

'''simple docstring'''

import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						import jax
						import jax.numpy as jnp
						import numpy as np

						from transformers.modeling_flax_pytorch_utils import (
						    convert_pytorch_state_dict_to_flax,
						    load_flax_weights_in_pytorch_model,
						)
						from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
						import torch







@require_flax
class lowerCamelCase      :

				def __init__(					self,	lowercase_,	lowercase_=14,	lowercase_=7,	lowercase_=True,	lowercase_=True,	lowercase_=True,	lowercase_=99,	lowercase_=32,	lowercase_=5,	lowercase_=4,	lowercase_=37,	lowercase_="gelu",	lowercase_=0.1,	lowercase_=0.1,	lowercase_=512,	lowercase_=0.02,	lowercase_=None,	)						->       Any:
					snake_case							 =				parent
					snake_case							 =				batch_size
					snake_case							 =				seq_length
					snake_case							 =				is_training
					snake_case							 =				use_input_mask
					snake_case							 =				use_labels
					snake_case							 =				vocab_size
					snake_case							 =				d_model
					snake_case							 =				num_hidden_layers
					snake_case							 =				num_attention_heads
					snake_case							 =				ffn_dim
					snake_case							 =				activation_function
					snake_case							 =				activation_dropout
					snake_case							 =				attention_dropout
					snake_case							 =				max_position_embeddings
					snake_case							 =				initializer_range
					snake_case							 =				None
					snake_case							 =				0
					snake_case							 =				2
					snake_case							 =				1

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					snake_case							 =				np.clip(ids_tensor([self.batch_size, self.seq_length],	self.vocab_size   ),	3,	self.vocab_size   )

					snake_case							 =				None
					if self.use_input_mask:
						snake_case							 =				random_attention_mask([self.batch_size, self.seq_length]   )

					snake_case							 =				XGLMConfig(
					    vocab_size=self.vocab_size,	d_model=self.hidden_size,	num_layers=self.num_hidden_layers,	attention_heads=self.num_attention_heads,	ffn_dim=self.ffn_dim,	activation_function=self.activation_function,	activation_dropout=self.activation_dropout,	attention_dropout=self.attention_dropout,	max_position_embeddings=self.max_position_embeddings,	initializer_range=self.initializer_range,	use_cache=lowercase_,	bos_token_id=self.bos_token_id,	eos_token_id=self.eos_token_id,	pad_token_id=self.pad_token_id,	)

					return (config, input_ids, input_mask)

				def 				_lowerCamelCase						(					self   )						->       Dict:
					snake_case							 =				self.prepare_config_and_inputs()
					snake_case , snake_case , snake_case							 =				config_and_inputs
					snake_case							 =				{'input_ids': input_ids, 'attention_mask': attention_mask}
					return config, inputs_dict

				def 				_lowerCamelCase						(					self   )						->       List[Any]:
					snake_case , snake_case , snake_case							 =				self.prepare_config_and_inputs()

					snake_case							 =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
					snake_case							 =				ids_tensor([self.batch_size, self.seq_length],	vocab_size=2   )

					return (
					    config,
					    input_ids,
					    attention_mask,
					    encoder_hidden_states,
					    encoder_attention_mask,
					)

				def 				_lowerCamelCase						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_   )						->       Optional[int]:
					snake_case							 =				20
					snake_case							 =				model_class_name(lowercase_   )

					snake_case							 =				model.init_cache(input_ids.shape[0],	lowercase_   )
					snake_case							 =				jnp.ones((input_ids.shape[0], max_decoder_length),	dtype='i4'   )

					snake_case							 =				jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1   )[None, :],	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
					snake_case							 =				model(
					    input_ids[:, :-1],	attention_mask=lowercase_,	past_key_values=lowercase_,	position_ids=lowercase_,	)

					snake_case							 =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],	dtype='i4'   )
					snake_case							 =				model(
					    input_ids[:, -1:],	attention_mask=lowercase_,	past_key_values=outputs_cache.past_key_values,	position_ids=lowercase_,	)

					snake_case							 =				model(lowercase_   )

					snake_case							 =				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						(					self,	lowercase_,	lowercase_,	lowercase_,	lowercase_   )						->       str:
					snake_case							 =				20
					snake_case							 =				model_class_name(lowercase_   )

