


from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerModel,
  )
  from transformers.models.roformer.modeling_tf_roformer import (
      TFRoFormerSelfAttention,
      TFRoFormerSinusoidalPositionalEmbedding,
  )






class        __snake_case	:







    """simple docstring"""



    def __init__(       self   :  str   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Optional[int]=13   ,						lowerCamelCase   :  Optional[int]=7   ,						lowerCamelCase   :  Optional[Any]=True   ,						lowerCamelCase   :  List[Any]=True   ,						lowerCamelCase   :  Union[str, Any]=True   ,						lowerCamelCase   :  Any=True   ,						lowerCamelCase   :  int=99   ,						lowerCamelCase   :  List[str]=32   ,						lowerCamelCase   :  Optional[int]=2   ,						lowerCamelCase   :  int=4   ,						lowerCamelCase   :  str=37   ,						lowerCamelCase   :  Any="gelu"   ,						lowerCamelCase   :  List[str]=0.1   ,						lowerCamelCase   :  Dict=0.1   ,						lowerCamelCase   :  List[Any]=5_12   ,						lowerCamelCase   :  Any=16   ,						lowerCamelCase   :  Optional[Any]=2   ,						lowerCamelCase   :  Optional[int]=0.02   ,						lowerCamelCase   :  str=3   ,						lowerCamelCase   :  Tuple=4   ,						lowerCamelCase   :  List[Any]=None   ,						)       ->  List[Any]:
      lowerCAmelCase_      :				Optional[int]      =       parent
      lowerCAmelCase_      :				int      =       13
      lowerCAmelCase_      :				Optional[int]      =       7
      lowerCAmelCase_      :				Dict      =       True
      lowerCAmelCase_      :				int      =       True
      lowerCAmelCase_      :				Union[str, Any]      =       True
      lowerCAmelCase_      :				List[Any]      =       True
      lowerCAmelCase_      :				Any      =       99
      lowerCAmelCase_      :				int      =       32
      lowerCAmelCase_      :				int      =       2
      lowerCAmelCase_      :				Tuple      =       4
      lowerCAmelCase_      :				str      =       37
      lowerCAmelCase_      :				Optional[int]      =       """gelu"""
      lowerCAmelCase_      :				Union[str, Any]      =       0.1
      lowerCAmelCase_      :				Optional[Any]      =       0.1
      lowerCAmelCase_      :				Union[str, Any]      =       5_12
      lowerCAmelCase_      :				Tuple      =       16
      lowerCAmelCase_      :				Tuple      =       2
      lowerCAmelCase_      :				Optional[Any]      =       0.02
      lowerCAmelCase_      :				Optional[Any]      =       3
      lowerCAmelCase_      :				List[Any]      =       4
      lowerCAmelCase_      :				Optional[int]      =       None

    def __lowercase       (       self   :  Optional[int]		)       ->  Optional[int]:
      lowerCAmelCase_      :				List[Any]      =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

      lowerCAmelCase_      :				Union[str, Any]      =       None
      if self.use_input_mask:
        lowerCAmelCase_      :				Any      =       random_attention_mask([self.batch_size, self.seq_length]		)

      lowerCAmelCase_      :				List[Any]      =       None
      if self.use_token_type_ids:
        lowerCAmelCase_      :				List[str]      =       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size		)

      lowerCAmelCase_      :				List[Any]      =       None
      lowerCAmelCase_      :				Optional[int]      =       None
      lowerCAmelCase_      :				int      =       None
      if self.use_labels:
        lowerCAmelCase_      :				Dict      =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
        lowerCAmelCase_      :				List[str]      =       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels		)
        lowerCAmelCase_      :				Optional[int]      =       ids_tensor([self.batch_size]   ,						self.num_choices		)

      lowerCAmelCase_      :				Tuple      =       RoFormerConfig(
          vocab_size=self.vocab_size   ,						hidden_size=self.hidden_size   ,						num_hidden_layers=self.num_hidden_layers   ,						num_attention_heads=self.num_attention_heads   ,						intermediate_size=self.intermediate_size   ,						hidden_act=self.hidden_act   ,						hidden_dropout_prob=self.hidden_dropout_prob   ,						attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,						max_position_embeddings=self.max_position_embeddings   ,						type_vocab_size=self.type_vocab_size   ,						initializer_range=self.initializer_range   ,						return_dict=lowerCamelCase   ,						)

