'''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'''





import os

import numpy
import onnx





def      lowerCAmelCase_    ( snake_case_	:			Union[str, Any] ,   snake_case_	:			Optional[int]      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       a.name
     UpperCAmelCase_	   =       b.name

     UpperCAmelCase_	   =       ""
     UpperCAmelCase_	   =       ""

     UpperCAmelCase_	   =       a == b

     UpperCAmelCase_	   =       name_a
     UpperCAmelCase_	   =       name_b

     return res





def      lowerCAmelCase_    ( snake_case_	:			Any ,   snake_case_	:			Tuple ,   snake_case_	:			Union[str, Any]      )   ->				Any:


     '''simple docstring'''




     for i, input_name in enumerate(node_proto.input      ):
          if input_name == name:
               node_proto.input.insert(snake_case_ ,   snake_case_      )
               node_proto.input.pop(i + 1      )

     if node_proto.op_type == "If":
          _graph_replace_input_with(node_proto.attribute[0].g ,   snake_case_ ,   snake_case_      )
          _graph_replace_input_with(node_proto.attribute[1].g ,   snake_case_ ,   snake_case_      )
     if node_proto.op_type == "Loop":
          _graph_replace_input_with(node_proto.attribute[0].g ,   snake_case_ ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			List[str] ,   snake_case_	:			Union[str, Any]      )   ->				Optional[int]:


     '''simple docstring'''




     for n in graph_proto.node:
          _node_replace_input_with(snake_case_ ,   snake_case_ ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			List[str] ,   snake_case_	:			Any ,   snake_case_	:			Optional[Any]      )   ->				Optional[int]:


     '''simple docstring'''




     UpperCAmelCase_	   =       list(model.graph.initializer      )
     UpperCAmelCase_	   =       list(model_without_ext.graph.initializer      )
     for i, ref_i in ind_to_replace:
          assert inits_with_data[i].name == inits[i].name
          assert inits_with_data[ref_i].name == inits[ref_i].name
          assert i > ref_i

          UpperCAmelCase_	   =       inits[i].name
          UpperCAmelCase_	   =       inits[ref_i].name

          model_without_ext.graph.initializer.remove(inits[i]      )

          # for n in model.graph.node:
          _graph_replace_input_with(model_without_ext.graph ,   snake_case_ ,   snake_case_      )






def      lowerCAmelCase_    ( snake_case_	:			int      )   ->				Optional[Any]:


     '''simple docstring'''




     UpperCAmelCase_	   =       os.path.dirname(snake_case_      )
     UpperCAmelCase_	   =       os.path.basename(snake_case_      )

     UpperCAmelCase_	   =       onnx.load(os.path.join(snake_case_ ,   snake_case_      )      )

     UpperCAmelCase_	   =       list(model.graph.initializer      )

     UpperCAmelCase_	   =       set()
     UpperCAmelCase_	   =       {}
     UpperCAmelCase_	   =       []

     UpperCAmelCase_	   =       0

     for i in range(len(snake_case_      )      ):
          if i in dup_set:
               continue

          for j in range(i + 1 ,   len(snake_case_      )      ):
               if j in dup_set:
                    continue
               if _is_equal_tensor_proto(inits[i] ,   inits[j]      ):
                    dup_set.add(snake_case_      )
                    dup_set.add(snake_case_      )

                    UpperCAmelCase_	   =       inits[j].data_type
                    UpperCAmelCase_	   =       numpy.prod(inits[j].dims      )
                    if dtype == 1:
                         mem_size *= 4
                    elif dtype == 6:
                         mem_size *= 4
                    elif dtype == 7 or dtype == 11:
                         mem_size *= 8
                    else:
                         print("unexpected data type: " ,   snake_case_      )
                    total_reduced_size += mem_size

                    UpperCAmelCase_	   =       inits[i].name
                    UpperCAmelCase_	   =       inits[j].name

                    if name_i in dup_map:
                         dup_map[name_i].append(snake_case_      )
                    else:
                         UpperCAmelCase_	   =       [name_j]
                    ind_to_replace.append((j, i)      )

     print("total reduced size: " ,   total_reduced_size / 10_24 / 10_24 / 10_24 ,   "GB"      )

     UpperCAmelCase_	   =       sorted(snake_case_      )
     _remove_dup_initializers_from_model(snake_case_ ,   snake_case_ ,   snake_case_      )

     UpperCAmelCase_	   =       "optimized_" + model_file_name
     UpperCAmelCase_	   =       os.path.join(snake_case_ ,   snake_case_      )
     onnx.save(snake_case_ ,   snake_case_      )

     return new_model




