





"""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_	 ="""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase=None			,		_lowercase=None  ):
						if attention_mask is None:
												_UpperCamelCase     :   List[str]      =       np.where(input_ids != config.pad_token_id			,		1			,		0  )
						return {
						    "input_ids": input_ids,
						    "attention_mask": attention_mask,
						}




@require_flax
class 					_a		:






				def __init__(					self						:       int,					lowerCAmelCase__						:       int,					lowerCAmelCase__						:       Tuple=1_3,					lowerCAmelCase__						:       Tuple=7,					lowerCAmelCase__						:       Dict=True,					lowerCAmelCase__						:       Optional[int]=False,					lowerCAmelCase__						:       int=9_9,					lowerCAmelCase__						:       Tuple=1_6,					lowerCAmelCase__						:       Optional[int]=2,					lowerCAmelCase__						:       Optional[int]=4,					lowerCAmelCase__						:       Any=4,					lowerCAmelCase__						:       Union[str, Any]="gelu",					lowerCAmelCase__						:       Optional[Any]=0.1,					lowerCAmelCase__						:       Tuple=0.1,					lowerCAmelCase__						:       Dict=2_0,					lowerCAmelCase__						:       List[Any]=2,					lowerCAmelCase__						:       int=1,					lowerCAmelCase__						:       Dict=0,					lowerCAmelCase__						:       Tuple=1_6,					lowerCAmelCase__						:       Union[str, Any]=1_6,					lowerCAmelCase__						:       int=0.02,					)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   Any      =       parent
										_UpperCamelCase     :   Optional[Any]      =       batch_size
										_UpperCamelCase     :   Any      =       seq_length
										_UpperCamelCase     :   List[Any]      =       is_training
										_UpperCamelCase     :   Optional[Any]      =       use_labels
										_UpperCamelCase     :   Optional[int]      =       vocab_size
										_UpperCamelCase     :   Optional[int]      =       hidden_size
										_UpperCamelCase     :   List[Any]      =       num_hidden_layers
										_UpperCamelCase     :   Any      =       num_attention_heads
										_UpperCamelCase     :   Union[str, Any]      =       intermediate_size
										_UpperCamelCase     :   Optional[Any]      =       hidden_act
										_UpperCamelCase     :   List[str]      =       hidden_dropout_prob
										_UpperCamelCase     :   Optional[Any]      =       attention_probs_dropout_prob
										_UpperCamelCase     :   List[Any]      =       max_position_embeddings
										_UpperCamelCase     :   int      =       eos_token_id
										_UpperCamelCase     :   Union[str, Any]      =       pad_token_id
										_UpperCamelCase     :   int      =       bos_token_id
										_UpperCamelCase     :   Any      =       embed_dim
										_UpperCamelCase     :   str      =       word_embed_proj_dim
										_UpperCamelCase     :   Any      =       initializer_range
										_UpperCamelCase     :   Optional[Any]      =       False






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




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       np.clip(ids_tensor([self.batch_size, self.seq_length - 1],					self.vocab_size				),					3,					self.vocab_size				)
										_UpperCamelCase     :   str      =       np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),					dtype=np.intaa				)),					-1				)

										_UpperCamelCase     :   List[str]      =       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=lowerCAmelCase__,					word_embed_proj_dim=self.word_embed_proj_dim,					initializer_range=self.initializer_range,					use_cache=lowerCAmelCase__,					)
										_UpperCamelCase     :   List[str]      =       prepare_opt_inputs_dict(lowerCAmelCase__,					lowerCAmelCase__				)
										return config, inputs_dict






				def 							snake_case      (					self						:       List[str]				)      -> List[str]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   int      =       self.prepare_config_and_inputs()
										return config, inputs_dict






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




										'''simple docstring'''



										_UpperCamelCase     :   int      =       2_0
										_UpperCamelCase     :   int      =       model_class_name(lowerCAmelCase__				)

										_UpperCamelCase     :   List[str]      =       inputs_dict['''input_ids''']
										_UpperCamelCase     :   Any      =       inputs_dict['''attention_mask''']

										_UpperCamelCase     :   Dict      =       model.init_cache(input_ids.shape[0],					lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       jnp.ones((input_ids.shape[0], max_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     :   Any      =       model(
										    input_ids[:, :-1],					attention_mask=lowerCAmelCase__,					past_key_values=lowerCAmelCase__,					position_ids=lowerCAmelCase__,					)

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

										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__				)

										_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}"""				)






				def 							snake_case      (					self						:       Union[str, Any],					lowerCAmelCase__						:       Tuple,					lowerCAmelCase__						:       List[str],					lowerCAmelCase__						:       int				)      -> int:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       2_0
										_UpperCamelCase     :   str      =       model_class_name(lowerCAmelCase__				)