      return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Any		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Optional[Any]      =       TFRoFormerModel(config=lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

      lowerCAmelCase_      :				int      =       [input_ids, input_mask]
      lowerCAmelCase_      :				List[str]      =       model(lowerCamelCase		)

      lowerCAmelCase_      :				int      =       model(lowerCamelCase		)

      self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Tuple      =       True
      lowerCAmelCase_      :				Union[str, Any]      =       TFRoFormerForCausalLM(config=lowerCamelCase		)
      lowerCAmelCase_      :				str      =       {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      lowerCAmelCase_      :				List[Any]      =       model(lowerCamelCase		)["""logits"""]
      self.parent.assertListEqual(
          list(prediction_scores.numpy().shape		)   ,						[self.batch_size, self.seq_length, self.vocab_size]		)

    def __lowercase       (       self   :  Dict   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Tuple   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Dict		)       ->  Optional[int]:
      lowerCAmelCase_      :				Optional[int]      =       TFRoFormerForMaskedLM(config=lowerCamelCase		)
      lowerCAmelCase_      :				List[str]      =       {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      lowerCAmelCase_      :				Union[str, Any]      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)		)

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  Union[str, Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				List[Any]      =       self.num_labels
      lowerCAmelCase_      :				Any      =       TFRoFormerForSequenceClassification(config=lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      lowerCAmelCase_      :				Dict      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)		)

    def __lowercase       (       self   :  int   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  List[Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Dict      =       self.num_choices
      lowerCAmelCase_      :				int      =       TFRoFormerForMultipleChoice(config=lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       tf.tile(tf.expand_dims(lowerCamelCase   ,						1		)   ,						(1, self.num_choices, 1)		)
      lowerCAmelCase_      :				str      =       tf.tile(tf.expand_dims(lowerCamelCase   ,						1		)   ,						(1, self.num_choices, 1)		)
      lowerCAmelCase_      :				Dict      =       tf.tile(tf.expand_dims(lowerCamelCase   ,						1		)   ,						(1, self.num_choices, 1)		)
      lowerCAmelCase_      :				int      =       {
          """input_ids""": multiple_choice_inputs_ids,
          """attention_mask""": multiple_choice_input_mask,
          """token_type_ids""": multiple_choice_token_type_ids,
      }
      lowerCAmelCase_      :				Union[str, Any]      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_choices)		)

    def __lowercase       (       self   :  Any   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  Optional[Any]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Dict   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  int		)       ->  Optional[int]:
      lowerCAmelCase_      :				Any      =       self.num_labels
      lowerCAmelCase_      :				Union[str, Any]      =       TFRoFormerForTokenClassification(config=lowerCamelCase		)
      lowerCAmelCase_      :				Optional[Any]      =       {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }
      lowerCAmelCase_      :				int      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.num_labels)		)

    def __lowercase       (       self   :  Tuple   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Optional[int]   ,						lowerCamelCase   :  List[Any]   ,						lowerCamelCase   :  Any   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  Dict		)       ->  Dict:
      lowerCAmelCase_      :				List[Any]      =       TFRoFormerForQuestionAnswering(config=lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       {
          """input_ids""": input_ids,
          """attention_mask""": input_mask,
          """token_type_ids""": token_type_ids,
      }

      lowerCAmelCase_      :				Tuple      =       model(lowerCamelCase		)
      self.parent.assertEqual(result.start_logits.shape   ,						(self.batch_size, self.seq_length)		)
      self.parent.assertEqual(result.end_logits.shape   ,						(self.batch_size, self.seq_length)		)





    def __lowercase       (       self   :  Union[str, Any]		)       ->  Any:
      lowerCAmelCase_      :				int      =       self.prepare_config_and_inputs()
      (
          (
          lowerCAmelCase_   
      ),							(
          lowerCAmelCase_   
      ),							(
          lowerCAmelCase_   
      ),							(
          lowerCAmelCase_   
      ),							(
          lowerCAmelCase_   
      ),							(
          lowerCAmelCase_   
      ),							(
          lowerCAmelCase_   
      ),							   
      )      :				Any      =       config_and_inputs
      lowerCAmelCase_      :				List[Any]      =       {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
      return config, inputs_dict







@require_tf
class        __snake_case	(       snake_case_     ,snake_case_     ,unittest.TestCase):







