
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
		lowerCAmelCase__:    int      = """platform"""
		import jax
		import jax.numpy as jnp
		import numpy as np

		from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel

@require_flax
class 							snake_case_ :
			__lowerCamelCase							:     List[Any]						=			PegasusConfig
			__lowerCamelCase							:     List[Any]						=			{}
			__lowerCamelCase							:     List[str]						=			'''gelu'''





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

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





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			ids_tensor([self.batch_size, self.seq_length - 1]      ,						self.vocab_size      ).clip(3      ,						self.vocab_size      )
					SCREAMING_SNAKE_CASE_					:	int		  =			np.expand_dims(np.array([self.eos_token_id] * self.batch_size      )      ,						1      )
					SCREAMING_SNAKE_CASE_					:	Dict		  =			np.concatenate([input_ids, eos_tensor]      ,						axis=1      )

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

					SCREAMING_SNAKE_CASE_					:	Optional[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_					:	Any		  =			prepare_pegasus_inputs_dict(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      )
					return config, inputs_dict





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			20
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			model_class_name(__lowerCAmelCase      )

					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_					:	Dict		  =			model.init_cache(decoder_input_ids.shape[0]      ,						__lowerCAmelCase      ,						__lowerCAmelCase      )
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,						dtype='i4'      )

					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_					:	Tuple		  =			model.decode(
					    decoder_input_ids[:, :-1]      ,						__lowerCAmelCase      ,						decoder_attention_mask=__lowerCAmelCase      ,						past_key_values=__lowerCAmelCase      ,						decoder_position_ids=__lowerCAmelCase      ,						)

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

					SCREAMING_SNAKE_CASE_					:	Any		  =			model.decode(__lowerCAmelCase      ,						__lowerCAmelCase      )

					SCREAMING_SNAKE_CASE_					:	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 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	str		  =			20
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			model_class_name(__lowerCAmelCase      )

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

					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			(
					    inputs_dict['decoder_input_ids'],
					    inputs_dict['decoder_attention_mask'],
					)

					SCREAMING_SNAKE_CASE_					:	Tuple		  =			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_					:	Tuple		  =			model.init_cache(decoder_input_ids.shape[0]      ,						__lowerCAmelCase      ,						__lowerCAmelCase      )
					SCREAMING_SNAKE_CASE_					:	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_					:	Tuple		  =			model.decode(
					    decoder_input_ids[:, :-1]      ,						__lowerCAmelCase      ,						decoder_attention_mask=__lowerCAmelCase      ,						past_key_values=__lowerCAmelCase      ,						decoder_position_ids=__lowerCAmelCase      ,						)
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,						dtype='i4'      )
					SCREAMING_SNAKE_CASE_					:	str		  =			model.decode(
					    decoder_input_ids[:, -1:]      ,						__lowerCAmelCase      ,						past_key_values=outputs_cache.past_key_values      ,						decoder_attention_mask=__lowerCAmelCase      ,						decoder_position_ids=__lowerCAmelCase      ,						)

					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			model.decode(__lowerCAmelCase      ,						__lowerCAmelCase      ,						decoder_attention_mask=__lowerCAmelCase      )

					SCREAMING_SNAKE_CASE_					:	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   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE ,    SCREAMING_SNAKE_CASE=None ,    SCREAMING_SNAKE_CASE=None ,    )		-> Tuple:






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





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			FlaxPegasusModelTester(self      )
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			ConfigTester(self      ,						config_class=__lowerCAmelCase      )





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





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	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(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      )





			def 						__A						(		self      ):
					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_with_attn_mask(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      )





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_  ,      SCREAMING_SNAKE_CASE_					:	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__      ):
									SCREAMING_SNAKE_CASE_					:	List[str]		  =			self._prepare_for_class(__lowerCAmelCase      ,						__lowerCAmelCase      )
									SCREAMING_SNAKE_CASE_					:	Tuple		  =			model_class(__lowerCAmelCase      )

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

									with self.subTest('JIT Enabled'      ):
											SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			encode_jitted(**__lowerCAmelCase      ).to_tuple()

									with self.subTest('JIT Disabled'      ):
											with jax.disable_jit():
													SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			encode_jitted(**__lowerCAmelCase      ).to_tuple()

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





			def 						__A						(		self      ):
					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:
							with self.subTest(model_class.__name__      ):
									SCREAMING_SNAKE_CASE_					:	Any		  =			model_class(__lowerCAmelCase      )
									SCREAMING_SNAKE_CASE_					:	List[Any]		  =			model.encode(inputs_dict['input_ids']      ,						inputs_dict['attention_mask']      )

									SCREAMING_SNAKE_CASE_					:	int		  =			{
									    '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(__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
											return model.decode(
											    decoder_input_ids=__lowerCAmelCase      ,						decoder_attention_mask=__lowerCAmelCase      ,						encoder_outputs=__lowerCAmelCase      ,						)

									with self.subTest('JIT Enabled'      ):
											SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			decode_jitted(**__lowerCAmelCase      ).to_tuple()

									with self.subTest('JIT Disabled'      ):
											with jax.disable_jit():
													SCREAMING_SNAKE_CASE_					:	str		  =			decode_jitted(**__lowerCAmelCase      ).to_tuple()

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





			@slow
			def 						__A						(		self      ):
					for model_class_name in self.all_model_classes:
							SCREAMING_SNAKE_CASE_					:	Tuple		  =			model_class_name.from_pretrained('google/pegasus-large'      ,						from_pt=__lowerCAmelCase      )
							SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			np.ones((1, 1)      )
							SCREAMING_SNAKE_CASE_					:	Any		  =			model(__lowerCAmelCase      )
							self.assertIsNotNone(__lowerCAmelCase      )






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

					SCREAMING_SNAKE_CASE_					:	Optional[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!" ',
					]

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









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_UpperCAmelCase						:  Union[str, Any]									=			logging.get_logger(__name__)

_UpperCAmelCase						:  Dict									=			{
    """microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}







class 					lowercase							(			lowercase_      ,    lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[str]			     =     '''resnet'''
				__SCREAMING_SNAKE_CASE   :					Dict			     =     ['''basic''', '''bottleneck''']




				def __init__(   self ,  snake_case=3 ,  snake_case=64 ,  snake_case=[256, 512, 1024, 2048] ,  snake_case=[3, 4, 6, 3] ,  snake_case="bottleneck" ,  snake_case="relu" ,  snake_case=False ,  snake_case=None ,  snake_case=None ,  **snake_case ,  ):
											super().__init__(**snake_case			)
											if layer_type not in self.layer_types:
																		raise ValueError(F'''layer_type={layer_type} is not one of {",".join(self.layer_types			)}'''			)
											snake_case_     							=       num_channels
											snake_case_     							=       embedding_size
											snake_case_     							=       hidden_sizes
											snake_case_     							=       depths
											snake_case_     							=       layer_type
											snake_case_     							=       hidden_act
											snake_case_     							=       downsample_in_first_stage
											snake_case_     							=       ['stem'] + [F'''stage{idx}''' for idx in range(1 ,  len(snake_case			) + 1			)]
											snake_case_							,       snake_case_     							=       get_aligned_output_features_output_indices(
											    out_features=snake_case ,  out_indices=snake_case ,  stage_names=self.stage_names			)







class 					lowercase							(			lowercase_     ):
				__SCREAMING_SNAKE_CASE   :					List[Any]			     =     version.parse('''1.11'''     )




				@property
				def        a				(   self			):
											return OrderedDict(
											    [
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]			)




				@property
				def        a				(   self			):
											return 1e-3




