




'''simple docstring'''


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      A       :







							def __init__(    self			:    Union[str, Any]				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Tuple=14				,     lowerCAmelCase_			:    str=7				,     lowerCAmelCase_			:    Optional[Any]=True				,     lowerCAmelCase_			:    Dict=True				,     lowerCAmelCase_			:    Any=False				,     lowerCAmelCase_			:    List[str]=True				,     lowerCAmelCase_			:    List[str]=99				,     lowerCAmelCase_			:    Tuple=32				,     lowerCAmelCase_			:    Optional[int]=4				,     lowerCAmelCase_			:    Optional[Any]=4				,     lowerCAmelCase_			:    Union[str, Any]=4				,     lowerCAmelCase_			:    Optional[int]=37				,     lowerCAmelCase_			:    List[Any]="gelu"				,     lowerCAmelCase_			:    List[str]=0.1				,     lowerCAmelCase_			:    str=0.1				,     lowerCAmelCase_			:    Any=5_12				,     lowerCAmelCase_			:    Union[str, Any]=0.0_2				,     )     ->				Dict:


														"""simple docstring"""


														_a      	=							parent
														_a      	=							batch_size
														_a      	=							seq_length
														_a      	=							is_training
														_a      	=							use_input_mask
														_a      	=							use_token_type_ids
														_a      	=							use_labels
														_a      	=							vocab_size
														_a      	=							hidden_size
														_a      	=							rotary_dim
														_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      	=							initializer_range
														_a      	=							None
														_a      	=							vocab_size - 1
														_a      	=							vocab_size - 1
														_a      	=							vocab_size - 1







							def   __lowerCAmelCase     (    self			:    str   )     ->				str:


														"""simple docstring"""


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

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

														_a      	=							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=lowerCAmelCase_				,     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   __lowerCAmelCase     (    self			:    List[Any]   )     ->				Optional[Any]:


														"""simple docstring"""


														_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			:    Optional[Any]				,     lowerCAmelCase_			:    Optional[int]				,     lowerCAmelCase_			:    Tuple				,     lowerCAmelCase_			:    Any				,     lowerCAmelCase_			:    Any   )     ->				Dict:


														"""simple docstring"""


														_a      	=							20
														_a      	=							model_class_name(lowerCAmelCase_   )

														_a      	=							model.init_cache(input_ids.shape[0]				,     lowerCAmelCase_   )
														_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=lowerCAmelCase_				,     past_key_values=lowerCAmelCase_				,     position_ids=lowerCAmelCase_				,     )

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

														_a      	=							model(lowerCAmelCase_   )

														_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			:    int				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Dict				,     lowerCAmelCase_			:    List[Any]				,     lowerCAmelCase_			:    List[str]   )     ->				List[Any]:


														"""simple docstring"""


														_a      	=							20
														_a      	=							model_class_name(lowerCAmelCase_   )

														_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]				,     lowerCAmelCase_   )
														_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=lowerCAmelCase_				,     past_key_values=lowerCAmelCase_				,     position_ids=lowerCAmelCase_				,     )
														_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=lowerCAmelCase_				,     position_ids=lowerCAmelCase_				,     )

														_a      	=							model(lowerCAmelCase_				,     attention_mask=lowerCAmelCase_   )

														_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_flax
class      A       (						_a	,_a	,unittest.TestCase       ):
							lowercase_							     =							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							lowercase_							     =							(FlaxGPTJForCausalLM,) if is_flax_available() else ()







							def   __lowerCAmelCase     (    self			:    Any   )     ->				int:


														"""simple docstring"""


														_a      	=							FlaxGPTJModelTester(self   )







							def   __lowerCAmelCase     (    self			:    int   )     ->				List[str]:


														"""simple docstring"""


														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(lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )







							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Optional[Any]:


														"""simple docstring"""


														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(
																					    lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_				,     lowerCAmelCase_   )







							@tooslow
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				Tuple:


														"""simple docstring"""


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

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

														_a      	=							jax.jit(model.generate   )

														_a      	=							jit_generate(
														    inputs['''input_ids''']				,     attention_mask=inputs['''attention_mask''']				,     pad_token_id=tokenizer.pad_token_id   ).sequences

														_a      	=							tokenizer.batch_decode(lowerCAmelCase_				,     skip_special_tokens=lowerCAmelCase_   )

														_a      	=							[
														    '''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(lowerCAmelCase_				,     lowerCAmelCase_   )







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


														"""simple docstring"""


														_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(lowerCAmelCase_				,     lowerCAmelCase_   )
																												_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(lowerCAmelCase_				,     lowerCAmelCase_   )

																												_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(lowerCAmelCase_   ):
																																			_a      	=							0
																																			_a      	=							1
																																			_a      	=							0
																																			_a      	=							1
																												_a      	=							pt_model_class(lowerCAmelCase_   ).eval()
																												_a      	=							model_class(lowerCAmelCase_				,     dtype=jnp.floataa   )

																												_a      	=							convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,     lowerCAmelCase_   )
																												_a      	=							fx_state

																												with torch.no_grad():
																																			_a      	=							pt_model(**lowerCAmelCase_   ).to_tuple()

																												_a      	=							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_   )
																																			_a      	=							model_class.from_pretrained(lowerCAmelCase_				,     from_pt=lowerCAmelCase_   )

