






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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 TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel




@require_tf
class     A    :






 """simple docstring"""





 __a			:						Dict      =						PegasusConfig
 __a			:						Any      =						{}
 __a			:						Optional[int]      =						'gelu'
 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase=13			,     __lowerCAmelCase=7			,     __lowerCAmelCase=True			,     __lowerCAmelCase=False			,     __lowerCAmelCase=99			,     __lowerCAmelCase=32			,     __lowerCAmelCase=2			,     __lowerCAmelCase=4			,     __lowerCAmelCase=37			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=40			,     __lowerCAmelCase=2			,     __lowerCAmelCase=1			,     __lowerCAmelCase=0			,     ):
       UpperCamelCase_				:   str					    =							parent
       UpperCamelCase_				:   Union[str, Any]					    =							batch_size
       UpperCamelCase_				:   List[str]					    =							seq_length
       UpperCamelCase_				:   Dict					    =							is_training
       UpperCamelCase_				:   Dict					    =							use_labels
       UpperCamelCase_				:   Any					    =							vocab_size
       UpperCamelCase_				:   Dict					    =							hidden_size
       UpperCamelCase_				:   Dict					    =							num_hidden_layers
       UpperCamelCase_				:   int					    =							num_attention_heads
       UpperCamelCase_				:   Any					    =							intermediate_size

       UpperCamelCase_				:   Tuple					    =							hidden_dropout_prob
       UpperCamelCase_				:   List[Any]					    =							attention_probs_dropout_prob
       UpperCamelCase_				:   int					    =							max_position_embeddings
       UpperCamelCase_				:   Any					    =							eos_token_id
       UpperCamelCase_				:   List[str]					    =							pad_token_id
       UpperCamelCase_				:   List[str]					    =							bos_token_id
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							ids_tensor([self.batch_size, self.seq_length - 1]			,     self.vocab_size )
       UpperCamelCase_				:   Union[str, Any]					    =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )			,     1 )
       UpperCamelCase_				:   Tuple					    =							tf.concat([input_ids, eos_tensor]			,     axis=1 )

       UpperCamelCase_				:   Tuple					    =							ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size )

       UpperCamelCase_				:   List[Any]					    =							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			,     **self.config_updates			,     )
       UpperCamelCase_				:   Union[str, Any]					    =							prepare_pegasus_inputs_dict(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )
       return config, inputs_dict





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Tuple					    =							TFPegasusModel(config=__lowerCAmelCase ).get_decoder()
       UpperCamelCase_				:   Optional[Any]					    =							inputs_dict["""input_ids"""]

       UpperCamelCase_				:   Optional[Any]					    =							input_ids[:1, :]
       UpperCamelCase_				:   str					    =							inputs_dict["""attention_mask"""][:1, :]
       UpperCamelCase_				:   Dict					    =							inputs_dict["""head_mask"""]
       UpperCamelCase_				:   int					    =							1

       # first forward pass
       UpperCamelCase_				:   Optional[int]					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase			,     head_mask=__lowerCAmelCase			,     use_cache=__lowerCAmelCase )

       UpperCamelCase_    ,			UpperCamelCase_				:   Union[str, Any]					    =							outputs.to_tuple()

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

       # append to next input_ids and
       UpperCamelCase_				:   Tuple					    =							tf.concat([input_ids, next_tokens]			,     axis=-1 )
       UpperCamelCase_				:   List[Any]					    =							tf.concat([attention_mask, next_attn_mask]			,     axis=-1 )

       UpperCamelCase_				:   Optional[Any]					    =							model(__lowerCAmelCase			,     attention_mask=__lowerCAmelCase )[0]
       UpperCamelCase_				:   Tuple					    =							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
       UpperCamelCase_				:   List[Any]					    =							int(ids_tensor((1,)			,     output_from_past.shape[-1] ) )
       UpperCamelCase_				:   List[Any]					    =							output_from_no_past[:, -3:, random_slice_idx]
       UpperCamelCase_				:   Optional[Any]					    =							output_from_past[:, :, random_slice_idx]

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






def 							snake_case					(     a_							: str    ,   a_							: Optional[int]    ,   a_							: Tuple    ,   a_							: Dict=None    ,   a_							: Any=None    ,   a_							: Dict=None    ,   a_							: Any=None    ,   a_							: Optional[int]=None    ,   )       ->				Dict:





      """simple docstring"""

      if attention_mask is None:
            UpperCamelCase_				:   Optional[Any]					    =							tf.cast(tf.math.not_equal(__SCREAMING_SNAKE_CASE    ,   config.pad_token_id	)    ,   tf.inta	)
      if decoder_attention_mask is None:
            UpperCamelCase_				:   List[Any]					    =							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_				:   List[Any]					    =							tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
      if decoder_head_mask is None:
            UpperCamelCase_				:   int					    =							tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
      if cross_attn_head_mask is None:
            UpperCamelCase_				:   Tuple					    =							tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }




@require_tf
class     A    (      lowercase__,   lowercase__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						Any      =						(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
 __a			:						Optional[int]      =						(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
 __a			:						Optional[Any]      =						(
     {
         'conversational': TFPegasusForConditionalGeneration,
         'feature-extraction': TFPegasusModel,
         'summarization': TFPegasusForConditionalGeneration,
         'text2text-generation': TFPegasusForConditionalGeneration,
         'translation': TFPegasusForConditionalGeneration,
     }
     if is_tf_available()
     else {}
 )
 __a			:						int      =						True
 __a			:						Tuple      =						False
 __a			:						List[str]      =						False
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							TFPegasusModelTester(self )
       UpperCamelCase_				:   List[str]					    =							ConfigTester(self			,     config_class=__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       self.config_tester.run_common_tests()





 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase )






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






 """simple docstring"""





 __a			:						Union[str, Any]      =						[
     ' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
     ' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
 ]
 __a			:						str      =						[
     'California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'
     ' reduce the risk of wildfires.',
     'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
 ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
 __a			:						Any      =						'google/pegasus-xsum'
 @cached_property
 def   _UpperCAmelCase       (							self ):
       return AutoTokenizer.from_pretrained(self.model_name )
 @cached_property
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   List[str]					    =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
       return model
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   Optional[Any]					    =							self.translate_src_text(**__lowerCAmelCase )
       assert self.expected_text == generated_words
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							self.tokenizer(self.src_text			,     **__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors="""tf""" )
       UpperCamelCase_				:   Dict					    =							self.model.generate(
           model_inputs.input_ids			,     attention_mask=model_inputs.attention_mask			,     num_beams=2			,     use_cache=__lowerCAmelCase			,     )
       UpperCamelCase_				:   Union[str, Any]					    =							self.tokenizer.batch_decode(generated_ids.numpy()			,     skip_special_tokens=__lowerCAmelCase )
       return generated_words





 @slow
 def   _UpperCAmelCase       (							self ):
       self._assert_generated_batch_equal_expected()














'''simple docstring'''

import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						int      =						0
 __a			:						bool      =						False
 __a			:						float      =						3.0






class     A    (      unittest.TestCase						):






 """simple docstring"""





 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       self.assertDictEqual(MockClass().to_kwargs()			,     {} )
       self.assertDictEqual(MockClass(a=2 ).to_kwargs()			,     {"""a""": 2} )
       self.assertDictEqual(MockClass(a=2			,     b=__lowerCAmelCase ).to_kwargs()			,     {"""a""": 2, """b""": True} )
       self.assertDictEqual(MockClass(a=2			,     c=2.25 ).to_kwargs()			,     {"""a""": 2, """c""": 2.25} )
 @require_cuda
 def   _UpperCAmelCase       (							self ):
       # If no defaults are changed, `to_kwargs` returns an empty dict.
       UpperCamelCase_				:   int					    =							GradScalerKwargs(init_scale=10_24			,     growth_factor=2 )
       AcceleratorState._reset_state()
       UpperCamelCase_				:   Dict					    =							Accelerator(mixed_precision="""fp16"""			,     kwargs_handlers=[scaler_handler] )
       print(accelerator.use_fpaa )
       UpperCamelCase_				:   Any					    =							accelerator.scaler

       # Check the kwargs have been applied
       self.assertEqual(scaler._init_scale			,     10_24.0 )
       self.assertEqual(scaler._growth_factor			,     2.0 )

       # Check the other values are at the default
       self.assertEqual(scaler._backoff_factor			,     0.5 )
       self.assertEqual(scaler._growth_interval			,     20_00 )
       self.assertEqual(scaler._enabled			,     __lowerCAmelCase )





 @require_multi_gpu
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							["""torchrun""", F"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__ )]
       execute_subprocess_async(__lowerCAmelCase			,     env=os.environ.copy() )


if __name__ == "__main__":
      UpperCamelCase							=DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
      UpperCamelCase							=Accelerator(kwargs_handlers=[ddp_scaler])
      UpperCamelCase							=torch.nn.Linear(100, 200)
      UpperCamelCase							=accelerator.prepare(model)

      # Check the values changed in kwargs
      UpperCamelCase							=""
      UpperCamelCase							=model.bucket_bytes_cap // (1024 * 1024)
      if observed_bucket_cap_map != 15:
            error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
      if model.find_unused_parameters is not True:
            error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

      # Check the values of the defaults
      if model.dim != 0:
            error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
      if model.broadcast_buffers is not True:
            error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
      if model.gradient_as_bucket_view is not False:
            error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

      # Raise error at the end to make sure we don't stop at the first failure.
      if len(error_msg) > 0:
            raise ValueError(error_msg)






