'''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 				_lowercase     :


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					LEDConfig
 _SCREAMING_SNAKE_CASE     :       Any      	=					{}
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					"""gelu"""
 def __init__(      self   :	List[Any]      ,       SCREAMING_SNAKE_CASE__   :	Any      ,       SCREAMING_SNAKE_CASE__   :	str=13      ,       SCREAMING_SNAKE_CASE__   :	int=7      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=True      ,       SCREAMING_SNAKE_CASE__   :	int=False      ,       SCREAMING_SNAKE_CASE__   :	Tuple=99      ,       SCREAMING_SNAKE_CASE__   :	str=32      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=2      ,       SCREAMING_SNAKE_CASE__   :	Tuple=4      ,       SCREAMING_SNAKE_CASE__   :	Tuple=37      ,       SCREAMING_SNAKE_CASE__   :	Any=0.1      ,       SCREAMING_SNAKE_CASE__   :	List[Any]=0.1      ,       SCREAMING_SNAKE_CASE__   :	Dict=20      ,       SCREAMING_SNAKE_CASE__   :	Optional[int]=2      ,       SCREAMING_SNAKE_CASE__   :	Union[str, Any]=1      ,       SCREAMING_SNAKE_CASE__   :	Tuple=0      ,       SCREAMING_SNAKE_CASE__   :	List[str]=4      ,       )  ->    Optional[int]:
  __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]							)  ->    str:
  __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(__snake_case      ,       __snake_case      ,       __snake_case							)
  __lowerCAmelCase											=						tf.concat(
      [tf.zeros_like(__snake_case							)[:, :-1], tf.ones_like(__snake_case							)[:, -1:]]      ,       axis=-1      ,       )
  __lowerCAmelCase											=						global_attention_mask
  return config, inputs_dict





 def a					(      self   :	Tuple      ,       SCREAMING_SNAKE_CASE__   :	str      ,       SCREAMING_SNAKE_CASE__   :	int							)  ->    int:
  __lowerCAmelCase											=						TFLEDModel(config=__snake_case							).get_decoder()
  __lowerCAmelCase											=						inputs_dict["""input_ids"""]

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

  # first forward pass
  __lowerCAmelCase											=						model(__snake_case      ,       attention_mask=__snake_case      ,       use_cache=__snake_case							)

  __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(__snake_case      ,       attention_mask=__snake_case							)[0]
  __lowerCAmelCase											=						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
  __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(__snake_case      ,       __snake_case      ,       rtol=1e-3							)







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






 '''simple docstring'''

 if attention_mask is None:
  __lowerCAmelCase											=						tf.cast(tf.math.not_equal(_snake_case						,		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 				_lowercase     (				lowerCAmelCase_  ,							lowerCAmelCase_  ,							unittest.TestCase			):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE     :       Optional[int]      	=					(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
 _SCREAMING_SNAKE_CASE     :       List[str]      	=					(TFLEDForConditionalGeneration,) if is_tf_available() else ()
 _SCREAMING_SNAKE_CASE     :       Union[str, Any]      	=					(
     {
         """conversational""": TFLEDForConditionalGeneration,
         """feature-extraction""": TFLEDModel,
         """summarization""": TFLEDForConditionalGeneration,
         """text2text-generation""": TFLEDForConditionalGeneration,
         """translation""": TFLEDForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 _SCREAMING_SNAKE_CASE     :       Dict      	=					True
 _SCREAMING_SNAKE_CASE     :       Dict      	=					False
 _SCREAMING_SNAKE_CASE     :       Dict      	=					False
 _SCREAMING_SNAKE_CASE     :       str      	=					False
 def a					(      self   :	int							)  ->    Optional[Any]:
  __lowerCAmelCase											=						TFLEDModelTester(self							)
  __lowerCAmelCase											=						ConfigTester(self      ,       config_class=__snake_case							)
 def a					(      self   :	str							)  ->    Optional[int]:
  self.config_tester.run_common_tests()
 def a					(      self   :	Optional[int]							)  ->    Tuple:
  __lowerCAmelCase											=						self.model_tester.prepare_config_and_inputs_for_common()
  self.model_tester.check_decoder_model_past_large_inputs(*__snake_case							)
 def a					(      self   :	List[Any]							)  ->    Any:
  __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(SCREAMING_SNAKE_CASE__   :	Optional[Any]							):
   __lowerCAmelCase											=						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(SCREAMING_SNAKE_CASE__   :	Optional[int]							):
   __lowerCAmelCase											=						[t.numpy() for t in outputs.encoder_attentions]
   __lowerCAmelCase											=						[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:
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						False
   __lowerCAmelCase											=						False
   __lowerCAmelCase											=						model_class(__snake_case							)
   __lowerCAmelCase											=						model(self._prepare_for_class(__snake_case      ,       __snake_case							)							)
   __lowerCAmelCase											=						len(__snake_case							)
   self.assertEqual(config.output_hidden_states      ,       __snake_case							)
   check_encoder_attentions_output(__snake_case							)

