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  :					Any          =  """platform"""

		import jax
		import jax.numpy as jnp

		from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=None							,      lowercase=None							):







				"""simple docstring"""



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




@require_flax
class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=13				,       lowerCAmelCase__=7				,       lowerCAmelCase__=True				,       lowerCAmelCase__=False				,       lowerCAmelCase__=99				,       lowerCAmelCase__=16				,       lowerCAmelCase__=2				,       lowerCAmelCase__=4				,       lowerCAmelCase__=4				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=20				,       lowerCAmelCase__=2				,       lowerCAmelCase__=1				,       lowerCAmelCase__=0				,       lowerCAmelCase__=16				,       lowerCAmelCase__=16				,       lowerCAmelCase__=0.02				,       )					-> Any:

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


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

							__lowercase			      =							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__				,       )
							__lowercase			      =							prepare_opt_inputs_dict(lowerCAmelCase__				,       lowerCAmelCase__						)
							return config, inputs_dict





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							self.prepare_config_and_inputs()
							return config, inputs_dict





			def 	_SCREAMING_SNAKE_CASE      (       self				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							20
							__lowercase			      =							model_class_name(lowerCAmelCase__						)

							__lowercase			      =							inputs_dict['''input_ids''']
							__lowercase			      =							inputs_dict['''attention_mask''']

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

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

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

							__lowercase			      =							model(lowerCAmelCase__						)

							__lowercase			      =							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				,       lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							20
							__lowercase			      =							model_class_name(lowerCAmelCase__						)

							__lowercase      ,					__lowercase			      =							(
							    inputs_dict['''input_ids'''],
							    inputs_dict['''attention_mask'''],
							)

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

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

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

							__lowercase			      =							model(lowerCAmelCase__				,       attention_mask=lowerCAmelCase__						)

							__lowercase			      =							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				(						_UpperCAmelCase						,unittest.TestCase						,_UpperCAmelCase			):


			"""simple docstring"""






			__a  :			int      			=						(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
			__a  :			Dict      			=						() if is_flax_available() else ()





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

							'''simple docstring'''


							__lowercase			      =							FlaxOPTModelTester(self						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


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




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


			"""simple docstring"""






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

							'''simple docstring'''


							__lowercase			      =							FlaxOPTModel.from_pretrained('''facebook/opt-350m'''						)
							__lowercase			      =							jnp.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]						)
							__lowercase			      =							model(input_ids=lowerCAmelCase__						).last_hidden_state
							__lowercase			      =							(1, 11, 5_12)
							self.assertEqual(output.shape				,       lowerCAmelCase__						)
							__lowercase			      =							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]				,       lowerCAmelCase__				,       atol=4E-2						)						)




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


			"""simple docstring"""






			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							super().setUp()
							__lowercase			      =							'''facebook/opt-350m'''





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


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

							__lowercase			      =							[
							    '''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
							__lowercase			      =							tokenizer(lowerCAmelCase__				,       return_tensors='''jax'''				,       padding=lowerCAmelCase__				,       add_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							model(inputs.input_ids				,       attention_mask=inputs.attention_mask						)[0].mean(axis=-1						)
							__lowercase			      =							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(lowerCAmelCase__				,       lowerCAmelCase__				,       atol=4E-2						)						)

							__lowercase			      =							jax.jit(lowerCAmelCase__						)
							__lowercase			      =							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        _UpperCamelCase				(						unittest.TestCase			):


			"""simple docstring"""






			@property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[int]:

							'''simple docstring'''


							__lowercase			      =							'''facebook/opt-125m'''

							__lowercase			      =							[
							    '''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''',
							]

							__lowercase			      =							[]

							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							GPTaTokenizer.from_pretrained(lowerCAmelCase__						)

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

											__lowercase			      =							model.generate(lowerCAmelCase__				,       max_length=10						)
											__lowercase			      =							generated_ids[0]

											__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)
											predicted_outputs += generated_string

							self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							'''facebook/opt-350m'''

							__lowercase			      =							[
							    '''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''',
							]

							__lowercase			      =							[]
							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							GPTaTokenizer.from_pretrained(lowerCAmelCase__						)

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

											__lowercase			      =							model.generate(lowerCAmelCase__				,       max_length=10						)
											__lowercase			      =							generated_ids[0]

											__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)
											predicted_outputs += generated_string

							self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)





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

							'''simple docstring'''


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

							__lowercase			      =							jax.jit(model.generate						)

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

							__lowercase			      =							tokenizer.batch_decode(lowerCAmelCase__				,       skip_special_tokens=lowerCAmelCase__						)

							self.assertIsNotNone(lowerCAmelCase__				,       lowerCAmelCase__						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							'''facebook/opt-350m'''

							__lowercase			      =							GPTaTokenizer.from_pretrained(lowerCAmelCase__						)
							__lowercase			      =							FlaxOPTForCausalLM.from_pretrained(lowerCAmelCase__						)

