
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
							lowercase__	:Optional[Any]										=							"platform"
							import jax
							import jax.numpy as jnp
							import numpy as np

							from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel







@require_flax
class        lowercase	:
							lowercase_ : str       =PegasusConfig
							lowercase_ : str       ={}
							lowercase_ : List[Any]       ='''gelu'''




							def __init__(       self     ,A__     ,A__=1_3     ,A__=7     ,A__=True     ,A__=False     ,A__=9_9     ,A__=3_2     ,A__=5     ,A__=4     ,A__=3_7     ,A__=0.1     ,A__=0.1     ,A__=2_0     ,A__=2     ,A__=1     ,A__=0     ,):
												lowercase					=		parent
												lowercase					=		batch_size
												lowercase					=		seq_length
												lowercase					=		is_training
												lowercase					=		use_labels
												lowercase					=		vocab_size
												lowercase					=		hidden_size
												lowercase					=		num_hidden_layers
												lowercase					=		num_attention_heads
												lowercase					=		intermediate_size

												lowercase					=		hidden_dropout_prob
												lowercase					=		attention_probs_dropout_prob
												lowercase					=		max_position_embeddings
												lowercase					=		eos_token_id
												lowercase					=		pad_token_id
												lowercase					=		bos_token_id




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

												lowercase					=		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

												lowercase					=		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					=		prepare_pegasus_inputs_dict(A__     ,A__     ,A__)
												return config, inputs_dict




							def 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		2_0
												lowercase					=		model_class_name(A__)

												lowercase					=		model.encode(inputs_dict['''input_ids'''])

												lowercase			,		lowercase					=		(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

												lowercase					=		model.init_cache(decoder_input_ids.shape[0]     ,A__     ,A__)
												lowercase					=		jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,dtype='''i4''')

												lowercase					=		jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)
												lowercase					=		model.decode(
												    decoder_input_ids[:, :-1]     ,A__     ,decoder_attention_mask=A__     ,past_key_values=A__     ,decoder_position_ids=A__     ,)

												lowercase					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype='''i4''')
												lowercase					=		model.decode(
												    decoder_input_ids[:, -1:]     ,A__     ,decoder_attention_mask=A__     ,past_key_values=outputs_cache.past_key_values     ,decoder_position_ids=A__     ,)

												lowercase					=		model.decode(A__     ,A__)

												lowercase					=		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 				A__   (       self     ,A__     ,A__     ,A__):
												lowercase					=		2_0
												lowercase					=		model_class_name(A__)

												lowercase					=		model.encode(inputs_dict['''input_ids'''])

												lowercase			,		lowercase					=		(
												    inputs_dict['''decoder_input_ids'''],
												    inputs_dict['''decoder_attention_mask'''],
												)

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

												lowercase					=		model.init_cache(decoder_input_ids.shape[0]     ,A__     ,A__)
												lowercase					=		jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]     ,(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,)

												lowercase					=		model.decode(
												    decoder_input_ids[:, :-1]     ,A__     ,decoder_attention_mask=A__     ,past_key_values=A__     ,decoder_position_ids=A__     ,)
												lowercase					=		jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]     ,dtype='''i4''')
												lowercase					=		model.decode(
												    decoder_input_ids[:, -1:]     ,A__     ,past_key_values=outputs_cache.past_key_values     ,decoder_attention_mask=A__     ,decoder_position_ids=A__     ,)

												lowercase					=		model.decode(A__     ,A__     ,decoder_attention_mask=A__)

												lowercase					=		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					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__=None				,    lowerCAmelCase__=None				,    ):
					'''simple docstring'''
					if attention_mask is None:
										lowercase					=		np.not_equal(lowerCAmelCase__				,    config.pad_token_id	).astype(np.inta	)
					if decoder_attention_mask is None:
										lowercase					=		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        lowercase	(      SCREAMING_SNAKE_CASE__ ,						unittest.TestCase	):
							lowercase_ : Union[str, Any]       =(
							    (
							        FlaxPegasusForConditionalGeneration,
							        FlaxPegasusModel,
							    )
							    if is_flax_available()
							    else ()
							)
							lowercase_ : str       =(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
							lowercase_ : Union[str, Any]       =True
							lowercase_ : Optional[Any]       =False
							lowercase_ : Any       =False
							lowercase_ : Optional[int]       =False




							def 				A__   (       self):
												lowercase					=		FlaxPegasusModelTester(self)
												lowercase					=		ConfigTester(self     ,config_class=A__)




