


'''simple docstring'''


import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax
				import jax.numpy as jnp
				import numpy as np

				from transformers.modeling_flax_pytorch_utils import (
				    convert_pytorch_state_dict_to_flax,
				    load_flax_weights_in_pytorch_model,
				)
				from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
				import torch





@require_flax
class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case=14					,      __snake_case=7					,      __snake_case=True					,      __snake_case=True					,      __snake_case=True					,      __snake_case=99					,      __snake_case=32					,      __snake_case=5					,      __snake_case=4					,      __snake_case=37					,      __snake_case="gelu"					,      __snake_case=0.1					,      __snake_case=0.1					,      __snake_case=512					,      __snake_case=0.02					,      __snake_case=None					,      ):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   parent
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   batch_size
											_SCREAMING_SNAKE_CASE				: str  	=   seq_length
											_SCREAMING_SNAKE_CASE				: int  	=   is_training
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   use_input_mask
											_SCREAMING_SNAKE_CASE				: Any  	=   use_labels
											_SCREAMING_SNAKE_CASE				: str  	=   vocab_size
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   d_model
											_SCREAMING_SNAKE_CASE				: int  	=   num_hidden_layers
											_SCREAMING_SNAKE_CASE				: int  	=   num_attention_heads
											_SCREAMING_SNAKE_CASE				: str  	=   ffn_dim
											_SCREAMING_SNAKE_CASE				: Tuple  	=   activation_function
											_SCREAMING_SNAKE_CASE				: str  	=   activation_dropout
											_SCREAMING_SNAKE_CASE				: List[str]  	=   attention_dropout
											_SCREAMING_SNAKE_CASE				: Tuple  	=   max_position_embeddings
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   initializer_range
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   None
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   0
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   2
											_SCREAMING_SNAKE_CASE				: List[str]  	=   1






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: str  	=   np.clip(ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size						)					,      3					,      self.vocab_size						)

											_SCREAMING_SNAKE_CASE				: Any  	=   None
											if self.use_input_mask:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   random_attention_mask([self.batch_size, self.seq_length]						)

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   XGLMConfig(
											    vocab_size=self.vocab_size					,      d_model=self.hidden_size					,      num_layers=self.num_hidden_layers					,      attention_heads=self.num_attention_heads					,      ffn_dim=self.ffn_dim					,      activation_function=self.activation_function					,      activation_dropout=self.activation_dropout					,      attention_dropout=self.attention_dropout					,      max_position_embeddings=self.max_position_embeddings					,      initializer_range=self.initializer_range					,      use_cache=__snake_case					,      bos_token_id=self.bos_token_id					,      eos_token_id=self.eos_token_id					,      pad_token_id=self.pad_token_id					,      )

											return (config, input_ids, input_mask)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: int  	=   self.prepare_config_and_inputs()
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Any  	=   config_and_inputs
											_SCREAMING_SNAKE_CASE				: List[str]  	=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.prepare_config_and_inputs()

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
											_SCREAMING_SNAKE_CASE				: List[Any]  	=   ids_tensor([self.batch_size, self.seq_length]					,      vocab_size=2						)

											return (
											    config,
											    input_ids,
											    attention_mask,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   20
											_SCREAMING_SNAKE_CASE				: int  	=   model_class_name(__snake_case						)

											_SCREAMING_SNAKE_CASE				: Dict  	=   model.init_cache(input_ids.shape[0]					,      __snake_case						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   jnp.ones((input_ids.shape[0], max_decoder_length)					,      dtype="""i4"""						)

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

											_SCREAMING_SNAKE_CASE				: str  	=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,      dtype="""i4"""						)
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   model(
											    input_ids[:, -1:]					,      attention_mask=__snake_case					,      past_key_values=outputs_cache.past_key_values					,      position_ids=__snake_case					,      )

											_SCREAMING_SNAKE_CASE				: int  	=   model(__snake_case						)

											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   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 UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   20
											_SCREAMING_SNAKE_CASE				: List[str]  	=   model_class_name(__snake_case						)

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

											_SCREAMING_SNAKE_CASE				: Dict  	=   model.init_cache(input_ids.shape[0]					,      __snake_case						)
											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1						)[None, :]					,      (input_ids.shape[0], input_ids.shape[-1] - 1)						)

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

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   model(__snake_case					,      attention_mask=__snake_case						)
											_SCREAMING_SNAKE_CASE				: Any  	=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
											self.parent.assertTrue(diff < 1e-3					,      msg=f"""Max diff is {diff}"""						)





@require_sentencepiece
@require_flax
class 					lowercase__						(		_snake_case  ,  _snake_case  ,  unittest.TestCase      ):





							'''simple docstring'''


							A_							:   Tuple   			=						(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
							A_							:   List[Any]   			=						(FlaxXGLMForCausalLM,) if is_flax_available() else ()






