





"""simple docstring"""





from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
						import tensorflow as tf

						from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel





@require_tf
class 							_lowerCamelCase   :
						UpperCAmelCase_			=      MarianConfig
						UpperCAmelCase_			=      {}
						UpperCAmelCase_			=      "gelu"


						def __init__(self ,				__a ,				__a=13 ,				__a=7 ,				__a=True ,				__a=False ,				__a=99 ,				__a=32 ,				__a=2 ,				__a=4 ,				__a=37 ,				__a=0.1 ,				__a=0.1 ,				__a=20 ,				__a=2 ,				__a=1 ,				__a=0 ,				)			-> List[str]:
								UpperCamelCase =						parent
								UpperCamelCase =						batch_size
								UpperCamelCase =						seq_length
								UpperCamelCase =						is_training
								UpperCamelCase =						use_labels
								UpperCamelCase =						vocab_size
								UpperCamelCase =						hidden_size
								UpperCamelCase =						num_hidden_layers
								UpperCamelCase =						num_attention_heads
								UpperCamelCase =						intermediate_size

								UpperCamelCase =						hidden_dropout_prob
								UpperCamelCase =						attention_probs_dropout_prob
								UpperCamelCase =						max_position_embeddings
								UpperCamelCase =						eos_token_id
								UpperCamelCase =						pad_token_id
								UpperCamelCase =						bos_token_id


						def      snake_case_ (self		)			-> Tuple:
								UpperCamelCase =						ids_tensor([self.batch_size, self.seq_length - 1] ,				self.vocab_size		)
								UpperCamelCase =						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		) ,				1		)
								UpperCamelCase =						tf.concat([input_ids, eos_tensor] ,				axis=1		)

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

								UpperCamelCase =						self.config_cls(
								    vocab_size=self.vocab_size ,				d_model=self.hidden_size ,				encoder_layers=self.num_hidden_layers ,				decoder_layers=self.num_hidden_layers ,				encoder_attention_heads=self.num_attention_heads ,				decoder_attention_heads=self.num_attention_heads ,				encoder_ffn_dim=self.intermediate_size ,				decoder_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_ids=[2] ,				bos_token_id=self.bos_token_id ,				pad_token_id=self.pad_token_id ,				decoder_start_token_id=self.pad_token_id ,				**self.config_updates ,				)
								UpperCamelCase =						prepare_marian_inputs_dict(__a ,				__a ,				__a		)
								return config, inputs_dict




						def      snake_case_ (self ,				__a ,				__a		)			-> Dict:
								UpperCamelCase =						TFMarianModel(config=__a		).get_decoder()
								UpperCamelCase =						inputs_dict["input_ids"]

								UpperCamelCase =						input_ids[:1, :]
								UpperCamelCase =						inputs_dict["attention_mask"][:1, :]
								UpperCamelCase =						inputs_dict["head_mask"]
								UpperCamelCase =						1

								# first forward pass
								UpperCamelCase =						model(__a ,				attention_mask=__a ,				head_mask=__a ,				use_cache=__a		)

								UpperCamelCase ,					UpperCamelCase =						outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCamelCase =						ids_tensor((self.batch_size, 3) ,				config.vocab_size		)
								UpperCamelCase =						tf.cast(ids_tensor((self.batch_size, 3) ,				2		) ,				tf.inta		)

								# append to next input_ids and
								UpperCamelCase =						tf.concat([input_ids, next_tokens] ,				axis=-1		)
								UpperCamelCase =						tf.concat([attention_mask, next_attn_mask] ,				axis=-1		)

								UpperCamelCase =						model(__a ,				attention_mask=__a		)[0]
								UpperCamelCase =						model(__a ,				attention_mask=__a ,				past_key_values=__a		)[0]

								self.parent.assertEqual(next_tokens.shape[1] ,				output_from_past.shape[1]		)

								# select random slice
								UpperCamelCase =						int(ids_tensor((1,) ,				output_from_past.shape[-1]		)		)
								UpperCamelCase =						output_from_no_past[:, -3:, random_slice_idx]
								UpperCamelCase =						output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(__a ,				__a ,				rtol=1e-3		)



def    a__       (       _SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE	,						_SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None	,						):




		"""simple docstring"""


		if attention_mask is None:
				UpperCamelCase =						tf.cast(tf.math.not_equal(_SCREAMING_SNAKE_CASE	,						config.pad_token_id     )	,						tf.inta     )
		if decoder_attention_mask is None:
				UpperCamelCase =						tf.concat(
				    [
				        tf.ones(decoder_input_ids[:, :1].shape	,						dtype=tf.inta     ),
				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]	,						config.pad_token_id     )	,						tf.inta     ),
				    ]	,						axis=-1	,						)
		if head_mask is None:
				UpperCamelCase =						tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
		if decoder_head_mask is None:
				UpperCamelCase =						tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
		if cross_attn_head_mask is None:
				UpperCamelCase =						tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
		return {
		    "input_ids": input_ids,
		    "decoder_input_ids": decoder_input_ids,
		    "attention_mask": attention_mask,
		    "decoder_attention_mask": decoder_attention_mask,
		    "head_mask": head_mask,
		    "decoder_head_mask": decoder_head_mask,
		    "cross_attn_head_mask": cross_attn_head_mask,
		}





