

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

			from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel






@require_flax
class _UpperCamelCase							:
			'''simple docstring'''







			lowerCamelCase__           =PegasusConfig
			lowerCamelCase__           ={}
			lowerCamelCase__           ='gelu'
			def __init__(			self							: List[Any]      ,      a							: Dict      ,      a							: Optional[int]=13      ,      a							: List[str]=7      ,      a							: Union[str, Any]=True      ,      a							: str=False      ,      a							: Tuple=99      ,      a							: Tuple=32      ,      a							: Dict=5      ,      a							: Optional[Any]=4      ,      a							: List[str]=37      ,      a							: Optional[int]=0.1      ,      a							: List[Any]=0.1      ,      a							: Dict=20      ,      a							: List[Any]=2      ,      a							: Tuple=1      ,      a							: Union[str, Any]=0      ,      )							->       Dict:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       hidden_dropout_prob
				SCREAMING_SNAKE_CASE   :      Dict				     =       attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_position_embeddings
				SCREAMING_SNAKE_CASE   :      str				     =       eos_token_id
				SCREAMING_SNAKE_CASE   :      Dict				     =       pad_token_id
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       bos_token_id
			def 							__UpperCamelCase (			self							: str	)							->       Tuple:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       ids_tensor([self.batch_size, self.seq_length]      ,      self.vocab_size	)

				SCREAMING_SNAKE_CASE   :      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      ,      )
				SCREAMING_SNAKE_CASE   :      int				     =       prepare_pegasus_inputs_dict(a      ,      a      ,      a	)
				return config, inputs_dict
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: Union[str, Any]      ,      a							: Dict	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Tuple				     =       20
				SCREAMING_SNAKE_CASE   :      str				     =       model_class_name(a	)

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model.encode(inputs_dict["input_ids"]	)

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      int				     =       (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       model.init_cache(decoder_input_ids.shape[0]      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      int				     =       jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,      dtype="i4"	)

				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]      ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,      )
				SCREAMING_SNAKE_CASE   :      Any				     =       model.decode(
				    decoder_input_ids[:, :-1]      ,      a      ,      decoder_attention_mask=a      ,      past_key_values=a      ,      decoder_position_ids=a      ,      )

				SCREAMING_SNAKE_CASE   :      str				     =       jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,      dtype="i4"	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model.decode(
				    decoder_input_ids[:, -1:]      ,      a      ,      decoder_attention_mask=a      ,      past_key_values=outputs_cache.past_key_values      ,      decoder_position_ids=a      ,      )

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model.decode(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      Tuple				     =       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 							__UpperCamelCase (			self							: List[Any]      ,      a							: Optional[Any]      ,      a							: Dict      ,      a							: Optional[Any]	)							->       Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       20
				SCREAMING_SNAKE_CASE   :      Dict				     =       model_class_name(a	)

				SCREAMING_SNAKE_CASE   :      List[Any]				     =       model.encode(inputs_dict["input_ids"]	)

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       (
				    inputs_dict["decoder_input_ids"],
				    inputs_dict["decoder_attention_mask"],
				)

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

				SCREAMING_SNAKE_CASE   :      Any				     =       model.init_cache(decoder_input_ids.shape[0]      ,      a      ,      a	)
				SCREAMING_SNAKE_CASE   :      int				     =       jnp.broadcast_to(
				    jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :]      ,      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,      )

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

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       model.decode(a      ,      a      ,      decoder_attention_mask=a	)

				SCREAMING_SNAKE_CASE   :      Optional[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  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a=None							,			_a=None							,			):
	if attention_mask is None:
		SCREAMING_SNAKE_CASE   :      Tuple				     =       np.not_equal(_a							,			config.pad_token_id).astype(np.inta)
	if decoder_attention_mask is None:
		SCREAMING_SNAKE_CASE   :      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 _UpperCamelCase							(							__A							,	unittest.TestCase   ):
			'''simple docstring'''







			lowerCamelCase__           =(
			    (
			        FlaxPegasusForConditionalGeneration,
			        FlaxPegasusModel,
			    )
			    if is_flax_available()
			    else ()
			)
			lowerCamelCase__           =(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
			lowerCamelCase__           =True
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			lowerCamelCase__           =False
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      int				     =       FlaxPegasusModelTester(self	)
				SCREAMING_SNAKE_CASE   :      Any				     =       ConfigTester(self      ,      config_class=a	)
			def 							__UpperCamelCase (			self							: Union[str, Any]	)							->       int:

				"""simple docstring"""

				self.config_tester.run_common_tests()
			def 							__UpperCamelCase (			self							: List[str]	)							->       Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[Any]				     =       self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes:
					self.model_tester.check_use_cache_forward(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Any	)							->       List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      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_with_attn_mask(a      ,      a      ,      a	)
			def 							__UpperCamelCase (			self							: Dict	)							->       List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      str				     =       self.model_tester.prepare_config_and_inputs_for_common()

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

						@jax.jit
						def encode_jitted(a							: str      ,      a							: Optional[Any]=None      ,      **a							: str	):
							return model.encode(input_ids=a      ,      attention_mask=a	)

						with self.subTest("JIT Enabled"	):
							SCREAMING_SNAKE_CASE   :      Any				     =       encode_jitted(**a	).to_tuple()

						with self.subTest("JIT Disabled"	):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE   :      Dict				     =       encode_jitted(**a	).to_tuple()

						self.assertEqual(len(a	)      ,      len(a	)	)
						for jitted_output, output in zip(a      ,      a	):
							self.assertEqual(jitted_output.shape      ,      output.shape	)
			def 							__UpperCamelCase (			self							: List[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
					with self.subTest(model_class.__name__	):
						SCREAMING_SNAKE_CASE   :      List[Any]				     =       model_class(a	)
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       model.encode(inputs_dict["input_ids"]      ,      inputs_dict["attention_mask"]	)

