'''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 		SCREAMING_SNAKE_CASE     :







				'''simple docstring'''





				def __init__(						self					:						Dict       ,		lowercase__					:						str       ,		lowercase__					:						Dict=14       ,		lowercase__					:						int=7       ,		lowercase__					:						int=True       ,		lowercase__					:						List[Any]=True       ,		lowercase__					:						Optional[Any]=False       ,		lowercase__					:						str=True       ,		lowercase__					:						int=99       ,		lowercase__					:						Optional[int]=32       ,		lowercase__					:						str=4       ,		lowercase__					:						str=4       ,		lowercase__					:						int=4       ,		lowercase__					:						Optional[Any]=37       ,		lowercase__					:						Any="gelu"       ,		lowercase__					:						Union[str, Any]=0.1       ,		lowercase__					:						Tuple=0.1       ,		lowercase__					:						Union[str, Any]=512       ,		lowercase__					:						Union[str, Any]=0.0_2       ,		)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						parent
										_UpperCamelCase			:			List[str]   			=						batch_size
										_UpperCamelCase			:			Optional[Any]   			=						seq_length
										_UpperCamelCase			:			Optional[int]   			=						is_training
										_UpperCamelCase			:			str   			=						use_input_mask
										_UpperCamelCase			:			Dict   			=						use_token_type_ids
										_UpperCamelCase			:			Dict   			=						use_labels
										_UpperCamelCase			:			Dict   			=						vocab_size
										_UpperCamelCase			:			int   			=						hidden_size
										_UpperCamelCase			:			Dict   			=						rotary_dim
										_UpperCamelCase			:			Dict   			=						num_hidden_layers
										_UpperCamelCase			:			int   			=						num_attention_heads
										_UpperCamelCase			:			Optional[int]   			=						intermediate_size
										_UpperCamelCase			:			Optional[Any]   			=						hidden_act
										_UpperCamelCase			:			int   			=						hidden_dropout_prob
										_UpperCamelCase			:			str   			=						attention_probs_dropout_prob
										_UpperCamelCase			:			Optional[int]   			=						max_position_embeddings
										_UpperCamelCase			:			List[Any]   			=						initializer_range
										_UpperCamelCase			:			List[str]   			=						None
										_UpperCamelCase			:			Union[str, Any]   			=						vocab_size - 1
										_UpperCamelCase			:			Optional[int]   			=						vocab_size - 1
										_UpperCamelCase			:			Union[str, Any]   			=						vocab_size - 1





				def  snake_case__				(						self					:						Tuple					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			str   			=						ids_tensor([self.batch_size, self.seq_length]       ,		self.vocab_size					)

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

										_UpperCamelCase			:			Any   			=						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=lowercase__       ,		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  snake_case__				(						self					:						List[str]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[Any]   			=						self.prepare_config_and_inputs()
										_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						config_and_inputs
										_UpperCamelCase			:			Dict   			=						{"input_ids": input_ids, "attention_mask": attention_mask}
										return config, inputs_dict





				def  snake_case__				(						self					:						str       ,		lowercase__					:						str       ,		lowercase__					:						int       ,		lowercase__					:						int       ,		lowercase__					:						str					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						20
										_UpperCamelCase			:			List[str]   			=						model_class_name(lowercase__					)

										_UpperCamelCase			:			List[Any]   			=						model.init_cache(input_ids.shape[0]       ,		lowercase__					)
										_UpperCamelCase			:			Any   			=						jnp.ones((input_ids.shape[0], max_decoder_length)       ,		dtype="i4"					)

										_UpperCamelCase			:			int   			=						jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1					)[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)					)
										_UpperCamelCase			:			Optional[Any]   			=						model(
										    input_ids[:, :-1]       ,		attention_mask=lowercase__       ,		past_key_values=lowercase__       ,		position_ids=lowercase__       ,		)

										_UpperCamelCase			:			Optional[int]   			=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype="i4"					)
										_UpperCamelCase			:			List[Any]   			=						model(
										    input_ids[:, -1:]       ,		attention_mask=lowercase__       ,		past_key_values=outputs_cache.past_key_values       ,		position_ids=lowercase__       ,		)

										_UpperCamelCase			:			Optional[Any]   			=						model(lowercase__					)

										_UpperCamelCase			:			int   			=						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  snake_case__				(						self					:						Optional[Any]       ,		lowercase__					:						Any       ,		lowercase__					:						Dict       ,		lowercase__					:						Dict       ,		lowercase__					:						Any					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						20
										_UpperCamelCase			:			Tuple   			=						model_class_name(lowercase__					)

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

										_UpperCamelCase			:			int   			=						model.init_cache(input_ids.shape[0]       ,		lowercase__					)
										_UpperCamelCase			:			Dict   			=						jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1					)[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)					)

										_UpperCamelCase			:			str   			=						model(
										    input_ids[:, :-1]       ,		attention_mask=lowercase__       ,		past_key_values=lowercase__       ,		position_ids=lowercase__       ,		)
										_UpperCamelCase			:			Tuple   			=						jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype="i4"					)
										_UpperCamelCase			:			Optional[int]   			=						model(
										    input_ids[:, -1:]       ,		past_key_values=outputs_cache.past_key_values       ,		attention_mask=lowercase__       ,		position_ids=lowercase__       ,		)