@require_tf
class 							_lowerCamelCase   (							_lowercase	,      _lowercase	,      unittest.TestCase			):
						UpperCAmelCase_			=      (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
						UpperCAmelCase_			=      (TFMarianMTModel,) if is_tf_available() else ()
						UpperCAmelCase_			=      (
						    {
						        "conversational": TFMarianMTModel,
						        "feature-extraction": TFMarianModel,
						        "summarization": TFMarianMTModel,
						        "text2text-generation": TFMarianMTModel,
						        "translation": TFMarianMTModel,
						    }
						    if is_tf_available()
						    else {}
						)
						UpperCAmelCase_			=      True
						UpperCAmelCase_			=      False
						UpperCAmelCase_			=      False


						def      snake_case_ (self		)			-> Union[str, Any]:
								UpperCamelCase =						TFMarianModelTester(self		)
								UpperCamelCase =						ConfigTester(self ,				config_class=__a		)


						def      snake_case_ (self		)			-> List[str]:
								self.config_tester.run_common_tests()




						def      snake_case_ (self		)			-> str:
								UpperCamelCase =						self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*__a		)





@require_tf
class 							_lowerCamelCase   (							unittest.TestCase			):
						UpperCAmelCase_			=      1_000  # show more chars for failing integration tests


						@classmethod
						def      snake_case_ (cls		)			-> None:
								UpperCamelCase =						F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
								return cls


						@cached_property
						def      snake_case_ (self		)			-> MarianTokenizer:
								return AutoTokenizer.from_pretrained(self.model_name		)


						@property
						def      snake_case_ (self		)			-> int:
								return self.tokenizer.eos_token_id


						@cached_property
						def      snake_case_ (self		)			-> int:
								warnings.simplefilter("error"		)
								UpperCamelCase =						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
								assert isinstance(__a ,				__a		)
								UpperCamelCase =						model.config
								self.assertListEqual(c.bad_words_ids ,				[[c.pad_token_id]]		)
								self.assertEqual(c.max_length ,				5_12		)
								self.assertEqual(c.decoder_start_token_id ,				c.pad_token_id		)
								return model


						def      snake_case_ (self ,				**__a		)			-> int:
								UpperCamelCase =						self.translate_src_text(**__a		)
								self.assertListEqual(self.expected_text ,				__a		)




						def      snake_case_ (self ,				**__a		)			-> Optional[Any]:
								UpperCamelCase =						self.tokenizer(self.src_text ,				**__a ,				padding=__a ,				return_tensors="tf"		)
								UpperCamelCase =						self.model.generate(
								    model_inputs.input_ids ,				attention_mask=model_inputs.attention_mask ,				num_beams=2 ,				max_length=1_28		)
								UpperCamelCase =						self.tokenizer.batch_decode(generated_ids.numpy() ,				skip_special_tokens=__a		)
								return generated_words





@require_sentencepiece
@require_tokenizers
@require_tf
class 							_lowerCamelCase   (							_lowercase			):
						UpperCAmelCase_			=      "mt"
						UpperCAmelCase_			=      "en"
						UpperCAmelCase_			=      ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
						UpperCAmelCase_			=      ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


						@unittest.skip("Skipping until #12647 is resolved."		)
						@slow
						def      snake_case_ (self		)			-> List[Any]:
								self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 							_lowerCamelCase   (							_lowercase			):
						UpperCAmelCase_			=      "en"
						UpperCAmelCase_			=      "zh"
						UpperCAmelCase_			=      ["My name is Wolfgang and I live in Berlin"]
						UpperCAmelCase_			=      ["我叫沃尔夫冈 我住在柏林"]


						@unittest.skip("Skipping until #12647 is resolved."		)
						@slow
						def      snake_case_ (self		)			-> Any:
								self._assert_generated_batch_equal_expected()





@require_sentencepiece
@require_tokenizers
@require_tf
class 							_lowerCamelCase   (							_lowercase			):
						UpperCAmelCase_			=      "en"
						UpperCAmelCase_			=      "ROMANCE"
						UpperCAmelCase_			=      [
						    ">>fr<< Don't spend so much time watching TV.",
						    ">>pt<< Your message has been sent.",
						    ">>es<< He's two years older than me.",
						]
						UpperCAmelCase_			=      [
						    "Ne passez pas autant de temps à regarder la télé.",
						    "A sua mensagem foi enviada.",
						    "Es dos años más viejo que yo.",
						]


						@unittest.skip("Skipping until #12647 is resolved."		)
						@slow
						def      snake_case_ (self		)			-> Optional[int]:
								self._assert_generated_batch_equal_expected()




						@unittest.skip("Skipping until #12647 is resolved."		)
						@slow
						def      snake_case_ (self		)			-> Union[str, Any]:
								UpperCamelCase =						TranslationPipeline(self.model ,				self.tokenizer ,				framework="tf"		)
								UpperCamelCase =						pipeline(self.src_text		)
								self.assertEqual(self.expected_text ,				[x["translation_text"] for x in output]		)













"""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    a__       (       _SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


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





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



def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


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





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Tuple:
								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      snake_case_ (self		)			-> Optional[int]:
								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__       (       ):




		"""simple docstring"""


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


if __name__ == "__main__":
						main()






