

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
						A__							=				"""platform"""

						import jax
						import jax.numpy as jnp

						from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer




def        _lowercase  (					a_					:       List[str]       ,a_					:       Dict       ,a_					:       List[Any]=None       ,a_					:       Dict=None					)					->						List[Any]:




					'''simple docstring'''







					if attention_mask is None:
										__magic_name__				=     np.where(input_ids != config.pad_token_id       ,1       ,0					)
					return {
					    "input_ids": input_ids,
					    "attention_mask": attention_mask,
					}

@require_flax
class 					__UpperCamelCase					:







	def __init__( self:			Optional[Any]	,			__UpperCamelCase:			Optional[Any]	,			__UpperCamelCase:			Dict=13	,			__UpperCamelCase:			int=7	,			__UpperCamelCase:			List[str]=True	,			__UpperCamelCase:			int=False	,			__UpperCamelCase:			List[str]=99	,			__UpperCamelCase:			List[str]=16	,			__UpperCamelCase:			str=2	,			__UpperCamelCase:			Optional[Any]=4	,			__UpperCamelCase:			Union[str, Any]=4	,			__UpperCamelCase:			List[str]="gelu"	,			__UpperCamelCase:			Optional[Any]=0.1	,			__UpperCamelCase:			List[str]=0.1	,			__UpperCamelCase:			Optional[int]=20	,			__UpperCamelCase:			Tuple=2	,			__UpperCamelCase:			Optional[Any]=1	,			__UpperCamelCase:			Tuple=0	,			__UpperCamelCase:			str=16	,			__UpperCamelCase:			List[Any]=16	,			__UpperCamelCase:			List[Any]=0.02	,			):

						'''simple docstring'''





						__magic_name__				=     parent
						__magic_name__				=     batch_size
						__magic_name__				=     seq_length
						__magic_name__				=     is_training
						__magic_name__				=     use_labels
						__magic_name__				=     vocab_size
						__magic_name__				=     hidden_size
						__magic_name__				=     num_hidden_layers
						__magic_name__				=     num_attention_heads
						__magic_name__				=     intermediate_size
						__magic_name__				=     hidden_act
						__magic_name__				=     hidden_dropout_prob
						__magic_name__				=     attention_probs_dropout_prob
						__magic_name__				=     max_position_embeddings
						__magic_name__				=     eos_token_id
						__magic_name__				=     pad_token_id
						__magic_name__				=     bos_token_id
						__magic_name__				=     embed_dim
						__magic_name__				=     word_embed_proj_dim
						__magic_name__				=     initializer_range
						__magic_name__				=     False







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





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

						__magic_name__				=     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=snake_case_	,			word_embed_proj_dim=self.word_embed_proj_dim	,			initializer_range=self.initializer_range	,			use_cache=snake_case_	,			)
						__magic_name__				=     prepare_opt_inputs_dict(snake_case_	,			snake_case_   )
						return config, inputs_dict







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     self.prepare_config_and_inputs()
						return config, inputs_dict







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]	,			__UpperCamelCase:			str	,			__UpperCamelCase:			Dict	,			__UpperCamelCase:			Tuple   ):

						'''simple docstring'''





						__magic_name__				=     20
						__magic_name__				=     model_class_name(snake_case_   )

						__magic_name__				=     inputs_dict["input_ids"]
						__magic_name__				=     inputs_dict["attention_mask"]

						__magic_name__				=     model.init_cache(input_ids.shape[0]	,			snake_case_   )
						__magic_name__				=     jnp.ones((input_ids.shape[0], max_length)	,			dtype='i4'   )

						__magic_name__				=     jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1   )[None, :]	,			(input_ids.shape[0], input_ids.shape[-1] - 1)	,			)
						__magic_name__				=     model(
						    input_ids[:, :-1]	,			attention_mask=snake_case_	,			past_key_values=snake_case_	,			position_ids=snake_case_	,			)

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

						__magic_name__				=     model(snake_case_   )

						__magic_name__				=     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:			Union[str, Any]	,			__UpperCamelCase:			int	,			__UpperCamelCase:			List[str]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     20
						__magic_name__				=     model_class_name(snake_case_   )

