

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
     snake_case_        =			'''platform'''
     import jax
     import jax.numpy as jnp
     import numpy as np

     from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel


@require_flax
class 				SCREAMING_SNAKE_CASE__ :
    __lowerCamelCase						:	int														=					PegasusConfig
    __lowerCamelCase						:	Union[str, Any]														=					{}
    __lowerCamelCase						:	List[str]														=					"""gelu"""





    def __init__(				self						,       a						,       a=13						,       a=7						,       a=True						,       a=False						,       a=99						,       a=32						,       a=5						,       a=4						,       a=37						,       a=0.1						,       a=0.1						,       a=20						,       a=2						,       a=1						,       a=0						,       ):
         lowercase__   :       str				=	parent
         lowercase__   :       Dict				=	batch_size
         lowercase__   :       int				=	seq_length
         lowercase__   :       str				=	is_training
         lowercase__   :       List[Any]				=	use_labels
         lowercase__   :       Optional[Any]				=	vocab_size
         lowercase__   :       str				=	hidden_size
         lowercase__   :       Optional[Any]				=	num_hidden_layers
         lowercase__   :       str				=	num_attention_heads
         lowercase__   :       Union[str, Any]				=	intermediate_size

         lowercase__   :       Union[str, Any]				=	hidden_dropout_prob
         lowercase__   :       List[Any]				=	attention_probs_dropout_prob
         lowercase__   :       Dict				=	max_position_embeddings
         lowercase__   :       List[str]				=	eos_token_id
         lowercase__   :       List[str]				=	pad_token_id
         lowercase__   :       List[str]				=	bos_token_id





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

         lowercase__   :       Dict				=	ids_tensor([self.batch_size, self.seq_length]						,       self.vocab_size)

         lowercase__   :       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						,       )
         lowercase__   :       Tuple				=	prepare_pegasus_inputs_dict(__lowercase						,       __lowercase						,       __lowercase)
         return config, inputs_dict





    def 				snake_case_    (				self						,       a						,       a						,       a):
         lowercase__   :       Union[str, Any]				=	20
         lowercase__   :       List[str]				=	model_class_name(__lowercase)

         lowercase__   :       str				=	model.encode(inputs_dict['input_ids'])

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

         lowercase__   :       str				=	model.init_cache(decoder_input_ids.shape[0]						,       __lowercase						,       __lowercase)
         lowercase__   :       str				=	jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						,       dtype='i4')

         lowercase__   :       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)						,       )
         lowercase__   :       int				=	model.decode(
             decoder_input_ids[:, :-1]						,       __lowercase						,       decoder_attention_mask=__lowercase						,       past_key_values=__lowercase						,       decoder_position_ids=__lowercase						,       )

         lowercase__   :       Union[str, Any]				=	jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						,       dtype='i4')
         lowercase__   :       Union[str, Any]				=	model.decode(
             decoder_input_ids[:, -1:]						,       __lowercase						,       decoder_attention_mask=__lowercase						,       past_key_values=outputs_cache.past_key_values						,       decoder_position_ids=__lowercase						,       )

         lowercase__   :       Dict				=	model.decode(__lowercase						,       __lowercase)

         lowercase__   :       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						,       a						,       a						,       a):
         lowercase__   :       Any				=	20
         lowercase__   :       List[Any]				=	model_class_name(__lowercase)

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

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

         lowercase__   :       Dict				=	jnp.concatenate(
             [
                 decoder_attention_mask,
                 jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
             ]						,       axis=-1						,       )

         lowercase__   :       Tuple				=	model.init_cache(decoder_input_ids.shape[0]						,       __lowercase						,       __lowercase)
         lowercase__   :       List[str]				=	jnp.broadcast_to(
             jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]						,       (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						,       )

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

         lowercase__   :       Optional[int]				=	model.decode(__lowercase						,       __lowercase						,       decoder_attention_mask=__lowercase)

         lowercase__   :       Dict				=	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__      (   SCREAMING_SNAKE_CASE_		:   str     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Any=None     ,					SCREAMING_SNAKE_CASE_		:   int=None     ,					):



     '''simple docstring'''





     if attention_mask is None:
          lowercase__   :       Tuple				=	np.not_equal(SCREAMING_SNAKE_CASE_     ,					config.pad_token_id      ).astype(np.inta      )
     if decoder_attention_mask is None:
          lowercase__   :       List[Any]				=	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 				SCREAMING_SNAKE_CASE__ (UpperCAmelCase_		,	unittest.TestCase					):
    __lowerCamelCase						:	List[str]														=					(
        (
            FlaxPegasusForConditionalGeneration,
            FlaxPegasusModel,
        )
        if is_flax_available()
        else ()
    )
    __lowerCamelCase						:	Optional[Any]														=					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
    __lowerCamelCase						:	List[Any]														=					True
    __lowerCamelCase						:	Optional[int]														=					False
    __lowerCamelCase						:	Tuple														=					False
    __lowerCamelCase						:	Union[str, Any]														=					False





