






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      :
      snake_case_	=				LEDConfig
      snake_case_	=				{}
      snake_case_	=				"""gelu"""


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=1_3       ,    __UpperCamelCase=7       ,    __UpperCamelCase=True       ,    __UpperCamelCase=False       ,    __UpperCamelCase=9_9       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=2       ,    __UpperCamelCase=4       ,    __UpperCamelCase=3_7       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=0.1       ,    __UpperCamelCase=2_0       ,    __UpperCamelCase=2       ,    __UpperCamelCase=1       ,    __UpperCamelCase=0       ,    __UpperCamelCase=4       ,    )->						Dict:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       seq_length
           __lowerCAmelCase             =       is_training
           __lowerCAmelCase             =       use_labels
           __lowerCAmelCase             =       vocab_size
           __lowerCAmelCase             =       hidden_size
           __lowerCAmelCase             =       num_hidden_layers
           __lowerCAmelCase             =       num_attention_heads
           __lowerCAmelCase             =       intermediate_size

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


      def 			__UpperCAmelCase		(						self						)->						str:
           __lowerCAmelCase             =       ids_tensor([self.batch_size, self.seq_length - 1]       ,    self.vocab_size						)
           __lowerCAmelCase             =       tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)       ,    1						)
           __lowerCAmelCase             =       tf.concat([input_ids, eos_tensor]       ,    axis=1						)

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

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


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       TFLEDModel(config=__UpperCamelCase						).get_decoder()
           __lowerCAmelCase             =       inputs_dict["input_ids"]

           __lowerCAmelCase             =       input_ids[:1, :]
           __lowerCAmelCase             =       inputs_dict["attention_mask"][:1, :]
           __lowerCAmelCase             =       1

           # first forward pass
           __lowerCAmelCase             =       model(__UpperCamelCase       ,    attention_mask=__UpperCamelCase       ,    use_cache=__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase             =       outputs.to_tuple()

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

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

           __lowerCAmelCase             =       model(__UpperCamelCase       ,    attention_mask=__UpperCamelCase						)[0]
           __lowerCAmelCase             =       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
           __lowerCAmelCase             =       int(ids_tensor((1,)       ,    output_from_past.shape[-1]						)						)
           __lowerCAmelCase             =       output_from_no_past[:, -3:, random_slice_idx]
           __lowerCAmelCase             =       output_from_past[:, :, random_slice_idx]

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





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case=None       ,			__snake_case=None       ,			__snake_case=None       ,			__snake_case=None       ,			):
     if attention_mask is None:
          __lowerCAmelCase             =       tf.cast(tf.math.not_equal(__snake_case       ,			config.pad_token_id							)       ,			tf.inta							)
     if decoder_attention_mask is None:
          __lowerCAmelCase             =       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:
          __lowerCAmelCase             =       tf.ones((config.encoder_layers, config.encoder_attention_heads)							)
     if decoder_head_mask is None:
          __lowerCAmelCase             =       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      ):
      snake_case_	=				(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
      snake_case_	=				(TFLEDForConditionalGeneration,) if is_tf_available() else ()
      snake_case_	=				(
          {
              """conversational""": TFLEDForConditionalGeneration,
              """feature-extraction""": TFLEDModel,
              """summarization""": TFLEDForConditionalGeneration,
              """text2text-generation""": TFLEDForConditionalGeneration,
              """translation""": TFLEDForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      snake_case_	=				True
      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       TFLEDModelTester(self						)
           __lowerCAmelCase             =       ConfigTester(self       ,    config_class=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						int:
           self.config_tester.run_common_tests()


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()
           __lowerCAmelCase             =       tf.zeros_like(inputs_dict["attention_mask"]						)
           __lowerCAmelCase             =       2
           __lowerCAmelCase             =       tf.where(
               tf.range(self.model_tester.seq_length						)[None, :] < num_global_attn_indices       ,    1       ,    inputs_dict["global_attention_mask"]       ,    )

           __lowerCAmelCase             =       True
           __lowerCAmelCase             =       self.model_tester.seq_length
           __lowerCAmelCase             =       self.model_tester.encoder_seq_length

           def check_decoder_attentions_output(__UpperCamelCase						):
                __lowerCAmelCase             =       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						):
                __lowerCAmelCase             =       [t.numpy() for t in outputs.encoder_attentions]
                __lowerCAmelCase             =       [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:
                __lowerCAmelCase             =       True
                __lowerCAmelCase             =       False
                __lowerCAmelCase             =       False
                __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                __lowerCAmelCase             =       model(self._prepare_for_class(__UpperCamelCase       ,    __UpperCamelCase						)						)
                __lowerCAmelCase             =       len(__UpperCamelCase						)
                self.assertEqual(config.output_hidden_states       ,    __UpperCamelCase						)
                check_encoder_attentions_output(__UpperCamelCase						)