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

					snake_case							 =				model.init_cache(input_ids.shape[0],	lowercase_   )
					snake_case							 =				jnp.broadcast_to(
					    jnp.arange(input_ids.shape[-1] - 1   )[None, :],	(input_ids.shape[0], input_ids.shape[-1] - 1)   )

					snake_case							 =				model(
					    input_ids[:, :-1],	attention_mask=lowercase_,	past_key_values=lowercase_,	position_ids=lowercase_,	)
					snake_case							 =				jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]],	dtype='i4'   )
					snake_case							 =				model(
					    input_ids[:, -1:],	past_key_values=outputs_cache.past_key_values,	attention_mask=lowercase_,	position_ids=lowercase_,	)

					snake_case							 =				model(lowercase_,	attention_mask=lowercase_   )
					snake_case							 =				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}'''   )







@require_sentencepiece
@require_flax
class lowerCamelCase      (						__lowerCAmelCase							,							__lowerCAmelCase							,							unittest.TestCase							):
				snake_case_           =   (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
				snake_case_           =   (FlaxXGLMForCausalLM,) if is_flax_available() else ()

				def 				_lowerCamelCase						(					self   )						->       Tuple:
					snake_case							 =				FlaxXGLMModelTester(self   )

				def 				_lowerCamelCase						(					self   )						->       Any:
					for model_class_name in self.all_model_classes:
						snake_case , snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward(lowercase_,	lowercase_,	lowercase_,	lowercase_   )

				def 				_lowerCamelCase						(					self   )						->       Union[str, Any]:
					for model_class_name in self.all_model_classes:
						snake_case , snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs()
						self.model_tester.check_use_cache_forward_with_attn_mask(
						    lowercase_,	lowercase_,	lowercase_,	lowercase_   )

				@slow
				def 				_lowerCamelCase						(					self   )						->       str:
					snake_case							 =				XGLMTokenizer.from_pretrained('XGLM',	padding_side='left'   )
					snake_case							 =				tokenizer(['Hello this is a long string', 'Hey'],	return_tensors='np',	padding=lowercase_,	truncation=lowercase_   )

					snake_case							 =				FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'   )
					snake_case							 =				1
					snake_case							 =				False

					snake_case							 =				jax.jit(model.generate   )

					snake_case							 =				jit_generate(inputs['input_ids'],	attention_mask=inputs['attention_mask']   ).sequences

					snake_case							 =				tokenizer.batch_decode(lowercase_,	skip_special_tokens=lowercase_   )

					snake_case							 =				[
					    'Hello this is a long string of questions, but I\'m not sure if I\'m',
					    'Hey, I\'m a newbie to the forum and I\'',
					]

					self.assertListEqual(lowercase_,	lowercase_   )

				@is_pt_flax_cross_test
				def 				_lowerCamelCase						(					self   )						->       int:
					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__   ):
							# prepare inputs
							snake_case							 =				self._prepare_for_class(lowercase_,	lowercase_   )
							snake_case							 =				{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

							# load corresponding PyTorch class
							snake_case							 =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
							snake_case							 =				getattr(lowercase_,	lowercase_   )

							snake_case , snake_case							 =				pt_inputs['input_ids'].shape
							snake_case							 =				np.random.randint(0,	seq_length - 1,	size=(batch_size,)   )
							for batch_idx, start_index in enumerate(lowercase_   ):
								snake_case							 =				0
								snake_case							 =				1
								snake_case							 =				0
								snake_case							 =				1
							snake_case							 =				pt_model_class(lowercase_   ).eval()
							# Flax models don't use the `use_cache` option and cache is not returned as a default.
							# So we disable `use_cache` here for PyTorch model.
							snake_case							 =				False
							snake_case							 =				model_class(lowercase_,	dtype=jnp.floataa   )
							snake_case							 =				convert_pytorch_state_dict_to_flax(pt_model.state_dict(),	lowercase_   )
							snake_case							 =				fx_state

							with torch.no_grad():
								snake_case							 =				pt_model(**lowercase_   ).to_tuple()

							snake_case							 =				fx_model(**lowercase_   ).to_tuple()
							self.assertEqual(len(lowercase_   ),	len(lowercase_   ),	'Output lengths differ between Flax and PyTorch'   )
							for fx_output, pt_output in zip(lowercase_,	lowercase_   ):
								self.assert_almost_equals(fx_output[:, -1],	pt_output[:, -1].numpy(),	4E-2   )