										_UpperCamelCase			:			List[Any]   			=						model(lowercase__       ,		attention_mask=lowercase__					)

										_UpperCamelCase			:			Optional[int]   			=						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 		SCREAMING_SNAKE_CASE     (					SCREAMING_SNAKE_CASE   , SCREAMING_SNAKE_CASE   , unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
				UpperCAmelCase__							=							(FlaxGPTJForCausalLM,) if is_flax_available() else ()





				def  snake_case__				(						self					:						Dict					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						FlaxGPTJModelTester(self					)





				def  snake_case__				(						self					:						Optional[int]					)							->Optional[int]:


										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Union[str, Any]   			=						self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward(lowercase__       ,		lowercase__       ,		lowercase__       ,		lowercase__					)





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


										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																_UpperCamelCase			,       _UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						self.model_tester.prepare_config_and_inputs()
																self.model_tester.check_use_cache_forward_with_attn_mask(
																    lowercase__       ,		lowercase__       ,		lowercase__       ,		lowercase__					)





				@tooslow
				def  snake_case__				(						self					:						List[str]					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						GPTaTokenizer.from_pretrained("gpt2"       ,		pad_token="<|endoftext|>"       ,		padding_side="left"					)
										_UpperCamelCase			:			Optional[int]   			=						tokenizer(["Hello this is a long string", "Hey"]       ,		return_tensors="np"       ,		padding=lowercase__       ,		truncation=lowercase__					)

										_UpperCamelCase			:			Optional[Any]   			=						FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B"					)
										_UpperCamelCase			:			Optional[int]   			=						False
										_UpperCamelCase			:			int   			=						model.config.eos_token_id

										_UpperCamelCase			:			Any   			=						jax.jit(model.generate					)

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

										_UpperCamelCase			:			Dict   			=						tokenizer.batch_decode(lowercase__       ,		skip_special_tokens=lowercase__					)

										_UpperCamelCase			:			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(lowercase__       ,		lowercase__					)





				@is_pt_flax_cross_test
				def  snake_case__				(						self					:						Any					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						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			:			int   			=						self._prepare_for_class(lowercase__       ,		lowercase__					)
																						_UpperCamelCase			:			Union[str, Any]   			=						{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						_UpperCamelCase			:			Dict   			=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						_UpperCamelCase			:			List[str]   			=						getattr(lowercase__       ,		lowercase__					)

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

																						_UpperCamelCase			:			Dict   			=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,		lowercase__					)
																						_UpperCamelCase			:			Optional[Any]   			=						fx_state

																						with torch.no_grad():
																												_UpperCamelCase			:			Dict   			=						pt_model(**lowercase__					).to_tuple()

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

																						with tempfile.TemporaryDirectory() as tmpdirname:
																												pt_model.save_pretrained(lowercase__					)
																												_UpperCamelCase			:			int   			=						model_class.from_pretrained(lowercase__       ,		from_pt=lowercase__					)

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





				@is_pt_flax_cross_test
				def  snake_case__				(						self					:						Dict					)							->List[str]:


										'''simple docstring'''




										_UpperCamelCase			,       _UpperCamelCase			:			Tuple   			=						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			:			Optional[int]   			=						self._prepare_for_class(lowercase__       ,		lowercase__					)
																						_UpperCamelCase			:			Union[str, Any]   			=						{k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

																						# load corresponding PyTorch class
																						_UpperCamelCase			:			Tuple   			=						model_class.__name__[4:]  # Skip the "Flax" at the beginning
																						_UpperCamelCase			:			List[str]   			=						getattr(lowercase__       ,		lowercase__					)

																						_UpperCamelCase			:			Union[str, Any]   			=						pt_model_class(lowercase__					).eval()
																						_UpperCamelCase			:			Union[str, Any]   			=						model_class(lowercase__       ,		dtype=jnp.floataa					)

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

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

																						with torch.no_grad():
																												_UpperCamelCase			:			Optional[int]   			=						pt_model(**lowercase__					).to_tuple()

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

																						with tempfile.TemporaryDirectory() as tmpdirname:
																												fx_model.save_pretrained(lowercase__					)
																												_UpperCamelCase			:			Union[str, Any]   			=						pt_model_class.from_pretrained(lowercase__       ,		from_flax=lowercase__					)

																						with torch.no_grad():
																												_UpperCamelCase			:			str   			=						pt_model_loaded(**lowercase__					).to_tuple()

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







				@tooslow
				def  snake_case__				(						self					:						Tuple					)							->str:


										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																_UpperCamelCase			:			Any   			=						model_class_name.from_pretrained("EleutherAI/gpt-j-6B"					)
																_UpperCamelCase			:			Union[str, Any]   			=						model(np.ones((1, 1)					)					)
																self.assertIsNotNone(lowercase__					)



'''simple docstring'''





lowerCAmelCase_	:		Union[str, Any]			=     """
# Transformers 설치 방법
! pip install transformers datasets
# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.
# ! pip install git+https://github.com/huggingface/transformers.git
"""

lowerCAmelCase_	:		List[Any]			=     [{"""type""": """code""", """content""": INSTALL_CONTENT}]
lowerCAmelCase_	:		int			=     {
    """{processor_class}""": """FakeProcessorClass""",
    """{model_class}""": """FakeModelClass""",
    """{object_class}""": """FakeObjectClass""",
}