    """simple docstring"""



    lowercase       			= (
        (
            TFRoFormerModel,
            TFRoFormerForCausalLM,
            TFRoFormerForMaskedLM,
            TFRoFormerForQuestionAnswering,
            TFRoFormerForSequenceClassification,
            TFRoFormerForTokenClassification,
            TFRoFormerForMultipleChoice,
        )
        if is_tf_available()
        else ()
    )
    lowercase       			= (
        {
            'feature-extraction': TFRoFormerModel,
            'fill-mask': TFRoFormerForMaskedLM,
            'question-answering': TFRoFormerForQuestionAnswering,
            'text-classification': TFRoFormerForSequenceClassification,
            'text-generation': TFRoFormerForCausalLM,
            'token-classification': TFRoFormerForTokenClassification,
            'zero-shot': TFRoFormerForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )

    lowercase       			= False
    lowercase       			= False

    def __lowercase       (       self   :  Union[str, Any]   ,						lowerCamelCase   :  str   ,						lowerCamelCase   :  List[str]   ,						lowerCamelCase   :  int   ,						lowerCamelCase   :  Union[str, Any]   ,						lowerCamelCase   :  Dict		)       ->  List[str]:
      if pipeline_test_casse_name == "TextGenerationPipelineTests":
        return True

      return False

    def __lowercase       (       self   :  int		)       ->  int:
      lowerCAmelCase_      :				Union[str, Any]      =       TFRoFormerModelTester(self		)
      lowerCAmelCase_      :				List[str]      =       ConfigTester(self   ,						config_class=lowerCamelCase   ,						hidden_size=37		)

    def __lowercase       (       self   :  int		)       ->  Optional[int]:
      self.config_tester.run_common_tests()

    def __lowercase       (       self   :  List[str]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowerCamelCase		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Any:
      lowerCAmelCase_      :				Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_lm(*lowerCamelCase		)

    def __lowercase       (       self   :  Union[str, Any]		)       ->  int:
      lowerCAmelCase_      :				Union[str, Any]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_lm_head(*lowerCamelCase		)

    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				Any      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_multiple_choice(*lowerCamelCase		)

    def __lowercase       (       self   :  str		)       ->  Tuple:
      lowerCAmelCase_      :				List[str]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_question_answering(*lowerCamelCase		)

    def __lowercase       (       self   :  Optional[int]		)       ->  Dict:
      lowerCAmelCase_      :				Optional[Any]      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase		)

    def __lowercase       (       self   :  Dict		)       ->  Optional[Any]:
      lowerCAmelCase_      :				int      =       self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_token_classification(*lowerCamelCase		)





    @slow
    def __lowercase       (       self   :  Dict		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				Union[str, Any]      =       TFRoFormerModel.from_pretrained("""junnyu/roformer_chinese_base"""		)
      self.assertIsNotNone(lowerCamelCase		)







@require_tf
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    @slow
    def __lowercase       (       self   :  Optional[int]		)       ->  Any:
      lowerCAmelCase_      :				str      =       TFRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""		)
      lowerCAmelCase_      :				List[str]      =       tf.constant([[0, 1, 2, 3, 4, 5]]		)
      lowerCAmelCase_      :				str      =       model(lowerCamelCase		)[0]

      # TODO Replace vocab size
      lowerCAmelCase_      :				Union[str, Any]      =       5_00_00

      lowerCAmelCase_      :				Optional[Any]      =       [1, 6, vocab_size]
      self.assertEqual(output.shape   ,						lowerCamelCase		)

      print(output[:, :3, :3]		)

      # TODO Replace values below with what was printed above.
      lowerCAmelCase_      :				Dict      =       tf.constant(
          [
              [
                  [-0.12_053_341, -1.0_264_901, 0.29_221_946],
                  [-1.5_133_783, 0.197_433, 0.15_190_607],
                  [-5.0_135_403, -3.900_256, -0.84_038_764],
              ]
          ]		)
      tf.debugging.assert_near(output[:, :3, :3]   ,						lowerCamelCase   ,						atol=1E-4		)