   if self.is_encoder_decoder:
    __lowerCAmelCase											=						model_class(__snake_case							)
    __lowerCAmelCase											=						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"]
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						model_class(__snake_case							)
   __lowerCAmelCase											=						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
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						True
   __lowerCAmelCase											=						model_class(__snake_case							)
   __lowerCAmelCase											=						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   :	Optional[int]							)  ->    str:
  pass





 def a					(      self   :	Dict							)  ->    List[Any]:
  pass







def      UpperCamelCase_						(					snake_case_       :       Dict      )					->     Union[str, Any]:






 '''simple docstring'''

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


_A			:      Optional[int]					      =				1e-4







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


 '''simple docstring'''






 def a					(      self   :	List[Any]							)  ->    Optional[Any]:
  __lowerCAmelCase											=						TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							).led

  # change to intended input here
  __lowerCAmelCase											=						_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						prepare_led_inputs_dict(model.config      ,       __snake_case      ,       __snake_case							)
  __lowerCAmelCase											=						model(**__snake_case							)[0]
  __lowerCAmelCase											=						(1, 10_24, 7_68)
  self.assertEqual(output.shape      ,       __snake_case							)
  # change to expected output here
  __lowerCAmelCase											=						tf.convert_to_tensor(
      [[2.3_0_5_0, 2.8_2_7_9, 0.6_5_3_1], [-1.8_4_5_7, -0.1_4_5_5, -3.5_6_6_1], [-1.0_1_8_6, 0.4_5_8_6, -2.2_0_4_3]]      ,       )
  tf.debugging.assert_near(output[:, :3, :3]      ,       __snake_case      ,       atol=1e-3							)





 def a					(      self   :	Any							)  ->    Tuple:
  __lowerCAmelCase											=						TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""							)

  # change to intended input here
  __lowerCAmelCase											=						_long_tensor([5_12 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						_long_tensor([1_28 * [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69]]							)
  __lowerCAmelCase											=						prepare_led_inputs_dict(model.config      ,       __snake_case      ,       __snake_case							)
  __lowerCAmelCase											=						model(**__snake_case							)[0]
  __lowerCAmelCase											=						(1, 10_24, model.config.vocab_size)
  self.assertEqual(output.shape      ,       __snake_case							)
  # change to expected output here
  __lowerCAmelCase											=						tf.convert_to_tensor(
      [[3_3.6_5_0_7, 6.4_5_7_2, 1_6.8_0_8_9], [5.8_7_3_9, -2.4_2_3_8, 1_1.2_9_0_2], [-3.2_1_3_9, -4.3_1_4_9, 4.2_7_8_3]]      ,       )
  tf.debugging.assert_near(output[:, :3, :3]      ,       __snake_case      ,       atol=1e-3      ,       rtol=1e-3							)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase					:			Tuple  				=			{
    "configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "LlamaConfig"],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Optional[int]  				=			["LlamaTokenizer"]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Optional[int]  				=			["LlamaTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			List[Any]  				=			[
						    "LlamaForCausalLM",
						    "LlamaModel",
						    "LlamaPreTrainedModel",
						    "LlamaForSequenceClassification",
						]


if TYPE_CHECKING:
						from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama import LlamaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_llama_fast import LlamaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel


else:
						import sys

						_lowerCAmelCase					:			int  				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