										_UpperCamelCase  ,				_UpperCamelCase     :   List[Any]      =       (
										    inputs_dict['''input_ids'''],
										    inputs_dict['''attention_mask'''],
										)

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

										_UpperCamelCase     :   str      =       model.init_cache(input_ids.shape[0],					lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1				)[None, :],					(input_ids.shape[0], input_ids.shape[-1] - 1),					)

										_UpperCamelCase     :   List[Any]      =       model(
										    input_ids[:, :-1],					attention_mask=lowerCAmelCase__,					past_key_values=lowerCAmelCase__,					position_ids=lowerCAmelCase__,					)
										_UpperCamelCase     :   str      =       jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],					dtype='''i4'''				)
										_UpperCamelCase     :   List[Any]      =       model(
										    input_ids[:, -1:],					past_key_values=outputs_cache.past_key_values,					attention_mask=lowerCAmelCase__,					position_ids=lowerCAmelCase__,					)

										_UpperCamelCase     :   List[Any]      =       model(lowerCAmelCase__,					attention_mask=lowerCAmelCase__				)

										_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}"""				)




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






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




										'''simple docstring'''



										_UpperCamelCase     :   int      =       FlaxOPTModelTester(self				)






				def 							snake_case      (					self						:       Any				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   str      =       self.model_tester.prepare_config_and_inputs()
										for model_class in self.all_model_classes:
																self.model_tester.check_use_cache_forward(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       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(lowerCAmelCase__,					lowerCAmelCase__,					lowerCAmelCase__				)






				@slow
				def 							snake_case      (					self						:       Optional[Any]				)      -> str:




										'''simple docstring'''



										for model_class_name in self.all_model_classes:
																_UpperCamelCase     :   str      =       model_class_name.from_pretrained('''facebook/opt-125m'''				)
																_UpperCamelCase     :   Optional[Any]      =       np.ones((1, 1)				) * model.config.eos_token_id
																_UpperCamelCase     :   Optional[Any]      =       model(lowerCAmelCase__				)
																self.assertIsNotNone(lowerCAmelCase__				)




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






				@slow
				def 							snake_case      (					self						:       Union[str, Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       FlaxOPTModel.from_pretrained('''facebook/opt-350m'''				)
										_UpperCamelCase     :   Dict      =       jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				)
										_UpperCamelCase     :   Tuple      =       model(input_ids=lowerCAmelCase__				).last_hidden_state
										_UpperCamelCase     :   Optional[int]      =       (1, 1_1, 5_1_2)
										self.assertEqual(output.shape,					lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, 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],					lowerCAmelCase__,					atol=4e-2				)				)




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






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




										'''simple docstring'''



										super().setUp()
										_UpperCamelCase     :   Any      =       '''facebook/opt-350m'''






				def 							snake_case      (					self						:       List[Any]				)      -> Optional[Any]:




										'''simple docstring'''



										_UpperCamelCase     :   Union[str, Any]      =       FlaxOPTForCausalLM.from_pretrained(self.path_model				)
										_UpperCamelCase     :   str      =       GPTaTokenizer.from_pretrained(self.path_model				)

										_UpperCamelCase     :   Any      =       [
										    '''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     :   Optional[int]      =       tokenizer(lowerCAmelCase__,					return_tensors='''jax''',					padding=lowerCAmelCase__,					add_special_tokens=lowerCAmelCase__				)
										_UpperCamelCase     :   Union[str, Any]      =       model(inputs.input_ids,					attention_mask=inputs.attention_mask				)[0].mean(axis=-1				)
										_UpperCamelCase     :   List[str]      =       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(lowerCAmelCase__,					lowerCAmelCase__,					atol=4e-2				)				)

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




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






				@property
				def 							snake_case      (					self						:       str				)      -> Any:




										'''simple docstring'''



										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 							snake_case      (					self						:       List[Any]				)      -> Optional[int]:




										'''simple docstring'''



										_UpperCamelCase     :   Dict      =       '''facebook/opt-125m'''

										_UpperCamelCase     :   Dict      =       [
										    '''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     :   Any      =       []

										_UpperCamelCase     :   str      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)

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

																_UpperCamelCase     :   Any      =       model.generate(lowerCAmelCase__,					max_length=1_0				)
																_UpperCamelCase     :   Union[str, Any]      =       generated_ids[0]

																_UpperCamelCase     :   Dict      =       tokenizer.batch_decode(lowerCAmelCase__,					skip_special_tokens=lowerCAmelCase__				)
																predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       List[str]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   List[Any]      =       '''facebook/opt-350m'''

										_UpperCamelCase     :   List[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     :   Dict      =       []
										_UpperCamelCase     :   Any      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)

										for prompt in self.prompts:
																_UpperCamelCase     :   List[Any]      =       tokenizer(lowerCAmelCase__,					return_tensors='''jax'''				).input_ids

