'''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 	__A				:
    a__	:						str	       =  LEDConfig
    a__	:						Any	       =  {}
    a__	:						Any	       =  """gelu"""



    def __init__(self   :   Tuple       ,      __a   :   Union[str, Any]       ,      __a   :   List[str]=13       ,      __a   :   Union[str, Any]=7       ,      __a   :   Optional[int]=True       ,      __a   :   Dict=False       ,      __a   :   Dict=99       ,      __a   :   List[str]=32       ,      __a   :   List[Any]=2       ,      __a   :   Any=4       ,      __a   :   Any=37       ,      __a   :   int=0.1       ,      __a   :   Tuple=0.1       ,      __a   :   Optional[int]=20       ,      __a   :   str=2       ,      __a   :   Union[str, Any]=1       ,      __a   :   Dict=0       ,      __a   :   Optional[int]=4       ,      ):
         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  _lowercase						(self   :   Any			):
         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(__a       ,      __a       ,      __a			)
         UpperCAmelCase_	   =       tf.concat(
             [tf.zeros_like(__a			)[:, :-1], tf.ones_like(__a			)[:, -1:]]       ,      axis=-1       ,      )
         UpperCAmelCase_	   =       global_attention_mask
         return config, inputs_dict






    def  _lowercase						(self   :   List[str]       ,      __a   :   str       ,      __a   :   Optional[int]			):
         UpperCAmelCase_	   =       TFLEDModel(config=__a			).get_decoder()
         UpperCAmelCase_	   =       inputs_dict["input_ids"]

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

         # first forward pass
         UpperCAmelCase_	   =       model(__a       ,      attention_mask=__a       ,      use_cache=__a			)

         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(__a       ,      attention_mask=__a			)[0]
         UpperCAmelCase_	   =       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_	   =       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(__a       ,      __a       ,      rtol=1E-3			)





def      lowerCAmelCase_    ( snake_case_	:			str ,   snake_case_	:			Optional[int] ,   snake_case_	:			Dict ,   snake_case_	:			Union[str, Any]=None ,   snake_case_	:			Dict=None ,   snake_case_	:			List[str]=None ,   snake_case_	:			Tuple=None ,   )   ->				str:


     '''simple docstring'''




     if attention_mask is None:
          UpperCAmelCase_	   =       tf.cast(tf.math.not_equal(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 	__A				(			UpperCamelCase__		,     UpperCamelCase__		,     unittest.TestCase     ):
    a__	:						List[Any]	       =  (TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
    a__	:						Any	       =  (TFLEDForConditionalGeneration,) if is_tf_available() else ()
    a__	:						Dict	       =  (
        {
            """conversational""": TFLEDForConditionalGeneration,
            """feature-extraction""": TFLEDModel,
            """summarization""": TFLEDForConditionalGeneration,
            """text2text-generation""": TFLEDForConditionalGeneration,
            """translation""": TFLEDForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    a__	:						Any	       =  True
    a__	:						str	       =  False
    a__	:						Optional[Any]	       =  False
    a__	:						int	       =  False



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       TFLEDModelTester(self			)
         UpperCAmelCase_	   =       ConfigTester(self       ,      config_class=__a			)



    def  _lowercase						(self   :   str			):
         self.config_tester.run_common_tests()



    def  _lowercase						(self   :   Union[str, Any]			):
         UpperCAmelCase_	   =       self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*__a			)



    def  _lowercase						(self   :   Optional[int]			):
         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(__a   :   Optional[int]			):
              UpperCAmelCase_	   =       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   :   Dict			):
              UpperCAmelCase_	   =       [t.numpy() for t in outputs.encoder_attentions]
              UpperCAmelCase_	   =       [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_	   =       True
              UpperCAmelCase_	   =       False
              UpperCAmelCase_	   =       False
              UpperCAmelCase_	   =       model_class(__a			)
              UpperCAmelCase_	   =       model(self._prepare_for_class(__a       ,      __a			)			)
              UpperCAmelCase_	   =       len(__a			)
              self.assertEqual(config.output_hidden_states       ,      __a			)
              check_encoder_attentions_output(__a			)

              if self.is_encoder_decoder:
                   UpperCAmelCase_	   =       model_class(__a			)
                   UpperCAmelCase_	   =       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_	   =       True
              UpperCAmelCase_	   =       model_class(__a			)
              UpperCAmelCase_	   =       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_	   =       True
              UpperCAmelCase_	   =       True
              UpperCAmelCase_	   =       model_class(__a			)
              UpperCAmelCase_	   =       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  _lowercase						(self   :   str			):
         pass






    def  _lowercase						(self   :   Union[str, Any]			):
         # TODO: Head-masking not yet implement
         pass





def      lowerCAmelCase_    ( snake_case_	:			Dict      )   ->				str:


     '''simple docstring'''




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


SCREAMING_SNAKE_CASE_:					Dict  =1E-4






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



    def  _lowercase						(self   :   str			):
         UpperCAmelCase_	   =       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"			).led

         # change to intended input here
         UpperCAmelCase_	   =       _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]			)
         UpperCAmelCase_	   =       _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]			)
         UpperCAmelCase_	   =       prepare_led_inputs_dict(model.config       ,      __a       ,      __a			)
         UpperCAmelCase_	   =       model(**__a			)[0]
         UpperCAmelCase_	   =       (1, 1024, 768)
         self.assertEqual(output.shape       ,      __a			)
         # change to expected output here
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]       ,      )
         tf.debugging.assert_near(output[:, :3, :3]       ,      __a       ,      atol=1E-3			)






    def  _lowercase						(self   :   Any			):
         UpperCAmelCase_	   =       TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"			)

         # change to intended input here
         UpperCAmelCase_	   =       _long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]			)
         UpperCAmelCase_	   =       _long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]			)
         UpperCAmelCase_	   =       prepare_led_inputs_dict(model.config       ,      __a       ,      __a			)
         UpperCAmelCase_	   =       model(**__a			)[0]
         UpperCAmelCase_	   =       (1, 1024, model.config.vocab_size)
         self.assertEqual(output.shape       ,      __a			)
         # change to expected output here
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]       ,      )
         tf.debugging.assert_near(output[:, :3, :3]       ,      __a       ,      atol=1E-3       ,      rtol=1E-3			)





'''simple docstring'''





from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import numpy as np
    import tensorflow as tf

    from transformers import TFCamembertModel




@require_tf
@require_sentencepiece
@require_tokenizers
class 	__A				(			unittest.TestCase     ):



    @slow
    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       TFCamembertModel.from_pretrained("jplu/tf-camembert-base"			)

         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[5, 121, 11, 660, 16, 730, 25543, 110, 83, 6]]       ,      dtype=tf.intaa       ,      )  # J'aime le camembert !"

         UpperCAmelCase_	   =       model(__a			)["last_hidden_state"]
         UpperCAmelCase_	   =       tf.TensorShape((1, 10, 768)			)
         self.assertEqual(output.shape       ,      __a			)
         # compare the actual values for a slice.
         UpperCAmelCase_	   =       tf.convert_to_tensor(
             [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]       ,      dtype=tf.floataa       ,      )
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(np.allclose(output[:, :3, :3].numpy()       ,      expected_slice.numpy()       ,      atol=1E-4			)			)




