


import unittest

from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     UpperCamelCase								=	"""platform"""
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class   _lowerCamelCase :




       """simple docstring"""
       snake_case      =    PegasusConfig
       snake_case      =    {}
       snake_case      =    "gelu"



       def __init__(      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=13				,       _SCREAMING_SNAKE_CASE=7				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=False				,       _SCREAMING_SNAKE_CASE=99				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=5				,       _SCREAMING_SNAKE_CASE=4				,       _SCREAMING_SNAKE_CASE=37				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=20				,       _SCREAMING_SNAKE_CASE=2				,       _SCREAMING_SNAKE_CASE=1				,       _SCREAMING_SNAKE_CASE=0				,       )->Optional[Any]:




         '''simple docstring'''





         A_      :				Any		     =					parent
         A_      :				Tuple		     =					batch_size
         A_      :				Union[str, Any]		     =					seq_length
         A_      :				Optional[Any]		     =					is_training
         A_      :				List[str]		     =					use_labels
         A_      :				Optional[Any]		     =					vocab_size
         A_      :				Dict		     =					hidden_size
         A_      :				Dict		     =					num_hidden_layers
         A_      :				int		     =					num_attention_heads
         A_      :				Optional[int]		     =					intermediate_size

         A_      :				str		     =					hidden_dropout_prob
         A_      :				Union[str, Any]		     =					attention_probs_dropout_prob
         A_      :				Any		     =					max_position_embeddings
         A_      :				int		     =					eos_token_id
         A_      :				int		     =					pad_token_id
         A_      :				Optional[int]		     =					bos_token_id



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_      :				str		     =					ids_tensor([self.batch_size, self.seq_length - 1]				,       self.vocab_size		).clip(3				,       self.vocab_size		)
         A_      :				Any		     =					np.expand_dims(np.array([self.eos_token_id] * self.batch_size		)				,       1		)
         A_      :				Any		     =					np.concatenate([input_ids, eos_tensor]				,       axis=1		)

         A_      :				int		     =					ids_tensor([self.batch_size, self.seq_length]				,       self.vocab_size		)

         A_      :				Optional[int]		     =					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				,       )
         A_      :				str		     =					prepare_pegasus_inputs_dict(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         return config, inputs_dict



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->str:




         '''simple docstring'''





         A_      :				Tuple		     =					20
         A_      :				Union[str, Any]		     =					model_class_name(_SCREAMING_SNAKE_CASE		)

         A_      :				Dict		     =					model.encode(inputs_dict['''input_ids''']		)

         A_     ,       A_      :				str		     =					(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         A_      :				Optional[Any]		     =					model.init_cache(decoder_input_ids.shape[0]				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				int		     =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)				,       dtype='''i4'''		)

         A_      :				List[Any]		     =					jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )
         A_      :				Any		     =					model.decode(
             decoder_input_ids[:, :-1]				,       _SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       past_key_values=_SCREAMING_SNAKE_CASE				,       decoder_position_ids=_SCREAMING_SNAKE_CASE				,       )

         A_      :				Optional[int]		     =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype='''i4'''		)
         A_      :				str		     =					model.decode(
             decoder_input_ids[:, -1:]				,       _SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       past_key_values=outputs_cache.past_key_values				,       decoder_position_ids=_SCREAMING_SNAKE_CASE				,       )

         A_      :				Optional[Any]		     =					model.decode(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)

         A_      :				Optional[int]		     =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
         self.parent.assertTrue(diff < 1e-3				,       msg=F'''Max diff is {diff}'''		)







       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)->Any:




         '''simple docstring'''





         A_      :				Tuple		     =					20
         A_      :				Union[str, Any]		     =					model_class_name(_SCREAMING_SNAKE_CASE		)

         A_      :				List[Any]		     =					model.encode(inputs_dict['''input_ids''']		)

         A_     ,       A_      :				List[Any]		     =					(
             inputs_dict['''decoder_input_ids'''],
             inputs_dict['''decoder_attention_mask'''],
         )

         A_      :				List[Any]		     =					jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
             ]				,       axis=-1				,       )

         A_      :				Union[str, Any]		     =					model.init_cache(decoder_input_ids.shape[0]				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
         A_      :				Dict		     =					jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :]				,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)				,       )

         A_      :				Tuple		     =					model.decode(
             decoder_input_ids[:, :-1]				,       _SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       past_key_values=_SCREAMING_SNAKE_CASE				,       decoder_position_ids=_SCREAMING_SNAKE_CASE				,       )
         A_      :				int		     =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]				,       dtype='''i4'''		)
         A_      :				Dict		     =					model.decode(
             decoder_input_ids[:, -1:]				,       _SCREAMING_SNAKE_CASE				,       past_key_values=outputs_cache.past_key_values				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       decoder_position_ids=_SCREAMING_SNAKE_CASE				,       )

