
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        _SCREAMING_SNAKE_CASE	:
	lowerCAmelCase__   =					PegasusConfig
	lowerCAmelCase__   =					{}
	lowerCAmelCase__   =					'gelu'



	def __init__(       self		,    lowercase		,    lowercase=13		,    lowercase=7		,    lowercase=True		,    lowercase=False		,    lowercase=99		,    lowercase=32		,    lowercase=5		,    lowercase=4		,    lowercase=37		,    lowercase=0.1		,    lowercase=0.1		,    lowercase=20		,    lowercase=2		,    lowercase=1		,    lowercase=0		,    )							->			Union[str, Any]:
					lowerCamelCase_         =     parent
					lowerCamelCase_         =     batch_size
					lowerCamelCase_         =     seq_length
					lowerCamelCase_         =     is_training
					lowerCamelCase_         =     use_labels
					lowerCamelCase_         =     vocab_size
					lowerCamelCase_         =     hidden_size
					lowerCamelCase_         =     num_hidden_layers
					lowerCamelCase_         =     num_attention_heads
					lowerCamelCase_         =     intermediate_size

					lowerCamelCase_         =     hidden_dropout_prob
					lowerCamelCase_         =     attention_probs_dropout_prob
					lowerCamelCase_         =     max_position_embeddings
					lowerCamelCase_         =     eos_token_id
					lowerCamelCase_         =     pad_token_id
					lowerCamelCase_         =     bos_token_id



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

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

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



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase		,    lowercase					)							->			Dict:
					lowerCamelCase_         =     20
					lowerCamelCase_         =     model_class_name(lowercase					)

					lowerCamelCase_         =     model.encode(inputs_dict["input_ids"]					)

					lowerCamelCase_      ,       lowerCamelCase_         =     (
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

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

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

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

					lowerCamelCase_         =     model.decode(lowercase		,    lowercase					)

					lowerCamelCase_         =     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_(       self		,    lowercase		,    lowercase		,    lowercase					)							->			Optional[int]:
					lowerCamelCase_         =     20
					lowerCamelCase_         =     model_class_name(lowercase					)

					lowerCamelCase_         =     model.encode(inputs_dict["input_ids"]					)

					lowerCamelCase_      ,       lowerCamelCase_         =     (
					    inputs_dict["decoder_input_ids"],
					    inputs_dict["decoder_attention_mask"],
					)

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

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

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

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

					lowerCamelCase_         =     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_					(							lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__      ,					lowerCamelCase__=None      ,					lowerCamelCase__=None      ,					):
				if attention_mask is None:
								lowerCamelCase_         =     np.not_equal(lowerCamelCase__      ,					config.pad_token_id       ).astype(np.inta       )
				if decoder_attention_mask is None:
								lowerCamelCase_         =     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	( snake_case_     ,			unittest.TestCase		):
	lowerCAmelCase__   =					(
	    (
	        FlaxPegasusForConditionalGeneration,
	        FlaxPegasusModel,
	    )
	    if is_flax_available()
	    else ()
	)
	lowerCAmelCase__   =					(FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
	lowerCAmelCase__   =					True
	lowerCAmelCase__   =					False
	lowerCAmelCase__   =					False
	lowerCAmelCase__   =					False



	def        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_         =     FlaxPegasusModelTester(self					)
					lowerCamelCase_         =     ConfigTester(self		,    config_class=lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					self.config_tester.run_common_tests()



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Tuple:
					lowerCamelCase_      ,       lowerCamelCase_         =     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        SCREAMING_SNAKE_CASE_(       self					)							->			str:
					lowerCamelCase_      ,       lowerCamelCase_         =     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        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[int]:
					lowerCamelCase_      ,       lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs_for_common()

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

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

													with self.subTest("JIT Enabled"					):
																	lowerCamelCase_         =     encode_jitted(**lowercase					).to_tuple()

													with self.subTest("JIT Disabled"					):
																	with jax.disable_jit():
																					lowerCamelCase_         =     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        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					lowerCamelCase_      ,       lowerCamelCase_         =     self.model_tester.prepare_config_and_inputs_for_common()

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

													lowerCamelCase_         =     {
													    "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(lowercase		,    lowercase		,    lowercase					):
																	return model.decode(
																	    decoder_input_ids=lowercase		,    decoder_attention_mask=lowercase		,    encoder_outputs=lowercase		,    )

													with self.subTest("JIT Enabled"					):
																	lowerCamelCase_         =     decode_jitted(**lowercase					).to_tuple()

													with self.subTest("JIT Disabled"					):
																	with jax.disable_jit():
																					lowerCamelCase_         =     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        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					for model_class_name in self.all_model_classes:
									lowerCamelCase_         =     model_class_name.from_pretrained("google/pegasus-large"		,    from_pt=lowercase					)
									lowerCamelCase_         =     np.ones((1, 1)					)
									lowerCamelCase_         =     model(lowercase					)
									self.assertIsNotNone(lowercase					)



	@slow
	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum"					)
					lowerCamelCase_         =     PegasusTokenizer.from_pretrained("google/pegasus-xsum"					)

					lowerCamelCase_         =     [
					    " 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!\" ",
					]

					lowerCamelCase_         =     [
					    "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.",
					]

					lowerCamelCase_         =     tokenizer(lowercase		,    return_tensors="np"		,    truncation=lowercase		,    max_length=512		,    padding=lowercase					)
					lowerCamelCase_         =     model.generate(**lowercase		,    num_beams=2					).sequences
					lowerCamelCase_         =     tokenizer.batch_decode(lowercase		,    skip_special_tokens=lowercase					)
					assert tgt_text == decoded




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


__A							={
    '''configuration_gpt_neo''': ['''GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoConfig''', '''GPTNeoOnnxConfig'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''GPTNeoForCausalLM''',
			    '''GPTNeoForQuestionAnswering''',
			    '''GPTNeoForSequenceClassification''',
			    '''GPTNeoForTokenClassification''',
			    '''GPTNeoModel''',
			    '''GPTNeoPreTrainedModel''',
			    '''load_tf_weights_in_gpt_neo''',
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			__A							=[
			    '''FlaxGPTNeoForCausalLM''',
			    '''FlaxGPTNeoModel''',
			    '''FlaxGPTNeoPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_gpt_neo import (
						    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
						    GPTNeoForCausalLM,
						    GPTNeoForQuestionAnswering,
						    GPTNeoForSequenceClassification,
						    GPTNeoForTokenClassification,
						    GPTNeoModel,
						    GPTNeoPreTrainedModel,
						    load_tf_weights_in_gpt_neo,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
			import sys

			__A							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


