAmelCase_     :   Optional[Any]       =   tokenizer(
								    [
								        '''Today is a beautiful day and I want to''',
								        '''In the city of''',
								    ]							,				return_tensors='''jax'''							,				padding=_A							,				)

								UpperCAmelCase_     :   str       =   jax.jit(model.generate     )

								UpperCAmelCase_     :   str       =   jit_generate(inputs['''input_ids''']							,				attention_mask=inputs['''attention_mask''']     ).sequences

								UpperCAmelCase_     :   int       =   tokenizer.batch_decode(_A							,				skip_special_tokens=_A     )

								self.assertIsNotNone(_A							,				_A     )





					def 						A     (       self       :						Any     )  ->    List[Any]:
								UpperCAmelCase_     :   Dict       =   '''facebook/opt-350m'''

								UpperCAmelCase_     :   List[str]       =   GPTaTokenizer.from_pretrained(_A     )
								UpperCAmelCase_     :   str       =   FlaxOPTForCausalLM.from_pretrained(_A     )

								UpperCAmelCase_     :   int       =   '''left'''

								# use different length sentences to test batching
								UpperCAmelCase_     :   List[str]       =   [
								    '''Hello, my dog is a little''',
								    '''Today, I''',
								]

								UpperCAmelCase_     :   List[Any]       =   tokenizer(_A							,				return_tensors='''jax'''							,				padding=_A     )
								UpperCAmelCase_     :   Dict       =   inputs['''input_ids''']

								UpperCAmelCase_     :   int       =   model.generate(input_ids=_A							,				attention_mask=inputs['''attention_mask''']							,				trace=_A     )

								UpperCAmelCase_     :   int       =   tokenizer(sentences[0]							,				return_tensors='''jax'''     ).input_ids
								UpperCAmelCase_     :   Optional[int]       =   model.generate(input_ids=_A     )

								UpperCAmelCase_     :   Dict       =   inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
								UpperCAmelCase_     :   List[str]       =   tokenizer(sentences[1]							,				return_tensors='''jax'''     ).input_ids
								UpperCAmelCase_     :   Tuple       =   model.generate(input_ids=_A							,				max_length=model.config.max_length - num_paddings     )

								UpperCAmelCase_     :   Dict       =   tokenizer.batch_decode(outputs[0]							,				skip_special_tokens=_A     )
								UpperCAmelCase_     :   List[str]       =   tokenizer.decode(output_non_padded[0][0]							,				skip_special_tokens=_A     )
								UpperCAmelCase_     :   Tuple       =   tokenizer.decode(output_padded[0][0]							,				skip_special_tokens=_A     )

								UpperCAmelCase_     :   Dict       =   [
								    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
								    '''Today, I was in the middle of a conversation with a friend about the''',
								]
								self.assertListEqual(_A							,				_A     )
								self.assertListEqual(_A							,				[non_padded_sentence, padded_sentence]     )










'''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						)


