





'''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 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				LEDConfig
  lowerCAmelCase_					=				{}
  lowerCAmelCase_					=				"""gelu"""


  def __init__(				self    :					Dict							,	UpperCamelCase__    :					Tuple							,	UpperCamelCase__    :					Optional[int]=13							,	UpperCamelCase__    :					List[str]=7							,	UpperCamelCase__    :					Dict=True							,	UpperCamelCase__    :					str=False							,	UpperCamelCase__    :					Optional[Any]=99							,	UpperCamelCase__    :					Tuple=32							,	UpperCamelCase__    :					Optional[int]=2							,	UpperCamelCase__    :					Tuple=4							,	UpperCamelCase__    :					List[str]=37							,	UpperCamelCase__    :					Optional[Any]=0.1							,	UpperCamelCase__    :					List[str]=0.1							,	UpperCamelCase__    :					List[str]=20							,	UpperCamelCase__    :					str=2							,	UpperCamelCase__    :					List[str]=1							,	UpperCamelCase__    :					Optional[Any]=0							,	UpperCamelCase__    :					List[str]=4							,	)     ->						Any:
    _UpperCamelCase       			=parent
    _UpperCamelCase       			=batch_size
    _UpperCamelCase       			=seq_length
    _UpperCamelCase       			=is_training
    _UpperCamelCase       			=use_labels
    _UpperCamelCase       			=vocab_size
    _UpperCamelCase       			=hidden_size
    _UpperCamelCase       			=num_hidden_layers
    _UpperCamelCase       			=num_attention_heads
    _UpperCamelCase       			=intermediate_size

    _UpperCamelCase       			=hidden_dropout_prob
    _UpperCamelCase       			=attention_probs_dropout_prob
    _UpperCamelCase       			=max_position_embeddings
    _UpperCamelCase       			=eos_token_id
    _UpperCamelCase       			=pad_token_id
    _UpperCamelCase       			=bos_token_id
    _UpperCamelCase       			=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       			=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       			=(
        self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
    )


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

    _UpperCamelCase       			=ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size  )

    _UpperCamelCase       			=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       			=prepare_led_inputs_dict(UpperCamelCase__							,	UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=tf.concat(
        [tf.zeros_like(UpperCamelCase__  )[:, :-1], tf.ones_like(UpperCamelCase__  )[:, -1:]]							,	axis=-1							,	)
    _UpperCamelCase       			=global_attention_mask
    return config, inputs_dict




  def   UpperCamelCase__      (				self    :					str							,	UpperCamelCase__    :					List[Any]							,	UpperCamelCase__    :					List[Any]  )     ->						Any:
    _UpperCamelCase       			=TFLEDModel(config=UpperCamelCase__  ).get_decoder()
    _UpperCamelCase       			=inputs_dict['''input_ids''']

    _UpperCamelCase       			=input_ids[:1, :]
    _UpperCamelCase       			=inputs_dict['''attention_mask'''][:1, :]
    _UpperCamelCase       			=1

    # first forward pass
    _UpperCamelCase       			=model(UpperCamelCase__							,	attention_mask=UpperCamelCase__							,	use_cache=UpperCamelCase__  )

    _UpperCamelCase			,     _UpperCamelCase       			=outputs.to_tuple()

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

    # append to next input_ids and
    _UpperCamelCase       			=tf.concat([input_ids, next_tokens]							,	axis=-1  )
    _UpperCamelCase       			=tf.concat([attention_mask, next_attn_mask]							,	axis=-1  )

    _UpperCamelCase       			=model(UpperCamelCase__							,	attention_mask=UpperCamelCase__  )[0]
    _UpperCamelCase       			=model(UpperCamelCase__							,	attention_mask=UpperCamelCase__							,	past_key_values=UpperCamelCase__  )[0]

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

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

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




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


  """simple docstring"""

  if attention_mask is None:
    _UpperCamelCase       			=tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE  ,      config.pad_token_id   )  ,      tf.inta   )
  if decoder_attention_mask is None:
    _UpperCamelCase       			=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       			=tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
  if decoder_head_mask is None:
    _UpperCamelCase       			=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   (				lowercase_   ,     lowercase_   ,     unittest.TestCase):


  """simple docstring"""







  lowerCAmelCase_					=				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
  lowerCAmelCase_					=				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
  lowerCAmelCase_					=				(
      {
          """conversational""": TFLEDForConditionalGeneration,
          """feature-extraction""": TFLEDModel,
          """summarization""": TFLEDForConditionalGeneration,
          """text2text-generation""": TFLEDForConditionalGeneration,
          """translation""": TFLEDForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  lowerCAmelCase_					=				True
  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False
  lowerCAmelCase_					=				False


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Dict:
    _UpperCamelCase       			=TFLEDModelTester(self  )
    _UpperCamelCase       			=ConfigTester(self							,	config_class=UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					str  )     ->						List[Any]:
    self.config_tester.run_common_tests()


  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						List[Any]:
    _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Any:
    _UpperCamelCase			,     _UpperCamelCase       			=self.model_tester.prepare_config_and_inputs_for_common()
    _UpperCamelCase       			=tf.zeros_like(inputs_dict['''attention_mask''']  )
    _UpperCamelCase       			=2
    _UpperCamelCase       			=tf.where(
        tf.range(self.model_tester.seq_length  )[None, :] < num_global_attn_indices							,	1							,	inputs_dict['''global_attention_mask''']							,	)

