






from __future__ import annotations

import unittest

from transformers import LEDConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TFLEDForConditionalGeneration, TFLEDModel


@require_tf
class        _UpperCAmelCase						:
    __lowerCamelCase:   List[Any]		       =    LEDConfig
    __lowerCamelCase:   Union[str, Any]		       =    {}
    __lowerCamelCase:   Any		       =    'gelu'




    def __init__(			self  :       List[Any]  ,     a  :       int  ,     a  :       Optional[int]=1_3  ,     a  :       List[str]=7  ,     a  :       int=True  ,     a  :       Dict=False  ,     a  :       Optional[Any]=9_9  ,     a  :       Tuple=3_2  ,     a  :       Dict=2  ,     a  :       Dict=4  ,     a  :       Dict=3_7  ,     a  :       List[str]=0.1  ,     a  :       Optional[int]=0.1  ,     a  :       Optional[Any]=2_0  ,     a  :       Tuple=2  ,     a  :       List[Any]=1  ,     a  :       str=0  ,     a  :       str=4  ,     ):






       '''simple docstring'''






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

       lowercase_	:    Optional[Any]           =     hidden_dropout_prob
       lowercase_	:    Tuple           =     attention_probs_dropout_prob
       lowercase_	:    List[Any]           =     max_position_embeddings
       lowercase_	:    int           =     eos_token_id
       lowercase_	:    Dict           =     pad_token_id
       lowercase_	:    Optional[Any]           =     bos_token_id
       lowercase_	:    int           =     attention_window

       # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
       # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
       # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
       # because its local attention only attends to `self.attention_window` and one before and one after
       lowercase_	:    str           =     self.attention_window + 2

       # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
       # the `test_attention_outputs` and `test_hidden_states_output` tests
       lowercase_	:    str           =     (
           self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
       )




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






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

       lowercase_	:    Union[str, Any]           =     ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size			)

       lowercase_	:    Union[str, Any]           =     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  ,     attention_window=self.attention_window  ,     **self.config_updates  ,     )
       lowercase_	:    int           =     prepare_led_inputs_dict(a  ,     a  ,     a			)
       lowercase_	:    Tuple           =     tf.concat(
           [tf.zeros_like(a			)[:, :-1], tf.ones_like(a			)[:, -1:]]  ,     axis=-1  ,     )
       lowercase_	:    List[Any]           =     global_attention_mask
       return config, inputs_dict





    def 				lowerCAmelCase__				(			self  :       str  ,     a  :       List[str]  ,     a  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    str           =     TFLEDModel(config=a			).get_decoder()
       lowercase_	:    Optional[int]           =     inputs_dict['input_ids']

       lowercase_	:    Dict           =     input_ids[:1, :]
       lowercase_	:    Tuple           =     inputs_dict['attention_mask'][:1, :]
       lowercase_	:    int           =     1

       # first forward pass
       lowercase_	:    str           =     model(a  ,     attention_mask=a  ,     use_cache=a			)

       lowercase_	:    Optional[int]           =     outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       lowercase_	:    Union[str, Any]           =     ids_tensor((self.batch_size, 3)  ,     config.vocab_size			)
       lowercase_	:    str           =     tf.cast(ids_tensor((self.batch_size, 3)  ,     2			)  ,     tf.inta			)

       # append to next input_ids and
       lowercase_	:    List[Any]           =     tf.concat([input_ids, next_tokens]  ,     axis=-1			)
       lowercase_	:    str           =     tf.concat([attention_mask, next_attn_mask]  ,     axis=-1			)

       lowercase_	:    Dict           =     model(a  ,     attention_mask=a			)[0]
       lowercase_	:    str           =     model(a  ,     attention_mask=a  ,     past_key_values=a			)[0]

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

       # select random slice
       lowercase_	:    Optional[Any]           =     int(ids_tensor((1,)  ,     output_from_past.shape[-1]			)			)
       lowercase_	:    Dict           =     output_from_no_past[:, -3:, random_slice_idx]
       lowercase_	:    str           =     output_from_past[:, :, random_slice_idx]

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		_UpperCamelCase=None						,		):