						SCREAMING_SNAKE_CASE   :      List[Any]				     =       {
						    "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							: List[str]      ,      a							: int      ,      a							: int	):
							return model.decode(
							    decoder_input_ids=a      ,      decoder_attention_mask=a      ,      encoder_outputs=a      ,      )

						with self.subTest("JIT Enabled"	):
							SCREAMING_SNAKE_CASE   :      str				     =       decode_jitted(**a	).to_tuple()

						with self.subTest("JIT Disabled"	):
							with jax.disable_jit():
								SCREAMING_SNAKE_CASE   :      Dict				     =       decode_jitted(**a	).to_tuple()

						self.assertEqual(len(a	)      ,      len(a	)	)
						for jitted_output, output in zip(a      ,      a	):
							self.assertEqual(jitted_output.shape      ,      output.shape	)
			@slow
			def 							__UpperCamelCase (			self							: Dict	)							->       int:

				"""simple docstring"""

				for model_class_name in self.all_model_classes:
					SCREAMING_SNAKE_CASE   :      Dict				     =       model_class_name.from_pretrained("google/pegasus-large"      ,      from_pt=a	)
					SCREAMING_SNAKE_CASE   :      List[Any]				     =       np.ones((1, 1)	)
					SCREAMING_SNAKE_CASE   :      List[str]				     =       model(a	)
					self.assertIsNotNone(a	)






			@slow
			def 							__UpperCamelCase (			self							: str	)							->       Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       PegasusTokenizer.from_pretrained("google/pegasus-xsum"	)

				SCREAMING_SNAKE_CASE   :      List[str]				     =       [
				    " 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!\" ",
				]

				SCREAMING_SNAKE_CASE   :      Optional[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.",
				]

				SCREAMING_SNAKE_CASE   :      Optional[int]				     =       tokenizer(a      ,      return_tensors="np"      ,      truncation=a      ,      max_length=512      ,      padding=a	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       model.generate(**a      ,      num_beams=2	).sequences
				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenizer.batch_decode(a      ,      skip_special_tokens=a	)
				assert tgt_text == decoded


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


a_						     =    datasets.utils.logging.get_logger(__name__)






@dataclass
class _UpperCamelCase							(							datasets.BuilderConfig   ):
			'''simple docstring'''







			lowerCamelCase__           =10000
			lowerCamelCase__           =None
			lowerCamelCase__           =None






class _UpperCamelCase							(							datasets.ArrowBasedBuilder   ):
			'''simple docstring'''







			lowerCamelCase__           =ParquetConfig
			def 							__UpperCamelCase (			self							: str	)							->       str:

				"""simple docstring"""

				return datasets.DatasetInfo(features=self.config.features	)
			def 							__UpperCamelCase (			self							: Dict      ,      a							: List[Any]	)							->       Tuple:

				"""simple docstring"""

				if not self.config.data_files:
					raise ValueError(F"At least one data file must be specified, but got data_files={self.config.data_files}"	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       dl_manager.download_and_extract(self.config.data_files	)
				if isinstance(a      ,      (str, list, tuple)	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       data_files
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       [dl_manager.iter_files(a	) for file in files]
					return [datasets.SplitGenerator(name=datasets.Split.TRAIN      ,      gen_kwargs={"files": files}	)]
				SCREAMING_SNAKE_CASE   :      str				     =       []
				for split_name, files in data_files.items():
					if isinstance(a      ,      a	):
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       [files]
					# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
					SCREAMING_SNAKE_CASE   :      Tuple				     =       [dl_manager.iter_files(a	) for file in files]
					# Infer features is they are stoed in the arrow schema
					if self.info.features is None:
						for file in itertools.chain.from_iterable(a	):
							with open(a      ,      "rb"	) as f:
								SCREAMING_SNAKE_CASE   :      Dict				     =       datasets.Features.from_arrow_schema(pq.read_schema(a	)	)
							break
					splits.append(datasets.SplitGenerator(name=a      ,      gen_kwargs={"files": files}	)	)
				return splits
			def 							__UpperCamelCase (			self							: Dict      ,      a							: pa.Table	)							->       pa.Table:

				"""simple docstring"""

				if self.info.features is not None:
					# more expensive cast to support nested features with keys in a different order
					# allows str <-> int/float or str to Audio for example
					SCREAMING_SNAKE_CASE   :      str				     =       table_cast(a      ,      self.info.features.arrow_schema	)
				return pa_table






			def 							__UpperCamelCase (			self							: List[str]      ,      a							: Optional[Any]	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      Any				     =       self.info.features.arrow_schema if self.info.features is not None else None
				if self.info.features is not None and self.config.columns is not None:
					if sorted(field.name for field in schema	) != sorted(self.config.columns	):
						raise ValueError(
						    F"Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'"	)
				for file_idx, file in enumerate(itertools.chain.from_iterable(a	)	):
					with open(a      ,      "rb"	) as f:
						SCREAMING_SNAKE_CASE   :      Optional[int]				     =       pq.ParquetFile(a	)
						try:
							for batch_idx, record_batch in enumerate(
							    parquet_file.iter_batches(batch_size=self.config.batch_size      ,      columns=self.config.columns	)	):
								SCREAMING_SNAKE_CASE   :      int				     =       pa.Table.from_batches([record_batch]	)
								# Uncomment for debugging (will print the Arrow table size and elements)
								# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
								# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
								yield F"{file_idx}_{batch_idx}", self._cast_table(a	)
						except ValueError as e:
							logger.error(F"Failed to read file '{file}' with error {type(a	)}: {e}"	)
							raise