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
 UpperCAmelCase      		=					'''platform'''

 import jax
 import jax.numpy as jnp

 from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer


def SCREAMING_SNAKE_CASE					(  lowercase_:						List[str]		,    lowercase_:						Optional[int]		,    lowercase_:						Union[str, Any]=None		,    lowercase_:						List[str]=None					):
       if attention_mask is None:
              _UpperCamelCase  :							Optional[Any]	    =	np.where(input_ids != config.pad_token_id		,    1		,    0					)
       return {
           "input_ids": input_ids,
           "attention_mask": attention_mask,
       }




@require_flax
class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[Any]							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		List[Any]=1_3							,      lowerCamelCase							:		List[Any]=7							,      lowerCamelCase							:		Union[str, Any]=True							,      lowerCamelCase							:		List[Any]=False							,      lowerCamelCase							:		Any=9_9							,      lowerCamelCase							:		Any=1_6							,      lowerCamelCase							:		Dict=2							,      lowerCamelCase							:		List[Any]=4							,      lowerCamelCase							:		Any=4							,      lowerCamelCase							:		List[str]="gelu"							,      lowerCamelCase							:		str=0.1							,      lowerCamelCase							:		List[str]=0.1							,      lowerCamelCase							:		Optional[Any]=2_0							,      lowerCamelCase							:		Tuple=2							,      lowerCamelCase							:		int=1							,      lowerCamelCase							:		List[str]=0							,      lowerCamelCase							:		Tuple=1_6							,      lowerCamelCase							:		str=1_6							,      lowerCamelCase							:		Optional[Any]=0.0_2							,      )			->				List[str]:
        _UpperCamelCase  :							Optional[int]	    =	parent
        _UpperCamelCase  :							Optional[Any]	    =	batch_size
        _UpperCamelCase  :							Optional[Any]	    =	seq_length
        _UpperCamelCase  :							int	    =	is_training
        _UpperCamelCase  :							Optional[int]	    =	use_labels
        _UpperCamelCase  :							Optional[Any]	    =	vocab_size
        _UpperCamelCase  :							Optional[Any]	    =	hidden_size
        _UpperCamelCase  :							Optional[Any]	    =	num_hidden_layers
        _UpperCamelCase  :							Tuple	    =	num_attention_heads
        _UpperCamelCase  :							Optional[int]	    =	intermediate_size
        _UpperCamelCase  :							Any	    =	hidden_act
        _UpperCamelCase  :							Dict	    =	hidden_dropout_prob
        _UpperCamelCase  :							Optional[int]	    =	attention_probs_dropout_prob
        _UpperCamelCase  :							Optional[Any]	    =	max_position_embeddings
        _UpperCamelCase  :							Tuple	    =	eos_token_id
        _UpperCamelCase  :							Dict	    =	pad_token_id
        _UpperCamelCase  :							str	    =	bos_token_id
        _UpperCamelCase  :							Optional[int]	    =	embed_dim
        _UpperCamelCase  :							Tuple	    =	word_embed_proj_dim
        _UpperCamelCase  :							Optional[Any]	    =	initializer_range
        _UpperCamelCase  :							str	    =	False

 def        __snake_case       (      self							:		Optional[Any]	)			->				Tuple:
        _UpperCamelCase  :							Dict	    =	np.clip(ids_tensor([self.batch_size, self.seq_length - 1]							,      self.vocab_size	)							,      3							,      self.vocab_size	)
        _UpperCamelCase  :							Dict	    =	np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)							,      dtype=np.intaa	))							,      -1	)

        _UpperCamelCase  :							Optional[int]	    =	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							,      )
        _UpperCamelCase  :							Optional[Any]	    =	prepare_opt_inputs_dict(lowerCamelCase							,      lowerCamelCase	)
        return config, inputs_dict

 def        __snake_case       (      self							:		List[Any]	)			->				Dict:
        _UpperCamelCase    ,   _UpperCamelCase  :							Union[str, Any]	    =	self.prepare_config_and_inputs()
        return config, inputs_dict

 def        __snake_case       (      self							:		Optional[Any]							,      lowerCamelCase							:		Any							,      lowerCamelCase							:		Dict							,      lowerCamelCase							:		List[str]	)			->				Optional[int]:
        _UpperCamelCase  :							Optional[int]	    =	2_0
        _UpperCamelCase  :							str	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase  :							str	    =	inputs_dict['input_ids']
        _UpperCamelCase  :							str	    =	inputs_dict['attention_mask']

