


'''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      SCREAMING_SNAKE_CASE_	:
			__a					:      List[Any]					=  MarianConfig
			__a					:      Optional[Any]					=  {}
			__a					:      Tuple					=  '''gelu'''




			def __init__(      self					,      lowercase					,      lowercase=1_3					,      lowercase=7					,      lowercase=True					,      lowercase=False					,      lowercase=9_9					,      lowercase=3_2					,      lowercase=2					,      lowercase=4					,      lowercase=3_7					,      lowercase=0.1					,      lowercase=0.1					,      lowercase=2_0					,      lowercase=2					,      lowercase=1					,      lowercase=0					,      )			->						List[str]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[Any]			   = parent
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = batch_size
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = seq_length
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = is_training
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = use_labels
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = vocab_size
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = hidden_size
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = num_hidden_layers
								__SCREAMING_SNAKE_CASE					:							str			   = num_attention_heads
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = intermediate_size

								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = hidden_dropout_prob
								__SCREAMING_SNAKE_CASE					:							Dict			   = attention_probs_dropout_prob
								__SCREAMING_SNAKE_CASE					:							Any			   = max_position_embeddings
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = eos_token_id
								__SCREAMING_SNAKE_CASE					:							List[str]			   = pad_token_id
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = bos_token_id




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


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = ids_tensor([self.batch_size, self.seq_length - 1]					,      self.vocab_size       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )					,      1       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = tf.concat([input_ids, eos_tensor]					,      axis=1       )

								__SCREAMING_SNAKE_CASE					:							str			   = ids_tensor([self.batch_size, self.seq_length]					,      self.vocab_size       )

								__SCREAMING_SNAKE_CASE					:							Tuple			   = 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					,      )
								__SCREAMING_SNAKE_CASE					:							Any			   = prepare_marian_inputs_dict(lowercase					,      lowercase					,      lowercase       )
								return config, inputs_dict




			def 			_snake_case   (      self					,      lowercase					,      lowercase       )			->						Optional[int]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Tuple			   = TFMarianModel(config=lowercase       ).get_decoder()
								__SCREAMING_SNAKE_CASE					:							Dict			   = inputs_dict['''input_ids''']

								__SCREAMING_SNAKE_CASE					:							str			   = input_ids[:1, :]
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = inputs_dict['''attention_mask'''][:1, :]
								__SCREAMING_SNAKE_CASE					:							str			   = inputs_dict['''head_mask''']
								__SCREAMING_SNAKE_CASE					:							str			   = 1

								# first forward pass
								__SCREAMING_SNAKE_CASE					:							List[Any]			   = model(lowercase					,      attention_mask=lowercase					,      head_mask=lowercase					,      use_cache=lowercase       )

								__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							List[str]			   = outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = ids_tensor((self.batch_size, 3)					,      config.vocab_size       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = tf.cast(ids_tensor((self.batch_size, 3)					,      2       )					,      tf.inta       )

								# append to next input_ids and
								__SCREAMING_SNAKE_CASE					:							int			   = tf.concat([input_ids, next_tokens]					,      axis=-1       )
								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = tf.concat([attention_mask, next_attn_mask]					,      axis=-1       )

								__SCREAMING_SNAKE_CASE					:							Tuple			   = model(lowercase					,      attention_mask=lowercase       )[0]
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = model(lowercase					,      attention_mask=lowercase					,      past_key_values=lowercase       )[0]

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

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

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




def A_					(				__SCREAMING_SNAKE_CASE :					str	,   __SCREAMING_SNAKE_CASE :					List[Any]	,   __SCREAMING_SNAKE_CASE :					Optional[int]	,   __SCREAMING_SNAKE_CASE :					List[str]=None	,   __SCREAMING_SNAKE_CASE :					Dict=None	,   __SCREAMING_SNAKE_CASE :					List[str]=None	,   __SCREAMING_SNAKE_CASE :					List[Any]=None	,   __SCREAMING_SNAKE_CASE :					Optional[int]=None	,   )     ->  int:
					if attention_mask is None:
										__SCREAMING_SNAKE_CASE					:							Tuple			   = tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE	,   config.pad_token_id       )	,   tf.inta       )
					if decoder_attention_mask is None:
										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = 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:
										__SCREAMING_SNAKE_CASE					:							Tuple			   = tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
					if decoder_head_mask is None:
										__SCREAMING_SNAKE_CASE					:							Optional[int]			   = tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
					if cross_attn_head_mask is None:
										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = 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      SCREAMING_SNAKE_CASE_	( snake_case					,					snake_case					,					unittest.TestCase		):
			__a					:      Tuple					=  (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
			__a					:      Optional[int]					=  (TFMarianMTModel,) if is_tf_available() else ()
			__a					:      Union[str, Any]					=  (
			    {
			        '''conversational''': TFMarianMTModel,
			        '''feature-extraction''': TFMarianModel,
			        '''summarization''': TFMarianMTModel,
			        '''text2text-generation''': TFMarianMTModel,
			        '''translation''': TFMarianMTModel,
			    }
			    if is_tf_available()
			    else {}
			)
			__a					:      Union[str, Any]					=  True
			__a					:      Union[str, Any]					=  False
			__a					:      List[Any]					=  False




			def 			_snake_case   (      self       )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[str]			   = TFMarianModelTester(self       )
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = ConfigTester(self					,      config_class=lowercase       )




