





import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
      import jax
      import jax.numpy as jnp
      import numpy as np

      from transformers.modeling_flax_pytorch_utils import (
          convert_pytorch_state_dict_to_flax,
          load_flax_weights_in_pytorch_model,
      )
      from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
      import torch



@require_flax
class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    Any					, UpperCamelCase					:    Dict					, UpperCamelCase					:    Dict=14					, UpperCamelCase					:    List[Any]=7					, UpperCamelCase					:    List[Any]=True					, UpperCamelCase					:    Optional[Any]=True					, UpperCamelCase					:    Any=True					, UpperCamelCase					:    int=99					, UpperCamelCase					:    Optional[int]=32					, UpperCamelCase					:    Dict=5					, UpperCamelCase					:    List[Any]=4					, UpperCamelCase					:    Union[str, Any]=37					, UpperCamelCase					:    str="gelu"					, UpperCamelCase					:    Any=0.1					, UpperCamelCase					:    int=0.1					, UpperCamelCase					:    Union[str, Any]=5_12					, UpperCamelCase					:    int=0.02					, UpperCamelCase					:    Optional[Any]=None					, ):


    '''simple docstring'''





    _snake_case      : Tuple														=		parent
    _snake_case      : str														=		batch_size
    _snake_case      : Tuple														=		seq_length
    _snake_case      : Union[str, Any]														=		is_training
    _snake_case      : Dict														=		use_input_mask
    _snake_case      : Any														=		use_labels
    _snake_case      : int														=		vocab_size
    _snake_case      : Dict														=		d_model
    _snake_case      : int														=		num_hidden_layers
    _snake_case      : Union[str, Any]														=		num_attention_heads
    _snake_case      : Union[str, Any]														=		ffn_dim
    _snake_case      : List[str]														=		activation_function
    _snake_case      : List[str]														=		activation_dropout
    _snake_case      : List[Any]														=		attention_dropout
    _snake_case      : Union[str, Any]														=		max_position_embeddings
    _snake_case      : str														=		initializer_range
    _snake_case      : Tuple														=		None
    _snake_case      : List[str]														=		0
    _snake_case      : Union[str, Any]														=		2
    _snake_case      : Union[str, Any]														=		1







  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		np.clip(ids_tensor([self.batch_size, self.seq_length]					, self.vocab_size							)					, 3					, self.vocab_size							)

    _snake_case      : List[Any]														=		None
    if self.use_input_mask:
      _snake_case      : Union[str, Any]														=		random_attention_mask([self.batch_size, self.seq_length]							)

    _snake_case      : List[str]														=		XGLMConfig(
        vocab_size=self.vocab_size					, d_model=self.hidden_size					, num_layers=self.num_hidden_layers					, attention_heads=self.num_attention_heads					, ffn_dim=self.ffn_dim					, activation_function=self.activation_function					, activation_dropout=self.activation_dropout					, attention_dropout=self.attention_dropout					, max_position_embeddings=self.max_position_embeddings					, initializer_range=self.initializer_range					, use_cache=UpperCamelCase					, bos_token_id=self.bos_token_id					, eos_token_id=self.eos_token_id					, pad_token_id=self.pad_token_id					, )

    return (config, input_ids, input_mask)







  def UpperCamelCase_       (					self					:    List[str]							):


    '''simple docstring'''





    _snake_case      : int														=		self.prepare_config_and_inputs()
    _snake_case							,    _snake_case							,    _snake_case      : int														=		config_and_inputs
    _snake_case      : Optional[int]														=		{'input_ids': input_ids, 'attention_mask': attention_mask}
    return config, inputs_dict







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


    '''simple docstring'''





    _snake_case							,    _snake_case							,    _snake_case      : Any														=		self.prepare_config_and_inputs()

