

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__		:






  """simple docstring"""


  a									= LEDConfig
  a									= {}
  a									= "gelu"






  def __init__(							self						:		List[str]     ,       __lowerCamelCase						:		str     ,       __lowerCamelCase						:		Union[str, Any]=13     ,       __lowerCamelCase						:		Any=7     ,       __lowerCamelCase						:		Union[str, Any]=True     ,       __lowerCamelCase						:		List[Any]=False     ,       __lowerCamelCase						:		str=99     ,       __lowerCamelCase						:		Tuple=32     ,       __lowerCamelCase						:		Optional[Any]=2     ,       __lowerCamelCase						:		Tuple=4     ,       __lowerCamelCase						:		Optional[int]=37     ,       __lowerCamelCase						:		str=0.1     ,       __lowerCamelCase						:		List[str]=0.1     ,       __lowerCamelCase						:		List[str]=20     ,       __lowerCamelCase						:		List[str]=2     ,       __lowerCamelCase						:		List[Any]=1     ,       __lowerCamelCase						:		List[str]=0     ,       __lowerCamelCase						:		Tuple=4     ,       )		->							Any:
       SCREAMING_SNAKE_CASE__   =   parent
       SCREAMING_SNAKE_CASE__   =   batch_size
       SCREAMING_SNAKE_CASE__   =   seq_length
       SCREAMING_SNAKE_CASE__   =   is_training
       SCREAMING_SNAKE_CASE__   =   use_labels
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   num_hidden_layers
       SCREAMING_SNAKE_CASE__   =   num_attention_heads
       SCREAMING_SNAKE_CASE__   =   intermediate_size

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






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

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

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







  def 				lowercase_		(							self						:		Dict     ,       __lowerCamelCase						:		Optional[int]     ,       __lowerCamelCase						:		Tuple )		->							int:
       SCREAMING_SNAKE_CASE__   =   TFLEDModel(config=__lowerCamelCase ).get_decoder()
       SCREAMING_SNAKE_CASE__   =   inputs_dict['''input_ids''']

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

       # first forward pass
       SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       attention_mask=__lowerCamelCase     ,       use_cache=__lowerCamelCase )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   outputs.to_tuple()

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

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

       SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       attention_mask=__lowerCamelCase )[0]
       SCREAMING_SNAKE_CASE__   =   model(__lowerCamelCase     ,       attention_mask=__lowerCamelCase     ,       past_key_values=__lowerCamelCase )[0]

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

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

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



def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A=None				,		_A=None				,		_A=None				,		_A=None				,		):





     '''simple docstring'''




     if attention_mask is None:
          SCREAMING_SNAKE_CASE__   =   tf.cast(tf.math.not_equal(_A				,		config.pad_token_id    )				,		tf.inta    )
     if decoder_attention_mask is None:
          SCREAMING_SNAKE_CASE__   =   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:
          SCREAMING_SNAKE_CASE__   =   tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
     if decoder_head_mask is None:
          SCREAMING_SNAKE_CASE__   =   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	):






  """simple docstring"""


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






  def 				lowercase_		(							self						:		str )		->							str:
       SCREAMING_SNAKE_CASE__   =   TFLEDModelTester(self )
       SCREAMING_SNAKE_CASE__   =   ConfigTester(self     ,       config_class=__lowerCamelCase )






  def 				lowercase_		(							self						:		str )		->							Tuple:
       self.config_tester.run_common_tests()






  def 				lowercase_		(							self						:		Any )		->							int:
       SCREAMING_SNAKE_CASE__   =   self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__lowerCamelCase )






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

       SCREAMING_SNAKE_CASE__   =   True
       SCREAMING_SNAKE_CASE__   =   self.model_tester.seq_length
       SCREAMING_SNAKE_CASE__   =   self.model_tester.encoder_seq_length

       def check_decoder_attentions_output(__lowerCamelCase						:		Union[str, Any] ):
            SCREAMING_SNAKE_CASE__   =   outputs.decoder_attentions
            self.assertEqual(len(__lowerCamelCase )     ,       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(__lowerCamelCase						:		Optional[Any] ):
            SCREAMING_SNAKE_CASE__   =   [t.numpy() for t in outputs.encoder_attentions]
            SCREAMING_SNAKE_CASE__   =   [t.numpy() for t in outputs.encoder_global_attentions]
            self.assertEqual(len(__lowerCamelCase )     ,       self.model_tester.num_hidden_layers )
            self.assertEqual(len(__lowerCamelCase )     ,       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:
            SCREAMING_SNAKE_CASE__   =   True
            SCREAMING_SNAKE_CASE__   =   False
            SCREAMING_SNAKE_CASE__   =   False
            SCREAMING_SNAKE_CASE__   =   model_class(__lowerCamelCase )
            SCREAMING_SNAKE_CASE__   =   model(self._prepare_for_class(__lowerCamelCase     ,       __lowerCamelCase ) )
            SCREAMING_SNAKE_CASE__   =   len(__lowerCamelCase )
            self.assertEqual(config.output_hidden_states     ,       __lowerCamelCase )
            check_encoder_attentions_output(__lowerCamelCase )

