





"""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 							snake_case							:

      '''simple docstring'''



      _A     :       Any         =       LEDConfig
      _A     :       str         =       {}
      _A     :       List[str]         =       "gelu"

      def __init__(							self       :      List[Any]						,				__lowercase       :      Union[str, Any]						,				__lowercase       :      List[Any]=13						,				__lowercase       :      int=7						,				__lowercase       :      str=True						,				__lowercase       :      Any=False						,				__lowercase       :      str=99						,				__lowercase       :      str=32						,				__lowercase       :      Union[str, Any]=2						,				__lowercase       :      Optional[Any]=4						,				__lowercase       :      List[Any]=37						,				__lowercase       :      List[Any]=0.1						,				__lowercase       :      Tuple=0.1						,				__lowercase       :      Dict=20						,				__lowercase       :      str=2						,				__lowercase       :      Dict=1						,				__lowercase       :      Any=0						,				__lowercase       :      List[Any]=4						,				):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[Any]       =						parent
        __UpperCAmelCase    :						List[str]       =						batch_size
        __UpperCAmelCase    :						str       =						seq_length
        __UpperCAmelCase    :						str       =						is_training
        __UpperCAmelCase    :						Any       =						use_labels
        __UpperCAmelCase    :						Any       =						vocab_size
        __UpperCAmelCase    :						List[str]       =						hidden_size
        __UpperCAmelCase    :						Optional[Any]       =						num_hidden_layers
        __UpperCAmelCase    :						Dict       =						num_attention_heads
        __UpperCAmelCase    :						Optional[Any]       =						intermediate_size

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

      def       A_					(							self       :      int ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						Tuple       =						ids_tensor([self.batch_size, self.seq_length]						,				self.vocab_size )

        __UpperCAmelCase    :						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						,				)
        __UpperCAmelCase    :						Dict       =						prepare_led_inputs_dict(__a						,				__a						,				__a )
        __UpperCAmelCase    :						Union[str, Any]       =						tf.concat(
            [tf.zeros_like(__a )[:, :-1], tf.ones_like(__a )[:, -1:]]						,				axis=-1						,				)
        __UpperCAmelCase    :						Union[str, Any]       =						global_attention_mask
        return config, inputs_dict







      def       A_					(							self       :      List[str]						,				__lowercase       :      List[Any]						,				__lowercase       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						TFLEDModel(config=__a ).get_decoder()
        __UpperCAmelCase    :						Tuple       =						inputs_dict["input_ids"]

        __UpperCAmelCase    :						int       =						input_ids[:1, :]
        __UpperCAmelCase    :						List[str]       =						inputs_dict["attention_mask"][:1, :]
        __UpperCAmelCase    :						List[Any]       =						1

        # first forward pass
        __UpperCAmelCase    :						Any       =						model(__a						,				attention_mask=__a						,				use_cache=__a )

        __UpperCAmelCase    :						Union[str, Any]       =						outputs.to_tuple()

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

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

        __UpperCAmelCase    :						Tuple       =						model(__a						,				attention_mask=__a )[0]
        __UpperCAmelCase    :						int       =						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
        __UpperCAmelCase    :						List[Any]       =						int(ids_tensor((1,)						,				output_from_past.shape[-1] ) )
        __UpperCAmelCase    :						List[str]       =						output_from_no_past[:, -3:, random_slice_idx]
        __UpperCAmelCase    :						Optional[int]       =						output_from_past[:, :, random_slice_idx]

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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			)						->Dict:





  """simple docstring"""


  if attention_mask is None:
    __UpperCAmelCase    :						str       =						tf.cast(tf.math.not_equal(lowercase_  ,			config.pad_token_id  )  ,			tf.inta  )
  if decoder_attention_mask is None:
    __UpperCAmelCase    :						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:
    __UpperCAmelCase    :						List[str]       =						tf.ones((config.encoder_layers, config.encoder_attention_heads)  )
  if decoder_head_mask is None:
    __UpperCAmelCase    :						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 							snake_case							(			_UpperCamelCase      ,  _UpperCamelCase      ,  unittest.TestCase ):

      '''simple docstring'''



      _A     :       Any         =       (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
      _A     :       List[str]         =       (TFLEDForConditionalGeneration,) if is_tf_available() else ()
      _A     :       List[str]         =       (
          {
              "conversational": TFLEDForConditionalGeneration,
              "feature-extraction": TFLEDModel,
              "summarization": TFLEDForConditionalGeneration,
              "text2text-generation": TFLEDForConditionalGeneration,
              "translation": TFLEDForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _A     :       Tuple         =       True
      _A     :       str         =       False
      _A     :       Optional[Any]         =       False
      _A     :       int         =       False

      def       A_					(							self       :      str ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						TFLEDModelTester(self )
        __UpperCAmelCase    :						Any       =						ConfigTester(self						,				config_class=__a )

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        self.config_tester.run_common_tests()

