



from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel






def     a(      A       :    Dict ,     A       :    str ,     A       :    Dict=None ,     A       :    Dict=None       )		->       Any:
					"""simple docstring"""





					if attention_mask is None:
										a					     =   tf.cast(tf.math.not_equal(__snake_case ,     config.pad_token_id       ) ,     tf.inta       )
					return {"input_ids": input_ids, "attention_mask": attention_mask}



@require_tf
class 	_lowercase							:





	"""simple docstring"""
	__A							=							OPTConfig
	__A							=							{}
	__A							=							"gelu"




	def __init__(self		,       lowerCamelCase_		,       lowerCamelCase_=13		,       lowerCamelCase_=7		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=99		,       lowerCamelCase_=16		,       lowerCamelCase_=2		,       lowerCamelCase_=4		,       lowerCamelCase_=4		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.1		,       lowerCamelCase_=20		,       lowerCamelCase_=2		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       lowerCamelCase_=16		,       lowerCamelCase_=16		,       ):
						"""simple docstring"""

						a					     =   parent
						a					     =   batch_size
						a					     =   seq_length
						a					     =   is_training
						a					     =   use_labels
						a					     =   vocab_size
						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					     =   max_position_embeddings
						a					     =   eos_token_id
						a					     =   pad_token_id
						a					     =   bos_token_id
						a					     =   embed_dim
						a					     =   word_embed_proj_dim
						a					     =   False




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   ids_tensor([self.batch_size, self.seq_length - 1]		,       self.vocab_size		)
						a					     =   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		)		,       1		)
						a					     =   tf.concat([input_ids, eos_tensor]		,       axis=1		)

						a					     =   self.config_cls(
						    vocab_size=self.vocab_size		,       hidden_size=self.hidden_size		,       num_hidden_layers=self.num_hidden_layers		,       num_attention_heads=self.num_attention_heads		,       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_id=self.eos_token_id		,       bos_token_id=self.bos_token_id		,       pad_token_id=self.pad_token_id		,       embed_dim=self.embed_dim		,       word_embed_proj_dim=self.word_embed_proj_dim		,       is_encoder_decoder=lowerCamelCase_		,       **self.config_updates		,       )
						a					     =   prepare_opt_inputs_dict(lowerCamelCase_		,       lowerCamelCase_		)
						return config, inputs_dict




	def      UpperCamelCase_       (self		,       lowerCamelCase_		,       lowerCamelCase_		):
						"""simple docstring"""

						a					     =   TFOPTModel(config=lowerCamelCase_		)
						a					     =   inputs_dict['''input_ids''']

						a					     =   input_ids[:1, :]
						a					     =   inputs_dict['''attention_mask'''][:1, :]
						a					     =   1

						# first forward pass
						a					     =   model(lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       use_cache=lowerCamelCase_		)

						a					     =   outputs.to_tuple()

						# create hypothetical next token and extent to next_input_ids
						a					     =   ids_tensor((self.batch_size, 3)		,       config.vocab_size		)
						a					     =   tf.cast(ids_tensor((self.batch_size, 3)		,       2		)		,       tf.inta		)

						# append to next input_ids and
						a					     =   tf.concat([input_ids, next_tokens]		,       axis=-1		)
						a					     =   tf.concat([attention_mask, next_attn_mask]		,       axis=-1		)

						a					     =   model(lowerCamelCase_		,       attention_mask=lowerCamelCase_		)[0]
						a					     =   model(lowerCamelCase_		,       attention_mask=lowerCamelCase_		,       past_key_values=lowerCamelCase_		)[0]

						self.parent.assertEqual(next_tokens.shape[1]		,       output_from_past.shape[1]		)

						# select random slice
						a					     =   int(ids_tensor((1,)		,       output_from_past.shape[-1]		)		)
						a					     =   output_from_no_past[:, -3:, random_slice_idx]
						a					     =   output_from_past[:, :, random_slice_idx]