    _snake_case      : int														=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
    _snake_case      : Any														=		ids_tensor([self.batch_size, self.seq_length]					, vocab_size=2							)

    return (
        config,
        input_ids,
        attention_mask,
        encoder_hidden_states,
        encoder_attention_mask,
    )







  def UpperCamelCase_       (					self					:    Any					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    Any					, UpperCamelCase					:    Tuple					, UpperCamelCase					:    Any							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		20
    _snake_case      : List[str]														=		model_class_name(UpperCamelCase							)

    _snake_case      : int														=		model.init_cache(input_ids.shape[0]					, UpperCamelCase							)
    _snake_case      : List[str]														=		jnp.ones((input_ids.shape[0], max_decoder_length)					, dtype='i4'							)

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

    _snake_case      : Tuple														=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					, dtype='i4'							)
    _snake_case      : List[Any]														=		model(
        input_ids[:, -1:]					, attention_mask=UpperCamelCase					, past_key_values=outputs_cache.past_key_values					, position_ids=UpperCamelCase					, )

    _snake_case      : int														=		model(UpperCamelCase							)

    _snake_case      : Optional[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 UpperCamelCase_       (					self					:    Tuple					, UpperCamelCase					:    str					, UpperCamelCase					:    Dict					, UpperCamelCase					:    str					, UpperCamelCase					:    Any							):


    '''simple docstring'''





    _snake_case      : str														=		20
    _snake_case      : List[str]														=		model_class_name(UpperCamelCase							)

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

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

    _snake_case      : str														=		model(
        input_ids[:, :-1]					, attention_mask=UpperCamelCase					, past_key_values=UpperCamelCase					, position_ids=UpperCamelCase					, )
    _snake_case      : Dict														=		jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					, dtype='i4'							)
    _snake_case      : Optional[Any]														=		model(
        input_ids[:, -1:]					, past_key_values=outputs_cache.past_key_values					, attention_mask=UpperCamelCase					, position_ids=UpperCamelCase					, )

    _snake_case      : List[str]														=		model(UpperCamelCase					, attention_mask=UpperCamelCase							)
    _snake_case      : Union[str, 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_sentencepiece
@require_flax
class 				_lowerCAmelCase			(	UpperCAmelCase_							,			UpperCAmelCase_							,			unittest.TestCase							):


  '''simple docstring'''
  a_							:				Union[str, Any]					    =(FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
  a_							:				List[Any]					    =(FlaxXGLMForCausalLM,) if is_flax_available() else ()







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    _snake_case      : Tuple														=		FlaxXGLMModelTester(self							)







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





    for model_class_name in self.all_model_classes:
      _snake_case							,    _snake_case							,    _snake_case      : Optional[int]														=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward(UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)







  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





    for model_class_name in self.all_model_classes:
      _snake_case							,    _snake_case							,    _snake_case      : List[Any]														=		self.model_tester.prepare_config_and_inputs()
      self.model_tester.check_use_cache_forward_with_attn_mask(
          UpperCamelCase					, UpperCamelCase					, UpperCamelCase					, UpperCamelCase							)







  @slow
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		XGLMTokenizer.from_pretrained('XGLM'					, padding_side='left'							)
    _snake_case      : int														=		tokenizer(['Hello this is a long string', 'Hey']					, return_tensors='np'					, padding=UpperCamelCase					, truncation=UpperCamelCase							)

    _snake_case      : Union[str, Any]														=		FlaxXGLMForCausalLM.from_pretrained('facebook/xglm-564M'							)
    _snake_case      : Optional[Any]														=		1
    _snake_case      : List[str]														=		False

    _snake_case      : Dict														=		jax.jit(model.generate							)

    _snake_case      : Union[str, Any]														=		jit_generate(inputs['input_ids']					, attention_mask=inputs['attention_mask']							).sequences

    _snake_case      : str														=		tokenizer.batch_decode(UpperCamelCase					, skip_special_tokens=UpperCamelCase							)

    _snake_case      : int														=		[
        'Hello this is a long string of questions, but I\'m not sure if I\'m',
        'Hey, I\'m a newbie to the forum and I\'',
    ]

    self.assertListEqual(UpperCamelCase					, UpperCamelCase							)







