






"""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 							__lowercase:






						def __init__(       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE=14     ,    _SCREAMING_SNAKE_CASE=7     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=True     ,    _SCREAMING_SNAKE_CASE=99     ,    _SCREAMING_SNAKE_CASE=32     ,    _SCREAMING_SNAKE_CASE=5     ,    _SCREAMING_SNAKE_CASE=4     ,    _SCREAMING_SNAKE_CASE=37     ,    _SCREAMING_SNAKE_CASE="gelu"     ,    _SCREAMING_SNAKE_CASE=0.1     ,    _SCREAMING_SNAKE_CASE=0.1     ,    _SCREAMING_SNAKE_CASE=5_12     ,    _SCREAMING_SNAKE_CASE=0.02     ,    _SCREAMING_SNAKE_CASE=None     ,    )      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					=parent
											lowerCamelCase__					=batch_size
											lowerCamelCase__					=seq_length
											lowerCamelCase__					=is_training
											lowerCamelCase__					=use_input_mask
											lowerCamelCase__					=use_labels
											lowerCamelCase__					=vocab_size
											lowerCamelCase__					=d_model
											lowerCamelCase__					=num_hidden_layers
											lowerCamelCase__					=num_attention_heads
											lowerCamelCase__					=ffn_dim
											lowerCamelCase__					=activation_function
											lowerCamelCase__					=activation_dropout
											lowerCamelCase__					=attention_dropout
											lowerCamelCase__					=max_position_embeddings
											lowerCamelCase__					=initializer_range
											lowerCamelCase__					=None
											lowerCamelCase__					=0
											lowerCamelCase__					=2
											lowerCamelCase__					=1






						def 		lowerCAmelCase (       self)      ->	List[str]:




											"""simple docstring"""





											lowerCamelCase__					=np.clip(ids_tensor([self.batch_size, self.seq_length]     ,    self.vocab_size)     ,    3     ,    self.vocab_size)

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

											lowerCamelCase__					=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=_lowerCAmelCase     ,    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:




											"""simple docstring"""





											lowerCamelCase__					=self.prepare_config_and_inputs()
											lowerCamelCase__					=config_and_inputs
											lowerCamelCase__					={"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict






						def 		lowerCAmelCase (       self)      ->	Optional[int]:




											"""simple docstring"""





											lowerCamelCase__					=self.prepare_config_and_inputs()

											lowerCamelCase__					=floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
											lowerCamelCase__					=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     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	List[Any]:




											"""simple docstring"""





											lowerCamelCase__					=20
											lowerCamelCase__					=model_class_name(_lowerCAmelCase)

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

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

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

											lowerCamelCase__					=model(_lowerCAmelCase)

											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 (       self     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE     ,    _SCREAMING_SNAKE_CASE)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					=20
											lowerCamelCase__					=model_class_name(_lowerCAmelCase)

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

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

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

											lowerCamelCase__					=model(_lowerCAmelCase     ,    attention_mask=_lowerCAmelCase)
											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}")




@require_sentencepiece
@require_flax
class 							__lowercase(							__UpperCamelCase    ,				__UpperCamelCase    ,				unittest.TestCase	):
						lowerCAmelCase_		:				List[str]=							(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
						lowerCAmelCase_		:				int=							(FlaxXGLMForCausalLM,) if is_flax_available() else ()






						def 		lowerCAmelCase (       self)      ->	Optional[int]:




											"""simple docstring"""





											lowerCamelCase__					=FlaxXGLMModelTester(self)






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward(_lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase)






						def 		lowerCAmelCase (       self)      ->	Dict:




											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																lowerCamelCase__					=self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward_with_attn_mask(
																    _lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase     ,    _lowerCAmelCase)






						@slow
						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											lowerCamelCase__					=XGLMTokenizer.from_pretrained('XGLM'     ,    padding_side='left')
											lowerCamelCase__					=tokenizer(['Hello this is a long string', 'Hey']     ,    return_tensors='np'     ,    padding=_lowerCAmelCase     ,    truncation=_lowerCAmelCase)

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

											lowerCamelCase__					=jax.jit(model.generate)

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

											lowerCamelCase__					=tokenizer.batch_decode(_lowerCAmelCase     ,    skip_special_tokens=_lowerCAmelCase)

											lowerCamelCase__					=[
											    """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(_lowerCAmelCase     ,    _lowerCAmelCase)






