






import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, slow

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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
	import torch

class        _SCREAMING_SNAKE_CASE			:





	def __init__(						self							:   Optional[int]					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   Union[str, Any]=14					,    __lowerCamelCase							:   Optional[Any]=7					,    __lowerCamelCase							:   Optional[Any]=True					,    __lowerCamelCase							:   List[Any]=True					,    __lowerCamelCase							:   str=False					,    __lowerCamelCase							:   List[str]=True					,    __lowerCamelCase							:   Any=99					,    __lowerCamelCase							:   Dict=32					,    __lowerCamelCase							:   Optional[int]=4					,    __lowerCamelCase							:   str=4					,    __lowerCamelCase							:   Dict=[[["global", "local"], 2]]					,    __lowerCamelCase							:   List[str]=37					,    __lowerCamelCase							:   Any="gelu"					,    __lowerCamelCase							:   int=0.1					,    __lowerCamelCase							:   int=0.1					,    __lowerCamelCase							:   List[Any]=512					,    __lowerCamelCase							:   str=7					,    __lowerCamelCase							:   Union[str, Any]=0.02					,    ):
		UpperCamelCase	:Tuple		=      parent
		UpperCamelCase	:Optional[Any]		=      batch_size
		UpperCamelCase	:List[str]		=      seq_length
		UpperCamelCase	:str		=      is_training
		UpperCamelCase	:Tuple		=      use_input_mask
		UpperCamelCase	:int		=      use_token_type_ids
		UpperCamelCase	:Any		=      use_labels
		UpperCamelCase	:List[str]		=      vocab_size
		UpperCamelCase	:Optional[int]		=      hidden_size
		UpperCamelCase	:Optional[Any]		=      num_hidden_layers
		UpperCamelCase	:Optional[int]		=      num_attention_heads
		UpperCamelCase	:Optional[Any]		=      attention_types
		UpperCamelCase	:Optional[int]		=      intermediate_size
		UpperCamelCase	:Tuple		=      hidden_act
		UpperCamelCase	:Dict		=      hidden_dropout_prob
		UpperCamelCase	:Tuple		=      attention_probs_dropout_prob
		UpperCamelCase	:str		=      max_position_embeddings
		UpperCamelCase	:List[str]		=      window_size
		UpperCamelCase	:Optional[int]		=      initializer_range
		UpperCamelCase	:str		=      None
		UpperCamelCase	:Dict		=      vocab_size - 1
		UpperCamelCase	:int		=      vocab_size - 1
		UpperCamelCase	:Union[str, Any]		=      vocab_size - 1





	def 		_A					(						self							:   Tuple      ):
		UpperCamelCase	:List[Any]		=      ids_tensor([self.batch_size, self.seq_length]					,    self.vocab_size      )

		UpperCamelCase	:List[str]		=      None
		if self.use_input_mask:
			UpperCamelCase	:Dict		=      random_attention_mask([self.batch_size, self.seq_length]      )

		UpperCamelCase	:Any		=      GPTNeoConfig(
		    vocab_size=self.vocab_size					,    hidden_size=self.hidden_size					,    num_layers=self.num_hidden_layers					,    num_heads=self.num_attention_heads					,    max_position_embeddings=self.max_position_embeddings					,    use_cache=__lowerCamelCase					,    bos_token_id=self.bos_token_id					,    eos_token_id=self.eos_token_id					,    pad_token_id=self.pad_token_id					,    window_size=self.window_size					,    attention_types=self.attention_types					,    )

		return (config, input_ids, input_mask)





	def 		_A					(						self							:   int      ):
		UpperCamelCase	:List[Any]		=      self.prepare_config_and_inputs()
		UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:Optional[Any]		=      config_and_inputs
		UpperCamelCase	:Optional[int]		=      {"""input_ids""": input_ids, """attention_mask""": attention_mask}
		return config, inputs_dict





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   List[Any]					,    __lowerCamelCase							:   Dict      ):
		UpperCamelCase	:Any		=      20
		UpperCamelCase	:int		=      model_class_name(__lowerCamelCase      )

