'''simple docstring'''

import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
      import os

      # The slow tests are often failing with OOM error on GPU
      # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
      # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
      _lowerCamelCase :				Union[str, Any]														= """platform"""

      import jax
      import jax.numpy as jnp

      from transformers import MarianTokenizer
      from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right



def  _lowerCAmelCase				(				__a			,      __a			,      __a=None			,      __a=None			,      __a=None			,      __a=None			,      __a=None			,      __a=None			,      )  ->      Any:
     '''simple docstring'''







     if attention_mask is None:
          _UpperCamelCase  :Union[str, Any]       =np.where(input_ids != config.pad_token_id			,      1			,      0      )
     if decoder_attention_mask is None:
          _UpperCamelCase  :Tuple       =np.where(decoder_input_ids != config.pad_token_id			,      1			,      0      )
     if head_mask is None:
          _UpperCamelCase  :int       =np.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          _UpperCamelCase  :int       =np.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          _UpperCamelCase  :Tuple       =np.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": attention_mask,
     }




class    lowerCamelCase__  :



   def __init__(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=13					,      lowerCAmelCase__=7					,      lowerCAmelCase__=True					,      lowerCAmelCase__=False					,      lowerCAmelCase__=99					,      lowerCAmelCase__=16					,      lowerCAmelCase__=2					,      lowerCAmelCase__=4					,      lowerCAmelCase__=4					,      lowerCAmelCase__="gelu"					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=0.1					,      lowerCAmelCase__=32					,      lowerCAmelCase__=2					,      lowerCAmelCase__=1					,      lowerCAmelCase__=0					,      lowerCAmelCase__=0.02					,      )				->     Optional[Any]:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =parent
        _UpperCamelCase  :Union[str, Any]       =batch_size
        _UpperCamelCase  :List[Any]       =seq_length
        _UpperCamelCase  :Any       =is_training
        _UpperCamelCase  :Tuple       =use_labels
        _UpperCamelCase  :str       =vocab_size
        _UpperCamelCase  :Tuple       =hidden_size
        _UpperCamelCase  :Dict       =num_hidden_layers
        _UpperCamelCase  :Union[str, Any]       =num_attention_heads
        _UpperCamelCase  :List[str]       =intermediate_size
        _UpperCamelCase  :str       =hidden_act
        _UpperCamelCase  :int       =hidden_dropout_prob
        _UpperCamelCase  :str       =attention_probs_dropout_prob
        _UpperCamelCase  :int       =max_position_embeddings
        _UpperCamelCase  :Any       =eos_token_id
        _UpperCamelCase  :Optional[Any]       =pad_token_id
        _UpperCamelCase  :int       =bos_token_id
        _UpperCamelCase  :List[Any]       =initializer_range



   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =np.clip(ids_tensor([self.batch_size, self.seq_length - 1]					,      self.vocab_size     )					,      3					,      self.vocab_size     )
        _UpperCamelCase  :Any       =np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)					,      dtype=np.intaa     ))					,      -1     )

        _UpperCamelCase  :Optional[int]       =shift_tokens_right(lowerCAmelCase__					,      1					,      2     )

        _UpperCamelCase  :List[Any]       =MarianConfig(
            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_id=self.eos_token_id					,      bos_token_id=self.bos_token_id					,      pad_token_id=self.pad_token_id					,      initializer_range=self.initializer_range					,      use_cache=lowerCAmelCase__					,      )
        _UpperCamelCase  :Dict       =prepare_marian_inputs_dict(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        return config, inputs_dict



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Dict       =self.prepare_config_and_inputs()
        return config, inputs_dict



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Optional[Any]       =20
        _UpperCamelCase  :Optional[int]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =model.encode(inputs_dict["""input_ids"""]     )

        _UpperCamelCase       ,      _UpperCamelCase  :Union[str, Any]       =(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCamelCase  :Dict       =model.init_cache(decoder_input_ids.shape[0]					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Optional[int]       =jnp.ones((decoder_input_ids.shape[0], max_decoder_length)					,      dtype="""i4"""     )

