




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 						A:



 '''simple docstring'''


 UpperCamelCase		     =		LEDConfig
 UpperCamelCase		     =		{}
 UpperCamelCase		     =		'''gelu'''

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

       """simple docstring"""






       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        a__				(       self      :						int						)     ->			Any:

       """simple docstring"""






       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(A_		,							A_		,							A_						)
       lowerCamelCase_	  =					tf.concat(
           [tf.zeros_like(A_						)[:, :-1], tf.ones_like(A_						)[:, -1:]]		,							axis=-1		,							)
       lowerCamelCase_	  =					global_attention_mask
       return config, inputs_dict





 def        a__				(       self      :						List[Any]		,							A_      :						Union[str, Any]		,							A_      :						str						)     ->			Dict:

       """simple docstring"""






       lowerCamelCase_	  =					TFLEDModel(config=A_						).get_decoder()
       lowerCamelCase_	  =					inputs_dict['input_ids']

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

       # first forward pass
       lowerCamelCase_	  =					model(A_		,							attention_mask=A_		,							use_cache=A_						)

       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(A_		,							attention_mask=A_						)[0]
       lowerCamelCase_	  =					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
       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(A_		,							A_		,							rtol=1E-3						)

def     _SCREAMING_SNAKE_CASE    (							lowercase			: str			,							lowercase			: Optional[Any]			,							lowercase			: Optional[int]			,							lowercase			: Dict=None			,							lowercase			: Union[str, Any]=None			,							lowercase			: List[Any]=None			,							lowercase			: Dict=None			,							):


      '''simple docstring'''



      if attention_mask is None:
            lowerCamelCase_	  =					tf.cast(tf.math.not_equal(lowercase			,							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 						A(		UpperCamelCase							,   UpperCamelCase							,   unittest.TestCase     ):



 '''simple docstring'''


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

 def        a__				(       self      :						Union[str, Any]						)     ->			Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					TFLEDModelTester(self						)
       lowerCamelCase_	  =					ConfigTester(self		,							config_class=A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Tuple:

       """simple docstring"""






       self.config_tester.run_common_tests()

 def        a__				(       self      :						List[str]						)     ->			Any:

       """simple docstring"""






       lowerCamelCase_	  =					self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_						)

 def        a__				(       self      :						Optional[Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       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(A_      :						Optional[int]						):
             lowerCamelCase_	  =					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_      :						Any						):
             lowerCamelCase_	  =					[t.numpy() for t in outputs.encoder_attentions]
             lowerCamelCase_	  =					[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:
             lowerCamelCase_	  =					True
             lowerCamelCase_	  =					False
             lowerCamelCase_	  =					False
             lowerCamelCase_	  =					model_class(A_						)
             lowerCamelCase_	  =					model(self._prepare_for_class(A_		,							A_						)						)
             lowerCamelCase_	  =					len(A_						)
             self.assertEqual(config.output_hidden_states		,							A_						)
             check_encoder_attentions_output(A_						)

             if self.is_encoder_decoder:
                   lowerCamelCase_	  =					model_class(A_						)
                   lowerCamelCase_	  =					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"]
             lowerCamelCase_	  =					True
             lowerCamelCase_	  =					model_class(A_						)
             lowerCamelCase_	  =					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
             lowerCamelCase_	  =					True
             lowerCamelCase_	  =					True
             lowerCamelCase_	  =					model_class(A_						)
             lowerCamelCase_	  =					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      :						int						)     ->			Union[str, Any]:

       """simple docstring"""






       pass





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

       """simple docstring"""






       pass

def     _SCREAMING_SNAKE_CASE    (							lowercase			: int  ):


      '''simple docstring'''



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


lowerCamelCase       :	int									=     1e-4







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



 '''simple docstring'''


 def        a__				(       self      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'						).led

       # change to intended input here
       lowerCamelCase_	  =					_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
       lowerCamelCase_	  =					_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
       lowerCamelCase_	  =					prepare_led_inputs_dict(model.config		,							A_		,							A_						)
       lowerCamelCase_	  =					model(**A_						)[0]
       lowerCamelCase_	  =					(1, 1024, 768)
       self.assertEqual(output.shape		,							A_						)
       # change to expected output here
       lowerCamelCase_	  =					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        a__				(       self      :						Tuple						)     ->			List[Any]:

       """simple docstring"""






       lowerCamelCase_	  =					TFLEDForConditionalGeneration.from_pretrained('allenai/led-base-16384'						)

       # change to intended input here
       lowerCamelCase_	  =					_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
       lowerCamelCase_	  =					_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]						)
       lowerCamelCase_	  =					prepare_led_inputs_dict(model.config		,							A_		,							A_						)
       lowerCamelCase_	  =					model(**A_						)[0]
       lowerCamelCase_	  =					(1, 1024, model.config.vocab_size)
       self.assertEqual(output.shape		,							A_						)
       # change to expected output here
       lowerCamelCase_	  =					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						)









class 						A:



 '''simple docstring'''


 def __init__(       self      :						Dict						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					0
       lowerCamelCase_	  =					0
       lowerCamelCase_	  =					{}

 def        a__				(       self      :						Union[str, Any]		,							A_      :						List[Any]						)     ->			int:

       """simple docstring"""






       if vertex not in self.adjacency:
             lowerCamelCase_	  =					{}
             self.num_vertices += 1

 def        a__				(       self      :						int		,							A_      :						int		,							A_      :						Optional[Any]		,							A_      :						List[str]						)     ->			Tuple:

       """simple docstring"""






       self.add_vertex(A_						)
       self.add_vertex(A_						)

       if head == tail:
             return

       lowerCamelCase_	  =					weight
       lowerCamelCase_	  =					weight

 def        a__				(       self      :						Optional[int]						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					self.get_edges()
       for edge in edges:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
             edges.remove((tail, head, weight)						)
       for i in range(len(A_						)						):
             lowerCamelCase_	  =					list(edges[i]						)

       edges.sort(key=lambda A_						: e[2]						)
       for i in range(len(A_						) - 1						):
             if edges[i][2] >= edges[i + 1][2]:
                   lowerCamelCase_	  =					edges[i][2] + 1
       for edge in edges:
             lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
             lowerCamelCase_	  =					weight
             lowerCamelCase_	  =					weight

 def __str__(       self      :						str						)     ->			Optional[int]:

       """simple docstring"""






       lowerCamelCase_	  =					''
       for tail in self.adjacency:
             for head in self.adjacency[tail]:
                   lowerCamelCase_	  =					self.adjacency[head][tail]
                   string += f"""{head} -> {tail} == {weight}\n"""
       return string.rstrip('\n'						)

 def        a__				(       self      :						Union[str, Any]						)     ->			Union[str, Any]:

       """simple docstring"""






       lowerCamelCase_	  =					[]
       for tail in self.adjacency:
             for head in self.adjacency[tail]:
                   output.append((tail, head, self.adjacency[head][tail])						)
       return output

 def        a__				(       self      :						List[str]						)     ->			int:

       """simple docstring"""






       return self.adjacency.keys()

 @staticmethod
 def        a__				(       A_      :						Optional[Any]=None		,							A_      :						List[str]=None						)     ->			List[str]:

       """simple docstring"""






       lowerCamelCase_	  =					Graph()
       if vertices is None:
             lowerCamelCase_	  =					[]
       if edges is None:
             lowerCamelCase_	  =					[]
       for vertex in vertices:
             g.add_vertex(A_						)
       for edge in edges:
             g.add_edge(*A_						)
       return g







 class 						A:



  '''simple docstring'''


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

        """simple docstring"""






        lowerCamelCase_	  =					{}
        lowerCamelCase_	  =					{}

  def __len__(       self      :						Any						)     ->			List[str]:

        """simple docstring"""






        return len(self.parent						)

  def        a__				(       self      :						List[str]		,							A_      :						Any						)     ->			Dict:

        """simple docstring"""






        if item in self.parent:
              return self.find(A_						)

        lowerCamelCase_	  =					item
        lowerCamelCase_	  =					0
        return item

  def        a__				(       self      :						List[str]		,							A_      :						Tuple						)     ->			Optional[int]:

        """simple docstring"""






        if item not in self.parent:
              return self.make_set(A_						)
        if item != self.parent[item]:
              lowerCamelCase_	  =					self.find(self.parent[item]						)
        return self.parent[item]





  def        a__				(       self      :						Any		,							A_      :						int		,							A_      :						Tuple						)     ->			Union[str, Any]:

        """simple docstring"""






        lowerCamelCase_	  =					self.find(A_						)
        lowerCamelCase_	  =					self.find(A_						)

        if roota == roota:
              return roota

        if self.rank[roota] > self.rank[roota]:
              lowerCamelCase_	  =					roota
              return roota

        if self.rank[roota] < self.rank[roota]:
              lowerCamelCase_	  =					roota
              return roota

        if self.rank[roota] == self.rank[roota]:
              self.rank[roota] += 1
              lowerCamelCase_	  =					roota
              return roota
        return None

 @staticmethod
 def        a__				(       A_      :						int						)     ->			Tuple:

       """simple docstring"""






       lowerCamelCase_	  =					graph.num_vertices

       lowerCamelCase_	  =					Graph.UnionFind()
       lowerCamelCase_	  =					[]
       while num_components > 1:
             lowerCamelCase_	  =					{}
             for vertex in graph.get_vertices():
                   lowerCamelCase_	  =					-1

             lowerCamelCase_	  =					graph.get_edges()
             for edge in edges:
                   lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
                   edges.remove((tail, head, weight)						)
             for edge in edges:
                   lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					edge
                   lowerCamelCase_	  =					union_find.find(A_						)
                   lowerCamelCase_	  =					union_find.find(A_						)
                   if seta != seta:
                         if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                               lowerCamelCase_	  =					[head, tail, weight]

                         if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                               lowerCamelCase_	  =					[head, tail, weight]
             for vertex in cheap_edge:
                   if cheap_edge[vertex] != -1:
                         lowerCamelCase_  ,       lowerCamelCase_  ,       lowerCamelCase_	  =					cheap_edge[vertex]
                         if union_find.find(A_						) != union_find.find(A_						):
                               union_find.union(A_		,							A_						)
                               mst_edges.append(cheap_edge[vertex]						)
                               lowerCamelCase_	  =					num_components - 1
       lowerCamelCase_	  =					Graph.build(edges=A_						)
       return mst