						__magic_name__				=     (
						    inputs_dict["input_ids"],
						    inputs_dict["attention_mask"],
						)

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

						__magic_name__				=     model.init_cache(input_ids.shape[0]	,			snake_case_   )
						__magic_name__				=     jnp.broadcast_to(
						    jnp.arange(input_ids.shape[-1] - 1   )[None, :]	,			(input_ids.shape[0], input_ids.shape[-1] - 1)	,			)

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

						__magic_name__				=     model(snake_case_	,			attention_mask=snake_case_   )

						__magic_name__				=     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 					__UpperCamelCase					(       _snake_case	,    unittest.TestCase	,    _snake_case      ):
	_lowercase	: Optional[int]												=	(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
	_lowercase	: int												=	() if is_flax_available() else ()







	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						__magic_name__				=     FlaxOPTModelTester(self   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__				=     self.model_tester.prepare_config_and_inputs()
						for model_class in self.all_model_classes:
											self.model_tester.check_use_cache_forward(snake_case_	,			snake_case_	,			snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     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(snake_case_	,			snake_case_	,			snake_case_   )







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						for model_class_name in self.all_model_classes:
											__magic_name__				=     model_class_name.from_pretrained('facebook/opt-125m'   )
											__magic_name__				=     np.ones((1, 1)   ) * model.config.eos_token_id
											__magic_name__				=     model(snake_case_   )
											self.assertIsNotNone(snake_case_   )




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







	@slow
	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__				=     FlaxOPTModel.from_pretrained('facebook/opt-350m'   )
						__magic_name__				=     jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]   )
						__magic_name__				=     model(input_ids=snake_case_   ).last_hidden_state
						__magic_name__				=     (1, 11, 5_12)
						self.assertEqual(output.shape	,			snake_case_   )
						__magic_name__				=     jnp.array(
						    [[-0.2867, -1.9256, -0.3062], [-1.2711, -0.1337, -0.1897], [0.4109, 0.1187, -1.3142]]   )
						self.assertTrue(jnp.allclose(output[:, :3, :3]	,			snake_case_	,			atol=4E-2   )   )




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







	def 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						super().setUp()
						__magic_name__				=     "facebook/opt-350m"







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     FlaxOPTForCausalLM.from_pretrained(self.path_model   )
						__magic_name__				=     GPTaTokenizer.from_pretrained(self.path_model   )

						__magic_name__				=     [
						    "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
						__magic_name__				=     tokenizer(snake_case_	,			return_tensors='jax'	,			padding=snake_case_	,			add_special_tokens=snake_case_   )
						__magic_name__				=     model(inputs.input_ids	,			attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
						__magic_name__				=     jnp.array(
						    [
						        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
						        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
						        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
						        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
						    ]   )
						self.assertTrue(jnp.allclose(snake_case_	,			snake_case_	,			atol=4E-2   )   )

						__magic_name__				=     jax.jit(snake_case_   )
						__magic_name__				=     model(inputs.input_ids	,			attention_mask=inputs.attention_mask   )[0].mean(axis=-1   )
						self.assertTrue(jnp.allclose(snake_case_	,			snake_case_	,			atol=4E-2   )   )




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







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''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 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     "facebook/opt-125m"

						__magic_name__				=     [
						    "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",
						]

						__magic_name__				=     []

						__magic_name__				=     FlaxOPTForCausalLM.from_pretrained(snake_case_   )
						__magic_name__				=     GPTaTokenizer.from_pretrained(snake_case_   )

						for prompt in self.prompts:
											__magic_name__				=     tokenizer(snake_case_	,			return_tensors='jax'   ).input_ids

											__magic_name__				=     model.generate(snake_case_	,			max_length=10   )
											__magic_name__				=     generated_ids[0]

											__magic_name__				=     tokenizer.batch_decode(snake_case_	,			skip_special_tokens=snake_case_   )
											predicted_outputs += generated_string

						self.assertListEqual(snake_case_	,			snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__				=     "facebook/opt-350m"

						__magic_name__				=     [
						    "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",
						]

						__magic_name__				=     []
						__magic_name__				=     FlaxOPTForCausalLM.from_pretrained(snake_case_   )
						__magic_name__				=     GPTaTokenizer.from_pretrained(snake_case_   )

						for prompt in self.prompts:
											__magic_name__				=     tokenizer(snake_case_	,			return_tensors='jax'   ).input_ids

											__magic_name__				=     model.generate(snake_case_	,			max_length=10   )
											__magic_name__				=     generated_ids[0]

											__magic_name__				=     tokenizer.batch_decode(snake_case_	,			skip_special_tokens=snake_case_   )
											predicted_outputs += generated_string

						self.assertListEqual(snake_case_	,			snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     "facebook/opt-125m"
						__magic_name__				=     [
						    "Today is a beautiful day and I want to thank",
						    "In the city of Rome Canaver Canaver Canaver Canaver",
						]
						__magic_name__				=     FlaxOPTForCausalLM.from_pretrained(snake_case_   )
						__magic_name__				=     GPTaTokenizer.from_pretrained(snake_case_   )
						__magic_name__				=     tokenizer(
						    [
						        'Today is a beautiful day and I want to',
						        'In the city of',
						    ]	,			return_tensors='jax'	,			padding=snake_case_	,			)

						__magic_name__				=     jax.jit(model.generate   )

						__magic_name__				=     jit_generate(inputs['input_ids']	,			attention_mask=inputs['attention_mask']   ).sequences

						__magic_name__				=     tokenizer.batch_decode(snake_case_	,			skip_special_tokens=snake_case_   )

						self.assertIsNotNone(snake_case_	,			snake_case_   )







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     "facebook/opt-350m"

						__magic_name__				=     GPTaTokenizer.from_pretrained(snake_case_   )
						__magic_name__				=     FlaxOPTForCausalLM.from_pretrained(snake_case_   )

						__magic_name__				=     "left"

						# use different length sentences to test batching
						__magic_name__				=     [
						    "Hello, my dog is a little",
						    "Today, I",
						]

						__magic_name__				=     tokenizer(snake_case_	,			return_tensors='jax'	,			padding=snake_case_   )
						__magic_name__				=     inputs["input_ids"]

						__magic_name__				=     model.generate(input_ids=snake_case_	,			attention_mask=inputs['attention_mask']	,			trace=snake_case_   )

						__magic_name__				=     tokenizer(sentences[0]	,			return_tensors='jax'   ).input_ids
						__magic_name__				=     model.generate(input_ids=snake_case_   )

						__magic_name__				=     inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].sum()
						__magic_name__				=     tokenizer(sentences[1]	,			return_tensors='jax'   ).input_ids
						__magic_name__				=     model.generate(input_ids=snake_case_	,			max_length=model.config.max_length - num_paddings   )

						__magic_name__				=     tokenizer.batch_decode(outputs[0]	,			skip_special_tokens=snake_case_   )
						__magic_name__				=     tokenizer.decode(output_non_padded[0][0]	,			skip_special_tokens=snake_case_   )
						__magic_name__				=     tokenizer.decode(output_padded[0][0]	,			skip_special_tokens=snake_case_   )

						__magic_name__				=     [
						    "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(snake_case_	,			snake_case_   )
						self.assertListEqual(snake_case_	,			[non_padded_sentence, padded_sentence]   )






"""simple docstring"""
from __future__ import annotations

from statistics import mean
def 					a__ ( a :					list[int]		):

  """simple docstring"""







  _snake_case  :    List[str]            = 2
  _snake_case  :    Union[str, Any]            = list(a		)
  _snake_case  :    Any            = [0] * len(a		)
  _snake_case  :    Any            = 0
  while True:
    _snake_case  :    str            = True
    for i, burst_time in enumerate(a		):
      if rem_burst_times[i] > 0:
        _snake_case  :    str            = False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          _snake_case  :    int            = t - burst_time
          _snake_case  :    Dict            = 0
    if done is True:
      return waiting_times
def 					a__ ( a :					list[int]							,  a :					list[int]		):

  """simple docstring"""







  return [burst + waiting for burst, waiting in zip(a							,  a		)]


if __name__ == "__main__":
    _a   :      Dict       =				[3, 5, 7]
    _a   :      Dict       =				calculate_waiting_times(burst_times)
    _a   :      Tuple       =				calculate_turn_around_times(burst_times, waiting_times)
    print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
    for i, burst_time in enumerate(burst_times):
        print(
            f'  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  '
            f'{turn_around_times[i]}'
        )
    print(f'\nAverage waiting time = {mean(waiting_times):.5f}')
    print(f'Average turn around time = {mean(turn_around_times):.5f}')

