




import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, 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

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
		import torch




class        _UpperCamelCase				:


			"""simple docstring"""






			def __init__(       self				,       lowerCAmelCase__				,       lowerCAmelCase__=14				,       lowerCAmelCase__=7				,       lowerCAmelCase__=True				,       lowerCAmelCase__=True				,       lowerCAmelCase__=False				,       lowerCAmelCase__=True				,       lowerCAmelCase__=99				,       lowerCAmelCase__=32				,       lowerCAmelCase__=5				,       lowerCAmelCase__=4				,       lowerCAmelCase__=37				,       lowerCAmelCase__="gelu"				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=0.1				,       lowerCAmelCase__=5_12				,       lowerCAmelCase__=0.02				,       )					-> List[Any]:

							'''simple docstring'''


							__lowercase			      =							parent
							__lowercase			      =							batch_size
							__lowercase			      =							seq_length
							__lowercase			      =							is_training
							__lowercase			      =							use_input_mask
							__lowercase			      =							use_token_type_ids
							__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			      =							initializer_range
							__lowercase			      =							None
							__lowercase			      =							vocab_size - 1
							__lowercase			      =							vocab_size - 1
							__lowercase			      =							vocab_size - 1





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

							'''simple docstring'''


							__lowercase			      =							ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size						)

							__lowercase			      =							None
							if self.use_input_mask:
											__lowercase			      =							random_attention_mask([self.batch_size, self.seq_length]						)

							__lowercase			      =							GPTaConfig(
							    vocab_size=self.vocab_size				,       n_embd=self.hidden_size				,       n_layer=self.num_hidden_layers				,       n_head=self.num_attention_heads				,       n_positions=self.max_position_embeddings				,       use_cache=_UpperCAmelCase				,       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 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[str]:

							'''simple docstring'''


							__lowercase			      =							self.prepare_config_and_inputs()
							__lowercase			      =							config_and_inputs
							__lowercase			      =							{"input_ids": input_ids, "attention_mask": attention_mask}
							return config, inputs_dict





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							__lowercase			      =							self.prepare_config_and_inputs()

							__lowercase			      =							floats_tensor([self.batch_size, self.seq_length, self.hidden_size]						)
							__lowercase			      =							ids_tensor([self.batch_size, self.seq_length]				,       vocab_size=2						)

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





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

							'''simple docstring'''


							__lowercase			      =							20
							__lowercase			      =							model_class_name(_UpperCAmelCase						)

							__lowercase			      =							model.init_cache(input_ids.shape[0]				,       _UpperCAmelCase						)
							__lowercase			      =							jnp.ones((input_ids.shape[0], max_decoder_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=_UpperCAmelCase				,       past_key_values=_UpperCAmelCase				,       position_ids=_UpperCAmelCase				,       )

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

							__lowercase			      =							model(_UpperCAmelCase						)

							__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__				,       lowerCAmelCase__						)					-> int:

							'''simple docstring'''


							__lowercase			      =							20
							__lowercase			      =							model_class_name(_UpperCAmelCase						)

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

							__lowercase			      =							model.init_cache(input_ids.shape[0]				,       _UpperCAmelCase						)
							__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=_UpperCAmelCase				,       past_key_values=_UpperCAmelCase				,       position_ids=_UpperCAmelCase				,       )
							__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=_UpperCAmelCase				,       position_ids=_UpperCAmelCase				,       )

							__lowercase			      =							model(_UpperCAmelCase				,       attention_mask=_UpperCAmelCase						)

							__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				(						SCREAMING_SNAKE_CASE__						,SCREAMING_SNAKE_CASE__						,unittest.TestCase			):


			"""simple docstring"""






			__a  :			int      			=						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
			__a  :			List[Any]      			=						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()





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

							'''simple docstring'''


							__lowercase			      =							FlaxGPTaModelTester(self						)





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							for model_class_name in self.all_model_classes:
											__lowercase			      =							self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(_UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase						)





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

							'''simple docstring'''


							for model_class_name in self.all_model_classes:
											__lowercase			      =							self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    _UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase				,       _UpperCAmelCase						)





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							GPTaTokenizer.from_pretrained('''gpt2'''				,       pad_token='''</s>'''				,       padding_side='''left'''						)
							__lowercase			      =							tokenizer(['''Hello this is a long string''', '''Hey''']				,       return_tensors='''np'''				,       padding=_UpperCAmelCase				,       truncation=_UpperCAmelCase						)

							__lowercase			      =							FlaxGPTaLMHeadModel.from_pretrained('''gpt2'''						)
							__lowercase			      =							False
							__lowercase			      =							model.config.eos_token_id

							__lowercase			      =							jax.jit(model.generate						)

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

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

							__lowercase			      =							[
							    "Hello this is a long string of words. I'm going to start with the first one.\n",
							    "Hey, I'm not sure if I'm going to be able to do",
							]

							self.assertListEqual(_UpperCAmelCase				,       _UpperCAmelCase						)





