import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

A__           =							logging.getLogger()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:str						  =     argparse.ArgumentParser()
			parser.add_argument('''-f''')
			lowerCamelCase						:Tuple						  =     parser.parse_args()
			return args.f





def  _lowerCamelCase					(   a_				: Any):
			lowerCamelCase						:List[Any]						  =     {}
			lowerCamelCase						:Tuple						  =     os.path.join(__snake_case						,			'''all_results.json''')
			if os.path.exists(__snake_case):
						with open(__snake_case						,			'''r''') as f:
									lowerCamelCase						:Tuple						  =     json.load(__snake_case)
			else:
						raise ValueError(F"can\'t find {path}")
			return results





def  _lowerCamelCase					(   ):
			lowerCamelCase						:List[Any]						  =     torch.cuda.is_available() and torch_device == '''cuda'''
			return is_using_cuda and is_apex_available()


A__           =							logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)


class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







							@classmethod
							def 				snake_case  (				cls     :    Optional[Any]		):
										lowerCamelCase						:List[str]						  =     tempfile.mkdtemp()
										lowerCamelCase						:Optional[int]						  =     os.path.join(cls.tmpdir	,					'''default_config.yml'''		)
										write_basic_config(save_location=cls.configPath		)
										lowerCamelCase						:List[str]						  =     ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]







							@classmethod
							def 				snake_case  (				cls     :    Optional[Any]		):
										shutil.rmtree(cls.tmpdir		)







							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:int						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:Optional[int]						  =     F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

										if is_cuda_and_apex_available():
													testargs.append('''--fp16'''		)

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:List[Any]						  =     get_results(__a		)
										self.assertGreaterEqual(result['''eval_accuracy''']	,					0.7_5		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''epoch_0'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''glue_no_trainer'''		)		)		)







							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:str						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:Tuple						  =     F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

										if torch.cuda.device_count() > 1:
													# Skipping because there are not enough batches to train the model + would need a drop_last to work.
													return

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Union[str, Any]						  =     get_results(__a		)
										self.assertLess(result['''perplexity''']	,					100		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''epoch_0'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''clm_no_trainer'''		)		)		)







							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:List[str]						  =     F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Optional[int]						  =     get_results(__a		)
										self.assertLess(result['''perplexity''']	,					42		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''epoch_0'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''mlm_no_trainer'''		)		)		)







							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:List[str]						  =     7 if get_gpu_count() > 1 else 2

										lowerCamelCase						:str						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:Optional[Any]						  =     F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Dict						  =     get_results(__a		)
										self.assertGreaterEqual(result['''eval_accuracy''']	,					0.7_5		)
										self.assertLess(result['''train_loss''']	,					0.5		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''epoch_0'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''ner_no_trainer'''		)		)		)







							@unittest.skip(reason='''Fix me @muellerzr'''		)
							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Tuple		):
										lowerCamelCase						:Tuple						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:Tuple						  =     F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Union[str, Any]						  =     get_results(__a		)
										# Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
										self.assertGreaterEqual(result['''eval_f1''']	,					28		)
										self.assertGreaterEqual(result['''eval_exact''']	,					28		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''epoch_0'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''qa_no_trainer'''		)		)		)







							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:List[str]						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:Dict						  =     F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Any						  =     get_results(__a		)
										self.assertGreaterEqual(result['''eval_accuracy''']	,					0.8		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''swag_no_trainer'''		)		)		)







							@slow
							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    int		):
										lowerCamelCase						:int						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:Dict						  =     F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Optional[int]						  =     get_results(__a		)
										self.assertGreaterEqual(result['''eval_rouge1''']	,					10		)
										self.assertGreaterEqual(result['''eval_rouge2''']	,					2		)
										self.assertGreaterEqual(result['''eval_rougeL''']	,					7		)
										self.assertGreaterEqual(result['''eval_rougeLsum''']	,					7		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''epoch_0'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''summarization_no_trainer'''		)		)		)







							@slow
							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:List[str]						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:Union[str, Any]						  =     F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:int						  =     get_results(__a		)
										self.assertGreaterEqual(result['''eval_bleu''']	,					30		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''epoch_0'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''translation_no_trainer'''		)		)		)







							@slow
							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:int						  =     logging.StreamHandler(sys.stdout		)
										logger.addHandler(__a		)

										lowerCamelCase						:int						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:List[str]						  =     F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Union[str, Any]						  =     get_results(__a		)
										self.assertGreaterEqual(result['''eval_overall_accuracy''']	,					0.1_0		)







							@mock.patch.dict(os.environ	,					{'''WANDB_MODE''': '''offline'''}		)
							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:Union[str, Any]						  =     self.get_auto_remove_tmp_dir()
										lowerCamelCase						:str						  =     F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

										if is_cuda_and_apex_available():
													testargs.append('''--fp16'''		)

										run_command(self._launch_args + testargs		)
										lowerCamelCase						:Optional[int]						  =     get_results(__a		)
										# The base model scores a 25%
										self.assertGreaterEqual(result['''eval_accuracy''']	,					0.6		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''step_1'''		)		)		)
										self.assertTrue(os.path.exists(os.path.join(__a	,					'''image_classification_no_trainer'''		)		)		)




import numpy
class   _lowerCAmelCase		:







							def __init__(				self     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:Dict						  =     input_array

										# Random initial weights are assigned where first argument is the
										# number of nodes in previous layer and second argument is the
										# number of nodes in the next layer.