@require_tf
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    lowercase       			= 1E-4

    def __lowercase       (       self   :  Union[str, Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Union[str, Any]      =       tf.constant([[4, 10]]		)
      lowerCAmelCase_      :				List[str]      =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=6   ,						embedding_dim=6		)

      lowerCAmelCase_      :				str      =       emba(input_ids.shape		)
      lowerCAmelCase_      :				Optional[Any]      =       tf.constant(
          [[0.0_000, 0.0_000, 0.0_000, 1.0_000, 1.0_000, 1.0_000], [0.8_415, 0.0_464, 0.0_022, 0.5_403, 0.9_989, 1.0_000]]		)

      tf.debugging.assert_near(lowerCamelCase   ,						lowerCamelCase   ,						atol=self.tolerance		)





    def __lowercase       (       self   :  Union[str, Any]		)       ->  List[str]:
      lowerCAmelCase_      :				Tuple      =       tf.constant(
          [
              [0.0_000, 0.0_000, 0.0_000, 0.0_000, 0.0_000],
              [0.8_415, 0.8_219, 0.8_020, 0.7_819, 0.7_617],
              [0.9_093, 0.9_364, 0.9_581, 0.9_749, 0.9_870],
          ]		)
      lowerCAmelCase_      :				Dict      =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=5_12   ,						embedding_dim=5_12		)
      emba([2, 16, 5_12]		)
      lowerCAmelCase_      :				int      =       emba.weight[:3, :5]

      tf.debugging.assert_near(lowerCamelCase   ,						lowerCamelCase   ,						atol=self.tolerance		)







@require_tf
class        __snake_case	(       unittest.TestCase):







    """simple docstring"""



    lowercase       			= 1E-4

    def __lowercase       (       self   :  Union[str, Any]		)       ->  Optional[Any]:
      # 2,12,16,64
      lowerCAmelCase_      :				Any      =       tf.reshape(tf.range(2 * 12 * 16 * 64   ,						dtype=tf.floataa		)   ,						shape=(2, 12, 16, 64)		) / 1_00

      lowerCAmelCase_      :				Union[str, Any]      =       -tf.reshape(tf.range(2 * 12 * 16 * 64   ,						dtype=tf.floataa		)   ,						shape=(2, 12, 16, 64)		) / 1_00

      lowerCAmelCase_      :				Optional[int]      =       TFRoFormerSinusoidalPositionalEmbedding(num_positions=32   ,						embedding_dim=64		)
      lowerCAmelCase_      :				Any      =       embed_positions([2, 16, 7_68]		)[None, None, :, :]

      lowerCAmelCase_,							lowerCAmelCase_      :				List[Any]      =       TFRoFormerSelfAttention.apply_rotary_position_embeddings(
          lowerCamelCase   ,						lowerCamelCase   ,						lowerCamelCase		)

      lowerCAmelCase_      :				Any      =       tf.constant(
          [
              [0.0_000, 0.0_100, 0.0_200, 0.0_300, 0.0_400, 0.0_500, 0.0_600, 0.0_700],
              [-0.2_012, 0.8_897, 0.0_263, 0.9_401, 0.2_074, 0.9_463, 0.3_481, 0.9_343],
              [-1.7_057, 0.6_271, -1.2_145, 1.3_897, -0.6_303, 1.7_647, -0.1_173, 1.8_985],
              [-2.1_731, -1.6_397, -2.7_358, 0.2_854, -2.1_840, 1.7_183, -1.3_018, 2.4_871],
              [0.2_717, -3.6_173, -2.9_206, -2.1_988, -3.6_638, 0.3_858, -2.9_155, 2.2_980],
              [3.9_859, -2.1_580, -0.7_984, -4.4_904, -4.1_181, -2.0_252, -4.4_782, 1.1_253],
          ]		)
      lowerCAmelCase_      :				Tuple      =       tf.constant(
          [
              [0.0_000, -0.0_100, -0.0_200, -0.0_300, -0.0_400, -0.0_500, -0.0_600, -0.0_700],
              [0.2_012, -0.8_897, -0.0_263, -0.9_401, -0.2_074, -0.9_463, -0.3_481, -0.9_343],
              [1.7_057, -0.6_271, 1.2_145, -1.3_897, 0.6_303, -1.7_647, 0.1_173, -1.8_985],
              [2.1_731, 1.6_397, 2.7_358, -0.2_854, 2.1_840, -1.7_183, 1.3_018, -2.4_871],
              [-0.2_717, 3.6_173, 2.9_206, 2.1_988, 3.6_638, -0.3_858, 2.9_155, -2.2_980],
              [-3.9_859, 2.1_580, 0.7_984, 4.4_904, 4.1_181, 2.0_252, 4.4_782, -1.1_253],
          ]		)

      tf.debugging.assert_near(query_layer[0, 0, :6, :8]   ,						lowerCamelCase   ,						atol=self.tolerance		)
      tf.debugging.assert_near(key_layer[0, 0, :6, :8]   ,						lowerCamelCase   ,						atol=self.tolerance		)







'''simple docstring'''






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


__A :  Tuple    							=				"sshleifer/mar_enro_6_3_student"






class        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase       (       self   :  List[Any]		)       ->  Optional[Any]:
      super().setUp()

      lowerCAmelCase_      :				Any      =       cached_path(
          """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""   ,						extract_compressed_file=lowerCamelCase   ,						)
      lowerCAmelCase_      :				Optional[Any]      =       F'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'

    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  str		)       ->  str:
      MarianMTModel.from_pretrained(lowerCamelCase		)





    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  List[Any]		)       ->  Union[str, Any]:
      lowerCAmelCase_      :				str      =       {
          """$MAX_LEN""": 64,
          """$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_      :				Dict      =       (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""		)[1].strip()
      lowerCAmelCase_      :				Optional[int]      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				Optional[Any]      =       self.get_auto_remove_tmp_dir()

      # bash_script = bash_script.replace("--fp16 ", "")
      lowerCAmelCase_      :				Tuple      =       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_      :				Tuple      =       ["""finetune.py"""] + bash_script.split() + args
      with patch.object(lowerCamelCase   ,						"""argv"""   ,						lowerCamelCase		):
        lowerCAmelCase_      :				Optional[Any]      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				Any      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationModule.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				Tuple      =       parser.parse_args()
        lowerCAmelCase_      :				Dict      =       main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				int      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Optional[Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Tuple      =       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
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Any      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Union[str, Any]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				List[str]      =       """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_      :				List[str]      =       {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        __snake_case	(       _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    @timeout_decorator.timeout(6_00		)
    @slow
    @require_torch_gpu
    def __lowercase       (       self   :  Optional[Any]		)       ->  Dict:
      lowerCAmelCase_      :				List[str]      =       F'{self.test_file_dir_str}/test_data/wmt_en_ro'
      lowerCAmelCase_      :				Dict      =       {
          """--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_      :				int      =       (
          (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""		)[1].strip()
      )
      lowerCAmelCase_      :				str      =       bash_script.replace("""\\\n"""   ,						""""""		).strip().replace("""\"$@\""""   ,						""""""		)
      lowerCAmelCase_      :				Tuple      =       bash_script.replace("""--fp16 """   ,						""" """		)

      for k, v in env_vars_to_replace.items():
        lowerCAmelCase_      :				Optional[int]      =       bash_script.replace(lowerCamelCase   ,						str(lowerCamelCase		)		)
      lowerCAmelCase_      :				int      =       self.get_auto_remove_tmp_dir()
      lowerCAmelCase_      :				Optional[Any]      =       bash_script.replace("""--fp16"""   ,						""""""		)
      lowerCAmelCase_      :				Dict      =       6
      lowerCAmelCase_      :				List[Any]      =       (
          ["""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		):
        lowerCAmelCase_      :				Dict      =       argparse.ArgumentParser()
        lowerCAmelCase_      :				int      =       pl.Trainer.add_argparse_args(lowerCamelCase		)
        lowerCAmelCase_      :				List[str]      =       SummarizationDistiller.add_model_specific_args(lowerCamelCase   ,						os.getcwd()		)
        lowerCAmelCase_      :				List[Any]      =       parser.parse_args()
        # assert args.gpus == gpus THIS BREAKS for multi_gpu

        lowerCAmelCase_      :				str      =       distill_main(lowerCamelCase		)

      # Check metrics
      lowerCAmelCase_      :				Union[str, Any]      =       load_json(model.metrics_save_path		)
      lowerCAmelCase_      :				Union[str, Any]      =       metrics["""val"""][0]
      lowerCAmelCase_      :				Union[str, Any]      =       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
      lowerCAmelCase_      :				Union[str, Any]      =       os.listdir(lowerCamelCase		)
      lowerCAmelCase_      :				Dict      =       [x for x in contents if x.endswith(""".ckpt"""		)][0]
      lowerCAmelCase_      :				Optional[int]      =       os.path.join(args.output_dir   ,						lowerCamelCase		)
      lowerCAmelCase_      :				int      =       torch.load(lowerCamelCase   ,						map_location="""cpu"""		)
      lowerCAmelCase_      :				Tuple      =       """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_      :				Union[str, Any]      =       {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



