


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      ,						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      ,						):

						'''simple docstring'''


						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 				snake_case__				(							self     ):

						'''simple docstring'''


						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=snake_case      ,						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     ):

						'''simple docstring'''


						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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       20
						UpperCamelCase__					=       model_class_name(snake_case     )

						UpperCamelCase__					=       model.init_cache(input_ids.shape[0]      ,						snake_case     )
						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=snake_case      ,						past_key_values=snake_case      ,						position_ids=snake_case      ,						)

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

						UpperCamelCase__					=       model(snake_case     )

						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 				snake_case__				(							self      ,						snake_case      ,						snake_case      ,						snake_case      ,						snake_case     ):

						'''simple docstring'''


						UpperCamelCase__					=       20
						UpperCamelCase__					=       model_class_name(snake_case     )

						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]      ,						snake_case     )
						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=snake_case      ,						past_key_values=snake_case      ,						position_ids=snake_case      ,						)
						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=snake_case      ,						position_ids=snake_case      ,						)

						UpperCamelCase__					=       model(snake_case      ,						attention_mask=snake_case     )

						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__	(							UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Tuple    =		(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
			_UpperCamelCase		:					str    =		(FlaxGPTJForCausalLM,) if is_flax_available() else ()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       FlaxGPTJModelTester(self     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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(snake_case      ,						snake_case      ,						snake_case      ,						snake_case     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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(
									    snake_case      ,						snake_case      ,						snake_case      ,						snake_case     )




			@tooslow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


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

						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(snake_case      ,						skip_special_tokens=snake_case     )

						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(snake_case      ,						snake_case     )




			@is_pt_flax_cross_test
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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(snake_case      ,						snake_case     )
												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(snake_case      ,						snake_case     )

												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(snake_case     ):
															UpperCamelCase__					=       0
															UpperCamelCase__					=       1
															UpperCamelCase__					=       0
															UpperCamelCase__					=       1
												UpperCamelCase__					=       pt_model_class(snake_case     ).eval()
												UpperCamelCase__					=       model_class(snake_case      ,						dtype=jnp.floataa     )

												UpperCamelCase__					=       convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,						snake_case     )
												UpperCamelCase__					=       fx_state

												with torch.no_grad():
															UpperCamelCase__					=       pt_model(**snake_case     ).to_tuple()

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

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

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




			@is_pt_flax_cross_test
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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(snake_case      ,						snake_case     )
												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(snake_case      ,						snake_case     )

												UpperCamelCase__					=       pt_model_class(snake_case     ).eval()
												UpperCamelCase__					=       model_class(snake_case      ,						dtype=jnp.floataa     )

												UpperCamelCase__					=       load_flax_weights_in_pytorch_model(snake_case      ,						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(snake_case     ):
															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(**snake_case     ).to_tuple()

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

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

												with torch.no_grad():
															UpperCamelCase__					=       pt_model_loaded(**snake_case     ).to_tuple()

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







			@tooslow
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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(snake_case     )






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__UpperCamelCase    		=       {
    'configuration_resnet': ['RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ResNetConfig', 'ResNetOnnxConfig']
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'ResNetForImageClassification',
						    'ResNetModel',
						    'ResNetPreTrainedModel',
						    'ResNetBackbone',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'TFResNetForImageClassification',
						    'TFResNetModel',
						    'TFResNetPreTrainedModel',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__UpperCamelCase    		=       [
						    'FlaxResNetForImageClassification',
						    'FlaxResNetModel',
						    'FlaxResNetPreTrainedModel',
						]

if TYPE_CHECKING:
						from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_resnet import (
												    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    ResNetBackbone,
												    ResNetForImageClassification,
												    ResNetModel,
												    ResNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_resnet import (
												    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFResNetForImageClassification,
												    TFResNetModel,
												    TFResNetPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
						import sys

						__UpperCamelCase    		=       _LazyModule(__name__, globals()['__file__'], _import_structure)