  @is_pt_flax_cross_test
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





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

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

        _snake_case							,    _snake_case      : Dict														=		pt_inputs['input_ids'].shape
        _snake_case      : Tuple														=		np.random.randint(0					, seq_length - 1					, size=(batch_size,)							)
        for batch_idx, start_index in enumerate(UpperCamelCase							):
          _snake_case      : Any														=		0
          _snake_case      : Tuple														=		1
          _snake_case      : int														=		0
          _snake_case      : str														=		1
        _snake_case      : List[Any]														=		pt_model_class(UpperCamelCase							).eval()
        # Flax models don't use the `use_cache` option and cache is not returned as a default.
        # So we disable `use_cache` here for PyTorch model.
        _snake_case      : List[Any]														=		False
        _snake_case      : List[str]														=		model_class(UpperCamelCase					, dtype=jnp.floataa							)
        _snake_case      : str														=		convert_pytorch_state_dict_to_flax(pt_model.state_dict()					, UpperCamelCase							)
        _snake_case      : Dict														=		fx_state

        with torch.no_grad():
          _snake_case      : str														=		pt_model(**UpperCamelCase							).to_tuple()

        _snake_case      : Any														=		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							)
          _snake_case      : Union[str, Any]														=		model_class.from_pretrained(UpperCamelCase					, from_pt=UpperCamelCase							)

        _snake_case      : Tuple														=		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 UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





    _snake_case							,    _snake_case      : Dict														=		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
        _snake_case      : int														=		self._prepare_for_class(UpperCamelCase					, UpperCamelCase							)
        _snake_case      : int														=		{k: torch.tensor(v.tolist()							) for k, v in prepared_inputs_dict.items()}

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

        _snake_case      : Tuple														=		pt_model_class(UpperCamelCase							).eval()
        _snake_case      : Dict														=		False
        _snake_case      : Optional[int]														=		model_class(UpperCamelCase					, dtype=jnp.floataa							)

        _snake_case      : List[str]														=		load_flax_weights_in_pytorch_model(UpperCamelCase					, fx_model.params							)
        _snake_case							,    _snake_case      : str														=		pt_inputs['input_ids'].shape
        _snake_case      : Optional[int]														=		np.random.randint(0					, seq_length - 1					, size=(batch_size,)							)
        for batch_idx, start_index in enumerate(UpperCamelCase							):
          _snake_case      : List[Any]														=		0
          _snake_case      : str														=		1
          _snake_case      : Dict														=		0
          _snake_case      : Optional[int]														=		1

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

        with torch.no_grad():
          _snake_case      : Any														=		pt_model(**UpperCamelCase							).to_tuple()

        _snake_case      : Union[str, Any]														=		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							)
          _snake_case      : str														=		pt_model_class.from_pretrained(UpperCamelCase					, from_flax=UpperCamelCase							)

        with torch.no_grad():
          _snake_case      : str														=		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							)







  @slow
  def UpperCamelCase_       (					self					:    List[Any]							):


    '''simple docstring'''





    for model_class_name in self.all_model_classes:
      _snake_case      : Dict														=		model_class_name.from_pretrained('facebook/xglm-564M'							)
      _snake_case      : Any														=		model(np.ones((1, 1)							)							)
      self.assertIsNotNone(UpperCamelCase							)











import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  # Save pytorch-model
  _snake_case      : List[Any]														=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _snake_case      : Optional[Any]														=		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
  torch.save(model.state_dict()			,							lowerCAmelCase )
  print(F"""Save configuration file to {pytorch_config_dump_path}""" )
  with open(lowerCAmelCase			,							'w'			,							encoding='utf-8' ) as f:
    f.write(config.to_json_string() )


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--imagegpt_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the TensorFlow checkpoint path.""",
      )
      parser.add_argument(
          """--model_size""",
          default=None,
          type=str,
          required=True,
          help="""Size of the model (can be either 'small', 'medium' or 'large').""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )




