





"""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  _UpperCAmelCase       :


					def __init__( self      :Optional[Any]					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :Optional[Any]=14					,	__UpperCamelCase      :int=7					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[Any]=True					,	__UpperCamelCase      :Optional[Any]=True					,	__UpperCamelCase      :Union[str, Any]=99					,	__UpperCamelCase      :List[str]=32					,	__UpperCamelCase      :Tuple=5					,	__UpperCamelCase      :List[Any]=4					,	__UpperCamelCase      :Optional[int]=37					,	__UpperCamelCase      :int="gelu"					,	__UpperCamelCase      :int=0.1					,	__UpperCamelCase      :Dict=0.1					,	__UpperCamelCase      :Optional[int]=5_12					,	__UpperCamelCase      :str=0.02					,	__UpperCamelCase      :List[str]=None					,	):
												A		=  parent
												A		=  batch_size
												A		=  seq_length
												A		=  is_training
												A		=  use_input_mask
												A		=  use_labels
												A		=  vocab_size
												A		=  d_model
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  ffn_dim
												A		=  activation_function
												A		=  activation_dropout
												A		=  attention_dropout
												A		=  max_position_embeddings
												A		=  initializer_range
												A		=  None
												A		=  0
												A		=  2
												A		=  1


					def      lowerCamelCase	( self      :List[str]		):
												A		=  np.clip(ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size		)					,	3					,	self.vocab_size		)

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

												A		=  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=__UpperCamelCase					,	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      :Union[str, Any]		):
												A		=  self.prepare_config_and_inputs()
												A,     A,     A		=  config_and_inputs
												A		=  {"input_ids": input_ids, "attention_mask": attention_mask}
												return config, inputs_dict


					def      lowerCamelCase	( self      :Dict		):
												A,     A,     A		=  self.prepare_config_and_inputs()

												A		=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]		)
												A		=  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      :Dict					,	__UpperCamelCase      :Any					,	__UpperCamelCase      :List[str]					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Union[str, Any]		):
												A		=  20
												A		=  model_class_name(__UpperCamelCase		)

												A		=  model.init_cache(input_ids.shape[0]					,	__UpperCamelCase		)
												A		=  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"		)

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

												A		=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"		)
												A		=  model(
												    input_ids[:, -1:]					,	attention_mask=__UpperCamelCase					,	past_key_values=outputs_cache.past_key_values					,	position_ids=__UpperCamelCase					,	)

												A		=  model(__UpperCamelCase		)

												A		=  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      :Any					,	__UpperCamelCase      :int					,	__UpperCamelCase      :Optional[int]					,	__UpperCamelCase      :str					,	__UpperCamelCase      :List[str]		):
												A		=  20
												A		=  model_class_name(__UpperCamelCase		)

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

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

												A		=  model(
												    input_ids[:, :-1]					,	attention_mask=__UpperCamelCase					,	past_key_values=__UpperCamelCase					,	position_ids=__UpperCamelCase					,	)
												A		=  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"		)
												A		=  model(
												    input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=__UpperCamelCase					,	position_ids=__UpperCamelCase					,	)

												A		=  model(__UpperCamelCase					,	attention_mask=__UpperCamelCase		)
												A		=  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  _UpperCAmelCase       (     lowercase_					,							lowercase_					,							unittest.TestCase			):
					UpperCamelCase								=							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
					UpperCamelCase								=							(FlaxXGLMForCausalLM,) if is_flax_available() else ()


					def      lowerCamelCase	( self      :Tuple		):
												A		=  FlaxXGLMModelTester(self		)


					def      lowerCamelCase	( self      :Dict		):
												for model_class_name in self.all_model_classes:
																			A,     A,     A		=  self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)


					def      lowerCamelCase	( self      :Tuple		):
												for model_class_name in self.all_model_classes:
																			A,     A,     A		=  self.model_tester.prepare_config_and_inputs()
																			self.model_tester.check_use_cache_forward_with_attn_mask(
																			    __UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase		)


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												A		=  XGLMTokenizer.from_pretrained("XGLM"					,	padding_side="left"		)
												A		=  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=__UpperCamelCase					,	truncation=__UpperCamelCase		)

												A		=  FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"		)
												A		=  1
												A		=  False

												A		=  jax.jit(model.generate		)

												A		=  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]		).sequences

												A		=  tokenizer.batch_decode(__UpperCamelCase					,	skip_special_tokens=__UpperCamelCase		)

												A		=  [
												    "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(__UpperCamelCase					,	__UpperCamelCase		)