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




							def 				A__   (       self):
												lowercase			,		lowercase					=		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 				A__   (       self):
												lowercase			,		lowercase					=		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 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__):
																						lowercase					=		self._prepare_for_class(A__     ,A__)
																						lowercase					=		model_class(A__)

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

																						with self.subTest('''JIT Enabled'''):
																											lowercase					=		encode_jitted(**A__).to_tuple()

																						with self.subTest('''JIT Disabled'''):
																											with jax.disable_jit():
																																lowercase					=		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 				A__   (       self):
												lowercase			,		lowercase					=		self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__):
																						lowercase					=		model_class(A__)
																						lowercase					=		model.encode(inputs_dict['''input_ids''']     ,inputs_dict['''attention_mask'''])

																						lowercase					=		{
																						    '''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=A__     ,decoder_attention_mask=A__     ,encoder_outputs=A__     ,)

																						with self.subTest('''JIT Enabled'''):
																											lowercase					=		decode_jitted(**A__).to_tuple()

																						with self.subTest('''JIT Disabled'''):
																											with jax.disable_jit():
																																lowercase					=		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 				A__   (       self):
												for model_class_name in self.all_model_classes:
																	lowercase					=		model_class_name.from_pretrained('''google/pegasus-large'''     ,from_pt=A__)
																	lowercase					=		np.ones((1, 1))
																	lowercase					=		model(A__)
																	self.assertIsNotNone(A__)





							@slow
							def 				A__   (       self):
												lowercase					=		FlaxPegasusForConditionalGeneration.from_pretrained('''google/pegasus-xsum''')
												lowercase					=		PegasusTokenizer.from_pretrained('''google/pegasus-xsum''')

												lowercase					=		[
												    ''' 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					=		[
												    '''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					=		tokenizer(A__     ,return_tensors='''np'''     ,truncation=A__     ,max_length=5_1_2     ,padding=A__)
												lowercase					=		model.generate(**A__     ,num_beams=2).sequences
												lowercase					=		tokenizer.batch_decode(A__     ,skip_special_tokens=A__)
												assert tgt_text == decoded






import argparse
from collections import defaultdict

import yaml


lowercase__	:Optional[int]										=					"docs/source/en/_toctree.yml"



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		defaultdict(lowerCAmelCase__	)
					for doc in model_doc:
										counts[doc["local"]] += 1
					lowercase					=		[key for key, value in counts.items() if value > 1]

					lowercase					=		[]
					for duplicate_key in duplicates:
										lowercase					=		list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}	)
										if len(lowerCAmelCase__	) > 1:
															raise ValueError(
															    f'{duplicate_key} is present several times in the documentation table of content at '
															    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
															    '''others.'''	)
										# Only add this once
										new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}	)

					# Add none duplicate-keys
					new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]	)

					# Sort
					return sorted(lowerCAmelCase__				,    key=lambda lowerCAmelCase__	: s["title"].lower()	)



def  UpperCamelCase					(							lowerCAmelCase__=False	):
					'''simple docstring'''
					with open(lowerCAmelCase__				,    encoding='''utf-8'''	) as f:
										lowercase					=		yaml.safe_load(f.read()	)

					# Get to the API doc
					lowercase					=		0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					lowercase					=		content[api_idx]['''sections''']

					# Then to the model doc
					lowercase					=		0
					while api_doc[model_idx]["title"] != "Models":
										model_idx += 1

					lowercase					=		api_doc[model_idx]['''sections''']

					lowercase					=		[(idx, section) for idx, section in enumerate(lowerCAmelCase__	) if '''sections''' in section]
					lowercase					=		False
					for idx, modality_doc in modalities_docs:
										lowercase					=		modality_doc['''sections''']
										lowercase					=		clean_model_doc_toc(lowerCAmelCase__	)

										if old_modality_doc != new_modality_doc:
															lowercase					=		True
															if overwrite:
																				lowercase					=		new_modality_doc

					if diff:
										if overwrite:
															lowercase					=		model_doc
															lowercase					=		api_doc
															with open(lowerCAmelCase__				,    '''w'''				,    encoding='''utf-8'''	) as f:
																				f.write(yaml.dump(lowerCAmelCase__				,    allow_unicode=lowerCAmelCase__	)	)
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''	)


if __name__ == "__main__":
							lowercase__	:Any										=					argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							lowercase__	:int										=					parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