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	FlaxPegasusModelTester(self)
         lowercase__   :       Optional[Any]				=	ConfigTester(self						,       config_class=__lowercase)





    def 				snake_case_    (				self):
         self.config_tester.run_common_tests()





    def 				snake_case_    (				self):
         lowercase__   :       Dict				=	self.model_tester.prepare_config_and_inputs_for_common()
         for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(__lowercase						,       __lowercase						,       __lowercase)





    def 				snake_case_    (				self):
         lowercase__   :       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_with_attn_mask(__lowercase						,       __lowercase						,       __lowercase)





    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                   lowercase__   :       int				=	self._prepare_for_class(__lowercase						,       __lowercase)
                   lowercase__   :       Union[str, Any]				=	model_class(__lowercase)

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

                   with self.subTest('JIT Enabled'):
                        lowercase__   :       Tuple				=	encode_jitted(**__lowercase).to_tuple()

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

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





    def 				snake_case_    (				self):
         lowercase__   :       int				=	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__):
                   lowercase__   :       List[Any]				=	model_class(__lowercase)
                   lowercase__   :       List[Any]				=	model.encode(inputs_dict['input_ids']						,       inputs_dict['attention_mask'])

                   lowercase__   :       str				=	{
                       '''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(a						,       a						,       a):
                        return model.decode(
                            decoder_input_ids=__lowercase						,       decoder_attention_mask=__lowercase						,       encoder_outputs=__lowercase						,       )

                   with self.subTest('JIT Enabled'):
                        lowercase__   :       Union[str, Any]				=	decode_jitted(**__lowercase).to_tuple()

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

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





    @slow
    def 				snake_case_    (				self):
         for model_class_name in self.all_model_classes:
              lowercase__   :       int				=	model_class_name.from_pretrained('google/pegasus-large'						,       from_pt=__lowercase)
              lowercase__   :       Union[str, Any]				=	np.ones((1, 1))
              lowercase__   :       Tuple				=	model(__lowercase)
              self.assertIsNotNone(__lowercase)





    @slow
    def 				snake_case_    (				self):
         lowercase__   :       List[str]				=	FlaxPegasusForConditionalGeneration.from_pretrained('google/pegasus-xsum')
         lowercase__   :       List[Any]				=	PegasusTokenizer.from_pretrained('google/pegasus-xsum')

         lowercase__   :       Optional[int]				=	[
             ''' 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!" ''',
         ]

         lowercase__   :       Tuple				=	[
             '''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.''',
         ]

         lowercase__   :       Optional[Any]				=	tokenizer(__lowercase						,       return_tensors='np'						,       truncation=__lowercase						,       max_length=512						,       padding=__lowercase)
         lowercase__   :       Union[str, Any]				=	model.generate(**__lowercase						,       num_beams=2).sequences
         lowercase__   :       Any				=	tokenizer.batch_decode(__lowercase						,       skip_special_tokens=__lowercase)
         assert tgt_text == decoded




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


snake_case_        =			logging.get_logger(__name__)

snake_case_        =			{
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}


class 				SCREAMING_SNAKE_CASE__ (__snake_case					):
    __lowerCamelCase						:	List[Any]														=					"""speech_to_text_2"""
    __lowerCamelCase						:	str														=					["""past_key_values"""]
    __lowerCamelCase						:	List[Any]														=					{"""num_attention_heads""": """decoder_attention_heads""", """hidden_size""": """d_model"""}





    def __init__(				self						,       a=1_0000						,       a=6						,       a=2048						,       a=4						,       a=0.0						,       a=True						,       a="relu"						,       a=256						,       a=0.1						,       a=0.0						,       a=0.0						,       a=0.02						,       a=2						,       a=True						,       a=1						,       a=0						,       a=2						,       a=1024						,       **a						,       ):
         lowercase__   :       Optional[int]				=	vocab_size
         lowercase__   :       List[str]				=	d_model
         lowercase__   :       int				=	decoder_ffn_dim
         lowercase__   :       Optional[Any]				=	decoder_layers
         lowercase__   :       int				=	decoder_attention_heads
         lowercase__   :       Dict				=	dropout
         lowercase__   :       Optional[int]				=	attention_dropout
         lowercase__   :       Optional[int]				=	activation_dropout
         lowercase__   :       Optional[int]				=	activation_function
         lowercase__   :       Dict				=	init_std
         lowercase__   :       List[Any]				=	decoder_layerdrop
         lowercase__   :       int				=	use_cache
         lowercase__   :       Any				=	decoder_layers
         lowercase__   :       Optional[int]				=	scale_embedding  # scale factor will be sqrt(d_model) if True
         lowercase__   :       Tuple				=	max_target_positions

         super().__init__(
             pad_token_id=a						,       bos_token_id=a						,       eos_token_id=a						,       decoder_start_token_id=a						,       **a						,       )

