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     :

							'''simple docstring'''

							def __init__(				self   ,	_snake_case   ,	_snake_case=14   ,	_snake_case=7   ,	_snake_case=True   ,	_snake_case=True   ,	_snake_case=False   ,	_snake_case=True   ,	_snake_case=99   ,	_snake_case=32   ,	_snake_case=4   ,	_snake_case=4   ,	_snake_case=4   ,	_snake_case=37   ,	_snake_case="gelu"   ,	_snake_case=0.1   ,	_snake_case=0.1   ,	_snake_case=512   ,	_snake_case=0.02   ,	):
												_lowerCAmelCase			:		Any          = parent
												_lowerCAmelCase			:		Tuple          = batch_size
												_lowerCAmelCase			:		int          = seq_length
												_lowerCAmelCase			:		Any          = is_training
												_lowerCAmelCase			:		Dict          = use_input_mask
												_lowerCAmelCase			:		int          = use_token_type_ids
												_lowerCAmelCase			:		Union[str, Any]          = use_labels
												_lowerCAmelCase			:		Union[str, Any]          = vocab_size
												_lowerCAmelCase			:		Optional[Any]          = hidden_size
												_lowerCAmelCase			:		Optional[Any]          = rotary_dim
												_lowerCAmelCase			:		Optional[int]          = num_hidden_layers
												_lowerCAmelCase			:		Tuple          = num_attention_heads
												_lowerCAmelCase			:		Any          = intermediate_size
												_lowerCAmelCase			:		Any          = hidden_act
												_lowerCAmelCase			:		List[Any]          = hidden_dropout_prob
												_lowerCAmelCase			:		str          = attention_probs_dropout_prob
												_lowerCAmelCase			:		str          = max_position_embeddings
												_lowerCAmelCase			:		Tuple          = initializer_range
												_lowerCAmelCase			:		List[Any]          = None
												_lowerCAmelCase			:		List[Any]          = vocab_size - 1
												_lowerCAmelCase			:		List[str]          = vocab_size - 1
												_lowerCAmelCase			:		Dict          = vocab_size - 1






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[str]          = ids_tensor([self.batch_size, self.seq_length]   ,	self.vocab_size )

												_lowerCAmelCase			:		List[Any]          = None
												if self.use_input_mask:
																	_lowerCAmelCase			:		int          = random_attention_mask([self.batch_size, self.seq_length] )

												_lowerCAmelCase			:		Tuple          = 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=UpperCamelCase_   ,	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 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[Any]          = self.prepare_config_and_inputs()
												_lowerCAmelCase			:		Optional[Any]          = config_and_inputs
												_lowerCAmelCase			:		Optional[Any]          = {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict






							def 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case ):
												_lowerCAmelCase			:		Optional[Any]          = 20
												_lowerCAmelCase			:		List[Any]          = model_class_name(UpperCamelCase_ )

												_lowerCAmelCase			:		List[str]          = model.init_cache(input_ids.shape[0]   ,	UpperCamelCase_ )
												_lowerCAmelCase			:		int          = jnp.ones((input_ids.shape[0], max_decoder_length)   ,	dtype="i4" )

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

												_lowerCAmelCase			:		List[str]          = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,	dtype="i4" )
												_lowerCAmelCase			:		int          = model(
												    input_ids[:, -1:]   ,	attention_mask=UpperCamelCase_   ,	past_key_values=outputs_cache.past_key_values   ,	position_ids=UpperCamelCase_   ,	)

												_lowerCAmelCase			:		Any          = model(UpperCamelCase_ )

												_lowerCAmelCase			:		Dict          = 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 	SCREAMING_SNAKE_CASE__			(				self   ,	_snake_case   ,	_snake_case   ,	_snake_case   ,	_snake_case ):
												_lowerCAmelCase			:		Union[str, Any]          = 20
												_lowerCAmelCase			:		int          = model_class_name(UpperCamelCase_ )

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

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

												_lowerCAmelCase			:		Optional[Any]          = model(
												    input_ids[:, :-1]   ,	attention_mask=UpperCamelCase_   ,	past_key_values=UpperCamelCase_   ,	position_ids=UpperCamelCase_   ,	)
												_lowerCAmelCase			:		List[Any]          = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]   ,	dtype="i4" )
												_lowerCAmelCase			:		Union[str, Any]          = model(
												    input_ids[:, -1:]   ,	past_key_values=outputs_cache.past_key_values   ,	attention_mask=UpperCamelCase_   ,	position_ids=UpperCamelCase_   ,	)

												_lowerCAmelCase			:		Dict          = model(UpperCamelCase_   ,	attention_mask=UpperCamelCase_ )

												_lowerCAmelCase			:		Tuple          = 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						):

							'''simple docstring'''

							a_     =					(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							a_     =					(FlaxGPTJForCausalLM,) if is_flax_available() else ()






							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		Optional[int]          = FlaxGPTJModelTester(self )






							def 	SCREAMING_SNAKE_CASE__			(				self ):


												for model_class_name in self.all_model_classes:
																	_lowerCAmelCase			:		List[Any]          = self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_   ,	UpperCamelCase_ )






							def 	SCREAMING_SNAKE_CASE__			(				self ):


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






							@tooslow
							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[str]          = GPTaTokenizer.from_pretrained("gpt2"   ,	pad_token="<|endoftext|>"   ,	padding_side="left" )
												_lowerCAmelCase			:		str          = tokenizer(["Hello this is a long string", "Hey"]   ,	return_tensors="np"   ,	padding=UpperCamelCase_   ,	truncation=UpperCamelCase_ )

												_lowerCAmelCase			:		Tuple          = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B" )
												_lowerCAmelCase			:		Dict          = False
												_lowerCAmelCase			:		List[Any]          = model.config.eos_token_id