							__lowercase			      =							'''left'''

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

							__lowercase			      =							tokenizer(lowerCAmelCase__				,       return_tensors='''jax'''				,       padding=lowerCAmelCase__						)
							__lowercase			      =							inputs['''input_ids''']

							__lowercase			      =							model.generate(input_ids=lowerCAmelCase__				,       attention_mask=inputs['''attention_mask''']				,       trace=lowerCAmelCase__						)

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

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

							__lowercase			      =							tokenizer.batch_decode(outputs[0]				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_non_padded[0][0]				,       skip_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.decode(output_padded[0][0]				,       skip_special_tokens=lowerCAmelCase__						)

							__lowercase			      =							[
							    '''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]						)
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser





def     UpperCAmelCase						(   lowercase=None							,      lowercase=None							):







				"""simple docstring"""



				return field(default_factory=lambda: default							,      metadata=lowercase							)




@dataclass
class        _UpperCamelCase				:


			"""simple docstring"""






			__a  :			str      			=						field(
			    metadata={'''help''': '''The csv file to plot.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Disable logarithmic scale when plotting'''}						,)
			__a  :			bool      			=						field(
			    default=_UpperCAmelCase						,metadata={
			        '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
			    }						,)
			__a  :			Optional[str]      			=						field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}						,)
			__a  :			Optional[List[str]]      			=						list_field(
			    default=_UpperCAmelCase						,metadata={'''help''': '''List of model names that are used instead of the ones in the csv file.'''}			)





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				try:
								int(lowercase							)
								return True
				except ValueError:
								return False





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				try:
								float(lowercase							)
								return True
				except ValueError:
								return False




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__						)					-> str:

							'''simple docstring'''


							__lowercase			      =							args
							__lowercase			      =							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

							with open(self.args.csv_file				,       newline=''''''						) as csv_file:
											__lowercase			      =							csv.DictReader(lowerCAmelCase__						)
											for row in reader:
															__lowercase			      =							row['''model''']
															self.result_dict[model_name]["bsz"].append(int(row['''batch_size''']						)						)
															self.result_dict[model_name]["seq_len"].append(int(row['''sequence_length''']						)						)
															if can_convert_to_int(row['''result''']						):
																			# value is not None
																			__lowercase			      =							int(row['''result''']						)
															elif can_convert_to_float(row['''result''']						):
																			# value is not None
																			__lowercase			      =							float(row['''result''']						)





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

							'''simple docstring'''


							__lowercase      ,					__lowercase			      =							plt.subplots()
							__lowercase			      =							'''Time usage''' if self.args.is_time else '''Memory usage'''
							__lowercase			      =							title_str + ''' for training''' if self.args.is_train else title_str + ''' for inference'''

							if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale('''log'''						)
											ax.set_yscale('''log'''						)

							for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter()						)

							for model_name_idx, model_name in enumerate(self.result_dict.keys()						):
											__lowercase			      =							sorted(set(self.result_dict[model_name]['''bsz''']						)						)
											__lowercase			      =							sorted(set(self.result_dict[model_name]['''seq_len''']						)						)
											__lowercase			      =							self.result_dict[model_name]['''result''']

											((__lowercase)      ,					(__lowercase))			      =							(
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__lowercase			      =							(
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
															if self.args.plot_along_batch:
																			__lowercase			      =							np.asarray(
																			    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]				,       dtype=lowerCAmelCase__				,       )
															else:
																			__lowercase			      =							np.asarray(
																			    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]				,       dtype=np.floataa				,       )

															((__lowercase)      ,					(__lowercase))			      =							(
															    ('''batch_size''', '''len''') if self.args.plot_along_batch else ('''in #tokens''', '''bsz''')
															)

															__lowercase			      =							np.asarray(lowerCAmelCase__				,       lowerCAmelCase__						)[: len(lowerCAmelCase__						)]
															plt.scatter(
															    lowerCAmelCase__				,       lowerCAmelCase__				,       label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"						)
															plt.plot(lowerCAmelCase__				,       lowerCAmelCase__				,       '''--'''						)

											title_str += F" {label_model_name} vs."

							__lowercase			      =							title_str[:-4]
							__lowercase			      =							'''Time in s''' if self.args.is_time else '''Memory in MB'''

							# plot
							plt.title(lowerCAmelCase__						)
							plt.xlabel(lowerCAmelCase__						)
							plt.ylabel(lowerCAmelCase__						)
							plt.legend()

							if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file						)
							else:
											plt.show()





def     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							HfArgumentParser(lowercase							)
				__lowercase			      =							parser.parse_args_into_dataclasses()[0]
				__lowercase			      =							Plot(args=lowercase							)
				plot.plot()


if __name__ == "__main__":
		main()