							with tempfile.TemporaryDirectory() as tmpdirname:
								pt_model.save_pretrained(lowercase_   )
								snake_case							 =				model_class.from_pretrained(lowercase_,	from_pt=lowercase_   )

							snake_case							 =				fx_model_loaded(**lowercase_   ).to_tuple()
							self.assertEqual(
							    len(lowercase_   ),	len(lowercase_   ),	'Output lengths differ between Flax and PyTorch'   )
							for fx_output_loaded, pt_output in zip(lowercase_,	lowercase_   ):
								self.assert_almost_equals(fx_output_loaded[:, -1],	pt_output[:, -1].numpy(),	4E-2   )

				@is_pt_flax_cross_test
				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					snake_case , snake_case							 =				self.model_tester.prepare_config_and_inputs_for_common()
					for model_class in self.all_model_classes:
						with self.subTest(model_class.__name__   ):
							# prepare inputs
							snake_case							 =				self._prepare_for_class(lowercase_,	lowercase_   )
							snake_case							 =				{k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

							# load corresponding PyTorch class
							snake_case							 =				model_class.__name__[4:]  # Skip the "Flax" at the beginning
							snake_case							 =				getattr(lowercase_,	lowercase_   )

							snake_case							 =				pt_model_class(lowercase_   ).eval()
							snake_case							 =				False
							snake_case							 =				model_class(lowercase_,	dtype=jnp.floataa   )

							snake_case							 =				load_flax_weights_in_pytorch_model(lowercase_,	fx_model.params   )
							snake_case , snake_case							 =				pt_inputs['input_ids'].shape
							snake_case							 =				np.random.randint(0,	seq_length - 1,	size=(batch_size,)   )
							for batch_idx, start_index in enumerate(lowercase_   ):
								snake_case							 =				0
								snake_case							 =				1
								snake_case							 =				0
								snake_case							 =				1

							# make sure weights are tied in PyTorch
							pt_model.tie_weights()

							with torch.no_grad():
								snake_case							 =				pt_model(**lowercase_   ).to_tuple()

							snake_case							 =				fx_model(**lowercase_   ).to_tuple()
							self.assertEqual(len(lowercase_   ),	len(lowercase_   ),	'Output lengths differ between Flax and PyTorch'   )
							for fx_output, pt_output in zip(lowercase_,	lowercase_   ):
								self.assert_almost_equals(fx_output[:, -1],	pt_output[:, -1].numpy(),	4E-2   )

							with tempfile.TemporaryDirectory() as tmpdirname:
								fx_model.save_pretrained(lowercase_   )
								snake_case							 =				pt_model_class.from_pretrained(lowercase_,	from_flax=lowercase_   )

							with torch.no_grad():
								snake_case							 =				pt_model_loaded(**lowercase_   ).to_tuple()

							self.assertEqual(
							    len(lowercase_   ),	len(lowercase_   ),	'Output lengths differ between Flax and PyTorch'   )
							for fx_output, pt_output in zip(lowercase_,	lowercase_   ):
								self.assert_almost_equals(fx_output[:, -1],	pt_output[:, -1].numpy(),	4E-2   )






				@slow
				def 				_lowerCamelCase						(					self   )						->       Optional[Any]:
					for model_class_name in self.all_model_classes:
						snake_case							 =				model_class_name.from_pretrained('facebook/xglm-564M'   )
						snake_case							 =				model(np.ones((1, 1)   )   )
						self.assertIsNotNone(lowercase_   )








'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_						   =			{
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["LEDTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			[
						    "LED_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "LEDForConditionalGeneration",
						    "LEDForQuestionAnswering",
						    "LEDForSequenceClassification",
						    "LEDModel",
						    "LEDPreTrainedModel",
						]


try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase_						   =			["TFLEDForConditionalGeneration", "TFLEDModel", "TFLEDPreTrainedModel"]


if TYPE_CHECKING:
						from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
						from .tokenization_led import LEDTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_led_fast import LEDTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_led import (
												    LED_PRETRAINED_MODEL_ARCHIVE_LIST,
												    LEDForConditionalGeneration,
												    LEDForQuestionAnswering,
												    LEDForSequenceClassification,
												    LEDModel,
												    LEDPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
						import sys

						lowerCAmelCase_						   =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