        _UpperCamelCase  :							Dict	    =	model.init_cache(input_ids.shape[0]							,      lowerCamelCase	)
        _UpperCamelCase  :							List[str]	    =	jnp.ones((input_ids.shape[0], max_length)							,      dtype='i4'	)

        _UpperCamelCase  :							Union[str, Any]	    =	jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1	)[None, :]							,      (input_ids.shape[0], input_ids.shape[-1] - 1)							,      )
        _UpperCamelCase  :							Optional[Any]	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Union[str, Any]	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							Optional[int]	    =	model(
            input_ids[:, -1:]							,      attention_mask=lowerCamelCase							,      past_key_values=outputs_cache.past_key_values							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							int	    =	model(lowerCamelCase	)

        _UpperCamelCase  :							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        __snake_case       (      self							:		Any							,      lowerCamelCase							:		int							,      lowerCamelCase							:		List[str]							,      lowerCamelCase							:		Union[str, Any]	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	2_0
        _UpperCamelCase  :							Optional[int]	    =	model_class_name(lowerCamelCase	)

        _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	(
            inputs_dict['input_ids'],
            inputs_dict['attention_mask'],
        )

        _UpperCamelCase  :							Optional[Any]	    =	jnp.concatenate(
            [
                attention_mask,
                jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])	),
            ]							,      axis=-1							,      )

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

        _UpperCamelCase  :							int	    =	model(
            input_ids[:, :-1]							,      attention_mask=lowerCamelCase							,      past_key_values=lowerCamelCase							,      position_ids=lowerCamelCase							,      )
        _UpperCamelCase  :							Union[str, Any]	    =	jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]							,      dtype='i4'	)
        _UpperCamelCase  :							Optional[int]	    =	model(
            input_ids[:, -1:]							,      past_key_values=outputs_cache.past_key_values							,      attention_mask=lowerCamelCase							,      position_ids=lowerCamelCase							,      )

        _UpperCamelCase  :							Any	    =	model(lowerCamelCase							,      attention_mask=lowerCamelCase	)

        _UpperCamelCase  :							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}"	)







@require_flax
class  __lowerCAmelCase	(							a_,						unittest.TestCase,						a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[str, Any]					     =			(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
 _lowerCamelCase    :						List[Any]					     =			() if is_flax_available() else ()

 def        __snake_case       (      self							:		List[Any]	)			->				int:
        _UpperCamelCase  :							Optional[Any]	    =	FlaxOPTModelTester(self	)

 def        __snake_case       (      self							:		List[str]	)			->				Any:
        _UpperCamelCase    ,   _UpperCamelCase  :							Dict	    =	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        __snake_case       (      self							:		List[str]	)			->				Union[str, Any]:
        _UpperCamelCase    ,   _UpperCamelCase  :							str	    =	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        __snake_case       (      self							:		str	)			->				Union[str, Any]:
        for model_class_name in self.all_model_classes:
               _UpperCamelCase  :							Any	    =	model_class_name.from_pretrained('facebook/opt-125m'	)
               _UpperCamelCase  :							List[str]	    =	np.ones((1, 1)	) * model.config.eos_token_id
               _UpperCamelCase  :							Union[str, Any]	    =	model(lowerCamelCase	)
               self.assertIsNotNone(lowerCamelCase	)







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



 '''simple docstring'''

 @slow
 def        __snake_case       (      self							:		Dict	)			->				Union[str, Any]:
        _UpperCamelCase  :							List[str]	    =	FlaxOPTModel.from_pretrained('facebook/opt-350m'	)
        _UpperCamelCase  :							str	    =	jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]	)
        _UpperCamelCase  :							Tuple	    =	model(input_ids=lowerCamelCase	).last_hidden_state
        _UpperCamelCase  :							Any	    =	(1, 1_1, 5_1_2)
        self.assertEqual(output.shape							,      lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	jnp.array(
            [[-0.2_8_6_7, -1.9_2_5_6, -0.3_0_6_2], [-1.2_7_1_1, -0.1_3_3_7, -0.1_8_9_7], [0.4_1_0_9, 0.1_1_8_7, -1.3_1_4_2]]	)
        self.assertTrue(jnp.allclose(output[:, :3, :3]							,      lowerCamelCase							,      atol=4e-2	)	)