			@is_pt_flax_cross_test
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							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
															__lowercase			      =							self._prepare_for_class(_UpperCAmelCase				,       _UpperCAmelCase						)
															__lowercase			      =							{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															__lowercase			      =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
															__lowercase			      =							getattr(_UpperCAmelCase				,       _UpperCAmelCase						)

															__lowercase			      =							pt_inputs["input_ids"].shape
															__lowercase			      =							np.random.randint(0				,       seq_length - 1				,       size=(batch_size,)						)
															for batch_idx, start_index in enumerate(_UpperCAmelCase						):
																			__lowercase			      =							0
																			__lowercase			      =							1
																			__lowercase			      =							0
																			__lowercase			      =							1
															__lowercase			      =							pt_model_class(_UpperCAmelCase						).eval()
															__lowercase			      =							model_class(_UpperCAmelCase				,       dtype=jnp.floataa						)

															__lowercase			      =							convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,       _UpperCAmelCase						)
															__lowercase			      =							fx_state

															with torch.no_grad():
																			__lowercase			      =							pt_model(**_UpperCAmelCase						).to_tuple()

															__lowercase			      =							fx_model(**_UpperCAmelCase						).to_tuple()
															self.assertEqual(len(_UpperCAmelCase						)				,       len(_UpperCAmelCase						)				,       '''Output lengths differ between Flax and PyTorch'''						)
															for fx_output, pt_output in zip(_UpperCAmelCase				,       _UpperCAmelCase						):
																			self.assert_almost_equals(fx_output[:, -1]				,       pt_output[:, -1].numpy()				,       4E-2						)

															with tempfile.TemporaryDirectory() as tmpdirname:
																			pt_model.save_pretrained(_UpperCAmelCase						)
																			__lowercase			      =							model_class.from_pretrained(_UpperCAmelCase				,       from_pt=_UpperCAmelCase						)

															__lowercase			      =							fx_model_loaded(**_UpperCAmelCase						).to_tuple()
															self.assertEqual(
															    len(_UpperCAmelCase						)				,       len(_UpperCAmelCase						)				,       '''Output lengths differ between Flax and PyTorch'''						)
															for fx_output_loaded, pt_output in zip(_UpperCAmelCase				,       _UpperCAmelCase						):
																			self.assert_almost_equals(fx_output_loaded[:, -1]				,       pt_output[:, -1].numpy()				,       4E-2						)





			@is_pt_flax_cross_test
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> int:

							'''simple docstring'''


							__lowercase			      =							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
															__lowercase			      =							self._prepare_for_class(_UpperCAmelCase				,       _UpperCAmelCase						)
															__lowercase			      =							{k: torch.tensor(v.tolist()						) for k, v in prepared_inputs_dict.items()}

															# load corresponding PyTorch class
															__lowercase			      =							model_class.__name__[4:]  # Skip the "Flax" at the beginning
															__lowercase			      =							getattr(_UpperCAmelCase				,       _UpperCAmelCase						)

															__lowercase			      =							pt_model_class(_UpperCAmelCase						).eval()
															__lowercase			      =							model_class(_UpperCAmelCase				,       dtype=jnp.floataa						)

															__lowercase			      =							load_flax_weights_in_pytorch_model(_UpperCAmelCase				,       fx_model.params						)
															__lowercase			      =							pt_inputs["input_ids"].shape
															__lowercase			      =							np.random.randint(0				,       seq_length - 1				,       size=(batch_size,)						)
															for batch_idx, start_index in enumerate(_UpperCAmelCase						):
																			__lowercase			      =							0
																			__lowercase			      =							1
																			__lowercase			      =							0
																			__lowercase			      =							1

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

															with torch.no_grad():
																			__lowercase			      =							pt_model(**_UpperCAmelCase						).to_tuple()

															__lowercase			      =							fx_model(**_UpperCAmelCase						).to_tuple()
															self.assertEqual(len(_UpperCAmelCase						)				,       len(_UpperCAmelCase						)				,       '''Output lengths differ between Flax and PyTorch'''						)
															for fx_output, pt_output in zip(_UpperCAmelCase				,       _UpperCAmelCase						):
																			self.assert_almost_equals(fx_output[:, -1]				,       pt_output[:, -1].numpy()				,       4E-2						)

															with tempfile.TemporaryDirectory() as tmpdirname:
																			fx_model.save_pretrained(_UpperCAmelCase						)
																			__lowercase			      =							pt_model_class.from_pretrained(_UpperCAmelCase				,       from_flax=_UpperCAmelCase						)

															with torch.no_grad():
																			__lowercase			      =							pt_model_loaded(**_UpperCAmelCase						).to_tuple()

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





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

							'''simple docstring'''


							for model_class_name in self.all_model_classes:
											__lowercase			      =							model_class_name.from_pretrained('''gpt2'''				,       from_pt=_UpperCAmelCase						)
											__lowercase			      =							model(np.ones((1, 1)						)						)
											self.assertIsNotNone(_UpperCAmelCase						)
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()