



from __future__ import annotations

import copy
import inspect
import json
import math
import os
import tempfile
import unittest
from importlib import import_module

import numpy as np

from transformers import ViTMAEConfig
from transformers.file_utils import cached_property, is_tf_available, is_vision_available
from transformers.testing_utils import require_tf, require_vision, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFViTMAEForPreTraining, TFViTMAEModel


if is_vision_available():
				from PIL import Image

				from transformers import ViTImageProcessor


class       a							:



		def __init__(     self     :List[Any]   ,__lowercase     :List[Any]   ,__lowercase     :Union[str, Any]=1_3   ,__lowercase     :str=3_0   ,__lowercase     :Optional[Any]=2   ,__lowercase     :int=3   ,__lowercase     :List[Any]=True   ,__lowercase     :Tuple=True   ,__lowercase     :List[Any]=3_2   ,__lowercase     :str=2   ,__lowercase     :Union[str, Any]=4   ,__lowercase     :Dict=3_7   ,__lowercase     :List[Any]="gelu"   ,__lowercase     :Optional[int]=0.1   ,__lowercase     :str=0.1   ,__lowercase     :Union[str, Any]=1_0   ,__lowercase     :Optional[Any]=0.02   ,__lowercase     :Union[str, Any]=3   ,__lowercase     :Any=0.6   ,__lowercase     :List[str]=None   ,):
						snake_case__	:      str     		=	parent
						snake_case__	:      int     		=	batch_size
						snake_case__	:      Dict     		=	image_size
						snake_case__	:      List[str]     		=	patch_size
						snake_case__	:      str     		=	num_channels
						snake_case__	:      int     		=	is_training
						snake_case__	:      List[str]     		=	use_labels
						snake_case__	:      List[str]     		=	hidden_size
						snake_case__	:      List[Any]     		=	num_hidden_layers
						snake_case__	:      str     		=	num_attention_heads
						snake_case__	:      Any     		=	intermediate_size
						snake_case__	:      Optional[Any]     		=	hidden_act
						snake_case__	:      List[str]     		=	hidden_dropout_prob
						snake_case__	:      Union[str, Any]     		=	attention_probs_dropout_prob
						snake_case__	:      Optional[Any]     		=	type_sequence_label_size
						snake_case__	:      List[str]     		=	initializer_range
						snake_case__	:      Optional[Any]     		=	mask_ratio
						snake_case__	:      List[str]     		=	scope

						# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
						# (we add 1 for the [CLS] token)
						snake_case__	:      str     		=	(image_size // patch_size) ** 2
						snake_case__	:      List[str]     		=	int(math.ceil((1 - mask_ratio) * (num_patches + 1)  )  )



		def 	__lowerCamelCase			(     self     :int  ):
						snake_case__	:      Optional[Any]     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						snake_case__	:      Any     		=	None
						if self.use_labels:
										snake_case__	:      Tuple     		=	ids_tensor([self.batch_size]   ,self.type_sequence_label_size  )

						snake_case__	:      List[Any]     		=	self.get_config()

						return config, pixel_values, labels



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						return ViTMAEConfig(
						    image_size=self.image_size   ,patch_size=self.patch_size   ,num_channels=self.num_channels   ,hidden_size=self.hidden_size   ,num_hidden_layers=self.num_hidden_layers   ,num_attention_heads=self.num_attention_heads   ,intermediate_size=self.intermediate_size   ,decoder_hidden_size=self.hidden_size   ,decoder_num_hidden_layers=self.num_hidden_layers   ,decoder_num_attention_heads=self.num_attention_heads   ,decoder_intermediate_size=self.intermediate_size   ,hidden_act=self.hidden_act   ,hidden_dropout_prob=self.hidden_dropout_prob   ,attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,is_decoder=__lowercase   ,initializer_range=self.initializer_range   ,mask_ratio=self.mask_ratio   ,)



		def 	__lowerCamelCase			(     self     :List[str]   ,__lowercase     :Union[str, Any]   ,__lowercase     :Dict   ,__lowercase     :List[str]  ):
						snake_case__	:      Optional[int]     		=	TFViTMAEModel(config=__lowercase  )
						snake_case__	:      int     		=	model(__lowercase   ,training=__lowercase  )
						self.parent.assertEqual(result.last_hidden_state.shape   ,(self.batch_size, self.seq_length, self.hidden_size)  )