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


								'''simple docstring'''






								self.config_tester.run_common_tests()




			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowercase       )






@require_tf
class      SCREAMING_SNAKE_CASE_	( unittest.TestCase		):
			__a					:      List[Any]					=  10_00  # show more chars for failing integration tests




			@classmethod
			def 			_snake_case   (      cls       )			->						None:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Tuple			   = f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
								return cls




			@cached_property
			def 			_snake_case   (      self       )			->						MarianTokenizer:


								'''simple docstring'''






								return AutoTokenizer.from_pretrained(self.model_name       )




			@property
			def 			_snake_case   (      self       )			->						int:


								'''simple docstring'''






								return self.tokenizer.eos_token_id




			@cached_property
			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






								warnings.simplefilter('''error'''       )
								__SCREAMING_SNAKE_CASE					:							TFMarianMTModel			   = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
								assert isinstance(lowercase					,      lowercase       )
								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = model.config
								self.assertListEqual(c.bad_words_ids					,      [[c.pad_token_id]]       )
								self.assertEqual(c.max_length					,      5_1_2       )
								self.assertEqual(c.decoder_start_token_id					,      c.pad_token_id       )
								return model




			def 			_snake_case   (      self					,      **lowercase       )			->						Optional[Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							Optional[int]			   = self.translate_src_text(**lowercase       )
								self.assertListEqual(self.expected_text					,      lowercase       )




			def 			_snake_case   (      self					,      **lowercase       )			->						List[str]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							List[str]			   = self.tokenizer(self.src_text					,      **lowercase					,      padding=lowercase					,      return_tensors='''tf'''       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = self.model.generate(
								    model_inputs.input_ids					,      attention_mask=model_inputs.attention_mask					,      num_beams=2					,      max_length=1_2_8       )
								__SCREAMING_SNAKE_CASE					:							Tuple			   = self.tokenizer.batch_decode(generated_ids.numpy()					,      skip_special_tokens=lowercase       )
								return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      Optional[Any]					=  '''mt'''
			__a					:      List[Any]					=  '''en'''
			__a					:      Optional[int]					=  ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
			__a					:      int					=  ['''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       )			->						Union[str, Any]:


								'''simple docstring'''






								self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      Optional[Any]					=  '''en'''
			__a					:      Dict					=  '''zh'''
			__a					:      str					=  ['''My name is Wolfgang and I live in Berlin''']
			__a					:      Optional[Any]					=  ['''我叫沃尔夫冈 我住在柏林''']




			@unittest.skip('''Skipping until #12647 is resolved.'''       )
			@slow
			def 			_snake_case   (      self       )			->						Any:


								'''simple docstring'''






								self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class      SCREAMING_SNAKE_CASE_	( snake_case		):
			__a					:      Optional[int]					=  '''en'''
			__a					:      List[Any]					=  '''ROMANCE'''
			__a					:      Any					=  [
			    '''>>fr<< Don\'t spend so much time watching TV.''',
			    '''>>pt<< Your message has been sent.''',
			    '''>>es<< He\'s two years older than me.''',
			]
			__a					:      Optional[Any]					=  [
			    '''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[Any]:


								'''simple docstring'''






								self._assert_generated_batch_equal_expected()




			@unittest.skip('''Skipping until #12647 is resolved.'''       )
			@slow
			def 			_snake_case   (      self       )			->						Union[str, Any]:


								'''simple docstring'''






								__SCREAMING_SNAKE_CASE					:							str			   = TranslationPipeline(self.model					,      self.tokenizer					,      framework='''tf'''       )
								__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = pipeline(self.src_text       )
								self.assertEqual(self.expected_text					,      [x['''translation_text'''] for x in output]       )






'''simple docstring'''






import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
_A      =     {
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}


def A_					(				__SCREAMING_SNAKE_CASE :					str       )     ->  Dict:
					__SCREAMING_SNAKE_CASE					:							Tuple			   = search.split()
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = dict((g.split('''='''       ) for g in groups)       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = list(entries.keys()       )
					__SCREAMING_SNAKE_CASE					:							Dict			   = [[f"""--{k} {v}""" for v in vs.split(''':'''       )] for k, vs in entries.items()]
					__SCREAMING_SNAKE_CASE					:							int			   = [list(__SCREAMING_SNAKE_CASE       ) for x in itertools.product(*__SCREAMING_SNAKE_CASE       )]
					return matrix, entry_names


def A_					(				)     ->  int:
					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = sys.argv[0]