																_UpperCamelCase     :   List[Any]      =       model.generate(lowerCAmelCase__,					max_length=1_0				)
																_UpperCamelCase     :   List[str]      =       generated_ids[0]

																_UpperCamelCase     :   Optional[Any]      =       tokenizer.batch_decode(lowerCAmelCase__,					skip_special_tokens=lowerCAmelCase__				)
																predicted_outputs += generated_string

										self.assertListEqual(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Any				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   Optional[int]      =       '''facebook/opt-125m'''
										_UpperCamelCase     :   Optional[Any]      =       [
										    '''Today is a beautiful day and I want to thank''',
										    '''In the city of Rome Canaver Canaver Canaver Canaver''',
										]
										_UpperCamelCase     :   Any      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Tuple      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   Any      =       tokenizer(
										    [
										        '''Today is a beautiful day and I want to''',
										        '''In the city of''',
										    ],					return_tensors='''jax''',					padding=lowerCAmelCase__,					)

										_UpperCamelCase     :   Any      =       jax.jit(model.generate				)

										_UpperCamelCase     :   Optional[int]      =       jit_generate(inputs['''input_ids'''],					attention_mask=inputs['''attention_mask''']				).sequences

										_UpperCamelCase     :   List[Any]      =       tokenizer.batch_decode(lowerCAmelCase__,					skip_special_tokens=lowerCAmelCase__				)

										self.assertIsNotNone(lowerCAmelCase__,					lowerCAmelCase__				)






				def 							snake_case      (					self						:       Optional[int]				)      -> Tuple:




										'''simple docstring'''



										_UpperCamelCase     :   List[str]      =       '''facebook/opt-350m'''

										_UpperCamelCase     :   Any      =       GPTaTokenizer.from_pretrained(lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__				)

										_UpperCamelCase     :   str      =       '''left'''

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

										_UpperCamelCase     :   int      =       tokenizer(lowerCAmelCase__,					return_tensors='''jax''',					padding=lowerCAmelCase__				)
										_UpperCamelCase     :   int      =       inputs['''input_ids''']

										_UpperCamelCase     :   Optional[int]      =       model.generate(input_ids=lowerCAmelCase__,					attention_mask=inputs['''attention_mask'''],					trace=lowerCAmelCase__				)

										_UpperCamelCase     :   List[Any]      =       tokenizer(sentences[0],					return_tensors='''jax'''				).input_ids
										_UpperCamelCase     :   Union[str, Any]      =       model.generate(input_ids=lowerCAmelCase__				)

										_UpperCamelCase     :   Union[str, Any]      =       inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
										_UpperCamelCase     :   Any      =       tokenizer(sentences[1],					return_tensors='''jax'''				).input_ids
										_UpperCamelCase     :   Union[str, Any]      =       model.generate(input_ids=lowerCAmelCase__,					max_length=model.config.max_length - num_paddings				)

										_UpperCamelCase     :   Tuple      =       tokenizer.batch_decode(outputs[0],					skip_special_tokens=lowerCAmelCase__				)
										_UpperCamelCase     :   List[str]      =       tokenizer.decode(output_non_padded[0][0],					skip_special_tokens=lowerCAmelCase__				)
										_UpperCamelCase     :   Optional[Any]      =       tokenizer.decode(output_padded[0][0],					skip_special_tokens=lowerCAmelCase__				)

										_UpperCamelCase     :   Optional[int]      =       [
										    '''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(lowerCAmelCase__,					lowerCAmelCase__				)
										self.assertListEqual(lowerCAmelCase__,					[non_padded_sentence, padded_sentence]				)




'''simple docstring'''





from itertools import count
from math import asin, pi, sqrt






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float  )   ->				float:







     """simple docstring"""
     return (
         (1 - 2 * point) * sqrt(point - point**2  ) + 2 * point + asin(sqrt(1 - point  )  )
     ) / 4






def SCREAMING_SNAKE_CASE__  (				snake_case       :				int  )   ->				float:







     """simple docstring"""
     a					:							List[str]							= (circles_number + 1 - sqrt(2 * circles_number  )) / (
         2 * (circles_number**2 + 1)
     )
     a					:							int							= circles_number * intersection_y

     a					:							List[Any]							= intersection_x * intersection_y / 2
     a					:							List[str]							= circle_bottom_arc_integral(
         1 / 2  ) - circle_bottom_arc_integral(snake_case  )

     return triangle_area + concave_region_area






def SCREAMING_SNAKE_CASE__  (				snake_case       :				float = 1 / 1_000  )   ->				int:







     """simple docstring"""
     a					:							str							= (1 - pi / 4) / 4

     for n in count(1  ):
          if concave_triangle_area(snake_case  ) / l_section_area < fraction:
               return n

     return -1


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