						# test that outputs are equal for slice
						tf.debugging.assert_near(lowerCamelCase_		,       lowerCamelCase_		,       rtol=1E-3		)




@require_tf
class 	_lowercase							(   _A,      _A,      unittest.TestCase	):





	"""simple docstring"""
	__A							=							(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
	__A							=							(TFOPTForCausalLM,) if is_tf_available() else ()
	__A							=							(
	    {"feature-extraction": TFOPTModel, "text-generation": TFOPTForCausalLM} if is_tf_available() else {}
	)
	__A							=							False
	__A							=							False
	__A							=							False
	__A							=							10




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   TFOPTModelTester(self		)
						a					     =   ConfigTester(self		,       config_class=lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						self.config_tester.run_common_tests()




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs_for_common()
						self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   self.model_tester.prepare_config_and_inputs_for_common()

						def _get_word_embedding_weight(lowerCamelCase_		,       lowerCamelCase_		):
											if hasattr(lowerCamelCase_		,       "weight"		):
																return embedding_layer.weight
											else:
																# Here we build the word embeddings weights if not exists.
																# And then we retry to get the attribute once built.
																model.build()
																if hasattr(lowerCamelCase_		,       "weight"		):
																					return embedding_layer.weight
																else:
																					return None

						for model_class in self.all_model_classes:
											for size in [config.vocab_size - 10, config.vocab_size + 10]:
																# build the embeddings
																a					     =   model_class(config=lowerCamelCase_		)
																a					     =   _get_word_embedding_weight(lowerCamelCase_		,       model.get_input_embeddings()		)
																a					     =   _get_word_embedding_weight(lowerCamelCase_		,       model.get_output_embeddings()		)

																# reshape the embeddings
																model.resize_token_embeddings(lowerCamelCase_		)
																a					     =   _get_word_embedding_weight(lowerCamelCase_		,       model.get_input_embeddings()		)
																a					     =   _get_word_embedding_weight(lowerCamelCase_		,       model.get_output_embeddings()		)

																# check that the resized embeddings size matches the desired size.
																a					     =   size if size is not None else config.vocab_size

																self.assertEqual(new_input_embeddings.shape[0]		,       lowerCamelCase_		)

																# check that weights remain the same after resizing
																a					     =   True
																for pa, pa in zip(old_input_embeddings.value()		,       new_input_embeddings.value()		):
																					if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
																										a					     =   False
																self.assertTrue(lowerCamelCase_		)

																if old_output_embeddings is not None and new_output_embeddings is not None:
																					self.assertEqual(new_output_embeddings.shape[0]		,       lowerCamelCase_		)

																					a					     =   True
																					for pa, pa in zip(old_output_embeddings.value()		,       new_output_embeddings.value()		):
																										if tf.math.reduce_sum(tf.math.abs(pa - pa		)		) > 0:
																															a					     =   False
																					self.assertTrue(lowerCamelCase_		)






def     a(      A       :    int       )		->       List[Any]:
					"""simple docstring"""





					return tf.constant(__snake_case ,     dtype=tf.intaa       )



@require_tf
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""
	__A							=							99




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   tf.ones((4, 1)		,       dtype=tf.intaa		) * 2
						a					     =   tf.concat([ids_tensor((4, 6)		,       self.vocab_size - 3		) + 3, eos_column_vector]		,       axis=1		)
						a					     =   input_ids.shape[0]
						a					     =   OPTConfig(
						    vocab_size=self.vocab_size		,       hidden_size=24		,       num_hidden_layers=2		,       num_attention_heads=2		,       ffn_dim=32		,       max_position_embeddings=48		,       eos_token_id=2		,       pad_token_id=1		,       bos_token_id=0		,       )
						return config, input_ids, batch_size