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



 '''simple docstring'''

 def        __snake_case       (      self							:		str	)			->				str:
        super().setUp()
        _UpperCamelCase  :							int	    =	'facebook/opt-350m'

 def        __snake_case       (      self							:		List[str]	)			->				List[Any]:
        _UpperCamelCase  :							Union[str, Any]	    =	FlaxOPTForCausalLM.from_pretrained(self.path_model	)
        _UpperCamelCase  :							List[Any]	    =	GPTaTokenizer.from_pretrained(self.path_model	)

        _UpperCamelCase  :							Any	    =	[
            '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
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'							,      padding=lowerCamelCase							,      add_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							int	    =	model(inputs.input_ids							,      attention_mask=inputs.attention_mask	)[0].mean(axis=-1	)
        _UpperCamelCase  :							Any	    =	jnp.array(
            [
                [1.3_8_5_1, -1_3.8_9_2_3, -1_0.5_2_2_9, -1_0.7_5_3_3, -0.2_3_0_9, -1_0.2_3_8_4, -0.5_3_6_5, -9.0_9_4_7, -5.1_6_7_0],
                [-4.7_0_7_3, -1_0.6_2_7_6, -3.9_4_1_5, -2_1.5_2_4_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2, -0.2_8_2_2],
                [0.6_2_4_7, -3.4_2_2_9, -8.9_1_7_9, -1.4_2_9_7, -1_4.1_6_5_0, 1.4_1_4_6, -9.0_2_1_8, -0.2_7_0_3, -0.2_7_0_3],
                [6.4_7_8_3, -1.9_9_1_3, -1_0.7_9_2_6, -2.3_3_3_6, 1.5_0_9_2, -0.9_9_7_4, -6.8_2_1_3, 1.3_4_7_7, 1.3_4_7_7],
            ]	)
        self.assertTrue(jnp.allclose(lowerCamelCase							,      lowerCamelCase							,      atol=4e-2	)	)

        _UpperCamelCase  :							Dict	    =	jax.jit(lowerCamelCase	)
        _UpperCamelCase  :							int	    =	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  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 @property
 def        __snake_case       (      self							:		List[str]	)			->				Optional[Any]:
        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        __snake_case       (      self							:		List[Any]	)			->				List[Any]:
        _UpperCamelCase  :							str	    =	'facebook/opt-125m'

        _UpperCamelCase  :							Any	    =	[
            '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',
        ]

        _UpperCamelCase  :							Tuple	    =	[]

        _UpperCamelCase  :							List[Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)

        for prompt in self.prompts:
               _UpperCamelCase  :							List[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'	).input_ids

               _UpperCamelCase  :							Optional[Any]	    =	model.generate(lowerCamelCase							,      max_length=1_0	)
               _UpperCamelCase  :							int	    =	generated_ids[0]

               _UpperCamelCase  :							Dict	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)
               predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Optional[int]:
        _UpperCamelCase  :							Tuple	    =	'facebook/opt-350m'

        _UpperCamelCase  :							Dict	    =	[
            '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',
        ]

        _UpperCamelCase  :							str	    =	[]
        _UpperCamelCase  :							Tuple	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Dict	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)

        for prompt in self.prompts:
               _UpperCamelCase  :							Optional[Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'	).input_ids

               _UpperCamelCase  :							Tuple	    =	model.generate(lowerCamelCase							,      max_length=1_0	)
               _UpperCamelCase  :							List[Any]	    =	generated_ids[0]

               _UpperCamelCase  :							Any	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)
               predicted_outputs += generated_string

        self.assertListEqual(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Optional[Any]	)			->				List[str]:
        _UpperCamelCase  :							List[Any]	    =	'facebook/opt-125m'
        _UpperCamelCase  :							List[Any]	    =	[
            'Today is a beautiful day and I want to thank',
            'In the city of Rome Canaver Canaver Canaver Canaver',
        ]
        _UpperCamelCase  :							List[Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							str	    =	tokenizer(
            [
                'Today is a beautiful day and I want to',
                'In the city of',
            ]							,      return_tensors='jax'							,      padding=lowerCamelCase							,      )

