



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__  :







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







					def  _lowercase			(       self      :     Optional[Any]):
											A__	:							Optional[Any]						    = ids_tensor([self.batch_size, self.seq_length]			,		self.vocab_size)

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

											A__	:							Optional[int]						    = 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=__A			,		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  _lowercase			(       self      :     Optional[int]):
											A__	:							List[str]						    = self.prepare_config_and_inputs()
											A__    ,       A__    ,       A__	:							str						    = config_and_inputs
											A__	:							str						    = {"input_ids": input_ids, "attention_mask": attention_mask}
											return config, inputs_dict







					def  _lowercase			(       self      :     int			,		_A      :     int			,		_A      :     Any			,		_A      :     Any			,		_A      :     Optional[Any]):
											A__	:							str						    = 20
											A__	:							Optional[int]						    = model_class_name(__A)

											A__	:							Union[str, Any]						    = model.init_cache(input_ids.shape[0]			,		__A)
											A__	:							int						    = jnp.ones((input_ids.shape[0], max_decoder_length)			,		dtype="i4")

											A__	:							Optional[int]						    = jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1)[None, :]			,		(input_ids.shape[0], input_ids.shape[-1] - 1))
											A__	:							str						    = model(
											    input_ids[:, :-1]			,		attention_mask=__A			,		past_key_values=__A			,		position_ids=__A			,		)

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

											A__	:							List[Any]						    = model(__A)

											A__	:							Any						    = 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  _lowercase			(       self      :     Dict			,		_A      :     List[Any]			,		_A      :     List[str]			,		_A      :     Optional[Any]			,		_A      :     Union[str, Any]):
											A__	:							Optional[int]						    = 20
											A__	:							Optional[int]						    = model_class_name(__A)

											A__	:							List[str]						    = jnp.concatenate(
											    [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]))]			,		axis=-1			,		)

											A__	:							Optional[int]						    = model.init_cache(input_ids.shape[0]			,		__A)
											A__	:							Union[str, Any]						    = jnp.broadcast_to(
											    jnp.arange(input_ids.shape[-1] - 1)[None, :]			,		(input_ids.shape[0], input_ids.shape[-1] - 1))

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

											A__	:							str						    = model(__A			,		attention_mask=__A)

											A__	:							List[Any]						    = 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						):
					__A     :    Optional[int]           =						(FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
					__A     :    Optional[int]           =						(FlaxGPTJForCausalLM,) if is_flax_available() else ()







					def  _lowercase			(       self      :     Tuple):
											A__	:							Tuple						    = FlaxGPTJModelTester(self)







					def  _lowercase			(       self      :     Union[str, Any]):



											for model_class_name in self.all_model_classes:
																	A__    ,       A__    ,       A__	:							Dict						    = self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward(__A			,		__A			,		__A			,		__A)







					def  _lowercase			(       self      :     Dict):



											for model_class_name in self.all_model_classes:
																	A__    ,       A__    ,       A__	:							Optional[Any]						    = self.model_tester.prepare_config_and_inputs()
																	self.model_tester.check_use_cache_forward_with_attn_mask(
																	    __A			,		__A			,		__A			,		__A)







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

											A__	:							Dict						    = FlaxGPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")
											A__	:							int						    = False
											A__	:							List[str]						    = model.config.eos_token_id

											A__	:							Optional[int]						    = jax.jit(model.generate)

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

											A__	:							List[Any]						    = tokenizer.batch_decode(__A			,		skip_special_tokens=__A)

											A__	:							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(__A			,		__A)







					@is_pt_flax_cross_test
					def  _lowercase			(       self      :     Any):
											A__    ,       A__	:							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
																							A__	:							Any						    = self._prepare_for_class(__A			,		__A)
																							A__	:							int						    = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

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

																							A__	:							Tuple						    = convert_pytorch_state_dict_to_flax(pt_model.state_dict()			,		__A)
																							A__	:							List[Any]						    = fx_state

																							with torch.no_grad():
																													A__	:							int						    = pt_model(**__A).to_tuple()

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

																							with tempfile.TemporaryDirectory() as tmpdirname:
																													pt_model.save_pretrained(__A)
																													A__	:							Optional[Any]						    = model_class.from_pretrained(__A			,		from_pt=__A)

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







					@is_pt_flax_cross_test
					def  _lowercase			(       self      :     Any):
											A__    ,       A__	:							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
																							A__	:							Optional[int]						    = self._prepare_for_class(__A			,		__A)
																							A__	:							List[str]						    = {k: torch.tensor(v.tolist()) for k, v in prepared_inputs_dict.items()}

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

																							A__	:							int						    = pt_model_class(__A).eval()
																							A__	:							List[str]						    = model_class(__A			,		dtype=jnp.floataa)

																							A__	:							Dict						    = load_flax_weights_in_pytorch_model(__A			,		fx_model.params)
																							A__    ,       A__	:							Dict						    = pt_inputs["input_ids"].shape
																							A__	:							int						    = np.random.randint(0			,		seq_length - 1			,		size=(batch_size,))
																							for batch_idx, start_index in enumerate(__A):
																													A__	:							int						    = 0
																													A__	:							Optional[int]						    = 1
																													A__	:							Tuple						    = 0
																													A__	:							Union[str, Any]						    = 1

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

																							with torch.no_grad():
																													A__	:							Optional[Any]						    = pt_model(**__A).to_tuple()

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

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

																							with torch.no_grad():
																													A__	:							str						    = pt_model_loaded(**__A).to_tuple()

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







					@tooslow
					def  _lowercase			(       self      :     Union[str, Any]):



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




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)