





'''simple docstring'''

import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
UpperCAmelCase					:		int		=							(7_2_0, 1_2_8_0)  # Height, Width
UpperCAmelCase					:		Union[str, Any]		=							(0.4, 0.6)  # if height or width lower than this scale, drop it.
UpperCAmelCase					:		List[str]		=							1 / 1_0_0
UpperCAmelCase					:		List[Any]		=							''
UpperCAmelCase					:		Union[str, Any]		=							''
UpperCAmelCase					:		List[str]		=							''
UpperCAmelCase					:		Optional[int]		=							2_5_0




def    _a    (							):





      """simple docstring"""



      _snake_case     ,							_snake_case		:	List[str]					     =     get_dataset(lowerCAmelCase_			,      lowerCAmelCase_	)
      for index in range(lowerCAmelCase_	):
            _snake_case		:	Dict					     =     random.sample(range(len(lowerCAmelCase_	)	)			,      4	)
            _snake_case     ,							_snake_case     ,							_snake_case		:	List[Any]					     =     update_image_and_anno(
                lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      filter_scale=lowerCAmelCase_			,      )

            # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
            _snake_case		:	Optional[Any]					     =     random_chars(32	)
            _snake_case		:	List[Any]					     =     path.split(os.sep	)[-1].rsplit('''.'''			,      1	)[0]
            _snake_case		:	str					     =     f'''{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'''
            cva.imwrite(f'''{file_root}.jpg'''			,      lowerCAmelCase_			,      [cva.IMWRITE_JPEG_QUALITY, 85]	)
            print(f'''Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'''	)
            _snake_case		:	Optional[int]					     =     []
            for anno in new_annos:
                  _snake_case		:	str					     =     anno[3] - anno[1]
                  _snake_case		:	Any					     =     anno[4] - anno[2]
                  _snake_case		:	Dict					     =     anno[1] + width / 2
                  _snake_case		:	Union[str, Any]					     =     anno[2] + height / 2
                  _snake_case		:	Union[str, Any]					     =     f'''{anno[0]} {x_center} {y_center} {width} {height}'''
                  annos_list.append(lowerCAmelCase_	)
            with open(f'''{file_root}.txt'''			,      '''w'''	) as outfile:
                  outfile.write('''\n'''.join(line for line in annos_list	)	)




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_	):





      """simple docstring"""



      _snake_case		:	Dict					     =     []
      _snake_case		:	str					     =     []
      for label_file in glob.glob(os.path.join(lowerCAmelCase_			,      '''*.txt'''	)	):
            _snake_case		:	Union[str, Any]					     =     label_file.split(os.sep	)[-1].rsplit('''.'''			,      1	)[0]
            with open(lowerCAmelCase_	) as in_file:
                  _snake_case		:	int					     =     in_file.readlines()
            _snake_case		:	Union[str, Any]					     =     os.path.join(lowerCAmelCase_			,      f'''{label_name}.jpg'''	)

            _snake_case		:	Any					     =     []
            for obj_list in obj_lists:
                  _snake_case		:	Dict					     =     obj_list.rstrip('''\n'''	).split(''' '''	)
                  _snake_case		:	Union[str, Any]					     =     float(obj[1]	) - float(obj[3]	) / 2
                  _snake_case		:	str					     =     float(obj[2]	) - float(obj[4]	) / 2
                  _snake_case		:	Any					     =     float(obj[1]	) + float(obj[3]	) / 2
                  _snake_case		:	Optional[Any]					     =     float(obj[2]	) + float(obj[4]	) / 2

                  boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
            if not boxes:
                  continue
            img_paths.append(lowerCAmelCase_	)
            labels.append(lowerCAmelCase_	)
      return img_paths, labels




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_ = 0.0			,      ):





      """simple docstring"""



      _snake_case		:	Optional[Any]					     =     np.zeros([output_size[0], output_size[1], 3]			,      dtype=np.uinta	)
      _snake_case		:	Tuple					     =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
      _snake_case		:	Dict					     =     scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
      _snake_case		:	List[str]					     =     int(scale_x * output_size[1]	)
      _snake_case		:	List[Any]					     =     int(scale_y * output_size[0]	)