		def 	__lowerCamelCase			(     self     :Optional[int]   ,__lowercase     :List[str]   ,__lowercase     :Optional[int]   ,__lowercase     :int  ):
						snake_case__	:      Dict     		=	TFViTMAEForPreTraining(__lowercase  )
						snake_case__	:      Optional[int]     		=	model(__lowercase   ,training=__lowercase  )
						# expected sequence length = num_patches
						snake_case__	:      Optional[Any]     		=	(self.image_size // self.patch_size) ** 2
						snake_case__	:      int     		=	self.patch_size**2 * self.num_channels
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )

						# test greyscale images
						snake_case__	:      Tuple     		=	1
						snake_case__	:      List[Any]     		=	TFViTMAEForPreTraining(__lowercase  )

						snake_case__	:      Any     		=	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
						snake_case__	:      Any     		=	model(__lowercase   ,training=__lowercase  )
						snake_case__	:      Any     		=	self.patch_size**2
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, num_patches, expected_num_channels)  )






		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Union[str, Any]     		=	self.prepare_config_and_inputs()
						((snake_case__)		, (snake_case__)		, (snake_case__))	:      Optional[Any]     		=	config_and_inputs
						snake_case__	:      List[Any]     		=	{'''pixel_values''': pixel_values}
						return config, inputs_dict


@require_tf
class       a							(			__lowerCamelCase  ,							__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Union[str, Any]											=					(TFViTMAEModel, TFViTMAEForPreTraining) if is_tf_available() else ()
		__lowerCAmelCase	:    Union[str, Any]											=					{"""feature-extraction""": TFViTMAEModel} if is_tf_available() else {}

		__lowerCAmelCase	:    Tuple											=					False
		__lowerCAmelCase	:    List[Any]											=					False
		__lowerCAmelCase	:    Dict											=					False
		__lowerCAmelCase	:    List[str]											=					False



		def 	__lowerCamelCase			(     self     :Any  ):
						snake_case__	:      Union[str, Any]     		=	TFViTMAEModelTester(self  )
						snake_case__	:      Dict     		=	ConfigTester(self   ,config_class=__lowercase   ,has_text_modality=__lowercase   ,hidden_size=3_7  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						self.config_tester.run_common_tests()



		@unittest.skip(reason='''ViTMAE does not use inputs_embeds'''  )
		def 	__lowerCamelCase			(     self     :Dict  ):
						pass



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__		, snake_case__	:      int     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      Union[str, Any]     		=	model_class(__lowercase  )
										self.assertIsInstance(model.get_input_embeddings()   ,(tf.keras.layers.Layer)  )
										snake_case__	:      List[Any]     		=	model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowercase   ,tf.keras.layers.Layer  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__		, snake_case__	:      str     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      str     		=	model_class(__lowercase  )
										snake_case__	:      Any     		=	inspect.signature(model.call  )
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										snake_case__	:      Tuple     		=	[*signature.parameters.keys()]

										snake_case__	:      Dict     		=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Dict     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase  )



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_pretraining(*__lowercase  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make the mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      Any     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Optional[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      List[str]     		=	model_class(__lowercase  )
										snake_case__	:      Union[str, Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

										snake_case__	:      Optional[int]     		=	copy.deepcopy(self._prepare_for_class(__lowercase   ,__lowercase  )  )
										snake_case__	:      List[Any]     		=	model(**__lowercase   ,noise=__lowercase  )
										snake_case__	:      Optional[Any]     		=	outputs_dict[0].numpy()
										snake_case__	:      Optional[Any]     		=	outputs_keywords[0].numpy()

										self.assertLess(np.sum(np.abs(output_dict - output_keywords  )  )   ,1e-6  )