                if self.is_encoder_decoder:
                     __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                     __lowerCAmelCase             =       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"]
                __lowerCAmelCase             =       True
                __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                __lowerCAmelCase             =       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
                __lowerCAmelCase             =       True
                __lowerCAmelCase             =       True
                __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                __lowerCAmelCase             =       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						)->						Tuple:
           pass


      def 			__UpperCAmelCase		(						self						)->						int:
           # TODO: Head-masking not yet implement
           pass





def    __lowerCAmelCase   (    __snake_case							):
     return tf.constant(__snake_case       ,			dtype=tf.intaa							)


lowerCamelCase      :       List[Any]    =						1E-4



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


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"						).led

           # change to intended input here
           __lowerCAmelCase             =       _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]]						)
           __lowerCAmelCase             =       _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]]						)
           __lowerCAmelCase             =       prepare_led_inputs_dict(model.config       ,    __UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       model(**__UpperCamelCase						)[0]
           __lowerCAmelCase             =       (1, 1_0_2_4, 7_6_8)
           self.assertEqual(output.shape       ,    __UpperCamelCase						)
           # change to expected output here
           __lowerCAmelCase             =       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]       ,    __UpperCamelCase       ,    atol=1e-3						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"						)

           # change to intended input here
           __lowerCAmelCase             =       _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]]						)
           __lowerCAmelCase             =       _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]]						)
           __lowerCAmelCase             =       prepare_led_inputs_dict(model.config       ,    __UpperCamelCase       ,    __UpperCamelCase						)
           __lowerCAmelCase             =       model(**__UpperCamelCase						)[0]
           __lowerCAmelCase             =       (1, 1_0_2_4, model.config.vocab_size)
           self.assertEqual(output.shape       ,    __UpperCamelCase						)
           # change to expected output here
           __lowerCAmelCase             =       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]       ,    __UpperCamelCase       ,    atol=1e-3       ,    rtol=1e-3						)














import itertools
import string
from collections.abc import Generator, Iterable





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       iter(__snake_case							)
     while True:
          __lowerCAmelCase             =       tuple(itertools.islice(__snake_case       ,			__snake_case							)							)
          if not chunk:
               return
          yield chunk





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       "".join([c.upper() for c in dirty if c in string.ascii_letters]							)
     __lowerCAmelCase             =       ""

     if len(__snake_case							) < 2:
          return dirty

     for i in range(len(__snake_case							) - 1							):
          clean += dirty[i]

          if dirty[i] == dirty[i + 1]:
               clean += "X"

     clean += dirty[-1]

     if len(__snake_case							) & 1:
          clean += "X"

     return clean





def    __lowerCAmelCase   (    __snake_case							):
     # I and J are used interchangeably to allow
     # us to use a 5x5 table (25 letters)
     __lowerCAmelCase             =       "ABCDEFGHIKLMNOPQRSTUVWXYZ"
     # we're using a list instead of a '2d' array because it makes the math
     # for setting up the table and doing the actual encoding/decoding simpler
     __lowerCAmelCase             =       []

     # copy key chars into the table if they are in `alphabet` ignoring duplicates
     for char in key.upper():
          if char not in table and char in alphabet:
               table.append(__snake_case							)

    # fill the rest of the table in with the remaining alphabet chars
     for char in alphabet:
          if char not in table:
               table.append(__snake_case							)

     return table





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       prepare_input(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
               ciphertext += table[rowa * 5 + (cola + 1) % 5]
          elif cola == cola:
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
               ciphertext += table[((rowa + 1) % 5) * 5 + cola]
          else:  # rectangle
               ciphertext += table[rowa * 5 + cola]
               ciphertext += table[rowa * 5 + cola]

     return ciphertext





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       generate_table(__snake_case							)
     __lowerCAmelCase             =       ""

     # https://en.wikipedia.org/wiki/Playfair_cipher#Description
     for chara, chara in chunker(__snake_case       ,			2							):
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)
          __lowerCAmelCase      ,			__lowerCAmelCase             =       divmod(table.index(__snake_case							)       ,			5							)

          if rowa == rowa:
               plaintext += table[rowa * 5 + (cola - 1) % 5]
               plaintext += table[rowa * 5 + (cola - 1) % 5]
          elif cola == cola:
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
               plaintext += table[((rowa - 1) % 5) * 5 + cola]
          else:  # rectangle
               plaintext += table[rowa * 5 + cola]
               plaintext += table[rowa * 5 + cola]

     return plaintext






