



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 	lowerCamelCase      :







					'''simple docstring'''







					def __init__(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=14    ,	lowerCAmelCase=7    ,	lowerCAmelCase=True    ,	lowerCAmelCase=True    ,	lowerCAmelCase=False    ,	lowerCAmelCase=True    ,	lowerCAmelCase=99    ,	lowerCAmelCase=32    ,	lowerCAmelCase=4    ,	lowerCAmelCase=4    ,	lowerCAmelCase=4    ,	lowerCAmelCase=37    ,	lowerCAmelCase="gelu"    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=0.1    ,	lowerCAmelCase=512    ,	lowerCAmelCase=0.02    ,	):
							UpperCAmelCase_						=				parent
							UpperCAmelCase_						=				batch_size
							UpperCAmelCase_						=				seq_length
							UpperCAmelCase_						=				is_training
							UpperCAmelCase_						=				use_input_mask
							UpperCAmelCase_						=				use_token_type_ids
							UpperCAmelCase_						=				use_labels
							UpperCAmelCase_						=				vocab_size
							UpperCAmelCase_						=				hidden_size
							UpperCAmelCase_						=				rotary_dim
							UpperCAmelCase_						=				num_hidden_layers
							UpperCAmelCase_						=				num_attention_heads
							UpperCAmelCase_						=				intermediate_size
							UpperCAmelCase_						=				hidden_act
							UpperCAmelCase_						=				hidden_dropout_prob
							UpperCAmelCase_						=				attention_probs_dropout_prob
							UpperCAmelCase_						=				max_position_embeddings
							UpperCAmelCase_						=				initializer_range
							UpperCAmelCase_						=				None
							UpperCAmelCase_						=				vocab_size - 1
							UpperCAmelCase_						=				vocab_size - 1
							UpperCAmelCase_						=				vocab_size - 1



					def    A__		(			self     ):
							UpperCAmelCase_						=				ids_tensor([self.batch_size, self.seq_length]    ,	self.vocab_size     )

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

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



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				20
							UpperCAmelCase_						=				model_class_name(lowerCAmelCase     )

							UpperCAmelCase_						=				model.init_cache(input_ids.shape[0]    ,	lowerCAmelCase     )
							UpperCAmelCase_						=				jnp.ones((input_ids.shape[0], max_decoder_length)    ,	dtype="i4"     )

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

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

							UpperCAmelCase_						=				model(lowerCAmelCase     )

							UpperCAmelCase_						=				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    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				20
							UpperCAmelCase_						=				model_class_name(lowerCAmelCase     )

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

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

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

							UpperCAmelCase_						=				model(lowerCAmelCase    ,	attention_mask=lowerCAmelCase     )

							UpperCAmelCase_						=				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 	lowerCamelCase      (						lowercase__,     lowercase__,     unittest.TestCase  ):







					'''simple docstring'''







					lowerCAmelCase_				:    Union[str, Any]									=  (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
					lowerCAmelCase_				:    Dict									=  (FlaxGPTJForCausalLM,) if is_flax_available() else ()



					def    A__		(			self     ):
							UpperCAmelCase_						=				FlaxGPTJModelTester(self     )



					def    A__		(			self     ):
							for model_class_name in self.all_model_classes:
									UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward(lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



					def    A__		(			self     ):
							for model_class_name in self.all_model_classes:
									UpperCAmelCase_					,						UpperCAmelCase_					,						UpperCAmelCase_						=				self.model_tester.prepare_config_and_inputs()
									self.model_tester.check_use_cache_forward_with_attn_mask(
									    lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     )



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

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

							UpperCAmelCase_						=				jax.jit(model.generate     )

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

							UpperCAmelCase_						=				tokenizer.batch_decode(lowerCAmelCase    ,	skip_special_tokens=lowerCAmelCase     )

							UpperCAmelCase_						=				[
							    "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    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				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
											UpperCAmelCase_						=				self._prepare_for_class(lowerCAmelCase    ,	lowerCAmelCase     )
											UpperCAmelCase_						=				{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

											UpperCAmelCase_					,						UpperCAmelCase_						=				pt_inputs["input_ids"].shape
											UpperCAmelCase_						=				np.random.randint(0    ,	seq_length - 1    ,	size=(batch_size,)     )
											for batch_idx, start_index in enumerate(lowerCAmelCase     ):
													UpperCAmelCase_						=				0
													UpperCAmelCase_						=				1
													UpperCAmelCase_						=				0
													UpperCAmelCase_						=				1
											UpperCAmelCase_						=				pt_model_class(lowerCAmelCase     ).eval()
											UpperCAmelCase_						=				model_class(lowerCAmelCase    ,	dtype=jnp.floataa     )

											UpperCAmelCase_						=				convert_pytorch_state_dict_to_flax(pt_model.state_dict()    ,	lowerCAmelCase     )
											UpperCAmelCase_						=				fx_state

											with torch.no_grad():
													UpperCAmelCase_						=				pt_model(**lowerCAmelCase     ).to_tuple()

											UpperCAmelCase_						=				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     )
													UpperCAmelCase_						=				model_class.from_pretrained(lowerCAmelCase    ,	from_pt=lowerCAmelCase     )

											UpperCAmelCase_						=				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    A__		(			self     ):
							UpperCAmelCase_					,						UpperCAmelCase_						=				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
											UpperCAmelCase_						=				self._prepare_for_class(lowerCAmelCase    ,	lowerCAmelCase     )
											UpperCAmelCase_						=				{k: torch.tensor(v.tolist()     ) for k, v in prepared_inputs_dict.items()}

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

											UpperCAmelCase_						=				pt_model_class(lowerCAmelCase     ).eval()
											UpperCAmelCase_						=				model_class(lowerCAmelCase    ,	dtype=jnp.floataa     )

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

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

											with torch.no_grad():
													UpperCAmelCase_						=				pt_model(**lowerCAmelCase     ).to_tuple()

											UpperCAmelCase_						=				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     )
													UpperCAmelCase_						=				pt_model_class.from_pretrained(lowerCAmelCase    ,	from_flax=lowerCAmelCase     )

											with torch.no_grad():
													UpperCAmelCase_						=				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    A__		(			self     ):
							for model_class_name in self.all_model_classes:
									UpperCAmelCase_						=				model_class_name.from_pretrained("EleutherAI/gpt-j-6B"     )
									UpperCAmelCase_						=				model(np.ones((1, 1)     )     )
									self.assertIsNotNone(lowerCAmelCase     )






import unittest

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


if is_torch_available():
			from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 	lowerCamelCase      (						unittest.TestCase  ):







					'''simple docstring'''







					@slow
					def    A__		(			self     ):
							UpperCAmelCase_						=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"    ,	return_dict=lowerCAmelCase     ).to(lowerCAmelCase     )
							UpperCAmelCase_						=				AutoTokenizer.from_pretrained("google/mt5-small"     )

							UpperCAmelCase_						=				tokenizer("Hello there"    ,	return_tensors="pt"     ).input_ids
							UpperCAmelCase_						=				tokenizer("Hi I am"    ,	return_tensors="pt"     ).input_ids

							UpperCAmelCase_						=				model(input_ids.to(lowerCAmelCase     )    ,	labels=labels.to(lowerCAmelCase     )     ).loss
							UpperCAmelCase_						=				-(labels.shape[-1] * loss.item())

							UpperCAmelCase_						=				-84.9127
							self.assertTrue(abs(mtf_score - EXPECTED_SCORE     ) < 1e-4     )