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						# make the mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      List[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      List[str]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						def prepare_numpy_arrays(__lowercase     :Dict  ):
										snake_case__	:      Any     		=	{}
										for k, v in inputs_dict.items():
														if tf.is_tensor(__lowercase  ):
																		snake_case__	:      Dict     		=	v.numpy()
														else:
																		snake_case__	:      str     		=	np.array(__lowercase  )

										return inputs_np_dict

						for model_class in self.all_model_classes:
										snake_case__	:      str     		=	model_class(__lowercase  )

										snake_case__	:      List[Any]     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      Dict     		=	prepare_numpy_arrays(__lowercase  )

										snake_case__	:      Tuple     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      Dict     		=	model(**__lowercase   ,noise=__lowercase  )
										self.assert_outputs_same(__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Optional[Any]   ,__lowercase     :List[str]  ):
						# make masks reproducible
						np.random.seed(2  )

						snake_case__	:      Union[str, Any]     		=	int((tf_model.config.image_size // tf_model.config.patch_size) ** 2  )
						snake_case__	:      int     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.constant(__lowercase  )

						# Add `noise` argument.
						# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
						snake_case__	:      Optional[Any]     		=	tf_noise

						super().check_pt_tf_models(__lowercase   ,__lowercase   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :Dict  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      List[str]     		=	{
						    module_member
						    for model_class in self.all_model_classes
						    for module in (import_module(model_class.__module__  ),)
						    for module_member_name in dir(__lowercase  )
						    if module_member_name.endswith('''MainLayer'''  )
						    # This condition is required, since `modeling_tf_clip.py` has 3 classes whose names end with `MainLayer`.
						    and module_member_name[: -len('''MainLayer'''  )] == model_class.__name__[: -len('''Model'''  )]
						    for module_member in (getattr(__lowercase   ,__lowercase  ),)
						    if isinstance(__lowercase   ,__lowercase  )
						    and tf.keras.layers.Layer in module_member.__bases__
						    and getattr(__lowercase   ,'''_keras_serializable'''   ,__lowercase  )
						}

						snake_case__	:      Optional[Any]     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[Any]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )
						snake_case__	:      Any     		=	tf.convert_to_tensor(__lowercase  )
						inputs_dict.update({'''noise''': noise}  )

						for main_layer_class in tf_main_layer_classes:
										snake_case__	:      List[Any]     		=	main_layer_class(__lowercase  )

										snake_case__	:      Union[str, Any]     		=	{
										    name: tf.keras.Input(tensor.shape[1:]   ,dtype=tensor.dtype  ) for name, tensor in inputs_dict.items()
										}

										snake_case__	:      Optional[Any]     		=	tf.keras.Model(__lowercase   ,outputs=main_layer(__lowercase  )  )
										snake_case__	:      List[str]     		=	model(__lowercase  )

										with tempfile.TemporaryDirectory() as tmpdirname:
														snake_case__	:      List[str]     		=	os.path.join(__lowercase   ,'''keras_model.h5'''  )
														model.save(__lowercase  )
														snake_case__	:      List[str]     		=	tf.keras.models.load_model(
														    __lowercase   ,custom_objects={main_layer_class.__name__: main_layer_class}  )
														assert isinstance(__lowercase   ,tf.keras.Model  )
														snake_case__	:      Union[str, Any]     		=	model(__lowercase  )
														self.assert_outputs_same(__lowercase   ,__lowercase  )



		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      Union[str, Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      str     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      Tuple     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      Optional[int]     		=	model_class(__lowercase  )
										snake_case__	:      str     		=	self._prepare_for_class(__lowercase   ,__lowercase  )
										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )

										if model_class.__name__ == "TFViTMAEModel":
														snake_case__	:      List[Any]     		=	outputs.last_hidden_state.numpy()
														snake_case__	:      List[Any]     		=	0
										else:
														snake_case__	:      Any     		=	outputs.logits.numpy()
														snake_case__	:      Tuple     		=	0

										with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(__lowercase   ,saved_model=__lowercase  )
														snake_case__	:      Optional[Any]     		=	model_class.from_pretrained(__lowercase  )
														snake_case__	:      Any     		=	model(__lowercase   ,noise=__lowercase  )

														if model_class.__name__ == "TFViTMAEModel":
																		snake_case__	:      Dict     		=	after_outputs['''last_hidden_state'''].numpy()
																		snake_case__	:      List[Any]     		=	0
														else:
																		snake_case__	:      Any     		=	after_outputs['''logits'''].numpy()
																		snake_case__	:      Optional[Any]     		=	0