        _UpperCamelCase  :List[Any]       =jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )
        _UpperCamelCase  :List[Any]       =model.decode(
            decoder_input_ids[:, :-1]					,      lowerCAmelCase__					,      decoder_attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      decoder_position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Union[str, Any]       =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :Optional[int]       =model.decode(
            decoder_input_ids[:, -1:]					,      lowerCAmelCase__					,      decoder_attention_mask=lowerCAmelCase__					,      past_key_values=outputs_cache.past_key_values					,      decoder_position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =model.decode(lowerCAmelCase__					,      lowerCAmelCase__     )

        _UpperCamelCase  :Dict       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =20
        _UpperCamelCase  :Optional[Any]       =model_class_name(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =model.encode(inputs_dict["""input_ids"""]     )

        _UpperCamelCase       ,      _UpperCamelCase  :int       =(
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        _UpperCamelCase  :Tuple       =jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])     ),
            ]					,      axis=-1					,      )

        _UpperCamelCase  :int       =model.init_cache(decoder_input_ids.shape[0]					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Union[str, Any]       =jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1     )[None, :]					,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)					,      )

        _UpperCamelCase  :int       =model.decode(
            decoder_input_ids[:, :-1]					,      lowerCAmelCase__					,      decoder_attention_mask=lowerCAmelCase__					,      past_key_values=lowerCAmelCase__					,      decoder_position_ids=lowerCAmelCase__					,      )
        _UpperCamelCase  :Dict       =jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]					,      dtype="""i4"""     )
        _UpperCamelCase  :List[str]       =model.decode(
            decoder_input_ids[:, -1:]					,      lowerCAmelCase__					,      past_key_values=outputs_cache.past_key_values					,      decoder_attention_mask=lowerCAmelCase__					,      decoder_position_ids=lowerCAmelCase__					,      )

        _UpperCamelCase  :Optional[Any]       =model.decode(lowerCAmelCase__					,      lowerCAmelCase__					,      decoder_attention_mask=lowerCAmelCase__     )

        _UpperCamelCase  :Union[str, Any]       =np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])     )     )
        self.parent.assertTrue(diff < 1e-3					,      msg=f'''Max diff is {diff}'''     )




@require_flax
class    lowerCamelCase__  (   __snake_case       ,				unittest.TestCase       ,				__snake_case     ):
   __UpperCAmelCase           =						True
   __UpperCAmelCase           =						(FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
   __UpperCAmelCase           =						(FlaxMarianMTModel,) if is_flax_available() else ()



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :List[str]       =FlaxMarianModelTester(self     )



   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :int       =self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :List[Any]       =self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )



   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :Optional[int]       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                  _UpperCamelCase  :List[str]       =self._prepare_for_class(lowerCAmelCase__					,      lowerCAmelCase__     )
                  _UpperCamelCase  :Union[str, Any]       =model_class(lowerCAmelCase__     )

                  @jax.jit
                  def encode_jitted(lowerCAmelCase__					,      lowerCAmelCase__=None					,      **lowerCAmelCase__     ):
                       return model.encode(input_ids=lowerCAmelCase__					,      attention_mask=lowerCAmelCase__     )

                  with self.subTest("""JIT Enabled"""     ):
                       _UpperCamelCase  :Optional[int]       =encode_jitted(**lowerCAmelCase__     ).to_tuple()

                  with self.subTest("""JIT Disabled"""     ):
                       with jax.disable_jit():
                            _UpperCamelCase  :Optional[int]       =encode_jitted(**lowerCAmelCase__     ).to_tuple()

                  self.assertEqual(len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )     )
                  for jitted_output, output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assertEqual(jitted_output.shape					,      output.shape     )



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase       ,      _UpperCamelCase  :int       =self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__     ):
                  _UpperCamelCase  :Optional[Any]       =model_class(lowerCAmelCase__     )
                  _UpperCamelCase  :Tuple       =model.encode(inputs_dict["""input_ids"""]					,      inputs_dict["""attention_mask"""]     )

                  _UpperCamelCase  :Union[str, Any]       ={
                      """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                      """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                      """encoder_outputs""": encoder_outputs,
                  }

                  @jax.jit
                  def decode_jitted(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     ):
                       return model.decode(
                           decoder_input_ids=lowerCAmelCase__					,      decoder_attention_mask=lowerCAmelCase__					,      encoder_outputs=lowerCAmelCase__					,      )

                  with self.subTest("""JIT Enabled"""     ):
                       _UpperCamelCase  :List[str]       =decode_jitted(**lowerCAmelCase__     ).to_tuple()

                  with self.subTest("""JIT Disabled"""     ):
                       with jax.disable_jit():
                            _UpperCamelCase  :List[str]       =decode_jitted(**lowerCAmelCase__     ).to_tuple()

                  self.assertEqual(len(lowerCAmelCase__     )					,      len(lowerCAmelCase__     )     )
                  for jitted_output, output in zip(lowerCAmelCase__					,      lowerCAmelCase__     ):
                       self.assertEqual(jitted_output.shape					,      output.shape     )




   @slow
   def  _UpperCamelCase		(    self     )				->     Tuple:
        """simple docstring"""





        for model_class_name in self.all_model_classes:
             _UpperCamelCase  :List[Any]       =model_class_name.from_pretrained("""Helsinki-NLP/opus-mt-en-de"""     )
             # FlaxMarianForSequenceClassification expects eos token in input_ids
             _UpperCamelCase  :List[Any]       =np.ones((1, 1)     ) * model.config.eos_token_id
             _UpperCamelCase  :Union[str, Any]       =model(lowerCAmelCase__     )
             self.assertIsNotNone(lowerCAmelCase__     )




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__  (   unittest.TestCase     ):
   __UpperCAmelCase           =						None
   __UpperCAmelCase           =						None



   @classmethod
   def  _UpperCamelCase		(    cls     )				->     None:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
        return cls



   @cached_property
   def  _UpperCamelCase		(    self     )				->     Dict:
        """simple docstring"""





        return MarianTokenizer.from_pretrained(self.model_name     )



   @property
   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        return self.tokenizer.eos_token_id



   @cached_property
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :FlaxMarianMTModel       =FlaxMarianMTModel.from_pretrained(self.model_name     )
        self.assertEqual(model.config.decoder_start_token_id					,      model.config.pad_token_id     )
        return model



   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       =self.translate_src_text(**lowerCAmelCase__     )
        self.assertListEqual(self.expected_text					,      lowerCAmelCase__     )




   def  _UpperCamelCase		(    self					,      **lowerCAmelCase__     )				->     Dict:
        """simple docstring"""





        _UpperCamelCase  :int       =self.tokenizer(self.src_text					,      padding=lowerCAmelCase__					,      return_tensors="""np"""					,      **lowerCAmelCase__     )
        _UpperCamelCase  :str       =self.model.generate(
            model_inputs.input_ids					,      attention_mask=model_inputs.attention_mask					,      num_beams=2					,      max_length=128					,      ).sequences
        _UpperCamelCase  :List[str]       =self.tokenizer.batch_decode(lowerCAmelCase__					,      skip_special_tokens=lowerCAmelCase__     )
        return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""en"""
   __UpperCAmelCase           =						"""fr"""
   __UpperCAmelCase           =						[
       """I am a small frog.""",
       """Now I can forget the 100 words of german that I know.""",
   ]
   __UpperCAmelCase           =						[
       """Je suis une petite grenouille.""",
       """Maintenant, je peux oublier les 100 mots d'allemand que je connais.""",
   ]



   @slow
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""fr"""
   __UpperCAmelCase           =						"""en"""
   __UpperCAmelCase           =						[
       """Donnez moi le micro.""",
       """Tom et Mary étaient assis à une table.""",  # Accents
   ]
   __UpperCAmelCase           =						[
       """Give me the microphone.""",
       """Tom and Mary were sitting at a table.""",
   ]



   @slow
   def  _UpperCamelCase		(    self     )				->     Optional[Any]:
        """simple docstring"""





        self._assert_generated_batch_equal_expected()




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



   @slow
   def  _UpperCamelCase		(    self     )				->     Union[str, Any]:
        """simple docstring"""





        self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""en"""
   __UpperCAmelCase           =						"""de"""
   __UpperCAmelCase           =						[
       """I am a small frog.""",
       """Now I can forget the 100 words of german that I know.""",
       """Tom asked his teacher for advice.""",
       """That's how I would do it.""",
       """Tom really admired Mary's courage.""",
       """Turn around and close your eyes.""",
   ]
   __UpperCAmelCase           =						[
       """Ich bin ein kleiner Frosch.""",
       """Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.""",
       """Tom bat seinen Lehrer um Rat.""",
       """So würde ich das machen.""",
       """Tom bewunderte Marias Mut wirklich.""",
       """Drehen Sie sich um und schließen Sie die Augen.""",
   ]



   @slow
   def  _UpperCamelCase		(    self     )				->     List[str]:
        """simple docstring"""





        self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""en"""
   __UpperCAmelCase           =						"""zh"""
   __UpperCAmelCase           =						["""My name is Wolfgang and I live in Berlin"""]
   __UpperCAmelCase           =						["""我叫沃尔夫冈 我住在柏林"""]



   @slow
   def  _UpperCamelCase		(    self     )				->     Any:
        """simple docstring"""





        self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						"""ru"""
   __UpperCAmelCase           =						"""fr"""
   __UpperCAmelCase           =						["""Он показал мне рукопись своей новой пьесы."""]
   __UpperCAmelCase           =						["""Il m'a montré le manuscrit de sa nouvelle pièce."""]



   @slow
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        self._assert_generated_batch_equal_expected()




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



   @slow
   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        self._assert_generated_batch_equal_expected()







"""simple docstring"""



import os
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import GPTaLMHeadModel


@require_torch
@require_sentencepiece
@require_tokenizers
class 	_UpperCamelCase ( unittest.TestCase							):



    '''simple docstring'''


    @slow
    @unittest.skip("""Model is not available."""			)
    def 		A__      (      self			):
     UpperCAmelCase__ 						=				"""nvidia/megatron-gpt2-345m/"""
     if "MYDIR" in os.environ:
      UpperCAmelCase__ 						=				os.path.join(os.environ["""MYDIR"""]   ,			__lowercase			)
     UpperCAmelCase__ 						=				GPTaLMHeadModel.from_pretrained(__lowercase			)
     model.to(__lowercase			)
     model.half()

     UpperCAmelCase__ 						=				torch.tensor(
         [[101, 7110, 1005, 1056, 2023, 11333, 17413, 1029, 102]]   ,			device=__lowercase   ,			dtype=torch.long   ,			)

     with torch.no_grad():
      UpperCAmelCase__ 						=				model(__lowercase			).logits

     UpperCAmelCase__ 						=				torch.Size((1, 9, 50257)			)
     self.assertEqual(output.shape   ,			__lowercase			)

     UpperCAmelCase__ 						=				torch.tensor(
         [
             4.9_414,
             -0.2_920,
             -1.2_148,
             -4.0_273,
             -0.5_161,
             -5.2_109,
             -1.2_412,
             -1.8_301,
             -1.7_734,
             -4.7_148,
             -0.2_317,
             -1.0_811,
             -2.1_777,
             0.4_141,
             -3.7_969,
             -4.0_586,
             -2.5_332,
             -3.3_809,
             4.3_867,
         ]   ,			device=__lowercase   ,			dtype=torch.half   ,			)

     for i in range(19			):
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				8 * i // 17, 2792 * i  # along the diagonal
      UpperCAmelCase__						,	UpperCAmelCase__ 						=				output[0, r, c], expected_diag[i]
      UpperCAmelCase__ 						=				F'''row={r} col={c} computed={computed} expected={expected}'''
      self.assertAlmostEqual(__lowercase   ,			__lowercase   ,			delta=1e-4   ,			msg=__lowercase			)



