



'''simple docstring'''
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, 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

					from transformers.modeling_flax_pytorch_utils import (
					    convert_pytorch_state_dict_to_flax,
					    load_flax_weights_in_pytorch_model,
					)
					from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
					import torch


class   SCREAMING_SNAKE_CASE__				:
	"""simple docstring"""


	def __init__(		self					,   A					,   A=14					,   A=7					,   A=True					,   A=True					,   A=False					,   A=True					,   A=99					,   A=32					,   A=5					,   A=4					,   A=37					,   A="gelu"					,   A=0.1					,   A=0.1					,   A=5_12					,   A=0.02					,   ) ->							Tuple:
				A:      int														=   parent
				A:      List[Any]														=   batch_size
				A:      str														=   seq_length
				A:      Dict														=   is_training
				A:      Union[str, Any]														=   use_input_mask
				A:      Optional[Any]														=   use_token_type_ids
				A:      str														=   use_labels
				A:      Any														=   vocab_size
				A:      Union[str, Any]														=   hidden_size
				A:      Tuple														=   num_hidden_layers
				A:      Any														=   num_attention_heads
				A:      List[Any]														=   intermediate_size
				A:      Any														=   hidden_act
				A:      Tuple														=   hidden_dropout_prob
				A:      Union[str, Any]														=   attention_probs_dropout_prob
				A:      str														=   max_position_embeddings
				A:      int														=   initializer_range
				A:      List[str]														=   None
				A:      Any														=   vocab_size - 1
				A:      Dict														=   vocab_size - 1
				A:      Union[str, Any]														=   vocab_size - 1


	def      a__ (		self			) ->							str:
				A:      Optional[Any]														=   ids_tensor([self.batch_size, self.seq_length]					,   self.vocab_size			)

				A:      Tuple														=   None
				if self.use_input_mask:
							A:      Optional[Any]														=   random_attention_mask([self.batch_size, self.seq_length]			)

				A:      List[str]														=   GPTaConfig(
				    vocab_size=self.vocab_size					,   n_embd=self.hidden_size					,   n_layer=self.num_hidden_layers					,   n_head=self.num_attention_heads					,   n_positions=self.max_position_embeddings					,   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			) ->							int:
				A:      Any														=   self.prepare_config_and_inputs()
				A							,		A							,		A:      Tuple														=   config_and_inputs
				A:      str														=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict


	def      a__ (		self			) ->							Optional[Any]:
				A							,		A							,		A:      Tuple														=   self.prepare_config_and_inputs()

				A:      Tuple														=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
				A:      Optional[int]														=   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					,   A					,   A					,   A					,   A			) ->							str:
				A:      Any														=   20
				A:      Union[str, Any]														=   model_class_name(A			)

				A:      Dict														=   model.init_cache(input_ids.shape[0]					,   A			)
				A:      Union[str, Any]														=   jnp.ones((input_ids.shape[0], max_decoder_length)					,   dtype="""i4"""			)

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

				A:      int														=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,   dtype="""i4"""			)
				A:      List[Any]														=   model(
				    input_ids[:, -1:]					,   attention_mask=A					,   past_key_values=outputs_cache.past_key_values					,   position_ids=A					,   )

				A:      Optional[int]														=   model(A			)

				A:      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      a__ (		self					,   A					,   A					,   A					,   A			) ->							List[Any]:
				A:      int														=   20
				A:      Dict														=   model_class_name(A			)

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

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

				A:      Union[str, Any]														=   model(
				    input_ids[:, :-1]					,   attention_mask=A					,   past_key_values=A					,   position_ids=A					,   )
				A:      Tuple														=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,   dtype="""i4"""			)
				A:      Tuple														=   model(
				    input_ids[:, -1:]					,   past_key_values=outputs_cache.past_key_values					,   attention_mask=A					,   position_ids=A					,   )

				A:      List[str]														=   model(A					,   attention_mask=A			)

				A:      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_flax
class   SCREAMING_SNAKE_CASE__				(   snake_case_     ,			snake_case_     ,			unittest.TestCase					):
	"""simple docstring"""


	A__    :    Optional[Any]						=				(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
	A__    :    Any						=				(FlaxGPTaLMHeadModel,) if is_flax_available() else ()


	def      a__ (		self			) ->							List[str]:
				A:      Dict														=   FlaxGPTaModelTester(self			)


	def      a__ (		self			) ->							Optional[Any]:
				for model_class_name in self.all_model_classes:
							A							,		A							,		A:      Optional[Any]														=   self.model_tester.prepare_config_and_inputs()
							self.model_tester.check_use_cache_forward(A					,   A					,   A					,   A			)


	def      a__ (		self			) ->							Any:
				for model_class_name in self.all_model_classes:
							A							,		A							,		A:      Union[str, Any]														=   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			) ->							List[Any]:
				A:      str														=   GPTaTokenizer.from_pretrained("""gpt2"""					,   pad_token="""</s>"""					,   padding_side="""left"""			)
				A:      Any														=   tokenizer(["""Hello this is a long string""", """Hey"""]					,   return_tensors="""np"""					,   padding=A					,   truncation=A			)

				A:      str														=   FlaxGPTaLMHeadModel.from_pretrained("""gpt2"""			)
				A:      List[str]														=   False
				A:      Union[str, Any]														=   model.config.eos_token_id