														snake_case__	:      int     		=	np.amax(np.abs(out_a - out_a  )  )
														self.assertLessEqual(__lowercase   ,1e-5  )



		def 	__lowerCamelCase			(     self     :int  ):
						# make mask reproducible
						np.random.seed(2  )

						snake_case__		, snake_case__	:      List[str]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						snake_case__	:      Dict     		=	int((config.image_size // config.patch_size) ** 2  )
						snake_case__	:      List[str]     		=	np.random.uniform(size=(self.model_tester.batch_size, num_patches)  )

						for model_class in self.all_model_classes:
										snake_case__	:      int     		=	model_class(__lowercase  )
										snake_case__	:      int     		=	self._prepare_for_class(__lowercase   ,__lowercase  )

										snake_case__	:      List[Any]     		=	model(__lowercase   ,noise=__lowercase  )
										snake_case__	:      int     		=	model.get_config()
										# make sure that returned config is jsonifiable, which is required by keras
										json.dumps(__lowercase  )
										snake_case__	:      Any     		=	model_class.from_config(model.get_config()  )
										# make sure it also accepts a normal config
										snake_case__	:      Optional[int]     		=	model_class.from_config(model.config  )
										snake_case__	:      Tuple     		=	new_model(__lowercase  )  # Build model
										new_model.set_weights(model.get_weights()  )
										snake_case__	:      List[Any]     		=	new_model(__lowercase   ,noise=__lowercase  )

										self.assert_outputs_same(__lowercase   ,__lowercase  )



		@unittest.skip(
		    reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results.'''  )
		def 	__lowerCamelCase			(     self     :List[Any]  ):
						pass



		@unittest.skip(reason='''ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load'''  )
		def 	__lowerCamelCase			(     self     :Tuple  ):
						pass






		@slow
		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      List[Any]     		=	TFViTMAEModel.from_pretrained('''google/vit-base-patch16-224'''  )
						self.assertIsNotNone(__lowercase  )



def 			_lowerCAmelCase       (					)   ->					Dict:


				"""simple docstring"""

				snake_case__	:      str     		=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
				return image





@require_tf
@require_vision
class       a							(			unittest.TestCase				):



		@cached_property
		def 	__lowerCamelCase			(     self     :List[str]  ):
						return ViTImageProcessor.from_pretrained('''facebook/vit-mae-base'''  ) if is_vision_available() else None






		@slow
		def 	__lowerCamelCase			(     self     :Any  ):
						# make random mask reproducible across the PT and TF model
						np.random.seed(2  )

						snake_case__	:      Tuple     		=	TFViTMAEForPreTraining.from_pretrained('''facebook/vit-mae-base'''  )

						snake_case__	:      int     		=	self.default_image_processor
						snake_case__	:      int     		=	prepare_img()
						snake_case__	:      Tuple     		=	image_processor(images=__lowercase   ,return_tensors='''tf'''  )

						# prepare a noise vector that will be also used for testing the TF model
						# (this way we can ensure that the PT and TF models operate on the same inputs)
						snake_case__	:      str     		=	ViTMAEConfig()
						snake_case__	:      List[str]     		=	int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2  )
						snake_case__	:      Dict     		=	np.random.uniform(size=(1, num_patches)  )

						# forward pass
						snake_case__	:      List[str]     		=	model(**__lowercase   ,noise=__lowercase  )

						# verify the logits
						snake_case__	:      Optional[Any]     		=	tf.convert_to_tensor([1, 1_9_6, 7_6_8]  )
						self.assertEqual(outputs.logits.shape   ,__lowercase  )

						snake_case__	:      Tuple     		=	tf.convert_to_tensor(
						    [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]  )

						tf.debugging.assert_near(outputs.logits[0, :3, :3]   ,__lowercase   ,atol=1e-4  )






import json
import multiprocessing
import os
import re
from collections import defaultdict

import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList


A__								=						['''\nclass''', '''\ndef''', '''\n#''', '''\n@''', '''\nprint''', '''\nif''']


class       a							(			__lowerCamelCase				):



