





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, 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, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class     __SCREAMING_SNAKE_CASE   :
							snake_case       : Tuple         =    MBartConfig
							snake_case       : Any         =    {}
							snake_case       : List[str]         =    """gelu"""






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase=13					,	__lowerCAmelCase=7					,	__lowerCAmelCase=True					,	__lowerCAmelCase=False					,	__lowerCAmelCase=99					,	__lowerCAmelCase=32					,	__lowerCAmelCase=2					,	__lowerCAmelCase=4					,	__lowerCAmelCase=37					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=0.1					,	__lowerCAmelCase=20					,	__lowerCAmelCase=2					,	__lowerCAmelCase=1					,	__lowerCAmelCase=0					,	):
														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 			_lowerCamelCase						(  self					):
														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_mbart_inputs_dict(__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)
														return config, inputs_dict







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     TFMBartModel(config=__lowerCAmelCase					).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(__lowerCAmelCase					,	attention_mask=__lowerCAmelCase					,	head_mask=__lowerCAmelCase					,	use_cache=__lowerCAmelCase					)

														UpperCamelCase__			       =     outputs.to_tuple()
														UpperCamelCase__			       =     past_key_values[1]






def       _UpperCamelCase       (a__				:List[str]				,     a__				:Tuple				,     a__				:Dict				,     a__				:Optional[int]=None				,     a__				:int=None				,     a__				:Dict=None				,     a__				:int=None				,     a__				:Optional[Any]=None				,     ):






							"""simple docstring"""





							if attention_mask is None:
														UpperCamelCase__			       =     tf.cast(tf.math.not_equal(_UpperCAmelCase				,     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     __SCREAMING_SNAKE_CASE   (       SCREAMING_SNAKE_CASE__				,						SCREAMING_SNAKE_CASE__				,						unittest.TestCase    ):
							snake_case       : List[str]         =    (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							snake_case       : Optional[int]         =    (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							snake_case       : Optional[Any]         =    (
							    {
							        """conversational""": TFMBartForConditionalGeneration,
							        """feature-extraction""": TFMBartModel,
							        """summarization""": TFMBartForConditionalGeneration,
							        """text2text-generation""": TFMBartForConditionalGeneration,
							        """translation""": TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							snake_case       : str         =    True
							snake_case       : Optional[int]         =    False
							snake_case       : int         =    False






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																					# Exception encountered when calling layer '...'
																					return True

														return False






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     TFMBartModelTester(self					)
														UpperCamelCase__			       =     ConfigTester(self					,	config_class=__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					):
														self.config_tester.run_common_tests()







							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase					)






@require_sentencepiece
@require_tokenizers
@require_tf
class     __SCREAMING_SNAKE_CASE   (       unittest.TestCase    ):
							snake_case       : Union[str, Any]         =    [
							    """ UN Chief Says There Is No Military Solution in Syria""",
							]
							snake_case       : Optional[int]         =    [
							    """Şeful ONU declară că nu există o soluţie militară în Siria""",
							]
							snake_case       : Optional[Any]         =    """facebook/mbart-large-en-ro"""






							@cached_property
							def 			_lowerCamelCase						(  self					):
														return AutoTokenizer.from_pretrained(self.model_name					)






							@cached_property
							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
														return model






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														UpperCamelCase__			       =     self.translate_src_text(**__lowerCAmelCase					)
														self.assertListEqual(self.expected_text					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	**__lowerCAmelCase					):
														UpperCamelCase__			       =     self.tokenizer(self.src_text					,	**__lowerCAmelCase					,	return_tensors="""tf"""					)
														UpperCamelCase__			       =     self.model.generate(
														    model_inputs.input_ids					,	attention_mask=model_inputs.attention_mask					,	num_beams=2					)
														UpperCamelCase__			       =     self.tokenizer.batch_decode(__lowerCAmelCase					,	skip_special_tokens=__lowerCAmelCase					)
														return generated_words







							@slow
							def 			_lowerCamelCase						(  self					):
														self._assert_generated_batch_equal_expected()











import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


UpperCamelCase__     =	"sshleifer/mar_enro_6_3_student"

class     __SCREAMING_SNAKE_CASE   (       _a    ):






							def 			_lowerCamelCase						(  self					):
														super().setUp()

														UpperCamelCase__			       =     cached_path(
														    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""					,	extract_compressed_file=__lowerCAmelCase					,	)
														UpperCamelCase__			       =     f"""{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"""






							@slow
							@require_torch_gpu
							def 			_lowerCamelCase						(  self					):
														MarianMTModel.from_pretrained(__lowerCAmelCase					)







							@slow
							@require_torch_gpu
							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     {
														    """$MAX_LEN""": 64,
														    """$BS""": 64,
														    """$GAS""": 1,
														    """$ENRO_DIR""": self.data_dir,
														    """facebook/mbart-large-cc25""": MARIAN_MODEL,
														    # "val_check_interval=0.25": "val_check_interval=1.0",
														    """--learning_rate=3e-5""": """--learning_rate 3e-4""",
														    """--num_train_epochs 6""": """--num_train_epochs 1""",
														}

														# Clean up bash script
														UpperCamelCase__			       =     (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""					)[1].strip()
														UpperCamelCase__			       =     bash_script.replace("""\\\n"""					,	""""""					).strip().replace("""\"$@\""""					,	""""""					)
														for k, v in env_vars_to_replace.items():
																					UpperCamelCase__			       =     bash_script.replace(__lowerCAmelCase					,	str(__lowerCAmelCase					)					)
														UpperCamelCase__			       =     self.get_auto_remove_tmp_dir()

														# bash_script = bash_script.replace("--fp16 ", "")
														UpperCamelCase__			       =     f"""
            --output_dir {output_dir}
            --tokenizer_name Helsinki-NLP/opus-mt-en-ro
            --sortish_sampler
            --do_predict
            --gpus 1
            --freeze_encoder
            --n_train 40000
            --n_val 500
            --n_test 500
            --fp16_opt_level O1
            --num_sanity_val_steps 0
            --eval_beams 2
        """.split()
														# XXX: args.gpus > 1 : handle multi_gpu in the future

														UpperCamelCase__			       =     ["""finetune.py"""] + bash_script.split() + args
														with patch.object(__lowerCAmelCase					,	"""argv"""					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     argparse.ArgumentParser()
																					UpperCamelCase__			       =     pl.Trainer.add_argparse_args(__lowerCAmelCase					)
																					UpperCamelCase__			       =     SummarizationModule.add_model_specific_args(__lowerCAmelCase					,	os.getcwd()					)
																					UpperCamelCase__			       =     parser.parse_args()
																					UpperCamelCase__			       =     main(__lowerCAmelCase					)

														# Check metrics
														UpperCamelCase__			       =     load_json(model.metrics_save_path					)
														UpperCamelCase__			       =     metrics["""val"""][0]
														UpperCamelCase__			       =     metrics["""val"""][-1]
														self.assertEqual(len(metrics["""val"""]					)					,	(args.max_epochs / args.val_check_interval)					)
														assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]					,	__lowerCAmelCase					)

														self.assertGreater(last_step_stats["""val_avg_gen_time"""]					,	0.01					)
														# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
														self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]					,	1.0					)

														# test learning requirements:

														# 1. BLEU improves over the course of training by more than 2 pts
														self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]					,	2					)

														# 2. BLEU finishes above 17
														self.assertGreater(last_step_stats["""val_avg_bleu"""]					,	17					)

														# 3. test BLEU and val BLEU within ~1.1 pt.
														self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]					)					,	1.1					)

														# check lightning ckpt can be loaded and has a reasonable statedict
														UpperCamelCase__			       =     os.listdir(__lowerCAmelCase					)
														UpperCamelCase__			       =     [x for x in contents if x.endswith(""".ckpt"""					)][0]
														UpperCamelCase__			       =     os.path.join(args.output_dir					,	__lowerCAmelCase					)
														UpperCamelCase__			       =     torch.load(__lowerCAmelCase					,	map_location="""cpu"""					)
														UpperCamelCase__			       =     """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
														assert expected_key in ckpt["state_dict"]
														assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