																												_a      	=							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			:    Optional[int]   )     ->				int:


														"""simple docstring"""


														_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(lowerCAmelCase_				,     lowerCAmelCase_   )
																												_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(lowerCAmelCase_				,     lowerCAmelCase_   )

																												_a      	=							pt_model_class(lowerCAmelCase_   ).eval()
																												_a      	=							model_class(lowerCAmelCase_				,     dtype=jnp.floataa   )

																												_a      	=							load_flax_weights_in_pytorch_model(lowerCAmelCase_				,     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(lowerCAmelCase_   ):
																																			_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(**lowerCAmelCase_   ).to_tuple()

																												_a      	=							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_   )
																																			_a      	=							pt_model_class.from_pretrained(lowerCAmelCase_				,     from_flax=lowerCAmelCase_   )

																												with torch.no_grad():
																																			_a      	=							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   )







							@tooslow
							def   __lowerCAmelCase     (    self			:    Optional[int]   )     ->				List[str]:


														"""simple docstring"""


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



class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase		,    lowercase=None		,    lowercase=None					)							->			List[Any]:
					lowerCamelCase_         =     data
					lowerCamelCase_         =     previous
					lowerCamelCase_         =     next_node



	def __str__(       self					)							->			str:
					return f'{self.data}'



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.data



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Union[str, Any]:
					return self.next



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.previous




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self		,    lowercase					)							->			Optional[int]:
					lowerCamelCase_         =     head



	def __iter__(       self					)							->			int:
					return self



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Optional[Any]:
					if not self.current:
									raise StopIteration
					else:
									lowerCamelCase_         =     self.current.get_data()
									lowerCamelCase_         =     self.current.get_next()
									return value




class        _SCREAMING_SNAKE_CASE	:



	def __init__(       self					)							->			Union[str, Any]:
					lowerCamelCase_         =     None  # First node in list
					lowerCamelCase_         =     None  # Last node in list



	def __str__(       self					)							->			Optional[Any]:
					lowerCamelCase_         =     self.head
					lowerCamelCase_         =     []
					while current is not None:
									nodes.append(current.get_data()					)
									lowerCamelCase_         =     current.get_next()
					return " ".join(str(lowercase					) for node in nodes					)



	def __contains__(       self		,    lowercase					)							->			Optional[int]:
					lowerCamelCase_         =     self.head
					while current:
									if current.get_data() == value:
													return True
									lowerCamelCase_         =     current.get_next()
					return False



	def __iter__(       self					)							->			List[str]:
					return LinkedListIterator(self.head					)



	def        SCREAMING_SNAKE_CASE_(       self					)							->			List[str]:
					if self.head:
									return self.head.get_data()
					return None



	def        SCREAMING_SNAKE_CASE_(       self					)							->			Any:
					if self.tail:
									return self.tail.get_data()
					return None



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			None:
					if self.head is None:
									lowerCamelCase_         =     node
									lowerCamelCase_         =     node
					else:
									self.insert_before_node(self.head		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			None:
					if self.head is None:
									self.set_head(lowercase					)
					else:
									self.insert_after_node(self.tail		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			None:
					lowerCamelCase_         =     Node(lowercase					)
					if self.head is None:
									self.set_head(lowercase					)
					else:
									self.set_tail(lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			None:
					lowerCamelCase_         =     node
					lowerCamelCase_         =     node.previous

					if node.get_previous() is None:
									lowerCamelCase_         =     node_to_insert
					else:
									lowerCamelCase_         =     node_to_insert

					lowerCamelCase_         =     node_to_insert



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			None:
					lowerCamelCase_         =     node
					lowerCamelCase_         =     node.next

					if node.get_next() is None:
									lowerCamelCase_         =     node_to_insert
					else:
									lowerCamelCase_         =     node_to_insert

					lowerCamelCase_         =     node_to_insert



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase		,    lowercase					)							->			None:
					lowerCamelCase_         =     1
					lowerCamelCase_         =     Node(lowercase					)
					lowerCamelCase_         =     self.head
					while node:
									if current_position == position:
													self.insert_before_node(lowercase		,    lowercase					)
													return
									current_position += 1
									lowerCamelCase_         =     node.next
					self.insert_after_node(self.tail		,    lowercase					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Node:
					lowerCamelCase_         =     self.head
					while node:
									if node.get_data() == item:
													return node
									lowerCamelCase_         =     node.get_next()
					raise Exception("Node not found"					)



	def        SCREAMING_SNAKE_CASE_(       self		,    lowercase					)							->			Union[str, Any]:
					if (node := self.get_node(lowercase					)) is not None:
									if node == self.head:
													lowerCamelCase_         =     self.head.get_next()

									if node == self.tail:
													lowerCamelCase_         =     self.tail.get_previous()

									self.remove_node_pointers(lowercase					)



	@staticmethod
	def        SCREAMING_SNAKE_CASE_(       lowercase					)							->			None:
					if node.get_next():
									lowerCamelCase_         =     node.previous

					if node.get_previous():
									lowerCamelCase_         =     node.next

					lowerCamelCase_         =     None
					lowerCamelCase_         =     None



	def        SCREAMING_SNAKE_CASE_(       self					)							->			int:
					return self.head is None





def      lowerCamelCase_					(							):
				pass


if __name__ == "__main__":
			import doctest

			doctest.testmod()