				A:      List[Any]														=   jax.jit(model.generate			)

				A:      Optional[Any]														=   jit_generate(inputs["""input_ids"""]					,   attention_mask=inputs["""attention_mask"""]			).sequences

				A:      int														=   tokenizer.batch_decode(A					,   skip_special_tokens=A			)

				A:      Union[str, Any]														=   [
				    """Hello this is a long string of words. I'm going to start with the first one.\n""",
				    """Hey, I'm not sure if I'm going to be able to do""",
				]

				self.assertListEqual(A					,   A			)


	@is_pt_flax_cross_test
	def      a__ (		self			) ->							str:
				A							,		A:      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
										A:      Dict														=   self._prepare_for_class(A					,   A			)
										A:      Tuple														=   {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

										# load corresponding PyTorch class
										A:      List[Any]														=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
										A:      List[Any]														=   getattr(A					,   A			)

										A							,		A:      Optional[Any]														=   pt_inputs["""input_ids"""].shape
										A:      Any														=   np.random.randint(0					,   seq_length - 1					,   size=(batch_size,)			)
										for batch_idx, start_index in enumerate(A			):
													A:      str														=   0
													A:      Union[str, Any]														=   1
													A:      str														=   0
													A:      int														=   1
										A:      str														=   pt_model_class(A			).eval()
										A:      Optional[Any]														=   model_class(A					,   dtype=jnp.floataa			)

										A:      int														=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,   A			)
										A:      str														=   fx_state

										with torch.no_grad():
													A:      Dict														=   pt_model(**A			).to_tuple()

										A:      List[str]														=   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			)
													A:      List[str]														=   model_class.from_pretrained(A					,   from_pt=A			)

										A:      List[str]														=   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			) ->							str:
				A							,		A:      str														=   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
										A:      int														=   self._prepare_for_class(A					,   A			)
										A:      List[str]														=   {k: torch.tensor(v.tolist()			) for k, v in prepared_inputs_dict.items()}

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

										A:      str														=   pt_model_class(A			).eval()
										A:      List[Any]														=   model_class(A					,   dtype=jnp.floataa			)

										A:      Any														=   load_flax_weights_in_pytorch_model(A					,   fx_model.params			)
										A							,		A:      Any														=   pt_inputs["""input_ids"""].shape
										A:      int														=   np.random.randint(0					,   seq_length - 1					,   size=(batch_size,)			)
										for batch_idx, start_index in enumerate(A			):
													A:      Optional[Any]														=   0
													A:      Any														=   1
													A:      Dict														=   0
													A:      Optional[int]														=   1

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

										with torch.no_grad():
													A:      Dict														=   pt_model(**A			).to_tuple()

										A:      Optional[int]														=   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			)
													A:      Any														=   pt_model_class.from_pretrained(A					,   from_flax=A			)

										with torch.no_grad():
													A:      Optional[Any]														=   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			) ->							Optional[Any]:
				for model_class_name in self.all_model_classes:
							A:      Any														=   model_class_name.from_pretrained("""gpt2"""					,   from_pt=A			)
							A:      Any														=   model(np.ones((1, 1)			)			)
							self.assertIsNotNone(A			)










'''simple docstring'''
from __future__ import annotations



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			A:      int														=   [0] * len(lowerCamelCase__				)
			for i in range(1							,							len(lowerCamelCase__				)				):
						A:      Dict														=   duration_times[i - 1] + waiting_times[i - 1]
			return waiting_times



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]							,							lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return [
			    duration_time + waiting_times[i]
			    for i, duration_time in enumerate(lowerCamelCase__				)
			]



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)



def 		_SCREAMING_SNAKE_CASE							(				lowerCamelCase__   :      list[int]				):


			'''simple docstring'''







			return sum(lowerCamelCase__				) / len(lowerCamelCase__				)


if __name__ == "__main__":
					# process id's
					__SCREAMING_SNAKE_CASE :      Optional[Any]             =[1, 2, 3]

					# ensure that we actually have processes
					if len(processes) == 0:
										print('Zero amount of processes')
										raise SystemExit(0)

					# duration time of all processes
					__SCREAMING_SNAKE_CASE :      Dict             =[19, 8, 9]

					# ensure we can match each id to a duration time
					if len(duration_times) != len(processes):
										print('Unable to match all id\'s with their duration time')
										raise SystemExit(0)

					# get the waiting times and the turnaround times
					__SCREAMING_SNAKE_CASE :      Tuple             =calculate_waiting_times(duration_times)
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_turnaround_times(duration_times, waiting_times)

					# get the average times
					__SCREAMING_SNAKE_CASE :      List[Any]             =calculate_average_waiting_time(waiting_times)
					__SCREAMING_SNAKE_CASE :      Union[str, Any]             =calculate_average_turnaround_time(turnaround_times)

					# print all the results
					print('Process ID\tDuration Time\tWaiting Time\tTurnaround Time')
					for i, process in enumerate(processes):
										print(
										    f"""{process}\t\t{duration_times[i]}\t\t{waiting_times[i]}\t\t"""
										    f"""{turnaround_times[i]}"""
										)
					print(f"""Average waiting time = {average_waiting_time}""")
					print(f"""Average turn around time = {average_turnaround_time}""")