         A_      :				Union[str, Any]		     =					model.decode(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE		)

         A_      :				Union[str, Any]		     =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
         self.parent.assertTrue(diff < 1e-3				,       msg=F'''Max diff is {diff}'''		)




def   _SCREAMING_SNAKE_CASE	( SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE=None ,			SCREAMING_SNAKE_CASE=None ,			):
  if attention_mask is None:
    A_      :				Union[str, Any]		     =					np.not_equal(SCREAMING_SNAKE_CASE ,			config.pad_token_id       ).astype(np.inta       )
  if decoder_attention_mask is None:
    A_      :				str		     =					np.concatenate(
        [
            np.ones(decoder_input_ids[:, :1].shape ,			dtype=np.inta       ),
            np.not_equal(decoder_input_ids[:, 1:] ,			config.pad_token_id       ).astype(np.inta       ),
        ] ,			axis=-1 ,			)
  return {
      "input_ids": input_ids,
      "decoder_input_ids": decoder_input_ids,
      "attention_mask": attention_mask,
      "decoder_attention_mask": decoder_attention_mask,
  }







@require_flax
class   _lowerCamelCase (					UpperCamelCase					,							unittest.TestCase			):




       """simple docstring"""
       snake_case      =    (
           (
               FlaxPegasusForConditionalGeneration,
               FlaxPegasusModel,
           )
           if is_flax_available()
           else ()
       )
       snake_case      =    (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
       snake_case      =    True
       snake_case      =    False
       snake_case      =    False
       snake_case      =    False



       def        _snake_case       (      self		)->List[str]:




         '''simple docstring'''





         A_      :				Any		     =					FlaxPegasusModelTester(self		)
         A_      :				Dict		     =					ConfigTester(self				,       config_class=_SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         self.config_tester.run_common_tests()



       def        _snake_case       (      self		)->List[Any]:




         '''simple docstring'''





         A_     ,       A_      :				List[str]		     =					self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				Optional[int]		     =					self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
           self.model_tester.check_use_cache_forward_with_attn_mask(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)



       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_     ,       A_      :				Optional[int]		     =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__		):
             A_      :				Dict		     =					self._prepare_for_class(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		)
             A_      :				Optional[Any]		     =					model_class(_SCREAMING_SNAKE_CASE		)

             @jax.jit
             def encode_jitted(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		):
               return model.encode(input_ids=_SCREAMING_SNAKE_CASE				,       attention_mask=_SCREAMING_SNAKE_CASE		)

             with self.subTest('''JIT Enabled'''		):
               A_      :				int		     =					encode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

             with self.subTest('''JIT Disabled'''		):
               with jax.disable_jit():
                 A_      :				List[str]		     =					encode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

             self.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       len(_SCREAMING_SNAKE_CASE		)		)
             for jitted_output, output in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
               self.assertEqual(jitted_output.shape				,       output.shape		)



       def        _snake_case       (      self		)->Tuple:




         '''simple docstring'''





         A_     ,       A_      :				int		     =					self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__		):
             A_      :				int		     =					model_class(_SCREAMING_SNAKE_CASE		)
             A_      :				Optional[Any]		     =					model.encode(inputs_dict['''input_ids''']				,       inputs_dict['''attention_mask''']		)

             A_      :				Dict		     =					{
                 '''decoder_input_ids''': inputs_dict['''decoder_input_ids'''],
                 '''decoder_attention_mask''': inputs_dict['''decoder_attention_mask'''],
                 '''encoder_outputs''': encoder_outputs,
             }

             @jax.jit
             def decode_jitted(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
               return model.decode(
                   decoder_input_ids=_SCREAMING_SNAKE_CASE				,       decoder_attention_mask=_SCREAMING_SNAKE_CASE				,       encoder_outputs=_SCREAMING_SNAKE_CASE				,       )

             with self.subTest('''JIT Enabled'''		):
               A_      :				Tuple		     =					decode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

             with self.subTest('''JIT Disabled'''		):
               with jax.disable_jit():
                 A_      :				Optional[int]		     =					decode_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

             self.assertEqual(len(_SCREAMING_SNAKE_CASE		)				,       len(_SCREAMING_SNAKE_CASE		)		)
             for jitted_output, output in zip(_SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE		):
               self.assertEqual(jitted_output.shape				,       output.shape		)



       @slow
       def        _snake_case       (      self		)->Optional[Any]:




         '''simple docstring'''





         for model_class_name in self.all_model_classes:
           A_      :				Any		     =					model_class_name.from_pretrained('''google/pegasus-large'''				,       from_pt=_SCREAMING_SNAKE_CASE		)
           A_      :				List[Any]		     =					np.ones((1, 1)		)
           A_      :				Optional[Any]		     =					model(_SCREAMING_SNAKE_CASE		)
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)







       @slow
       def        _snake_case       (      self		)->Union[str, Any]:




         '''simple docstring'''





