






'''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 	UpperCamelCase__					:

		"""simple docstring"""




		def __init__( self						:						Union[str, Any]							,    __A						:						Dict							,    __A						:						Any=1_4							,    __A						:						Union[str, Any]=7							,    __A						:						Dict=True							,    __A						:						int=True							,    __A						:						int=False							,    __A						:						Any=True							,    __A						:						str=9_9							,    __A						:						int=3_2							,    __A						:						Optional[Any]=4							,    __A						:						Optional[Any]=4							,    __A						:						int=4							,    __A						:						str=3_7							,    __A						:						str="gelu"							,    __A						:						int=0.1							,    __A						:						Tuple=0.1							,    __A						:						Optional[int]=5_1_2							,    __A						:						Optional[Any]=0.0_2							,    ):







							"""simple docstring"""
							_lowercase			= parent
							_lowercase			= batch_size
							_lowercase			= seq_length
							_lowercase			= is_training
							_lowercase			= use_input_mask
							_lowercase			= use_token_type_ids
							_lowercase			= use_labels
							_lowercase			= vocab_size
							_lowercase			= hidden_size
							_lowercase			= rotary_dim
							_lowercase			= num_hidden_layers
							_lowercase			= num_attention_heads
							_lowercase			= intermediate_size
							_lowercase			= hidden_act
							_lowercase			= hidden_dropout_prob
							_lowercase			= attention_probs_dropout_prob
							_lowercase			= max_position_embeddings
							_lowercase			= initializer_range
							_lowercase			= None
							_lowercase			= vocab_size - 1
							_lowercase			= vocab_size - 1
							_lowercase			= vocab_size - 1




		def       snake_case     ( self						:						List[Any]    ):







							"""simple docstring"""
							_lowercase			= ids_tensor([self.batch_size, self.seq_length]							,    self.vocab_size    )

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

							_lowercase			= 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       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""
							_lowercase			= self.prepare_config_and_inputs()
							_lowercase			= config_and_inputs
							_lowercase			= {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
							return config, inputs_dict




		def       snake_case     ( self						:						str							,    __A						:						List[str]							,    __A						:						Dict							,    __A						:						List[str]							,    __A						:						List[str]    ):







							"""simple docstring"""
							_lowercase			= 2_0
							_lowercase			= model_class_name(UpperCamelCase__    )

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

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

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

							_lowercase			= model(UpperCamelCase__    )

							_lowercase			= 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[int]							,    __A						:						Any							,    __A						:						str							,    __A						:						Tuple							,    __A						:						Optional[int]    ):







							"""simple docstring"""
							_lowercase			= 2_0
							_lowercase			= model_class_name(UpperCamelCase__    )

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

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

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

							_lowercase			= model(UpperCamelCase__							,    attention_mask=UpperCamelCase__    )

							_lowercase			= 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 	UpperCamelCase__					(     __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						:						Tuple    ):







							"""simple docstring"""
							_lowercase			= FlaxGPTJModelTester(self    )




		def       snake_case     ( self						:						List[str]    ):







							"""simple docstring"""







							for model_class_name in self.all_model_classes:
												_lowercase			= self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__    )




		def       snake_case     ( self						:						Optional[Any]    ):







							"""simple docstring"""







							for model_class_name in self.all_model_classes:
												_lowercase			= self.model_tester.prepare_config_and_inputs()
												self.model_tester.check_use_cache_forward_with_attn_mask(
												    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__    )




		@tooslow
		def       snake_case     ( self						:						Dict    ):







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

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

							_lowercase			= jax.jit(model.generate    )

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

							_lowercase			= tokenizer.batch_decode(UpperCamelCase__							,    skip_special_tokens=UpperCamelCase__    )

							_lowercase			= [
							    '''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       snake_case     ( self						:						str    ):







							"""simple docstring"""
							_lowercase			= 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
																	_lowercase			= self._prepare_for_class(UpperCamelCase__							,    UpperCamelCase__    )
																	_lowercase			= {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

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

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

																	_lowercase			= 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__    )
																						_lowercase			= model_class.from_pretrained(UpperCamelCase__							,    from_pt=UpperCamelCase__    )

																	_lowercase			= 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       snake_case     ( self						:						str    ):







							"""simple docstring"""
							_lowercase			= 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
																	_lowercase			= self._prepare_for_class(UpperCamelCase__							,    UpperCamelCase__    )
																	_lowercase			= {k: torch.tensor(v.tolist()    ) for k, v in prepared_inputs_dict.items()}

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

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

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

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

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

																	_lowercase			= 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__    )
																						_lowercase			= pt_model_class.from_pretrained(UpperCamelCase__							,    from_flax=UpperCamelCase__    )

																	with torch.no_grad():
																						_lowercase			= 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       snake_case     ( self						:						Tuple    ):







							"""simple docstring"""







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



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase__						       =      {
    "configuration_poolformer": [
        "POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "PoolFormerConfig",
        "PoolFormerOnnxConfig",
    ]
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      ["PoolFormerFeatureExtractor"]
						lowercase__						       =      ["PoolFormerImageProcessor"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowercase__						       =      [
						    "POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "PoolFormerForImageClassification",
						    "PoolFormerModel",
						    "PoolFormerPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_poolformer import (
						    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    PoolFormerConfig,
						    PoolFormerOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_poolformer import PoolFormerFeatureExtractor
												from .image_processing_poolformer import PoolFormerImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_poolformer import (
												    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    PoolFormerForImageClassification,
												    PoolFormerModel,
												    PoolFormerPreTrainedModel,
												)


else:
						import sys

						lowercase__						       =      _LazyModule(__name__, globals()["__file__"], _import_structure)