      _snake_case		:	List[Any]					     =     []
      _snake_case		:	Any					     =     []
      for i, index in enumerate(lowerCAmelCase_	):
            _snake_case		:	Tuple					     =     all_img_list[index]
            path_list.append(lowerCAmelCase_	)
            _snake_case		:	List[Any]					     =     all_annos[index]
            _snake_case		:	Optional[Any]					     =     cva.imread(lowerCAmelCase_	)
            if i == 0:  # top-left
                  _snake_case		:	List[str]					     =     cva.resize(lowerCAmelCase_			,      (divid_point_x, divid_point_y)	)
                  _snake_case		:	str					     =     img
                  for bbox in img_annos:
                        _snake_case		:	List[Any]					     =     bbox[1] * scale_x
                        _snake_case		:	str					     =     bbox[2] * scale_y
                        _snake_case		:	Any					     =     bbox[3] * scale_x
                        _snake_case		:	List[str]					     =     bbox[4] * scale_y
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
            elif i == 1:  # top-right
                  _snake_case		:	List[Any]					     =     cva.resize(lowerCAmelCase_			,      (output_size[1] - divid_point_x, divid_point_y)	)
                  _snake_case		:	Union[str, Any]					     =     img
                  for bbox in img_annos:
                        _snake_case		:	int					     =     scale_x + bbox[1] * (1 - scale_x)
                        _snake_case		:	List[str]					     =     bbox[2] * scale_y
                        _snake_case		:	List[str]					     =     scale_x + bbox[3] * (1 - scale_x)
                        _snake_case		:	Dict					     =     bbox[4] * scale_y
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
            elif i == 2:  # bottom-left
                  _snake_case		:	Optional[int]					     =     cva.resize(lowerCAmelCase_			,      (divid_point_x, output_size[0] - divid_point_y)	)
                  _snake_case		:	Dict					     =     img
                  for bbox in img_annos:
                        _snake_case		:	int					     =     bbox[1] * scale_x
                        _snake_case		:	Optional[int]					     =     scale_y + bbox[2] * (1 - scale_y)
                        _snake_case		:	int					     =     bbox[3] * scale_x
                        _snake_case		:	int					     =     scale_y + bbox[4] * (1 - scale_y)
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
            else:  # bottom-right
                  _snake_case		:	Union[str, Any]					     =     cva.resize(
                      lowerCAmelCase_			,      (output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
                  _snake_case		:	Union[str, Any]					     =     img
                  for bbox in img_annos:
                        _snake_case		:	Union[str, Any]					     =     scale_x + bbox[1] * (1 - scale_x)
                        _snake_case		:	Dict					     =     scale_y + bbox[2] * (1 - scale_y)
                        _snake_case		:	str					     =     scale_x + bbox[3] * (1 - scale_x)
                        _snake_case		:	str					     =     scale_y + bbox[4] * (1 - scale_y)
                        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
      if filter_scale > 0:
            _snake_case		:	int					     =     [
                anno
                for anno in new_anno
                if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
            ]

      return output_img, new_anno, path_list[0]




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



      assert number_char > 1, "The number of character should greater than 1"
      _snake_case		:	Dict					     =     ascii_lowercase + digits
      return "".join(random.choice(lowerCAmelCase_	) for _ in range(lowerCAmelCase_	)	)


if __name__ == "__main__":
   main()
   print('DONE ✅')









'''simple docstring'''

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 lowerCamelCase						:
  _lowercase  :	Any       					=				LEDConfig
  _lowercase  :	Any       					=				{}
  _lowercase  :	Optional[Any]       					=				"""gelu"""







  def __init__(		self     ,     lowercase__     ,     lowercase__=13     ,     lowercase__=7     ,     lowercase__=True     ,     lowercase__=False     ,     lowercase__=99     ,     lowercase__=32     ,     lowercase__=2     ,     lowercase__=4     ,     lowercase__=37     ,     lowercase__=0.1     ,     lowercase__=0.1     ,     lowercase__=20     ,     lowercase__=2     ,     lowercase__=1     ,     lowercase__=0     ,     lowercase__=4     ,     )		->						Any:






        """simple docstring"""