										# Random initial weights are assigned.
										# self.input_array.shape[1] is used to represent number of nodes in input layer.
										# First hidden layer consists of 4 nodes.
										lowerCamelCase						:Dict						  =     numpy.random.rand(
										    self.input_array.shape[1]	,					4		)

										# Random initial values for the first hidden layer.
										# First hidden layer has 4 nodes.
										# Second hidden layer has 3 nodes.
										lowerCamelCase						:Dict						  =     numpy.random.rand(
										    4	,					3		)

										# Random initial values for the second hidden layer.
										# Second hidden layer has 3 nodes.
										# Output layer has 1 node.
										lowerCamelCase						:Any						  =     numpy.random.rand(3	,					1		)

										# Real output values provided.
										lowerCamelCase						:Union[str, Any]						  =     output_array

										# Predicted output values by the neural network.
										# Predicted_output array initially consists of zeroes.
										lowerCamelCase						:List[str]						  =     numpy.zeros(output_array.shape		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:Any						  =     sigmoid(
										    numpy.dot(self.input_array	,					self.input_layer_and_first_hidden_layer_weights		)		)

										# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
										# connecting the first hidden set of nodes with the second hidden set of nodes.
										lowerCamelCase						:Any						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_input_and_first_hidden_layer	,					self.first_hidden_layer_and_second_hidden_layer_weights	,					)		)

										# layer_between_second_hidden_layer_and_output is the layer connecting
										# second hidden layer with the output node.
										lowerCamelCase						:Dict						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_first_hidden_layer_and_second_hidden_layer	,					self.second_hidden_layer_and_output_layer_weights	,					)		)

										return self.layer_between_second_hidden_layer_and_output







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:Union[str, Any]						  =     numpy.dot(
										    self.layer_between_first_hidden_layer_and_second_hidden_layer.T	,					2
										    * (self.output_array - self.predicted_output)
										    * sigmoid_derivative(self.predicted_output		)	,					)
										lowerCamelCase						:Dict						  =     numpy.dot(
										    self.layer_between_input_and_first_hidden_layer.T	,					numpy.dot(
										        2
										        * (self.output_array - self.predicted_output)
										        * sigmoid_derivative(self.predicted_output		)	,					self.second_hidden_layer_and_output_layer_weights.T	,					)
										    * sigmoid_derivative(
										        self.layer_between_first_hidden_layer_and_second_hidden_layer		)	,					)
										lowerCamelCase						:int						  =     numpy.dot(
										    self.input_array.T	,					numpy.dot(
										        numpy.dot(
										            2
										            * (self.output_array - self.predicted_output)
										            * sigmoid_derivative(self.predicted_output		)	,					self.second_hidden_layer_and_output_layer_weights.T	,					)
										        * sigmoid_derivative(
										            self.layer_between_first_hidden_layer_and_second_hidden_layer		)	,					self.first_hidden_layer_and_second_hidden_layer_weights.T	,					)
										    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer		)	,					)

										self.input_layer_and_first_hidden_layer_weights += (
										    updated_input_layer_and_first_hidden_layer_weights
										)
										self.first_hidden_layer_and_second_hidden_layer_weights += (
										    updated_first_hidden_layer_and_second_hidden_layer_weights
										)
										self.second_hidden_layer_and_output_layer_weights += (
										    updated_second_hidden_layer_and_output_layer_weights
										)







							def 				snake_case  (				self     :    Dict	,					__snake_case     :    numpy.ndarray	,					__snake_case     :    int	,					__snake_case     :    bool		):
										for iteration in range(1	,					iterations + 1		):
													lowerCamelCase						:Union[str, Any]						  =     self.feedforward()
													self.back_propagation()
													if give_loss:
																lowerCamelCase						:Tuple						  =     numpy.mean(numpy.square(output - self.feedforward()		)		)
																print(F"Iteration {iteration} Loss: {loss}"		)







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    numpy.ndarray		):
										lowerCamelCase						:int						  =     input_arr

										lowerCamelCase						:Union[str, Any]						  =     sigmoid(
										    numpy.dot(self.array	,					self.input_layer_and_first_hidden_layer_weights		)		)

										lowerCamelCase						:Optional[Any]						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_input_and_first_hidden_layer	,					self.first_hidden_layer_and_second_hidden_layer_weights	,					)		)

										lowerCamelCase						:Optional[int]						  =     sigmoid(
										    numpy.dot(
										        self.layer_between_first_hidden_layer_and_second_hidden_layer	,					self.second_hidden_layer_and_output_layer_weights	,					)		)

										return int(self.layer_between_second_hidden_layer_and_output > 0.6		)





def  _lowerCamelCase					(   a_				: numpy.ndarray):
			return 1 / (1 + numpy.exp(-value))





def  _lowerCamelCase					(   a_				: numpy.ndarray):
			return (value) * (1 - (value))





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     numpy.array(
			    (
			        [0, 0, 0],
			        [0, 0, 1],
			        [0, 1, 0],
			        [0, 1, 1],
			        [1, 0, 0],
			        [1, 0, 1],
			        [1, 1, 0],
			        [1, 1, 1],
			    )						,			dtype=numpy.floataa						,			)

			# True output values for the given input values.
			lowerCamelCase						:int						  =     numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])						,			dtype=numpy.floataa)

			# Calling neural network class.
			lowerCamelCase						:List[Any]						  =     TwoHiddenLayerNeuralNetwork(
			    input_array=a_						,			output_array=a_)

			# Calling training function.
			# Set give_loss to True if you want to see loss in every iteration.
			neural_network.train(output=a_						,			iterations=10						,			give_loss=a_)

			return neural_network.predict(numpy.array(([1, 1, 1])						,			dtype=numpy.floataa))


if __name__ == "__main__":
			example()