		def __init__(     self     :str   ,__lowercase     :Dict   ,__lowercase     :str   ,__lowercase     :Any=None   ,__lowercase     :Union[str, Any]=1  ):
						snake_case__	:      Optional[int]     		=	tokenizer
						snake_case__	:      List[Any]     		=	dataset
						snake_case__	:      Any     		=	len(__lowercase  ) if n_tasks is None else n_tasks
						snake_case__	:      List[Any]     		=	n_copies






		def __iter__(     self     :Tuple  ):
						snake_case__	:      Union[str, Any]     		=	[]
						for task in range(self.n_tasks  ):
										# without strip, the model generate commented codes ...
										prompts.append(self.tokenizer.eos_token + self.dataset[task]['''prompt'''].strip()  )
						snake_case__	:      Optional[int]     		=	self.tokenizer(__lowercase   ,padding=__lowercase   ,return_tensors='''pt'''  )
						for task in range(self.n_tasks  ):
										for _ in range(self.n_copies  ):
														yield {
														    "ids": outputs.input_ids[task],
														    "task_id": task,
														    "input_len": outputs.attention_mask[task].sum(),
														}


class       a							(			__lowerCamelCase				):



		def __init__(     self     :Union[str, Any]   ,__lowercase     :List[Any]   ,__lowercase     :int   ,__lowercase     :List[str]  ):
						snake_case__	:      List[str]     		=	start_length
						snake_case__	:      Any     		=	eof_strings
						snake_case__	:      Optional[Any]     		=	tokenizer






		def __call__(     self     :str   ,__lowercase     :str   ,__lowercase     :Any   ,**__lowercase     :Optional[int]  ):
						snake_case__	:      Tuple     		=	self.tokenizer.batch_decode(input_ids[:, self.start_length :]  )
						snake_case__	:      Union[str, Any]     		=	[]
						for decoded_generation in decoded_generations:
										done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings  )  )
						return all(__lowercase  )



def 			_lowerCAmelCase       (					__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      List[Any]     		=	re.split('''(%s)''' % '''|'''.join(__lowerCAmelCase      )  ,  __lowerCAmelCase      )
				# last string should be ""
				return "".join(string_list[:-2]      )



def 			_lowerCAmelCase       (					__lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase=20  ,  **__lowerCAmelCase      )   ->					List[Any]:


				"""simple docstring"""

				snake_case__	:      Optional[int]     		=	defaultdict(__lowerCAmelCase      )  # dict of list of generated tokens
				for step, batch in tqdm(enumerate(__lowerCAmelCase      )      ):
								with torch.no_grad():
												snake_case__	:      List[Any]     		=	batch['''ids'''].shape[-1]
												snake_case__	:      str     		=	accelerator.unwrap_model(__lowerCAmelCase      ).generate(
												    input_ids=batch['''ids'''][:, : batch['''input_len''']]  ,  num_return_sequences=__lowerCAmelCase  ,  **__lowerCAmelCase      )
												# each task is generated batch_size times
												snake_case__	:      str     		=	batch['''task_id'''].repeat(__lowerCAmelCase      )
												snake_case__	:      Optional[int]     		=	accelerator.pad_across_processes(
												    __lowerCAmelCase  ,  dim=1  ,  pad_index=tokenizer.pad_token_id      )

												snake_case__		, snake_case__	:      Any     		=	accelerator.gather((generated_tokens, generated_tasks)      )
												snake_case__	:      Union[str, Any]     		=	generated_tokens.cpu().numpy()
												snake_case__	:      Optional[Any]     		=	generated_tasks.cpu().numpy()

												for task, generated_tokens in zip(__lowerCAmelCase  ,  __lowerCAmelCase      ):
																gen_token_dict[task].append(__lowerCAmelCase      )

				snake_case__	:      List[Any]     		=	[[] for _ in range(__lowerCAmelCase      )]
				for task, generated_tokens in gen_token_dict.items():
								for s in generated_tokens:
												snake_case__	:      int     		=	tokenizer.decode(__lowerCAmelCase  ,  skip_special_tokens=__lowerCAmelCase  ,  clean_up_tokenization_spaces=__lowerCAmelCase      )
												code_gens[task].append(remove_last_block(__lowerCAmelCase      )      )
				return code_gens



def 			_lowerCAmelCase       (					)   ->					Optional[int]:


				"""simple docstring"""

				snake_case__	:      List[str]     		=	HfArgumentParser(__lowerCAmelCase      )
				snake_case__	:      Optional[Any]     		=	parser.parse_args()

				transformers.logging.set_verbosity_error()
				# enables code execution in code_eval metric
				snake_case__	:      Union[str, Any]     		=	args.HF_ALLOW_CODE_EVAL
				# make sure tokenizer plays nice with multiprocessing
				snake_case__	:      Union[str, Any]     		=	'''false'''

				if args.num_workers is None:
								snake_case__	:      Union[str, Any]     		=	multiprocessing.cpu_count()

				# Use dataset load to feed to accelerate
				snake_case__	:      Dict     		=	Accelerator()
				set_seed(args.seed  ,  device_specific=__lowerCAmelCase      )

				# Load model and tokenizer
				snake_case__	:      str     		=	AutoTokenizer.from_pretrained(args.model_ckpt      )
				snake_case__	:      List[str]     		=	tokenizer.eos_token
				snake_case__	:      Optional[Any]     		=	AutoModelForCausalLM.from_pretrained(args.model_ckpt      )

				# Generation settings
				snake_case__	:      List[Any]     		=	{
				    '''do_sample''': args.do_sample,
				    '''temperature''': args.temperature,
				    '''max_new_tokens''': args.max_new_tokens,
				    '''top_p''': args.top_p,
				    '''top_k''': args.top_k,
				    '''stopping_criteria''': StoppingCriteriaList([EndOfFunctionCriteria(0  ,  __lowerCAmelCase  ,  __lowerCAmelCase      )]      ),
				}

				# Load evaluation dataset and metric
				snake_case__	:      List[str]     		=	load_dataset('''openai_humaneval'''      )
				snake_case__	:      str     		=	load_metric('''code_eval'''      )

				snake_case__	:      List[Any]     		=	args.num_tasks if args.num_tasks is not None else len(human_eval['''test''']      )
				snake_case__	:      str     		=	args.n_samples // args.batch_size

				snake_case__	:      Tuple     		=	TokenizedDataset(__lowerCAmelCase  ,  human_eval['''test''']  ,  n_copies=__lowerCAmelCase  ,  n_tasks=__lowerCAmelCase      )
				# do not confuse args.batch_size, which is actually the num_return_sequences
				snake_case__	:      Tuple     		=	DataLoader(__lowerCAmelCase  ,  batch_size=1      )

				# Run a quick test to see if code evaluation is enabled
				try:
								snake_case__	:      Optional[Any]     		=	code_eval_metric.compute(references=['''''']  ,  predictions=[['''''']]      )
				except ValueError as exception:
								print(
								    '''Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'''
								    ''' flag to enable code evaluation.'''      )
								raise exception

				snake_case__		, snake_case__	:      Optional[Any]     		=	accelerator.prepare(__lowerCAmelCase  ,  __lowerCAmelCase      )

				snake_case__	:      Dict     		=	complete_code(
				    __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  __lowerCAmelCase  ,  n_tasks=__lowerCAmelCase  ,  batch_size=args.batch_size  ,  **__lowerCAmelCase  ,  )

				if accelerator.is_main_process:
								snake_case__	:      Optional[Any]     		=	[]

								for task in tqdm(range(__lowerCAmelCase      )      ):
												snake_case__	:      str     		=	human_eval['''test'''][task]['''test''']
												snake_case__	:      int     		=	f"""check({human_eval['test'][task]['entry_point']})"""
												references.append('''\n''' + test_func + '''\n''' + entry_point      )

								# Evaluate completions with "code_eval" metric
								snake_case__		, snake_case__	:      Any     		=	code_eval_metric.compute(
								    references=__lowerCAmelCase  ,  predictions=__lowerCAmelCase  ,  num_workers=args.num_workers      )
								print(f"""Results: {pass_at_k}"""      )

								# Save results to json file
								with open(args.output_file  ,  '''w'''      ) as fp:
												json.dump(__lowerCAmelCase  ,  __lowerCAmelCase      )


# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
				main()