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

        _snake_case		:	List[Any]					     =     hidden_dropout_prob
        _snake_case		:	List[str]					     =     attention_probs_dropout_prob
        _snake_case		:	Optional[int]					     =     max_position_embeddings
        _snake_case		:	Any					     =     eos_token_id
        _snake_case		:	List[Any]					     =     pad_token_id
        _snake_case		:	Optional[int]					     =     bos_token_id
        _snake_case		:	Any					     =     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
        _snake_case		:	Any					     =     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
        _snake_case		:	Tuple					     =     (
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )







  def 				UpperCAmelCase_				(		self		)		->						Optional[int]:






        """simple docstring"""





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

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

        _snake_case		:	List[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     ,     )
        _snake_case		:	Dict					     =     prepare_led_inputs_dict(lowercase__     ,     lowercase__     ,     lowercase__		)
        _snake_case		:	Dict					     =     tf.concat(
            [tf.zeros_like(lowercase__		)[:, :-1], tf.ones_like(lowercase__		)[:, -1:]]     ,     axis=-1     ,     )
        _snake_case		:	Dict					     =     global_attention_mask
        return config, inputs_dict







  def 				UpperCAmelCase_				(		self     ,     lowercase__     ,     lowercase__		)		->						int:






        """simple docstring"""





        _snake_case		:	int					     =     TFLEDModel(config=lowercase__		).get_decoder()
        _snake_case		:	Union[str, Any]					     =     inputs_dict['''input_ids''']

        _snake_case		:	List[str]					     =     input_ids[:1, :]
        _snake_case		:	Tuple					     =     inputs_dict['''attention_mask'''][:1, :]
        _snake_case		:	Dict					     =     1

        # first forward pass
        _snake_case		:	Optional[int]					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     use_cache=lowercase__		)

        _snake_case     ,							_snake_case		:	Dict					     =     outputs.to_tuple()

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

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

        _snake_case		:	List[Any]					     =     model(lowercase__     ,     attention_mask=lowercase__		)[0]
        _snake_case		:	Tuple					     =     model(lowercase__     ,     attention_mask=lowercase__     ,     past_key_values=lowercase__		)[0]

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

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

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




def    _a    (							lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      lowerCAmelCase_=None			,      ):





      """simple docstring"""



      if attention_mask is None:
            _snake_case		:	Union[str, Any]					     =     tf.cast(tf.math.not_equal(lowerCAmelCase_			,      config.pad_token_id	)			,      tf.inta	)
      if decoder_attention_mask is None:
            _snake_case		:	str					     =     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:
            _snake_case		:	int					     =     tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
      if decoder_head_mask is None:
            _snake_case		:	List[str]					     =     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 lowerCamelCase						(a__						,      a__						,      unittest.TestCase					):
  _lowercase  :	Optional[int]       					=				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  _lowercase  :	int       					=				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
  _lowercase  :	Dict       					=				(
      {
          """conversational""": TFLEDForConditionalGeneration,
          """feature-extraction""": TFLEDModel,
          """summarization""": TFLEDForConditionalGeneration,
          """text2text-generation""": TFLEDForConditionalGeneration,
          """translation""": TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  _lowercase  :	int       					=				True
  _lowercase  :	List[Any]       					=				False
  _lowercase  :	str       					=				False
  _lowercase  :	Union[str, Any]       					=				False







  def 				UpperCAmelCase_				(		self		)		->						Optional[Any]:






        """simple docstring"""





        _snake_case		:	str					     =     TFLEDModelTester(self		)
        _snake_case		:	Union[str, Any]					     =     ConfigTester(self     ,     config_class=lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						Tuple:






        """simple docstring"""





        self.config_tester.run_common_tests()







  def 				UpperCAmelCase_				(		self		)		->						List[str]:






        """simple docstring"""





        _snake_case		:	List[Any]					     =     self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowercase__		)







  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        _snake_case     ,							_snake_case		:	List[Any]					     =     self.model_tester.prepare_config_and_inputs_for_common()
        _snake_case		:	Any					     =     tf.zeros_like(inputs_dict['''attention_mask''']		)
        _snake_case		:	Optional[Any]					     =     2
        _snake_case		:	Any					     =     tf.where(
            tf.range(self.model_tester.seq_length		)[None, :] < num_global_attn_indices     ,     1     ,     inputs_dict['''global_attention_mask''']     ,     )

        _snake_case		:	Dict					     =     True
        _snake_case		:	str					     =     self.model_tester.seq_length
        _snake_case		:	Dict					     =     self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(lowercase__		):
              _snake_case		:	Optional[int]					     =     outputs.decoder_attentions
              self.assertEqual(len(lowercase__		)     ,     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(lowercase__		):
              _snake_case		:	int					     =     [t.numpy() for t in outputs.encoder_attentions]
              _snake_case		:	Tuple					     =     [t.numpy() for t in outputs.encoder_global_attentions]
              self.assertEqual(len(lowercase__		)     ,     self.model_tester.num_hidden_layers		)
              self.assertEqual(len(lowercase__		)     ,     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:
              _snake_case		:	Union[str, Any]					     =     True
              _snake_case		:	Dict					     =     False
              _snake_case		:	Union[str, Any]					     =     False
              _snake_case		:	List[Any]					     =     model_class(lowercase__		)
              _snake_case		:	Optional[Any]					     =     model(self._prepare_for_class(lowercase__     ,     lowercase__		)		)
              _snake_case		:	List[Any]					     =     len(lowercase__		)
              self.assertEqual(config.output_hidden_states     ,     lowercase__		)
              check_encoder_attentions_output(lowercase__		)

              if self.is_encoder_decoder:
                    _snake_case		:	Union[str, Any]					     =     model_class(lowercase__		)
                    _snake_case		:	List[Any]					     =     model(self._prepare_for_class(lowercase__     ,     lowercase__		)		)
                    self.assertEqual(config.output_hidden_states     ,     lowercase__		)
                    check_decoder_attentions_output(lowercase__		)

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

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

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







  @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''		)
  def 				UpperCAmelCase_				(		self		)		->						int:






        """simple docstring"""





        pass







  def 				UpperCAmelCase_				(		self		)		->						str:






        """simple docstring"""





        pass




def    _a    (							lowerCAmelCase_	):





      """simple docstring"""



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


UpperCAmelCase					:		Dict		=							1E-4





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







  def 				UpperCAmelCase_				(		self		)		->						Dict:






        """simple docstring"""





        _snake_case		:	List[str]					     =     TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		).led

        # change to intended input here
        _snake_case		:	List[str]					     =     _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	Tuple					     =     _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	Tuple					     =     prepare_led_inputs_dict(model.config     ,     lowercase__     ,     lowercase__		)
        _snake_case		:	int					     =     model(**lowercase__		)[0]
        _snake_case		:	Dict					     =     (1, 1_024, 768)
        self.assertEqual(output.shape     ,     lowercase__		)
        # change to expected output here
        _snake_case		:	List[Any]					     =     tf.convert_to_tensor(
            [[2.3_050, 2.8_279, 0.6_531], [-1.8_457, -0.1_455, -3.5_661], [-1.0_186, 0.4_586, -2.2_043]]     ,     )
        tf.debugging.assert_near(output[:, :3, :3]     ,     lowercase__     ,     atol=1E-3		)







  def 				UpperCAmelCase_				(		self		)		->						List[Any]:






        """simple docstring"""





        _snake_case		:	Any					     =     TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''		)

        # change to intended input here
        _snake_case		:	Dict					     =     _long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	Dict					     =     _long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]]		)
        _snake_case		:	List[str]					     =     prepare_led_inputs_dict(model.config     ,     lowercase__     ,     lowercase__		)
        _snake_case		:	Tuple					     =     model(**lowercase__		)[0]
        _snake_case		:	Any					     =     (1, 1_024, model.config.vocab_size)
        self.assertEqual(output.shape     ,     lowercase__		)
        # change to expected output here
        _snake_case		:	Dict					     =     tf.convert_to_tensor(
            [[33.6_507, 6.4_572, 16.8_089], [5.8_739, -2.4_238, 11.2_902], [-3.2_139, -4.3_149, 4.2_783]]     ,     )
        tf.debugging.assert_near(output[:, :3, :3]     ,     lowercase__     ,     atol=1E-3     ,     rtol=1E-3		)