    _UpperCamelCase       			=True
    _UpperCamelCase       			=self.model_tester.seq_length
    _UpperCamelCase       			=self.model_tester.encoder_seq_length

    def check_decoder_attentions_output(UpperCamelCase__    :					Any  ):
      _UpperCamelCase       			=outputs.decoder_attentions
      self.assertEqual(len(UpperCamelCase__  )							,	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(UpperCamelCase__    :					Optional[Any]  ):
      _UpperCamelCase       			=[t.numpy() for t in outputs.encoder_attentions]
      _UpperCamelCase       			=[t.numpy() for t in outputs.encoder_global_attentions]
      self.assertEqual(len(UpperCamelCase__  )							,	self.model_tester.num_hidden_layers  )
      self.assertEqual(len(UpperCamelCase__  )							,	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       			=True
      _UpperCamelCase       			=False
      _UpperCamelCase       			=False
      _UpperCamelCase       			=model_class(UpperCamelCase__  )
      _UpperCamelCase       			=model(self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )  )
      _UpperCamelCase       			=len(UpperCamelCase__  )
      self.assertEqual(config.output_hidden_states							,	UpperCamelCase__  )
      check_encoder_attentions_output(UpperCamelCase__  )

      if self.is_encoder_decoder:
        _UpperCamelCase       			=model_class(UpperCamelCase__  )
        _UpperCamelCase       			=model(self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )  )
        self.assertEqual(config.output_hidden_states							,	UpperCamelCase__  )
        check_decoder_attentions_output(UpperCamelCase__  )

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

      # Check attention is always last and order is fine
      _UpperCamelCase       			=True
      _UpperCamelCase       			=True
      _UpperCamelCase       			=model_class(UpperCamelCase__  )
      _UpperCamelCase       			=model(self._prepare_for_class(UpperCamelCase__							,	UpperCamelCase__  )  )

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


  @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''  )
  def   UpperCamelCase__      (				self    :					int  )     ->						Optional[Any]:
    pass




  def   UpperCamelCase__      (				self    :					Any  )     ->						List[Any]:
    # TODO: Head-masking not yet implement
    pass




def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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


__lowerCamelCase						: Union[str, Any]		=   1E-4







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


  """simple docstring"""







  def   UpperCamelCase__      (				self    :					Dict  )     ->						Optional[int]:
    _UpperCamelCase       			=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''  ).led

    # change to intended input here
    _UpperCamelCase       			=_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]  )
    _UpperCamelCase       			=_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]  )
    _UpperCamelCase       			=prepare_led_inputs_dict(model.config							,	UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=model(**UpperCamelCase__  )[0]
    _UpperCamelCase       			=(1, 1024, 768)
    self.assertEqual(output.shape							,	UpperCamelCase__  )
    # change to expected output here
    _UpperCamelCase       			=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]							,	UpperCamelCase__							,	atol=1E-3  )




  def   UpperCamelCase__      (				self    :					List[Any]  )     ->						Union[str, Any]:
    _UpperCamelCase       			=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''  )

    # change to intended input here
    _UpperCamelCase       			=_long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]  )
    _UpperCamelCase       			=_long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]]  )
    _UpperCamelCase       			=prepare_led_inputs_dict(model.config							,	UpperCamelCase__							,	UpperCamelCase__  )
    _UpperCamelCase       			=model(**UpperCamelCase__  )[0]
    _UpperCamelCase       			=(1, 1024, model.config.vocab_size)
    self.assertEqual(output.shape							,	UpperCamelCase__  )
    # change to expected output here
    _UpperCamelCase       			=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]							,	UpperCamelCase__							,	atol=1E-3							,	rtol=1E-3  )













'''simple docstring'''






def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1 or len(__SCREAMING_SNAKE_CASE   ) <= key:
    return input_string

  for position, character in enumerate(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[''''''.join(__SCREAMING_SNAKE_CASE   ) for row in temp_grid]
  _UpperCamelCase       			=''''''.join(__SCREAMING_SNAKE_CASE   )

  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=[]
  _UpperCamelCase       			=key - 1

  if key <= 0:
    raise ValueError('''Height of grid can\'t be 0 or negative'''   )
  if key == 1:
    return input_string

  _UpperCamelCase       			=[[] for _ in range(__SCREAMING_SNAKE_CASE   )]  # generates template
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    temp_grid[num].append('''*'''   )

  _UpperCamelCase       			=0
  for row in temp_grid:  # fills in the characters
    _UpperCamelCase       			=input_string[counter : counter + len(__SCREAMING_SNAKE_CASE   )]
    grid.append(list(__SCREAMING_SNAKE_CASE   )   )
    counter += len(__SCREAMING_SNAKE_CASE   )

  _UpperCamelCase       			=''''''  # reads as zigzag
  for position in range(len(__SCREAMING_SNAKE_CASE   )   ):
    _UpperCamelCase       			=position % (lowest * 2)  # puts it in bounds
    _UpperCamelCase       			=min(__SCREAMING_SNAKE_CASE  ,      lowest * 2 - num   )  # creates zigzag pattern
    output_string += grid[num][0]
    grid[num].pop(0   )
  return output_string


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			={}
  for key_guess in range(1  ,      len(__SCREAMING_SNAKE_CASE   )   ):  # tries every key
    _UpperCamelCase       			=decrypt(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  return results


if __name__ == "__main__":
      import doctest

      doctest.testmod()