														# TODO: turn on args.do_predict when PL bug fixed.
														if args.do_predict:
																					UpperCamelCase__			       =     {os.path.basename(__lowerCAmelCase					) for p in contents}
																					assert "test_generations.txt" in contents
																					assert "test_results.txt" in contents
																					# assert len(metrics["val"]) ==  desired_n_evals
																					assert len(metrics["""test"""]					) == 1






class     __SCREAMING_SNAKE_CASE   (       _a    ):






							@timeout_decorator.timeout(600					)
							@slow
							@require_torch_gpu
							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     f"""{self.test_file_dir_str}/test_data/wmt_en_ro"""
														UpperCamelCase__			       =     {
														    """--fp16_opt_level=O1""": """""",
														    """$MAX_LEN""": 128,
														    """$BS""": 16,
														    """$GAS""": 1,
														    """$ENRO_DIR""": data_dir,
														    """$m""": """sshleifer/student_marian_en_ro_6_1""",
														    """val_check_interval=0.25""": """val_check_interval=1.0""",
														}

														# Clean up bash script
														UpperCamelCase__			       =     (
														    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""					)[1].strip()
														)
														UpperCamelCase__			       =     bash_script.replace("""\\\n"""					,	""""""					).strip().replace("""\"$@\""""					,	""""""					)
														UpperCamelCase__			       =     bash_script.replace("""--fp16 """					,	""" """					)

														for k, v in env_vars_to_replace.items():
																					UpperCamelCase__			       =     bash_script.replace(__lowerCAmelCase					,	str(__lowerCAmelCase					)					)
														UpperCamelCase__			       =     self.get_auto_remove_tmp_dir()
														UpperCamelCase__			       =     bash_script.replace("""--fp16"""					,	""""""					)
														UpperCamelCase__			       =     6
														UpperCamelCase__			       =     (
														    ["""distillation.py"""]
														    + bash_script.split()
														    + [
														        f"""--output_dir={output_dir}""",
														        """--gpus=1""",
														        """--learning_rate=1e-3""",
														        f"""--num_train_epochs={epochs}""",
														        """--warmup_steps=10""",
														        """--val_check_interval=1.0""",
														        """--do_predict""",
														    ]
														)
														with patch.object(__lowerCAmelCase					,	"""argv"""					,	__lowerCAmelCase					):
																					UpperCamelCase__			       =     argparse.ArgumentParser()
																					UpperCamelCase__			       =     pl.Trainer.add_argparse_args(__lowerCAmelCase					)
																					UpperCamelCase__			       =     SummarizationDistiller.add_model_specific_args(__lowerCAmelCase					,	os.getcwd()					)
																					UpperCamelCase__			       =     parser.parse_args()
																					# assert args.gpus == gpus THIS BREAKS for multi_gpu

																					UpperCamelCase__			       =     distill_main(__lowerCAmelCase					)

														# Check metrics
														UpperCamelCase__			       =     load_json(model.metrics_save_path					)
														UpperCamelCase__			       =     metrics["""val"""][0]
														UpperCamelCase__			       =     metrics["""val"""][-1]
														assert len(metrics["""val"""]					) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

														assert last_step_stats["val_avg_gen_time"] >= 0.01

														assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
														assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
														assert isinstance(last_step_stats[f"""val_avg_{model.val_metric}"""]					,	__lowerCAmelCase					)

														# check lightning ckpt can be loaded and has a reasonable statedict
														UpperCamelCase__			       =     os.listdir(__lowerCAmelCase					)
														UpperCamelCase__			       =     [x for x in contents if x.endswith(""".ckpt"""					)][0]
														UpperCamelCase__			       =     os.path.join(args.output_dir					,	__lowerCAmelCase					)
														UpperCamelCase__			       =     torch.load(__lowerCAmelCase					,	map_location="""cpu"""					)
														UpperCamelCase__			       =     """model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
														assert expected_key in ckpt["state_dict"]
														assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

														# TODO: turn on args.do_predict when PL bug fixed.
														if args.do_predict:
																					UpperCamelCase__			       =     {os.path.basename(__lowerCAmelCase					) for p in contents}
																					assert "test_generations.txt" in contents
																					assert "test_results.txt" in contents
																					# assert len(metrics["val"]) ==  desired_n_evals
																					assert len(metrics["""test"""]					) == 1