					@is_pt_flax_cross_test
					def      lowerCamelCase	( self      :Any		):
												A,     A		=  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
																										A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase		)
																										A		=  {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

																										A,     A		=  pt_inputs["input_ids"].shape
																										A		=  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)		)
																										for batch_idx, start_index in enumerate(__UpperCamelCase		):
																																	A		=  0
																																	A		=  1
																																	A		=  0
																																	A		=  1
																										A		=  pt_model_class(__UpperCamelCase		).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.
																										A		=  False
																										A		=  model_class(__UpperCamelCase					,	dtype=jnp.floataa		)
																										A		=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	__UpperCamelCase		)
																										A		=  fx_state

																										with torch.no_grad():
																																	A		=  pt_model(**__UpperCamelCase		).to_tuple()

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

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

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


					@is_pt_flax_cross_test
					def      lowerCamelCase	( self      :Optional[int]		):
												A,     A		=  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
																										A		=  self._prepare_for_class(__UpperCamelCase					,	__UpperCamelCase		)
																										A		=  {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

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

																										A		=  pt_model_class(__UpperCamelCase		).eval()
																										A		=  False
																										A		=  model_class(__UpperCamelCase					,	dtype=jnp.floataa		)

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

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

																										with torch.no_grad():
																																	A		=  pt_model(**__UpperCamelCase		).to_tuple()

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

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

																										with torch.no_grad():
																																	A		=  pt_model_loaded(**__UpperCamelCase		).to_tuple()

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


					@slow
					def      lowerCamelCase	( self      :Optional[int]		):
												for model_class_name in self.all_model_classes:
																			A		=  model_class_name.from_pretrained("facebook/xglm-564M"		)
																			A		=  model(np.ones((1, 1)		)		)
																			self.assertIsNotNone(__UpperCamelCase		)









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case					:		Optional[int]							=			logging.get_logger(__name__)

_snake_case					:		List[Any]							=			{
    'ZinengTang/tvlt-base': 'https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json',
}






class  _UpperCAmelCase       (     lowercase_			):
					UpperCamelCase								=							'''tvlt'''


					def __init__( self      :Dict					,	__UpperCamelCase      :Tuple=2_24					,	__UpperCamelCase      :Dict=20_48					,	__UpperCamelCase      :Optional[Any]=1_28					,	__UpperCamelCase      :Optional[int]=[16, 16]					,	__UpperCamelCase      :Any=[16, 16]					,	__UpperCamelCase      :List[str]=3					,	__UpperCamelCase      :str=1					,	__UpperCamelCase      :List[str]=8					,	__UpperCamelCase      :str=7_68					,	__UpperCamelCase      :List[str]=12					,	__UpperCamelCase      :Tuple=12					,	__UpperCamelCase      :Union[str, Any]=30_72					,	__UpperCamelCase      :List[Any]="gelu"					,	__UpperCamelCase      :List[str]=0.0					,	__UpperCamelCase      :Union[str, Any]=0.0					,	__UpperCamelCase      :List[str]=0.02					,	__UpperCamelCase      :Dict=1e-6					,	__UpperCamelCase      :Any=True					,	__UpperCamelCase      :List[str]=False					,	__UpperCamelCase      :str=16					,	__UpperCamelCase      :List[str]=5_12					,	__UpperCamelCase      :int=8					,	__UpperCamelCase      :int=20_48					,	__UpperCamelCase      :int=0.75					,	__UpperCamelCase      :Tuple=0.15					,	__UpperCamelCase      :Optional[Any]="frame-level"					,	__UpperCamelCase      :int=True					,	__UpperCamelCase      :str=True					,	__UpperCamelCase      :Tuple="classification"					,	**__UpperCamelCase      :Any					,	):
												super().__init__(**__UpperCamelCase		)

												if audio_mask_type not in ("frame-level", "patch_level"):
																			raise ValueError(
																			    "audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') "
																			    f"got {audio_mask_type}"		)

												A		=  image_size
												A		=  spectrogram_length
												A		=  frequency_length
												A		=  image_patch_size
												A		=  audio_patch_size
												A		=  num_image_channels
												A		=  num_audio_channels
												A		=  num_frames

												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps
												A		=  qkv_bias
												A		=  use_mean_pooling

												A		=  decoder_num_attention_heads
												A		=  decoder_hidden_size
												A		=  decoder_num_hidden_layers
												A		=  decoder_intermediate_size
												A		=  pixel_mask_ratio
												A		=  audio_mask_ratio
												A		=  audio_mask_type

												A		=  task_matching
												A		=  task_mae
												A		=  loss_type


