






from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
					import tensorflow as tf

					from transformers.models.xglm.modeling_tf_xglm import (
					    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFXGLMForCausalLM,
					    TFXGLMModel,
					)


@require_tf
class       __SCREAMING_SNAKE_CASE   :
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   XGLMConfig
						_SCREAMING_SNAKE_CASE		:    str						=   {}
						_SCREAMING_SNAKE_CASE		:    List[Any]						=   '''gelu'''






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase=14       ,  _UpperCamelCase=7       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=True       ,  _UpperCamelCase=99       ,  _UpperCamelCase=32       ,  _UpperCamelCase=2       ,  _UpperCamelCase=4       ,  _UpperCamelCase=37       ,  _UpperCamelCase="gelu"       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=0.1       ,  _UpperCamelCase=5_12       ,  _UpperCamelCase=0.02       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  parent
												lowerCAmelCase__        =  batch_size
												lowerCAmelCase__        =  seq_length
												lowerCAmelCase__        =  is_training
												lowerCAmelCase__        =  use_input_mask
												lowerCAmelCase__        =  use_labels
												lowerCAmelCase__        =  vocab_size
												lowerCAmelCase__        =  d_model
												lowerCAmelCase__        =  num_hidden_layers
												lowerCAmelCase__        =  num_attention_heads
												lowerCAmelCase__        =  ffn_dim
												lowerCAmelCase__        =  activation_function
												lowerCAmelCase__        =  activation_dropout
												lowerCAmelCase__        =  attention_dropout
												lowerCAmelCase__        =  max_position_embeddings
												lowerCAmelCase__        =  initializer_range
												lowerCAmelCase__        =  None
												lowerCAmelCase__        =  0
												lowerCAmelCase__        =  2
												lowerCAmelCase__        =  1






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return XGLMConfig.from_pretrained('facebook/xglm-564M'				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  tf.clip_by_value(
												    ids_tensor([self.batch_size, self.seq_length]       ,  self.vocab_size				)       ,  clip_value_min=0       ,  clip_value_max=3				)

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

												lowerCAmelCase__        =  self.get_config()

												lowerCAmelCase__        =  floats_tensor([self.num_hidden_layers, self.num_attention_heads]       ,  2				)

												return (
												    config,
												    input_ids,
												    input_mask,
												    head_mask,
												)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												return 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=_UpperCamelCase       ,  bos_token_id=self.bos_token_id       ,  eos_token_id=self.eos_token_id       ,  pad_token_id=self.pad_token_id       ,  return_dict=_UpperCamelCase       ,  )







						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  self.prepare_config_and_inputs()

												(
												    (
												    lowerCAmelCase__
												)  ,    (
												    lowerCAmelCase__
												)  ,    (
												    lowerCAmelCase__
												)  ,    (
												    lowerCAmelCase__
												)  ,    
												)        =  config_and_inputs

												lowerCAmelCase__        =  {
												    'input_ids': input_ids,
												    'head_mask': head_mask,
												}

												return config, inputs_dict




@require_tf
class       __SCREAMING_SNAKE_CASE   (				__lowercase						,   __lowercase						,   unittest.TestCase):
						_SCREAMING_SNAKE_CASE		:    int						=   (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE		:    Any						=   (TFXGLMForCausalLM,) if is_tf_available() else ()
						_SCREAMING_SNAKE_CASE		:    Union[str, Any]						=   (
						    {'''feature-extraction''': TFXGLMModel, '''text-generation''': TFXGLMForCausalLM} if is_tf_available() else {}
						)
						_SCREAMING_SNAKE_CASE		:    Any						=   False
						_SCREAMING_SNAKE_CASE		:    int						=   False
						_SCREAMING_SNAKE_CASE		:    int						=   False






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFXGLMModelTester(self				)
												lowerCAmelCase__        =  ConfigTester(self       ,  config_class=_UpperCamelCase       ,  n_embd=37				)






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												self.config_tester.run_common_tests()






						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		lowerCAmelCase__        =  TFXGLMModel.from_pretrained(_UpperCamelCase				)
																		self.assertIsNotNone(_UpperCamelCase				)







						@unittest.skip(reason='Currently, model embeddings are going to undergo a major refactor.'				)
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().test_resize_token_embeddings()




@require_tf
class       __SCREAMING_SNAKE_CASE   (				unittest.TestCase):






						@slow
						def  UpperCamelCase__ (		self       ,  _UpperCamelCase=True				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
												lowerCAmelCase__        =  tf.convert_to_tensor([[2, 2_68, 98_65]]       ,  dtype=tf.intaa				)  # The dog
												# </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
												# fmt: off
												lowerCAmelCase__        =  [2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
												# fmt: on
												lowerCAmelCase__        =  model.generate(_UpperCamelCase       ,  do_sample=_UpperCamelCase       ,  num_beams=1				)
												if verify_outputs:
																		self.assertListEqual(output_ids[0].numpy().tolist()       ,  _UpperCamelCase				)






						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'				)
												lowerCAmelCase__        =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)

												tf.random.set_seed(0				)
												lowerCAmelCase__        =  tokenizer('Today is a nice day and'       ,  return_tensors='tf'				)
												lowerCAmelCase__        =  tokenized.input_ids
												# forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
												with tf.device(':/CPU:0'				):
																		lowerCAmelCase__        =  model.generate(_UpperCamelCase       ,  do_sample=_UpperCamelCase       ,  seed=[7, 0]				)
												lowerCAmelCase__        =  tokenizer.decode(output_ids[0]       ,  skip_special_tokens=_UpperCamelCase				)

												lowerCAmelCase__        =  (
												    'Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'
												)
												self.assertEqual(_UpperCamelCase       ,  _UpperCamelCase				)







						@slow
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  TFXGLMForCausalLM.from_pretrained('facebook/xglm-564M'				)
												lowerCAmelCase__        =  XGLMTokenizer.from_pretrained('facebook/xglm-564M'				)

												lowerCAmelCase__        =  'left'

												# use different length sentences to test batching
												lowerCAmelCase__        =  [
												    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
												    'left-padding, such as in batched generation. The output for the sequence below should be the same '
												    'regardless of whether left padding is applied or not. When',
												    'Hello, my dog is a little',
												]

												lowerCAmelCase__        =  tokenizer(_UpperCamelCase       ,  return_tensors='tf'       ,  padding=_UpperCamelCase				)
												lowerCAmelCase__        =  inputs['input_ids']

												lowerCAmelCase__        =  model.generate(input_ids=_UpperCamelCase       ,  attention_mask=inputs['attention_mask']       ,  max_new_tokens=12				)

												lowerCAmelCase__        =  tokenizer(sentences[0]       ,  return_tensors='tf'				).input_ids
												lowerCAmelCase__        =  model.generate(input_ids=_UpperCamelCase       ,  max_new_tokens=12				)

												lowerCAmelCase__        =  tokenizer(sentences[1]       ,  return_tensors='tf'				).input_ids
												lowerCAmelCase__        =  model.generate(input_ids=_UpperCamelCase       ,  max_new_tokens=12				)

												lowerCAmelCase__        =  tokenizer.batch_decode(_UpperCamelCase       ,  skip_special_tokens=_UpperCamelCase				)
												lowerCAmelCase__        =  tokenizer.decode(output_non_padded[0]       ,  skip_special_tokens=_UpperCamelCase				)
												lowerCAmelCase__        =  tokenizer.decode(output_padded[0]       ,  skip_special_tokens=_UpperCamelCase				)

												lowerCAmelCase__        =  [
												    'This is an extremelly long sentence that only exists to test the ability of the model to cope with '
												    'left-padding, such as in batched generation. The output for the sequence below should be the same '
												    'regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '
												    'a single',
												    'Hello, my dog is a little bit of a shy one, but he is very friendly',
												]
												self.assertListEqual(_UpperCamelCase       ,  _UpperCamelCase				)
												self.assertListEqual(_UpperCamelCase       ,  [non_padded_sentence, padded_sentence]				)













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


__snake_case      :      Tuple             =       """sshleifer/mar_enro_6_3_student"""


class       __SCREAMING_SNAKE_CASE   (				__lowercase):






						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												super().setUp()

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






						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												MarianMTModel.from_pretrained(_UpperCamelCase				)







						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  {
												    '$MAX_LEN': 64,
												    '$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
												lowerCAmelCase__        =  (self.test_file_dir / 'train_mbart_cc25_enro.sh').open().read().split('finetune.py'				)[1].strip()
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()

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

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

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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}"]       ,  _UpperCamelCase				)

												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
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) 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   (				__lowercase):






						@timeout_decorator.timeout(6_00				)
						@slow
						@require_torch_gpu
						def  UpperCamelCase__ (		self				):






												"""simple docstring"""
												lowerCAmelCase__        =  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
												lowerCAmelCase__        =  {
												    '--fp16_opt_level=O1': '',
												    '$MAX_LEN': 1_28,
												    '$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
												lowerCAmelCase__        =  (
												    (self.test_file_dir / 'distil_marian_no_teacher.sh').open().read().split('distillation.py'				)[1].strip()
												)
												lowerCAmelCase__        =  bash_script.replace('\\\n'       ,  ''				).strip().replace('"$@"'       ,  ''				)
												lowerCAmelCase__        =  bash_script.replace('--fp16 '       ,  ' '				)

												for k, v in env_vars_to_replace.items():
																		lowerCAmelCase__        =  bash_script.replace(_UpperCamelCase       ,  str(_UpperCamelCase				)				)
												lowerCAmelCase__        =  self.get_auto_remove_tmp_dir()
												lowerCAmelCase__        =  bash_script.replace('--fp16'       ,  ''				)
												lowerCAmelCase__        =  6
												lowerCAmelCase__        =  (
												    ['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(_UpperCamelCase       ,  'argv'       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  argparse.ArgumentParser()
																		lowerCAmelCase__        =  pl.Trainer.add_argparse_args(_UpperCamelCase				)
																		lowerCAmelCase__        =  SummarizationDistiller.add_model_specific_args(_UpperCamelCase       ,  os.getcwd()				)
																		lowerCAmelCase__        =  parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		lowerCAmelCase__        =  distill_main(_UpperCamelCase				)

												# Check metrics
												lowerCAmelCase__        =  load_json(model.metrics_save_path				)
												lowerCAmelCase__        =  metrics['val'][0]
												lowerCAmelCase__        =  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}"]       ,  _UpperCamelCase				)

												# check lightning ckpt can be loaded and has a reasonable statedict
												lowerCAmelCase__        =  os.listdir(_UpperCamelCase				)
												lowerCAmelCase__        =  [x for x in contents if x.endswith('.ckpt'				)][0]
												lowerCAmelCase__        =  os.path.join(args.output_dir       ,  _UpperCamelCase				)
												lowerCAmelCase__        =  torch.load(_UpperCamelCase       ,  map_location='cpu'				)
												lowerCAmelCase__        =  '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:
																		lowerCAmelCase__        =  {os.path.basename(_UpperCamelCase				) 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





