


import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


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

		from transformers.modeling_flax_pytorch_utils import (
		    convert_pytorch_state_dict_to_flax,
		    load_flax_weights_in_pytorch_model,
		)
		from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
		import torch







class        __lowercase       :



		"""simple docstring"""

		def __init__( self		,      A		,      A=14		,      A=7		,      A=True		,      A=True		,      A=False		,      A=True		,      A=99		,      A=32		,      A=4		,      A=4		,      A=4		,      A=37		,      A="gelu"		,      A=0.1		,      A=0.1		,      A=5_12		,      A=0.02		,      )  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  parent
					lowerCamelCase    		=  batch_size
					lowerCamelCase    		=  seq_length
					lowerCamelCase    		=  is_training
					lowerCamelCase    		=  use_input_mask
					lowerCamelCase    		=  use_token_type_ids
					lowerCamelCase    		=  use_labels
					lowerCamelCase    		=  vocab_size
					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  rotary_dim
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  max_position_embeddings
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  None
					lowerCamelCase    		=  vocab_size - 1
					lowerCamelCase    		=  vocab_size - 1
					lowerCamelCase    		=  vocab_size - 1







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







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

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

					lowerCamelCase    		=  GPTJConfig(
					    vocab_size=self.vocab_size		,      n_embd=self.hidden_size		,      n_layer=self.num_hidden_layers		,      n_head=self.num_attention_heads		,      n_positions=self.max_position_embeddings		,      use_cache=A		,      bos_token_id=self.bos_token_id		,      eos_token_id=self.eos_token_id		,      pad_token_id=self.pad_token_id		,      rotary_dim=self.rotary_dim		,      )

					return (config, input_ids, input_mask)







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A		,      A		,      A	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  20
					lowerCamelCase    		=  model_class_name(A	)

					lowerCamelCase    		=  model.init_cache(input_ids.shape[0]		,      A	)
					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=A		,      past_key_values=A		,      position_ids=A		,      )

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

					lowerCamelCase    		=  model(A	)

					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 					__A   ( self		,      A		,      A		,      A		,      A	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  20
					lowerCamelCase    		=  model_class_name(A	)

					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]		,      A	)
					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=A		,      past_key_values=A		,      position_ids=A		,      )
					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=A		,      position_ids=A		,      )

					lowerCamelCase    		=  model(A		,      attention_mask=A	)

					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_flax
class        __lowercase       (							a_			,							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Union[str, Any]			       =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
		UpperCamelCase     :					Optional[Any]			       =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()







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







					'''simple docstring'''







					lowerCamelCase    		=  FlaxGPTJModelTester(self	)







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







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







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







					'''simple docstring'''







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







		@tooslow
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					lowerCamelCase    		=  GPTaTokenizer.from_pretrained("""gpt2"""		,      pad_token="""<|endoftext|>"""		,      padding_side="""left"""	)
					lowerCamelCase    		=  tokenizer(["""Hello this is a long string""", """Hey"""]		,      return_tensors="""np"""		,      padding=A		,      truncation=A	)

					lowerCamelCase    		=  FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""	)
					lowerCamelCase    		=  False
					lowerCamelCase    		=  model.config.eos_token_id

					lowerCamelCase    		=  jax.jit(model.generate	)

					lowerCamelCase    		=  jit_generate(
					    inputs["""input_ids"""]		,      attention_mask=inputs["""attention_mask"""]		,      pad_token_id=tokenizer.pad_token_id	).sequences

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

					lowerCamelCase    		=  [
					    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
					    """Hey, I'm a little late to the party. I'm going to""",
					]

					self.assertListEqual(A		,      A	)







		@is_pt_flax_cross_test
		def 					__A   ( self	)  -> Optional[int]:







					'''simple docstring'''







					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__	):
											# prepare inputs
											lowerCamelCase    		=  self._prepare_for_class(A		,      A	)
											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(A		,      A	)

											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(A	):
														lowerCamelCase    		=  0
														lowerCamelCase    		=  1
														lowerCamelCase    		=  0
														lowerCamelCase    		=  1
											lowerCamelCase    		=  pt_model_class(A	).eval()
											lowerCamelCase    		=  model_class(A		,      dtype=jnp.floataa	)

											lowerCamelCase    		=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()		,      A	)
											lowerCamelCase    		=  fx_state

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

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

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

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







		@is_pt_flax_cross_test
		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







					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__	):
											# prepare inputs
											lowerCamelCase    		=  self._prepare_for_class(A		,      A	)
											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(A		,      A	)

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

											lowerCamelCase    		=  load_flax_weights_in_pytorch_model(A		,      fx_model.params	)
											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(A	):
														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(**A	).to_tuple()

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

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

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

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







		@tooslow
		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					for model_class_name in self.all_model_classes:
								lowerCamelCase    		=  model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""	)
								lowerCamelCase    		=  model(np.ones((1, 1)	)	)
								self.assertIsNotNone(A	)





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


UpperCAmelCase   :    str     =		logging.get_logger(__name__)

UpperCAmelCase   :    List[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        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[Any]			       =		"vit_mae"







		def __init__( self		,      A=7_68		,      A=12		,      A=12		,      A=30_72		,      A="gelu"		,      A=0.0		,      A=0.0		,      A=0.02		,      A=1e-1_2		,      A=2_24		,      A=16		,      A=3		,      A=True		,      A=16		,      A=5_12		,      A=8		,      A=20_48		,      A=0.75		,      A=False		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(**A	)

					lowerCamelCase    		=  hidden_size
					lowerCamelCase    		=  num_hidden_layers
					lowerCamelCase    		=  num_attention_heads
					lowerCamelCase    		=  intermediate_size
					lowerCamelCase    		=  hidden_act
					lowerCamelCase    		=  hidden_dropout_prob
					lowerCamelCase    		=  attention_probs_dropout_prob
					lowerCamelCase    		=  initializer_range
					lowerCamelCase    		=  layer_norm_eps
					lowerCamelCase    		=  image_size
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  qkv_bias
					lowerCamelCase    		=  decoder_num_attention_heads
					lowerCamelCase    		=  decoder_hidden_size
					lowerCamelCase    		=  decoder_num_hidden_layers
					lowerCamelCase    		=  decoder_intermediate_size
					lowerCamelCase    		=  mask_ratio
					lowerCamelCase    		=  norm_pix_loss