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*__a )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						Dict       =						True
        __UpperCAmelCase    :						str       =						self.model_tester.seq_length
        __UpperCAmelCase    :						Union[str, Any]       =						self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(__lowercase       :      Optional[int] ):
          __UpperCAmelCase    :						Optional[int]       =						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(__lowercase       :      Optional[Any] ):
          __UpperCAmelCase    :						Union[str, Any]       =						[t.numpy() for t in outputs.encoder_attentions]
          __UpperCAmelCase    :						List[Any]       =						[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:
          __UpperCAmelCase    :						Dict       =						True
          __UpperCAmelCase    :						Optional[Any]       =						False
          __UpperCAmelCase    :						int       =						False
          __UpperCAmelCase    :						Optional[int]       =						model_class(__a )
          __UpperCAmelCase    :						int       =						model(self._prepare_for_class(__a						,				__a ) )
          __UpperCAmelCase    :						Any       =						len(__a )
          self.assertEqual(config.output_hidden_states						,				__a )
          check_encoder_attentions_output(__a )

          if self.is_encoder_decoder:
            __UpperCAmelCase    :						Optional[Any]       =						model_class(__a )
            __UpperCAmelCase    :						List[Any]       =						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"]
          __UpperCAmelCase    :						int       =						True
          __UpperCAmelCase    :						Tuple       =						model_class(__a )
          __UpperCAmelCase    :						str       =						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
          __UpperCAmelCase    :						Any       =						True
          __UpperCAmelCase    :						List[str]       =						True
          __UpperCAmelCase    :						Tuple       =						model_class(__a )
          __UpperCAmelCase    :						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       A_					(							self       :      str ):


        '''simple docstring'''




        pass







      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        pass


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


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


lowercase__				:List[Any]				   =       1E-4







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

      '''simple docstring'''



      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




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

        # change to intended input here
        __UpperCAmelCase    :						int       =						_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
        __UpperCAmelCase    :						Tuple       =						_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
        __UpperCAmelCase    :						Optional[Any]       =						prepare_led_inputs_dict(model.config						,				__a						,				__a )
        __UpperCAmelCase    :						Optional[int]       =						model(**__a )[0]
        __UpperCAmelCase    :						Optional[int]       =						(1, 1_024, 768)
        self.assertEqual(output.shape						,				__a )
        # change to expected output here
        __UpperCAmelCase    :						Tuple       =						tf.convert_to_tensor(
            [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]						,				)
        tf.debugging.assert_near(output[:, :3, :3]						,				__a						,				atol=1e-3 )







      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )

        # change to intended input here
        __UpperCAmelCase    :						Optional[int]       =						_long_tensor([512 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
        __UpperCAmelCase    :						List[str]       =						_long_tensor([128 * [0, 31_414, 232, 328, 740, 1_140, 12_695, 69]] )
        __UpperCAmelCase    :						Optional[Any]       =						prepare_led_inputs_dict(model.config						,				__a						,				__a )
        __UpperCAmelCase    :						Union[str, Any]       =						model(**__a )[0]
        __UpperCAmelCase    :						int       =						(1, 1_024, model.config.vocab_size)
        self.assertEqual(output.shape						,				__a )
        # change to expected output here
        __UpperCAmelCase    :						Optional[int]       =						tf.convert_to_tensor(
            [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]						,				)
        tf.debugging.assert_near(output[:, :3, :3]						,				__a						,				atol=1e-3						,				rtol=1e-3 )






"""simple docstring"""





def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->Tuple:





  """simple docstring"""


  return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_=0  )						->Dict:





  """simple docstring"""


  return sorted(UpperCAmelCase_  ,			key=lambda UpperCAmelCase_  : x[column]  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(points_counts - 1  ):
    for j in range(i + 1  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						List[Any]       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=float('''inf'''  )  )						->str:





  """simple docstring"""


  for i in range(min(6  ,			points_counts - 1  )  ,			UpperCAmelCase_  ):
    for j in range(max(0  ,			i - 6  )  ,			UpperCAmelCase_  ):
      __UpperCAmelCase    :						Dict       =						euclidean_distance_sqr(points[i]  ,			points[j]  )
      if current_dis < min_dis:
        __UpperCAmelCase    :						Tuple       =						current_dis
  return min_dis


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Any:





  """simple docstring"""


  if points_counts <= 3:
    return dis_between_closest_pair(UpperCAmelCase_  ,			UpperCAmelCase_  )

  # recursion
  __UpperCAmelCase    :						Any       =						points_counts // 2
  __UpperCAmelCase    :						Any       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[:mid]  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						Tuple       =						closest_pair_of_points_sqr(
      UpperCAmelCase_  ,			points_sorted_on_y[mid:]  ,			points_counts - mid  )
  __UpperCAmelCase    :						List[Any]       =						min(UpperCAmelCase_  ,			UpperCAmelCase_  )
  __UpperCAmelCase    :						int       =						[]
  for point in points_sorted_on_x:
    if abs(point[0] - points_sorted_on_x[mid][0]  ) < closest_pair_dis:
      cross_strip.append(UpperCAmelCase_  )

  __UpperCAmelCase    :						Union[str, Any]       =						dis_between_closest_in_strip(
      UpperCAmelCase_  ,			len(UpperCAmelCase_  )  ,			UpperCAmelCase_  )
  return min(UpperCAmelCase_  ,			UpperCAmelCase_  )


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						str       =						column_based_sort(UpperCAmelCase_  ,			column=0  )
  __UpperCAmelCase    :						Any       =						column_based_sort(UpperCAmelCase_  ,			column=1  )
  return (
      closest_pair_of_points_sqr(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
  ) ** 0.5


if __name__ == "__main__":
  lowercase__				:Optional[Any]				   =       [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
  print('Distance:', closest_pair_of_points(points, len(points)))