@require_sentencepiece
@require_tf
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	@slow
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   TFOPTModel.from_pretrained("facebook/opt-350m"		)
						a					     =   _long_tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]		)
						a					     =   tf.not_equal(lowerCamelCase_		,       model.config.pad_token_id		)
						with tf.GradientTape():
											a					     =   model(input_ids=lowerCamelCase_		,       attention_mask=lowerCamelCase_		).last_hidden_state
						a					     =   (1, 11, 512)
						self.assertEqual(output.shape		,       lowerCamelCase_		)
						a					     =   tf.constant(
						    [[-0.2873, -1.9218, -0.3033], [-1.2710, -0.1338, -0.1902], [0.4095, 0.1214, -1.3121]]		)
						self.assertTrue(np.allclose(output[:, :3, :3]		,       lowerCamelCase_		,       atol=4E-3		)		)

						a					     =   tf.function(lowerCamelCase_		,       jit_compile=lowerCamelCase_		)
						a					     =   xla_generate(lowerCamelCase_		,       lowerCamelCase_		)[0]
						self.assertTrue(np.allclose(output[:, :3, :3]		,       lowerCamelCase_		,       atol=4E-2		)		)




@require_tf
@slow
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						super().setUp()
						a					     =   '''facebook/opt-350m'''




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   TFOPTForCausalLM.from_pretrained(self.path_model		)
						a					     =   GPTaTokenizer.from_pretrained(self.path_model		)

						a					     =   [
						    '''Today is a beautiful day and I want to''',
						    '''In the city of''',
						    '''Paris is the capital of France and''',
						    '''Computers and mobile phones have taken''',
						]
						# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="tf"		,       padding=lowerCamelCase_		,       add_special_tokens=lowerCamelCase_		)
						a					     =   tf.math.reduce_mean(model(inputs.input_ids		,       attention_mask=inputs.attention_mask		)[0]		,       axis=-1		)
						a					     =   tf.constant(
						    [
						        [1.3851, -13.8923, -10.5229, -10.7533, -0.2309, -10.2384, -0.5365, -9.0947, -5.1670],
						        [-4.7073, -10.6276, -3.9415, -21.5242, -0.2822, -0.2822, -0.2822, -0.2822, -0.2822],
						        [0.6247, -3.4229, -8.9179, -1.4297, -14.1650, 1.4146, -9.0218, -0.2703, -0.2703],
						        [6.4783, -1.9913, -10.7926, -2.3336, 1.5092, -0.9974, -6.8213, 1.3477, 1.3477],
						    ]		)
						self.assertTrue(np.allclose(lowerCamelCase_		,       lowerCamelCase_		,       atol=1E-4		)		)

						a					     =   tf.function(lowerCamelCase_		,       jit_compile=lowerCamelCase_		)
						a					     =   tf.math.reduce_mean(xla_generate(inputs.input_ids		,       attention_mask=inputs.attention_mask		)[0]		,       axis=-1		)
						self.assertTrue(np.allclose(lowerCamelCase_		,       lowerCamelCase_		,       atol=1E-4		)		)




@require_tf
@slow
class 	_lowercase							(   unittest.TestCase	):





	"""simple docstring"""




	@property
	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						return [
						    "Today is a beautiful day and I want",
						    "In the city of",
						    "Paris is the capital of France and",
						    "Computers and mobile phones have taken",
						]




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   '''facebook/opt-125m'''

						a					     =   [
						    '''Today is a beautiful day and I want to''',
						    '''In the city of New York, the city''',
						    '''Paris is the capital of France and the capital''',
						    '''Computers and mobile phones have taken over the''',
						]

						a					     =   []
						a					     =   GPTaTokenizer.from_pretrained(lowerCamelCase_		)
						a					     =   TFOPTForCausalLM.from_pretrained(lowerCamelCase_		)

						for prompt in self.prompts:
											a					     =   tokenizer(lowerCamelCase_		,       return_tensors="tf"		).input_ids

											a					     =   model.generate(lowerCamelCase_		,       max_length=10		)

											a					     =   tokenizer.batch_decode(lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		)
											predicted_outputs += generated_string

						self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   '''facebook/opt-350m'''

						a					     =   GPTaTokenizer.from_pretrained(lowerCamelCase_		)
						a					     =   TFOPTForCausalLM.from_pretrained(lowerCamelCase_		)

						a					     =   '''left'''