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: int  	=   FlaxXGLMModelTester(self						)






							def UpperCAmelCase_			(					self						):
											for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Tuple  	=   self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward(__snake_case					,      __snake_case					,      __snake_case					,      __snake_case						)






							def UpperCAmelCase_			(					self						):
											for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[Any]  	=   self.model_tester.prepare_config_and_inputs()
															self.model_tester.check_use_cache_forward_with_attn_mask(
															    __snake_case					,      __snake_case					,      __snake_case					,      __snake_case						)






							@slow
							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: Dict  	=   XGLMTokenizer.from_pretrained("""XGLM"""					,      padding_side="""left"""						)
											_SCREAMING_SNAKE_CASE				: str  	=   tokenizer(["""Hello this is a long string""", """Hey"""]					,      return_tensors="""np"""					,      padding=__snake_case					,      truncation=__snake_case						)

											_SCREAMING_SNAKE_CASE				: int  	=   FlaxXGLMForCausalLM.from_pretrained("""facebook/xglm-564M"""						)
											_SCREAMING_SNAKE_CASE				: str  	=   1
											_SCREAMING_SNAKE_CASE				: Tuple  	=   False

											_SCREAMING_SNAKE_CASE				: Tuple  	=   jax.jit(model.generate						)

											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   jit_generate(inputs["""input_ids"""]					,      attention_mask=inputs["""attention_mask"""]						).sequences

											_SCREAMING_SNAKE_CASE				: List[Any]  	=   tokenizer.batch_decode(__snake_case					,      skip_special_tokens=__snake_case						)

											_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   [
											    """Hello this is a long string of questions, but I'm not sure if I'm""",
											    """Hey, I'm a newbie to the forum and I'""",
											]

											self.assertListEqual(__snake_case					,      __snake_case						)






							@is_pt_flax_cross_test
							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Dict  	=   self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																			# prepare inputs
																			_SCREAMING_SNAKE_CASE				: Any  	=   self._prepare_for_class(__snake_case					,      __snake_case						)
																			_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

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

																			_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Optional[int]  	=   pt_inputs["""input_ids"""].shape
																			_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   np.random.randint(0					,      seq_length - 1					,      size=(batch_size,)						)
																			for batch_idx, start_index in enumerate(__snake_case						):
																							_SCREAMING_SNAKE_CASE				: Optional[int]  	=   0
																							_SCREAMING_SNAKE_CASE				: Optional[int]  	=   1
																							_SCREAMING_SNAKE_CASE				: Optional[int]  	=   0
																							_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   1
																			_SCREAMING_SNAKE_CASE				: Tuple  	=   pt_model_class(__snake_case						).eval()
																			# Flax models don't use the `use_cache` option and cache is not returned as a default.
																			# So we disable `use_cache` here for PyTorch model.
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   False
																			_SCREAMING_SNAKE_CASE				: List[str]  	=   model_class(__snake_case					,      dtype=jnp.floataa						)
																			_SCREAMING_SNAKE_CASE				: str  	=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,      __snake_case						)
																			_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   fx_state

																			with torch.no_grad():
																							_SCREAMING_SNAKE_CASE				: List[str]  	=   pt_model(**__snake_case						).to_tuple()

																			_SCREAMING_SNAKE_CASE				: str  	=   fx_model(**__snake_case						).to_tuple()
																			self.assertEqual(len(__snake_case						)					,      len(__snake_case						)					,      """Output lengths differ between Flax and PyTorch"""						)
																			for fx_output, pt_output in zip(__snake_case					,      __snake_case						):
																							self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2						)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																							pt_model.save_pretrained(__snake_case						)
																							_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   model_class.from_pretrained(__snake_case					,      from_pt=__snake_case						)

																			_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   fx_model_loaded(**__snake_case						).to_tuple()
																			self.assertEqual(
																			    len(__snake_case						)					,      len(__snake_case						)					,      """Output lengths differ between Flax and PyTorch"""						)
																			for fx_output_loaded, pt_output in zip(__snake_case					,      __snake_case						):
																							self.assert_almost_equals(fx_output_loaded[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2						)






							@is_pt_flax_cross_test
							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Tuple  	=   self.model_tester.prepare_config_and_inputs_for_common()
											for model_class in self.all_model_classes:
															with self.subTest(model_class.__name__						):
																			# prepare inputs
																			_SCREAMING_SNAKE_CASE				: Dict  	=   self._prepare_for_class(__snake_case					,      __snake_case						)
																			_SCREAMING_SNAKE_CASE				: List[Any]  	=   {k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

																			# load corresponding PyTorch class
																			_SCREAMING_SNAKE_CASE				: Optional[int]  	=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
																			_SCREAMING_SNAKE_CASE				: Dict  	=   getattr(__snake_case					,      __snake_case						)

