




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

       """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					:  List[str]  )   ->	Optional[int]:

       """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					:  Tuple						,							A_					:  Optional[int]						,							A_					:  Any  )   ->	Any:

       """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	:       Any	,							lowercase	:       Any	,							lowercase	:       List[str]	,							lowercase	:       Tuple=None	,							lowercase	:       Dict=None	,							lowercase	:       str=None	,							lowercase	:       Any=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					:  List[str]  )   ->	str:

       """simple docstring"""






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

 def 				a__ (				self					:  Tuple  )   ->	str:

       """simple docstring"""






       self.config_tester.run_common_tests()

 def 				a__ (				self					:  int  )   ->	Tuple:

       """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]  )   ->	Tuple:

       """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_					:  Any  ):
             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_					:  Optional[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					:  Tuple  )   ->	Any:

       """simple docstring"""






       pass





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

       """simple docstring"""






       pass

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict     ):


      '''simple docstring'''



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


lowerCamelCase				:		Dict     =   1e-4







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



 '''simple docstring'''


 def 				a__ (				self					:  Any  )   ->	int:

       """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					:  List[str]  )   ->	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  )









import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


lowerCamelCase				:		Union[str, Any]     =   logging.get_logger(__name__)







class   A(						UpperCamelCase   ):



 '''simple docstring'''


 def __init__(				self					:  int						,							*A_					:  str						,							**A_					:  Optional[int]  )   ->	None:

       """simple docstring"""






       warnings.warn(
           'The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
           ' use GLPNImageProcessor instead.'						,							A_						,							)
       super().__init__(*A_						,							**A_  )



