

'''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 						__magic_name__   :
 UpperCamelCase__     =       LEDConfig
 UpperCamelCase__     =       {}
 UpperCamelCase__     =       'gelu'







 def __init__(       self  , snake_case_  , snake_case_=13  , snake_case_=7  , snake_case_=True  , snake_case_=False  , snake_case_=99  , snake_case_=32  , snake_case_=2  , snake_case_=4  , snake_case_=37  , snake_case_=0.1  , snake_case_=0.1  , snake_case_=20  , snake_case_=2  , snake_case_=1  , snake_case_=0  , snake_case_=4  , ):
   lowercase									=parent
   lowercase									=batch_size
   lowercase									=seq_length
   lowercase									=is_training
   lowercase									=use_labels
   lowercase									=vocab_size
   lowercase									=hidden_size
   lowercase									=num_hidden_layers
   lowercase									=num_attention_heads
   lowercase									=intermediate_size

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







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

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

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







 def 						_A(       self  , snake_case_  , snake_case_   ):
   lowercase									=TFLEDModel(config=snake_case_   ).get_decoder()
   lowercase									=inputs_dict['''input_ids''']

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

   # first forward pass
   lowercase									=model(snake_case_  , attention_mask=snake_case_  , use_cache=snake_case_   )

   lowercase							, lowercase									=outputs.to_tuple()

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

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

   lowercase									=model(snake_case_  , attention_mask=snake_case_   )[0]
   lowercase									=model(snake_case_  , attention_mask=snake_case_  , past_key_values=snake_case_   )[0]

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

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

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




def 	UpperCamelCase   (       lowercase_						:    List[str]     ,					lowercase_						:    Tuple     ,					lowercase_						:    Optional[int]     ,					lowercase_						:    Optional[int]=None     ,					lowercase_						:    Union[str, Any]=None     ,					lowercase_						:    Optional[int]=None     ,					lowercase_						:    str=None     ,					)		->	Optional[Any]:




  '''simple docstring'''



  if attention_mask is None:
    lowercase									=tf.cast(tf.math.not_equal(lowercase_     ,					config.pad_token_id	)     ,					tf.inta	)
  if decoder_attention_mask is None:
    lowercase									=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:
    lowercase									=tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
  if decoder_head_mask is None:
    lowercase									=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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				unittest.TestCase ):
 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   ):
   lowercase									=TFLEDModelTester(self   )
   lowercase									=ConfigTester(self  , config_class=snake_case_   )







 def 						_A(       self   ):
   self.config_tester.run_common_tests()







 def 						_A(       self   ):
   lowercase									=self.model_tester.prepare_config_and_inputs_for_common()
   self.model_tester.check_decoder_model_past_large_inputs(*snake_case_   )







 def 						_A(       self   ):
   lowercase							, lowercase									=self.model_tester.prepare_config_and_inputs_for_common()
   lowercase									=tf.zeros_like(inputs_dict['''attention_mask''']   )
   lowercase									=2
   lowercase									=tf.where(
       tf.range(self.model_tester.seq_length   )[None, :] < num_global_attn_indices  , 1  , inputs_dict['''global_attention_mask''']  , )

   lowercase									=True
   lowercase									=self.model_tester.seq_length
   lowercase									=self.model_tester.encoder_seq_length

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

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

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

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

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







 @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.'''   )
 def 						_A(       self   ):
   pass







 def 						_A(       self   ):
   # TODO: Head-masking not yet implement
   pass




def 	UpperCamelCase   (       lowercase_						:    int	)		->	Optional[int]:




  '''simple docstring'''



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


_UpperCAmelCase  :		List[Any]  =		1e-4




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







 def 						_A(       self   ):
   lowercase									=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   ).led

   # change to intended input here
   lowercase									=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
   lowercase									=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
   lowercase									=prepare_led_inputs_dict(model.config  , snake_case_  , snake_case_   )
   lowercase									=model(**snake_case_   )[0]
   lowercase									=(1, 10_24, 7_68)
   self.assertEqual(output.shape  , snake_case_   )
   # change to expected output here
   lowercase									=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]  , snake_case_  , atol=1E-3   )







 def 						_A(       self   ):
   lowercase									=TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384'''   )

   # change to intended input here
   lowercase									=_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
   lowercase									=_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]   )
   lowercase									=prepare_led_inputs_dict(model.config  , snake_case_  , snake_case_   )
   lowercase									=model(**snake_case_   )[0]
   lowercase									=(1, 10_24, model.config.vocab_size)
   self.assertEqual(output.shape  , snake_case_   )
   # change to expected output here
   lowercase									=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]  , snake_case_  , atol=1E-3  , rtol=1E-3   )



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A									=  logging.get_logger(__name__)

A									=  {
    'google/bit-50': 'https://huggingface.co/google/bit-50/resolve/main/config.json',
}


class SCREAMING_SNAKE_CASE	(		__snake_case				,    __snake_case				):






			"""simple docstring"""




			__A    	=	"""bit"""
			__A    	=	["""preactivation""", """bottleneck"""]
			__A    	=	["""SAME""", """VALID"""]


			def __init__(     self			,					__UpperCamelCase=3			,					__UpperCamelCase=64			,					__UpperCamelCase=[2_56, 5_12, 10_24, 20_48]			,					__UpperCamelCase=[3, 4, 6, 3]			,					__UpperCamelCase="preactivation"			,					__UpperCamelCase="relu"			,					__UpperCamelCase=None			,					__UpperCamelCase=32			,					__UpperCamelCase=0.0			,					__UpperCamelCase=False			,					__UpperCamelCase=32			,					__UpperCamelCase=1			,					__UpperCamelCase=None			,					__UpperCamelCase=None			,					**__UpperCamelCase			,					):

				"""simple docstring"""


				super().__init__(**__UpperCamelCase				)
				if layer_type not in self.layer_types:
					raise ValueError(f"""layer_type={layer_type} is not one of {','.join(self.layer_types				)}"""				)
				if global_padding is not None:
					if global_padding.upper() in self.supported_padding:
						snake_case_   		=			global_padding.upper()
					else:
						raise ValueError(f"""Padding strategy {global_padding} not supported"""				)
				snake_case_   		=			num_channels
				snake_case_   		=			embedding_size
				snake_case_   		=			hidden_sizes
				snake_case_   		=			depths
				snake_case_   		=			layer_type
				snake_case_   		=			hidden_act
				snake_case_   		=			global_padding
				snake_case_   		=			num_groups
				snake_case_   		=			drop_path_rate
				snake_case_   		=			embedding_dynamic_padding
				snake_case_   		=			output_stride
				snake_case_   		=			width_factor

				snake_case_   		=			['stem'] + [f"""stage{idx}""" for idx in range(1			,					len(__UpperCamelCase				) + 1				)]
				snake_case_		,							snake_case_   		=			get_aligned_output_features_output_indices(
				    out_features=__UpperCamelCase			,					out_indices=__UpperCamelCase			,					stage_names=self.stage_names				)