         A_      :				Dict		     =					FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum'''		)
         A_      :				List[str]		     =					PegasusTokenizer.from_pretrained('''google/pegasus-xsum'''		)

         A_      :				Tuple		     =					[
             ''' 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_      :				Union[str, Any]		     =					[
             '''California\'s largest electricity provider has turned off power to hundreds of thousands of customers.''',
             '''Pop group N-Dubz have revealed they were surprised to get four nominations for this year\'s Mobo Awards.''',
         ]

         A_      :				Tuple		     =					tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors='''np'''				,       truncation=_SCREAMING_SNAKE_CASE				,       max_length=512				,       padding=_SCREAMING_SNAKE_CASE		)
         A_      :				List[str]		     =					model.generate(**_SCREAMING_SNAKE_CASE				,       num_beams=2		).sequences
         A_      :				Dict		     =					tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				,       skip_special_tokens=_SCREAMING_SNAKE_CASE		)
         assert tgt_text == decoded








from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase								=	logging.get_logger(__name__)

UpperCamelCase								=	{
    """t5-small""": """https://huggingface.co/t5-small/resolve/main/config.json""",
    """t5-base""": """https://huggingface.co/t5-base/resolve/main/config.json""",
    """t5-large""": """https://huggingface.co/t5-large/resolve/main/config.json""",
    """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/config.json""",
    """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/config.json""",
}






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""
       snake_case      =    "t5"
       snake_case      =    ["past_key_values"]
       snake_case      =    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}



       def __init__(      self				,       _SCREAMING_SNAKE_CASE=3_2128				,       _SCREAMING_SNAKE_CASE=512				,       _SCREAMING_SNAKE_CASE=64				,       _SCREAMING_SNAKE_CASE=2048				,       _SCREAMING_SNAKE_CASE=6				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=8				,       _SCREAMING_SNAKE_CASE=32				,       _SCREAMING_SNAKE_CASE=128				,       _SCREAMING_SNAKE_CASE=0.1				,       _SCREAMING_SNAKE_CASE=1e-6				,       _SCREAMING_SNAKE_CASE=1.0				,       _SCREAMING_SNAKE_CASE="relu"				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=True				,       _SCREAMING_SNAKE_CASE=0				,       _SCREAMING_SNAKE_CASE=1				,       **_SCREAMING_SNAKE_CASE				,       )->List[Any]:




         '''simple docstring'''





         A_      :				List[Any]		     =					vocab_size
         A_      :				int		     =					d_model
         A_      :				Optional[Any]		     =					d_kv
         A_      :				str		     =					d_ff
         A_      :				int		     =					num_layers
         A_      :				Any		     =					(
             num_decoder_layers if num_decoder_layers is not None else self.num_layers
         )  # default = symmetry
         A_      :				Optional[Any]		     =					num_heads
         A_      :				Union[str, Any]		     =					relative_attention_num_buckets
         A_      :				Dict		     =					relative_attention_max_distance
         A_      :				List[str]		     =					dropout_rate
         A_      :				Dict		     =					layer_norm_epsilon
         A_      :				str		     =					initializer_factor
         A_      :				Dict		     =					feed_forward_proj
         A_      :				int		     =					use_cache

         A_      :				Optional[int]		     =					self.feed_forward_proj.split('''-'''		)
         A_      :				Optional[Any]		     =					act_info[-1]
         A_      :				Optional[Any]		     =					act_info[0] == '''gated'''

         if len(_SCREAMING_SNAKE_CASE		) > 1 and act_info[0] != "gated" or len(_SCREAMING_SNAKE_CASE		) > 2:
           raise ValueError(
               F'''`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'''
               '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
               '''\'gated-gelu\' or \'relu\''''		)

         # for backwards compatibility
         if feed_forward_proj == "gated-gelu":
           A_      :				Tuple		     =					'''gelu_new'''

         super().__init__(
             pad_token_id=_SCREAMING_SNAKE_CASE				,       eos_token_id=_SCREAMING_SNAKE_CASE				,       is_encoder_decoder=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE				,       )






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       @property
       def        _snake_case       (      self		)->Mapping[str, Mapping[int, str]]:




         '''simple docstring'''





         A_      :				Union[str, Any]		     =					{
             '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
             '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
         }
         if self.use_past:
           A_      :				List[str]		     =					'''past_encoder_sequence + sequence'''
           A_      :				Optional[int]		     =					{0: '''batch'''}
           A_      :				str		     =					{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
         else:
           A_      :				Optional[int]		     =					{0: '''batch''', 1: '''decoder_sequence'''}
           A_      :				List[Any]		     =					{0: '''batch''', 1: '''decoder_sequence'''}

         if self.use_past:
           self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE				,       direction='''inputs'''		)

         return common_inputs







       @property
       def        _snake_case       (      self		)->int:




         '''simple docstring'''





         return 13