																			_SCREAMING_SNAKE_CASE				: Optional[int]  	=   pt_model_class(__snake_case						).eval()
																			_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   False
																			_SCREAMING_SNAKE_CASE				: Any  	=   model_class(__snake_case					,      dtype=jnp.floataa						)

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

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

																			with torch.no_grad():
																							_SCREAMING_SNAKE_CASE				: Any  	=   pt_model(**__snake_case						).to_tuple()

																			_SCREAMING_SNAKE_CASE				: Tuple  	=   fx_model(**__snake_case						).to_tuple()
																			self.assertEqual(len(__snake_case						)					,      len(__snake_case						)					,      """Output lengths differ between Flax and PyTorch"""						)
																			for fx_output, pt_output in zip(__snake_case					,      __snake_case						):
																							self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2						)

																			with tempfile.TemporaryDirectory() as tmpdirname:
																							fx_model.save_pretrained(__snake_case						)
																							_SCREAMING_SNAKE_CASE				: List[Any]  	=   pt_model_class.from_pretrained(__snake_case					,      from_flax=__snake_case						)

																			with torch.no_grad():
																							_SCREAMING_SNAKE_CASE				: Any  	=   pt_model_loaded(**__snake_case						).to_tuple()

																			self.assertEqual(
																			    len(__snake_case						)					,      len(__snake_case						)					,      """Output lengths differ between Flax and PyTorch"""						)
																			for fx_output, pt_output in zip(__snake_case					,      __snake_case						):
																							self.assert_almost_equals(fx_output[:, -1]					,      pt_output[:, -1].numpy()					,      4e-2						)






							@slow
							def UpperCAmelCase_			(					self						):
											for model_class_name in self.all_model_classes:
															_SCREAMING_SNAKE_CASE				: List[str]  	=   model_class_name.from_pretrained("""facebook/xglm-564M"""						)
															_SCREAMING_SNAKE_CASE				: int  	=   model(np.ones((1, 1)						)						)
															self.assertIsNotNone(__snake_case						)








'''simple docstring'''


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     snake_case_     (						SCREAMING_SNAKE_CASE__=None							,   SCREAMING_SNAKE_CASE__=None  ):







				"""simple docstring"""





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





@dataclass
class 					lowercase__						:





							'''simple docstring'''


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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





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





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Optional[int]  	=   args
											_SCREAMING_SNAKE_CASE				: Dict  	=   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}						)

											with open(self.args.csv_file					,      newline=""""""						) as csv_file:
															_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   csv.DictReader(__snake_case						)
															for row in reader:
																			_SCREAMING_SNAKE_CASE				: int  	=   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
																							_SCREAMING_SNAKE_CASE				: Any  	=   int(row["""result"""]						)
																			elif can_convert_to_float(row["""result"""]						):
																							# value is not None
																							_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   float(row["""result"""]						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: List[Any]  	=   plt.subplots()
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   """Time usage""" if self.args.is_time else """Memory usage"""
											_SCREAMING_SNAKE_CASE				: int  	=   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()						):
															_SCREAMING_SNAKE_CASE				: str  	=   sorted(set(self.result_dict[model_name]["""bsz"""]						)						)
															_SCREAMING_SNAKE_CASE				: List[str]  	=   sorted(set(self.result_dict[model_name]["""seq_len"""]						)						)
															_SCREAMING_SNAKE_CASE				: Optional[int]  	=   self.result_dict[model_name]["""result"""]

															((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: Tuple  	=   (
															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
															)

															_SCREAMING_SNAKE_CASE				: Any  	=   (
															    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:
																							_SCREAMING_SNAKE_CASE				: Any  	=   np.asarray(
																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]					,      dtype=__snake_case					,      )
																			else:
																							_SCREAMING_SNAKE_CASE				: Dict  	=   np.asarray(
																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]					,      dtype=np.floataa					,      )

																			((_SCREAMING_SNAKE_CASE)	, (_SCREAMING_SNAKE_CASE))				: List[Any]  	=   (
																			    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
																			)

																			_SCREAMING_SNAKE_CASE				: str  	=   np.asarray(__snake_case					,      __snake_case						)[: len(__snake_case						)]
																			plt.scatter(
																			    __snake_case					,      __snake_case					,      label=f"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""						)
																			plt.plot(__snake_case					,      __snake_case					,      """--"""						)

															title_str += f""" {label_model_name} vs."""

											_SCREAMING_SNAKE_CASE				: str  	=   title_str[:-4]
											_SCREAMING_SNAKE_CASE				: int  	=   """Time in s""" if self.args.is_time else """Memory in MB"""

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

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







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Dict  	=   HfArgumentParser(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   parser.parse_args_into_dataclasses()[0]
				_SCREAMING_SNAKE_CASE				: int  	=   Plot(args=SCREAMING_SNAKE_CASE__  )
				plot.plot()


if __name__ == "__main__":
				main()