												_lowerCAmelCase			:		str          = jax.jit(model.generate )

												_lowerCAmelCase			:		List[Any]          = jit_generate(
												    inputs["input_ids"]   ,	attention_mask=inputs["attention_mask"]   ,	pad_token_id=tokenizer.pad_token_id ).sequences

												_lowerCAmelCase			:		Any          = tokenizer.batch_decode(UpperCamelCase_   ,	skip_special_tokens=UpperCamelCase_ )

												_lowerCAmelCase			:		Optional[Any]          = [
												    '''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(UpperCamelCase_   ,	UpperCamelCase_ )






							@is_pt_flax_cross_test
							def 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[Any]          = 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			:		Union[str, Any]          = self._prepare_for_class(UpperCamelCase_   ,	UpperCamelCase_ )
																						_lowerCAmelCase			:		Optional[Any]          = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

																						_lowerCAmelCase			:		Union[str, Any]          = pt_inputs['''input_ids'''].shape
																						_lowerCAmelCase			:		Dict          = np.random.randint(0   ,	seq_length - 1   ,	size=(batch_size,) )
																						for batch_idx, start_index in enumerate(UpperCamelCase_ ):
																											_lowerCAmelCase			:		int          = 0
																											_lowerCAmelCase			:		Union[str, Any]          = 1
																											_lowerCAmelCase			:		Tuple          = 0
																											_lowerCAmelCase			:		List[str]          = 1
																						_lowerCAmelCase			:		Optional[int]          = pt_model_class(UpperCamelCase_ ).eval()
																						_lowerCAmelCase			:		Optional[int]          = model_class(UpperCamelCase_   ,	dtype=jnp.floataa )

																						_lowerCAmelCase			:		List[Any]          = convert_pytorch_state_dict_to_flax(pt_model.state_dict()   ,	UpperCamelCase_ )
																						_lowerCAmelCase			:		List[str]          = fx_state

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

																						_lowerCAmelCase			:		Optional[int]          = 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_ )
																											_lowerCAmelCase			:		List[str]          = model_class.from_pretrained(UpperCamelCase_   ,	from_pt=UpperCamelCase_ )

																						_lowerCAmelCase			:		str          = 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 	SCREAMING_SNAKE_CASE__			(				self ):
												_lowerCAmelCase			:		List[Any]          = 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			:		Any          = self._prepare_for_class(UpperCamelCase_   ,	UpperCamelCase_ )
																						_lowerCAmelCase			:		Any          = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

																						_lowerCAmelCase			:		Tuple          = pt_model_class(UpperCamelCase_ ).eval()
																						_lowerCAmelCase			:		Tuple          = model_class(UpperCamelCase_   ,	dtype=jnp.floataa )

																						_lowerCAmelCase			:		Optional[int]          = load_flax_weights_in_pytorch_model(UpperCamelCase_   ,	fx_model.params )
																						_lowerCAmelCase			:		Union[str, Any]          = pt_inputs['''input_ids'''].shape
																						_lowerCAmelCase			:		int          = np.random.randint(0   ,	seq_length - 1   ,	size=(batch_size,) )
																						for batch_idx, start_index in enumerate(UpperCamelCase_ ):
																											_lowerCAmelCase			:		List[Any]          = 0
																											_lowerCAmelCase			:		Optional[Any]          = 1
																											_lowerCAmelCase			:		Optional[int]          = 0
																											_lowerCAmelCase			:		Any          = 1

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

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

																						_lowerCAmelCase			:		Optional[Any]          = 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_ )
																											_lowerCAmelCase			:		Any          = pt_model_class.from_pretrained(UpperCamelCase_   ,	from_flax=UpperCamelCase_ )

																						with torch.no_grad():
																											_lowerCAmelCase			:		Dict          = 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 )






							@tooslow
							def 	SCREAMING_SNAKE_CASE__			(				self ):


												for model_class_name in self.all_model_classes:
																	_lowerCAmelCase			:		Dict          = model_class_name.from_pretrained("EleutherAI/gpt-j-6B" )
																	_lowerCAmelCase			:		Optional[Any]          = model(np.ones((1, 1) ) )
																	self.assertIsNotNone(UpperCamelCase_ )










'''simple docstring'''
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
		import torch

		from transformers import XLMRobertaModel
@require_sentencepiece
@require_tokenizers
@require_torch
class   lowercase      (				unittest.TestCase   ):

		"""simple docstring"""

		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :Optional[Any]				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-base'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Union[str, Any]				   =					torch.Size((1, 12, 768)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :Union[str, Any]				   =					torch.tensor(
							    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Any				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)




		@slow
		def        lowerCAmelCase__							(						self				):

							'''simple docstring'''


							UpperCamelCase__  :int				   =					XLMRobertaModel.from_pretrained('''xlm-roberta-large'''				)
							UpperCamelCase__  :List[str]				   =					torch.tensor([[0, 581, 10269, 83, 99942, 136, 60742, 23, 70, 80583, 18276, 2]]				)
							# The dog is cute and lives in the garden house

							UpperCamelCase__  :Any				   =					torch.Size((1, 12, 1024)				)  # batch_size, sequence_length, embedding_vector_dim
							UpperCamelCase__  :List[Any]				   =					torch.tensor(
							    [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]				)
							#  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
							#  xlmr.eval()
							#  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
							with torch.no_grad():
												UpperCamelCase__  :Optional[int]				   =					model(UpperCamelCase_				)['''last_hidden_state'''].detach()
							self.assertEqual(output.shape       ,  UpperCamelCase_				)
							# compare the actual values for a slice of last dim
							self.assertTrue(torch.allclose(output[:, :, -1]       ,  UpperCamelCase_       ,  atol=1e-3				)				)