					__SCREAMING_SNAKE_CASE					:							Optional[Any]			   = argparse.ArgumentParser(
					    usage=(
					        '''\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore'''
					        ''' refer to `run_eval.py -h` for the complete list.'''
					    )       )
					parser.add_argument(
					    '''--search'''	,   type=__SCREAMING_SNAKE_CASE	,   required=__SCREAMING_SNAKE_CASE	,   help='''param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'''	,   )
					parser.add_argument(
					    '''--bs'''	,   type=__SCREAMING_SNAKE_CASE	,   default=8	,   required=__SCREAMING_SNAKE_CASE	,   help='''initial batch size (may get reduced if it\'s too big)'''       )
					parser.add_argument('''--task'''	,   type=__SCREAMING_SNAKE_CASE	,   help='''used for task_specific_params + metrics'''       )
					parser.add_argument(
					    '''--info'''	,   nargs='''?'''	,   type=__SCREAMING_SNAKE_CASE	,   const=datetime_now()	,   help=(
					        '''add custom notes to be printed before the results table. If no value is passed, the current datetime'''
					        ''' string will be used.'''
					    )	,   )
					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							str			   = parser.parse_known_args()
					# we share some of the args
					args_main.extend(['''--task''', args.task]       )
					__SCREAMING_SNAKE_CASE					:							List[Any]			   = [prog] + args_main

					# to support variations like translation_en_to_de"
					__SCREAMING_SNAKE_CASE					:							List[str]			   = '''translation''' if '''translation''' in args.task else '''summarization'''

					__SCREAMING_SNAKE_CASE   ,   __SCREAMING_SNAKE_CASE					:							Tuple			   = parse_search_arg(args.search       )
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = task_score_names[task]  # score cols first
					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = {col: len(str(__SCREAMING_SNAKE_CASE       )       ) for col in col_names}
					__SCREAMING_SNAKE_CASE					:							Dict			   = []
					for r in matrix:
										__SCREAMING_SNAKE_CASE					:							List[str]			   = dict((x.replace('''--'''	,   ''''''       ).split() for x in r)       )
										__SCREAMING_SNAKE_CASE					:							Tuple			   = ''' '''.join(__SCREAMING_SNAKE_CASE       ).split()
										args_exp.extend(['''--bs''', str(args.bs       )]       )  # in case we need to reduce its size due to CUDA OOM
										__SCREAMING_SNAKE_CASE					:							Dict			   = args_normal + args_exp

										# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

										__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = run_generate(verbose=__SCREAMING_SNAKE_CASE       )
										# make sure scores are first in the table
										__SCREAMING_SNAKE_CASE					:							Any			   = OrderedDict()
										for score in task_score_names[task]:
															__SCREAMING_SNAKE_CASE					:							int			   = scores[score]
										result.update(__SCREAMING_SNAKE_CASE       )
										results.append(__SCREAMING_SNAKE_CASE       )

										# find widest entries
										for k, v in result.items():
															__SCREAMING_SNAKE_CASE					:							List[Any]			   = len(str(__SCREAMING_SNAKE_CASE       )       )
															if l > col_widths[k]:
																				__SCREAMING_SNAKE_CASE					:							Any			   = l

					__SCREAMING_SNAKE_CASE					:							Optional[int]			   = sorted(__SCREAMING_SNAKE_CASE	,   key=operator.itemgetter(*task_score_names[task]       )	,   reverse=__SCREAMING_SNAKE_CASE       )
					print(''' | '''.join([f"""{col:{col_widths[col]}}""" for col in col_names]       )       )
					print(''' | '''.join([f"""{"-"*col_widths[col]}""" for col in col_names]       )       )
					for row in results_sorted:
										print(''' | '''.join([f"""{row[col]:{col_widths[col]}}""" for col in col_names]       )       )

					__SCREAMING_SNAKE_CASE					:							Any			   = results_sorted[0]
					for score in task_score_names[task]:
										del best[score]
					__SCREAMING_SNAKE_CASE					:							Union[str, Any]			   = [f"""--{k} {v}""" for k, v in best.items()]
					__SCREAMING_SNAKE_CASE					:							Tuple			   = ['''--bs''', str(args.bs       )]
					if args.info:
										print(f"""\nInfo: {args.info}"""       )
					print('''\nBest score args:'''       )
					print(''' '''.join(args_main + best_args + dyn_args       )       )

					return results_sorted


if __name__ == "__main__":
						# Usage:
						# [normal-run_eval_search.py cmd plus] \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						#
						# Example:
						# PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
						# $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
						# --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
						# --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
						run_search()