        _UpperCamelCase  :							Optional[Any]	    =	jax.jit(model.generate	)

        _UpperCamelCase  :							str	    =	jit_generate(inputs['input_ids']							,      attention_mask=inputs['attention_mask']	).sequences

        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.batch_decode(lowerCamelCase							,      skip_special_tokens=lowerCamelCase	)

        self.assertIsNotNone(lowerCamelCase							,      lowerCamelCase	)

 def        __snake_case       (      self							:		Dict	)			->				Dict:
        _UpperCamelCase  :							Optional[Any]	    =	'facebook/opt-350m'

        _UpperCamelCase  :							Optional[Any]	    =	GPTaTokenizer.from_pretrained(lowerCamelCase	)
        _UpperCamelCase  :							Union[str, Any]	    =	FlaxOPTForCausalLM.from_pretrained(lowerCamelCase	)

        _UpperCamelCase  :							List[Any]	    =	'left'

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

        _UpperCamelCase  :							Union[str, Any]	    =	tokenizer(lowerCamelCase							,      return_tensors='jax'							,      padding=lowerCamelCase	)
        _UpperCamelCase  :							Optional[int]	    =	inputs['input_ids']

        _UpperCamelCase  :							Union[str, Any]	    =	model.generate(input_ids=lowerCamelCase							,      attention_mask=inputs['attention_mask']							,      trace=lowerCamelCase	)

        _UpperCamelCase  :							Any	    =	tokenizer(sentences[0]							,      return_tensors='jax'	).input_ids
        _UpperCamelCase  :							List[str]	    =	model.generate(input_ids=lowerCamelCase	)

        _UpperCamelCase  :							Tuple	    =	inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].sum()
        _UpperCamelCase  :							List[str]	    =	tokenizer(sentences[1]							,      return_tensors='jax'	).input_ids
        _UpperCamelCase  :							List[Any]	    =	model.generate(input_ids=lowerCamelCase							,      max_length=model.config.max_length - num_paddings	)

        _UpperCamelCase  :							int	    =	tokenizer.batch_decode(outputs[0]							,      skip_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.decode(output_non_padded[0][0]							,      skip_special_tokens=lowerCamelCase	)
        _UpperCamelCase  :							Optional[Any]	    =	tokenizer.decode(output_padded[0][0]							,      skip_special_tokens=lowerCamelCase	)

        _UpperCamelCase  :							Dict	    =	[
            '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  A			(   lowercase=None       ,			lowercase=None					)  ->    Tuple:
		'''simple docstring'''

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


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






def  A			(   lowercase					)  ->    List[Any]:
		'''simple docstring'''

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


def  A			(   lowercase					)  ->    Optional[Any]:
		'''simple docstring'''

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


class 							lowercase   :



		def __init__(					self							, A_			)    ->   List[str]:
				"""simple docstring"""
				UpperCamelCase            =	args
				UpperCamelCase            =	defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}			)

				with open(self.args.csv_file							, newline=''			) as csv_file:
						UpperCamelCase            =	csv.DictReader(A_			)
						for row in reader:
								UpperCamelCase            =	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
										UpperCamelCase            =	int(row['result']			)
								elif can_convert_to_float(row['result']			):
										# value is not None
										UpperCamelCase            =	float(row['result']			)



		def __UpperCamelCase					(					self			)    ->   Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase      ,    UpperCamelCase            =	plt.subplots()
				UpperCamelCase            =	'Time usage' if self.args.is_time else 'Memory usage'
				UpperCamelCase            =	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()			):
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['bsz']			)			)
						UpperCamelCase            =	sorted(set(self.result_dict[model_name]['seq_len']			)			)
						UpperCamelCase            =	self.result_dict[model_name]['result']

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

						UpperCamelCase            =	(
						    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:
										UpperCamelCase            =	np.asarray(
										    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							, dtype=A_							, )
								else:
										UpperCamelCase            =	np.asarray(
										    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							, dtype=np.floataa							, )

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

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

						title_str += F''' {label_model_name} vs.'''

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

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

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






def  A			(   )  ->    Dict:
		'''simple docstring'''

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


if __name__ == "__main__":
					main()