						@is_pt_flax_cross_test
						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=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
																					lowerCamelCase__					=self._prepare_for_class(_lowerCAmelCase     ,    _lowerCAmelCase)
																					lowerCamelCase__					={k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

																					with torch.no_grad():
																										lowerCamelCase__					=pt_model(**_lowerCAmelCase).to_tuple()

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

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

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






						@is_pt_flax_cross_test
						def 		lowerCAmelCase (       self)      ->	Any:




											"""simple docstring"""





											lowerCamelCase__					=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
																					lowerCamelCase__					=self._prepare_for_class(_lowerCAmelCase     ,    _lowerCAmelCase)
																					lowerCamelCase__					={k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

																					lowerCamelCase__					=pt_model_class(_lowerCAmelCase).eval()
																					lowerCamelCase__					=False
																					lowerCamelCase__					=model_class(_lowerCAmelCase     ,    dtype=jnp.floataa)

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

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

																					with torch.no_grad():
																										lowerCamelCase__					=pt_model(**_lowerCAmelCase).to_tuple()

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

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

																					with torch.no_grad():
																										lowerCamelCase__					=pt_model_loaded(**_lowerCAmelCase).to_tuple()

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






						@slow
						def 		lowerCAmelCase (       self)      ->	Optional[Any]:




											"""simple docstring"""





											for model_class_name in self.all_model_classes:
																lowerCamelCase__					=model_class_name.from_pretrained('facebook/xglm-564M')
																lowerCamelCase__					=model(np.ones((1, 1)))
																self.assertIsNotNone(_lowerCAmelCase)
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class      SCREAMING_SNAKE_CASE__					(  __UpperCamelCase    ):
       A							:	Optional[Any]          =	["image_processor", "tokenizer"]
       A							:	Dict          =	"VideoMAEImageProcessor"
       A							:	Tuple          =	("CLIPTokenizer", "CLIPTokenizerFast")

       def __init__(							self					:     str			,			_lowerCAmelCase					:     Tuple=None			,			_lowerCAmelCase					:     Any=None			,			**_lowerCAmelCase					:     Union[str, Any]			):
           __snake_case       :				int 			=    None
           if "feature_extractor" in kwargs:
               warnings.warn(
                   """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                   """ instead."""			,			_lowerCAmelCase			,			)
               __snake_case       :				Optional[Any] 			=    kwargs.pop("""feature_extractor"""			)

           __snake_case       :				int 			=    image_processor if image_processor is not None else feature_extractor
           if image_processor is None:
               raise ValueError("""You need to specify an `image_processor`."""			)
           if tokenizer is None:
               raise ValueError("""You need to specify a `tokenizer`."""			)

           super().__init__(_lowerCAmelCase			,			_lowerCAmelCase			)
           __snake_case       :				Optional[Any] 			=    self.image_processor

       def __call__(							self					:     str			,			_lowerCAmelCase					:     int=None			,			_lowerCAmelCase					:     Dict=None			,			_lowerCAmelCase					:     Optional[int]=None			,			**_lowerCAmelCase					:     List[str]			):

           if text is None and videos is None:
               raise ValueError("""You have to specify either text or videos. Both cannot be none."""			)

           if text is not None:
               __snake_case       :				Optional[Any] 			=    self.tokenizer(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if videos is not None:
               __snake_case       :				Optional[int] 			=    self.image_processor(_lowerCAmelCase			,			return_tensors=_lowerCAmelCase			,			**_lowerCAmelCase			)

           if text is not None and videos is not None:
               __snake_case       :				Union[str, Any] 			=    image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**_lowerCAmelCase			)			,			tensor_type=_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     str			,			**_lowerCAmelCase					:     List[Any]			):
           return self.tokenizer.batch_decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       def 			snake_case__    (							self					:     Tuple			,			*_lowerCAmelCase					:     List[Any]			,			**_lowerCAmelCase					:     Dict			):
           return self.tokenizer.decode(*_lowerCAmelCase			,			**_lowerCAmelCase			)

       @property
       def 			snake_case__    (							self					:     str			):
           return ["input_ids", "attention_mask", "position_ids", "pixel_values"]

       @property
       def 			snake_case__    (							self					:     str			):
           warnings.warn(
               """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor_class





       @property
       def 			snake_case__    (							self					:     Union[str, Any]			):
           warnings.warn(
               """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""			,			_lowerCAmelCase			,			)
           return self.image_processor