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

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

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

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






  @unittest.skip('''LED keeps using potentially symbolic tensors in conditionals and breaks tracing.''' )
  def 				lowercase_		(							self						:		Union[str, Any] )		->							str:
       pass







  def 				lowercase_		(							self						:		int )		->							Optional[Any]:
       # TODO: Head-masking not yet implement
       pass



def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




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


_SCREAMING_SNAKE_CASE					:  Dict             =      1e-4







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






  """simple docstring"""


  def 				lowercase_		(							self						:		Optional[int] )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' ).led

       # change to intended input here
       SCREAMING_SNAKE_CASE__   =   _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
       SCREAMING_SNAKE_CASE__   =   _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
       SCREAMING_SNAKE_CASE__   =   prepare_led_inputs_dict(model.config     ,       __lowerCamelCase     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   model(**__lowerCamelCase )[0]
       SCREAMING_SNAKE_CASE__   =   (1, 1024, 768)
       self.assertEqual(output.shape     ,       __lowerCamelCase )
       # change to expected output here
       SCREAMING_SNAKE_CASE__   =   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]     ,       __lowerCamelCase     ,       atol=1e-3 )







  def 				lowercase_		(							self						:		str )		->							List[Any]:
       SCREAMING_SNAKE_CASE__   =   TFLEDForConditionalGeneration.from_pretrained('''allenai/led-base-16384''' )

       # change to intended input here
       SCREAMING_SNAKE_CASE__   =   _long_tensor([512 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
       SCREAMING_SNAKE_CASE__   =   _long_tensor([128 * [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69]] )
       SCREAMING_SNAKE_CASE__   =   prepare_led_inputs_dict(model.config     ,       __lowerCamelCase     ,       __lowerCamelCase )
       SCREAMING_SNAKE_CASE__   =   model(**__lowerCamelCase )[0]
       SCREAMING_SNAKE_CASE__   =   (1, 1024, model.config.vocab_size)
       self.assertEqual(output.shape     ,       __lowerCamelCase )
       # change to expected output here
       SCREAMING_SNAKE_CASE__   =   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]     ,       __lowerCamelCase     ,       atol=1e-3     ,       rtol=1e-3 )





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  int             =      {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}






class 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "decision_transformer"
  a									= ["past_key_values"]
  a									= {
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		Any=17     ,       __lowerCamelCase						:		Any=4     ,       __lowerCamelCase						:		List[Any]=128     ,       __lowerCamelCase						:		Union[str, Any]=4096     ,       __lowerCamelCase						:		Optional[Any]=True     ,       __lowerCamelCase						:		Any=1     ,       __lowerCamelCase						:		List[Any]=1024     ,       __lowerCamelCase						:		Any=3     ,       __lowerCamelCase						:		Dict=1     ,       __lowerCamelCase						:		List[Any]=None     ,       __lowerCamelCase						:		str="relu"     ,       __lowerCamelCase						:		Dict=0.1     ,       __lowerCamelCase						:		int=0.1     ,       __lowerCamelCase						:		List[str]=0.1     ,       __lowerCamelCase						:		str=1e-5     ,       __lowerCamelCase						:		Optional[Any]=0.02     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		Optional[int]=5_0256     ,       __lowerCamelCase						:		Tuple=5_0256     ,       __lowerCamelCase						:		int=False     ,       __lowerCamelCase						:		Optional[int]=False     ,       **__lowerCamelCase						:		Tuple     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   state_dim
       SCREAMING_SNAKE_CASE__   =   act_dim
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   max_ep_len
       SCREAMING_SNAKE_CASE__   =   action_tanh
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   n_positions
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   n_inner
       SCREAMING_SNAKE_CASE__   =   activation_function
       SCREAMING_SNAKE_CASE__   =   resid_pdrop
       SCREAMING_SNAKE_CASE__   =   embd_pdrop
       SCREAMING_SNAKE_CASE__   =   attn_pdrop
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   scale_attn_weights
       SCREAMING_SNAKE_CASE__   =   use_cache
       SCREAMING_SNAKE_CASE__   =   scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE__   =   reorder_and_upcast_attn

       SCREAMING_SNAKE_CASE__   =   bos_token_id
       SCREAMING_SNAKE_CASE__   =   eos_token_id

       super().__init__(bos_token_id=__lowerCamelCase     ,       eos_token_id=__lowerCamelCase     ,       **__lowerCamelCase )