   """simple docstring"""




   if attention_mask is None:
      lowercase_	:    Dict           =     tf.cast(tf.math.not_equal(__lowercase						,		config.pad_token_id							)						,		tf.inta							)
   if decoder_attention_mask is None:
      lowercase_	:    int           =     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:
      lowercase_	:    Dict           =     tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
   if decoder_head_mask is None:
      lowercase_	:    List[Any]           =     tf.ones((config.decoder_layers, config.decoder_attention_heads)							)
   return {
       "input_ids": input_ids,
       "attention_mask": attention_mask,
       "decoder_input_ids": decoder_input_ids,
       "decoder_attention_mask": decoder_attention_mask,
       "head_mask": head_mask,
       "decoder_head_mask": decoder_head_mask,
   }


@require_tf
class        _UpperCAmelCase						(		__A				,					__A				,					unittest.TestCase			):
    __lowerCamelCase:   Any		       =    (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    __lowerCamelCase:   Any		       =    (TFLEDForConditionalGeneration,) if is_tf_available() else ()
    __lowerCamelCase:   Optional[Any]		       =    (
        {
            'conversational': TFLEDForConditionalGeneration,
            'feature-extraction': TFLEDModel,
            'summarization': TFLEDForConditionalGeneration,
            'text2text-generation': TFLEDForConditionalGeneration,
            'translation': TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    __lowerCamelCase:   str		       =    True
    __lowerCamelCase:   List[str]		       =    False
    __lowerCamelCase:   Tuple		       =    False
    __lowerCamelCase:   str		       =    False




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    List[Any]           =     TFLEDModelTester(self			)
       lowercase_	:    str           =     ConfigTester(self  ,     config_class=a			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       self.config_tester.run_common_tests()




    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    str           =     self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*a			)




    def 				lowerCAmelCase__				(			self  :       Dict			):






       '''simple docstring'''






       lowercase_	:    Dict           =     self.model_tester.prepare_config_and_inputs_for_common()
       lowercase_	:    Tuple           =     tf.zeros_like(inputs_dict["attention_mask"]			)
       lowercase_	:    Tuple           =     2
       lowercase_	:    Any           =     tf.where(
           tf.range(self.model_tester.seq_length			)[None, :] < num_global_attn_indices  ,     1  ,     inputs_dict["global_attention_mask"]  ,     )

       lowercase_	:    List[str]           =     True
       lowercase_	:    Dict           =     self.model_tester.seq_length
       lowercase_	:    str           =     self.model_tester.encoder_seq_length

       def check_decoder_attentions_output(a  :       Dict			):
          lowercase_	:    Dict           =     outputs.decoder_attentions
          self.assertEqual(len(a			)  ,     self.model_tester.num_hidden_layers			)
          self.assertListEqual(
              list(decoder_attentions[0].shape[-3:]			)  ,     [self.model_tester.num_attention_heads, seq_length, seq_length]  ,     )

       def check_encoder_attentions_output(a  :       int			):
          lowercase_	:    Optional[int]           =     [t.numpy() for t in outputs.encoder_attentions]
          lowercase_	:    Tuple           =     [t.numpy() for t in outputs.encoder_global_attentions]
          self.assertEqual(len(a			)  ,     self.model_tester.num_hidden_layers			)
          self.assertEqual(len(a			)  ,     self.model_tester.num_hidden_layers			)
          self.assertListEqual(
              list(attentions[0].shape[-3:]			)  ,     [self.model_tester.num_attention_heads, seq_length, seq_length]  ,     )
          self.assertListEqual(
              list(global_attentions[0].shape[-3:]			)  ,     [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]  ,     )

       for model_class in self.all_model_classes:
          lowercase_	:    List[str]           =     True
          lowercase_	:    Union[str, Any]           =     False
          lowercase_	:    List[Any]           =     False
          lowercase_	:    Any           =     model_class(a			)
          lowercase_	:    Any           =     model(self._prepare_for_class(a  ,     a			)			)
          lowercase_	:    Any           =     len(a			)
          self.assertEqual(config.output_hidden_states  ,     a			)
          check_encoder_attentions_output(a			)

          if self.is_encoder_decoder:
             lowercase_	:    Dict           =     model_class(a			)
             lowercase_	:    int           =     model(self._prepare_for_class(a  ,     a			)			)
             self.assertEqual(config.output_hidden_states  ,     a			)
             check_decoder_attentions_output(a			)