						# use different length sentences to test batching
						a					     =   [
						    '''Hello, my dog is a little''',
						    '''Today, I''',
						]

						a					     =   tokenizer(lowerCamelCase_		,       return_tensors="tf"		,       padding=lowerCamelCase_		)
						a					     =   inputs['''input_ids''']

						a					     =   model.generate(input_ids=lowerCamelCase_		,       attention_mask=inputs["attention_mask"]		)

						a					     =   tokenizer(sentences[0]		,       return_tensors="tf"		).input_ids
						a					     =   model.generate(input_ids=lowerCamelCase_		)

						a					     =   inputs_non_padded.shape[-1] - tf.math.reduce_sum(
						    tf.cast(inputs["attention_mask"][-1]		,       tf.intaa		)		)
						a					     =   tokenizer(sentences[1]		,       return_tensors="tf"		).input_ids
						a					     =   model.generate(input_ids=lowerCamelCase_		,       max_length=model.config.max_length - num_paddings		)

						a					     =   tokenizer.batch_decode(lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		)
						a					     =   tokenizer.decode(output_non_padded[0]		,       skip_special_tokens=lowerCamelCase_		)
						a					     =   tokenizer.decode(output_padded[0]		,       skip_special_tokens=lowerCamelCase_		)

						a					     =   [
						    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
						    '''Today, I was in the middle of a conversation with a friend about the''',
						]
						self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)
						self.assertListEqual(lowerCamelCase_		,       [non_padded_sentence, padded_sentence]		)




	def      UpperCamelCase_       (self		):
						"""simple docstring"""

						a					     =   '''facebook/opt-350m'''

						a					     =   [
						    '''Today is a beautiful day and I want to''',
						    '''In the city of San Francisco, the city''',
						    '''Paris is the capital of France and the capital''',
						    '''Computers and mobile phones have taken over the''',
						]

						a					     =   []
						a					     =   GPTaTokenizer.from_pretrained(lowerCamelCase_		)
						a					     =   TFOPTForCausalLM.from_pretrained(lowerCamelCase_		)

						for prompt in self.prompts:
											a					     =   tokenizer(lowerCamelCase_		,       return_tensors="tf"		).input_ids

											a					     =   model.generate(lowerCamelCase_		,       max_length=10		)

											a					     =   tokenizer.batch_decode(lowerCamelCase_		,       skip_special_tokens=lowerCamelCase_		)
											predicted_outputs += generated_string

						self.assertListEqual(lowerCamelCase_		,       lowerCamelCase_		)








"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A		:       Dict  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		{
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   "vit_mae"




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

							lowercase_ : List[Any]							       =   hidden_size
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : List[Any]							       =   num_attention_heads
							lowercase_ : Any							       =   intermediate_size
							lowercase_ : Optional[int]							       =   hidden_act
							lowercase_ : List[Any]							       =   hidden_dropout_prob
							lowercase_ : int							       =   attention_probs_dropout_prob
							lowercase_ : int							       =   initializer_range
							lowercase_ : Dict							       =   layer_norm_eps
							lowercase_ : Optional[Any]							       =   image_size
							lowercase_ : str							       =   patch_size
							lowercase_ : Dict							       =   num_channels
							lowercase_ : Any							       =   qkv_bias
							lowercase_ : Union[str, Any]							       =   decoder_num_attention_heads
							lowercase_ : Optional[Any]							       =   decoder_hidden_size
							lowercase_ : List[str]							       =   decoder_num_hidden_layers
							lowercase_ : List[Any]							       =   decoder_intermediate_size
							lowercase_ : Optional[Any]							       =   mask_ratio
							lowercase_ : Optional[Any]							       =   norm_pix_loss