          # Check that output attentions can also be changed via the config
          del inputs_dict["output_attentions"]
          lowercase_	:    Tuple           =     True
          lowercase_	:    Tuple           =     model_class(a			)
          lowercase_	:    Tuple           =     model(self._prepare_for_class(a  ,     a			)			)
          self.assertEqual(config.output_hidden_states  ,     a			)
          check_encoder_attentions_output(a			)

          # Check attention is always last and order is fine
          lowercase_	:    List[str]           =     True
          lowercase_	:    Optional[int]           =     True
          lowercase_	:    str           =     model_class(a			)
          lowercase_	:    int           =     model(self._prepare_for_class(a  ,     a			)			)

          self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)  ,     len(a			)			)
          self.assertEqual(model.config.output_hidden_states  ,     a			)
          check_encoder_attentions_output(a			)




    @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."			)
    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       pass





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       pass




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   return tf.constant(__lowercase						,		dtype=tf.intaa							)


UpperCamelCase__ =			1e-4





@slow
@require_tf
class        _UpperCAmelCase						(		unittest.TestCase			):




    def 				lowerCAmelCase__				(			self  :       int			):






       '''simple docstring'''






       lowercase_	:    str           =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"			).led

       # change to intended input here
       lowercase_	:    Any           =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]			)
       lowercase_	:    int           =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]			)
       lowercase_	:    List[str]           =     prepare_led_inputs_dict(model.config  ,     a  ,     a			)
       lowercase_	:    str           =     model(**a			)[0]
       lowercase_	:    Union[str, Any]           =     (1, 1_0_2_4, 7_6_8)
       self.assertEqual(output.shape  ,     a			)
       # change to expected output here
       lowercase_	:    int           =     tf.convert_to_tensor(
           [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]  ,     )
       tf.debugging.assert_near(output[:, :3, :3]  ,     a  ,     atol=1e-3			)





    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"			)

       # change to intended input here
       lowercase_	:    Union[str, Any]           =     _long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]			)
       lowercase_	:    List[str]           =     _long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]			)
       lowercase_	:    List[Any]           =     prepare_led_inputs_dict(model.config  ,     a  ,     a			)
       lowercase_	:    List[str]           =     model(**a			)[0]
       lowercase_	:    Dict           =     (1, 1_0_2_4, model.config.vocab_size)
       self.assertEqual(output.shape  ,     a			)
       # change to expected output here
       lowercase_	:    Any           =     tf.convert_to_tensor(
           [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]  ,     )
       tf.debugging.assert_near(output[:, :3, :3]  ,     a  ,     atol=1e-3  ,     rtol=1e-3			)












'''simple docstring'''
import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase__ =			namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   return (abs(source - target							) / target) < 0.01





@pytest.mark.integration
def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     _TestCommandArgs(dataset=_UpperCamelCase						,		all_configs=_UpperCamelCase						,		save_infos=_UpperCamelCase							)
   lowercase_	:    int           =     TestCommand(*_UpperCamelCase							)
   test_command.run()
   lowercase_	:    List[str]           =     os.path.join(_UpperCamelCase						,		"README.md"							)
   assert os.path.exists(_UpperCamelCase							)
   lowercase_	:    Any           =     DatasetInfosDict.from_directory(_UpperCamelCase							)
   lowercase_	:    Optional[int]           =     DatasetInfosDict(
       {
           "default": DatasetInfo(
               features=Features(
                   {
                       "tokens": Sequence(Value("string"							)							),
                       "ner_tags": Sequence(
                           ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]							)							),
                       "langs": Sequence(Value("string"							)							),
                       "spans": Sequence(Value("string"							)							),
                   }							)						,		splits=[
                   {
                       "name": "train",
                       "num_bytes": 235_1563,
                       "num_examples": 1_0000,
                   },
                   {
                       "name": "validation",
                       "num_bytes": 23_8418,
                       "num_examples": 1000,
                   },
               ]						,		download_size=394_0680						,		dataset_size=258_9981						,		)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      lowercase_    ,		lowercase_	:    Optional[int]           =     getattr(dataset_infos["default"]						,		_UpperCamelCase							), getattr(expected_dataset_infos["default"]						,		_UpperCamelCase							)
      if key == "num_bytes":
         assert is_apercent_close(_UpperCamelCase						,		_UpperCamelCase							)
      elif key == "splits":
         assert list(_UpperCamelCase							) == list(_UpperCamelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes						,		expected[split].num_bytes							)
      else:
         result == expected