		UpperCamelCase	:Optional[Any]		=      model.init_cache(input_ids.shape[0]					,    __lowerCamelCase      )
		UpperCamelCase	:Optional[Any]		=      jnp.ones((input_ids.shape[0], max_decoder_length)					,    dtype="""i4"""      )

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

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

		UpperCamelCase	:Optional[int]		=      model(__lowerCamelCase      )

		UpperCamelCase	:str		=      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 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   Optional[int]					,    __lowerCamelCase							:   int					,    __lowerCamelCase							:   Optional[Any]					,    __lowerCamelCase							:   str      ):
		UpperCamelCase	:Dict		=      20
		UpperCamelCase	:Union[str, Any]		=      model_class_name(__lowerCamelCase      )

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

		UpperCamelCase	:List[str]		=      model.init_cache(input_ids.shape[0]					,    __lowerCamelCase      )
		UpperCamelCase	:Optional[Any]		=      jnp.broadcast_to(
		    jnp.arange(input_ids.shape[-1] - 1      )[None, :]					,    (input_ids.shape[0], input_ids.shape[-1] - 1)      )

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

		UpperCamelCase	:str		=      model(__lowerCamelCase					,    attention_mask=__lowerCamelCase      )

		UpperCamelCase	:Dict		=      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			(     _a						,  _a						,  unittest.TestCase ):
	snake_case__				:	Optional[int]	      =    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
	snake_case__				:	Dict	      =    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()





	def 		_A					(						self							:   Dict      ):
		UpperCamelCase	:Any		=      FlaxGPTNeoModelTester(self      )





	def 		_A					(						self							:   List[str]      ):
		for model_class_name in self.all_model_classes:
			UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:Optional[int]		=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.check_use_cache_forward(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )





	def 		_A					(						self							:   Optional[int]      ):
		for model_class_name in self.all_model_classes:
			UpperCamelCase	, UpperCamelCase	, UpperCamelCase	:Optional[Any]		=      self.model_tester.prepare_config_and_inputs()
			self.model_tester.check_use_cache_forward_with_attn_mask(
			    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase      )





	@slow
	def 		_A					(						self							:   List[str]      ):
		UpperCamelCase	:Dict		=      GPTaTokenizer.from_pretrained("""gpt2"""					,    pad_token="""<|endoftext|>"""					,    padding_side="""left"""      )
		UpperCamelCase	:str		=      tokenizer(["""Hello this is a long string""", """Hey"""]					,    return_tensors="""np"""					,    padding=__lowerCamelCase					,    truncation=__lowerCamelCase      )

		UpperCamelCase	:List[str]		=      FlaxGPTNeoForCausalLM.from_pretrained("""EleutherAI/gpt-neo-125M"""      )
		UpperCamelCase	:int		=      False
		UpperCamelCase	:Dict		=      model.config.eos_token_id

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

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

		UpperCamelCase	:Optional[int]		=      tokenizer.batch_decode(__lowerCamelCase					,    skip_special_tokens=__lowerCamelCase      )

		UpperCamelCase	:int		=      [
		    """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(__lowerCamelCase					,    __lowerCamelCase      )





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

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

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

				UpperCamelCase	:Optional[Any]		=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,    __lowerCamelCase      )
				UpperCamelCase	:Union[str, Any]		=      fx_state

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

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

				with tempfile.TemporaryDirectory() as tmpdirname:
					pt_model.save_pretrained(__lowerCamelCase      )
					UpperCamelCase	:Union[str, Any]		=      model_class.from_pretrained(__lowerCamelCase					,    from_pt=__lowerCamelCase      )

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





	@is_pt_flax_cross_test
	def 		_A					(						self							:   List[str]      ):
		UpperCamelCase	, UpperCamelCase	:Optional[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
				UpperCamelCase	:Tuple		=      self._prepare_for_class(__lowerCamelCase					,    __lowerCamelCase      )
				UpperCamelCase	:Dict		=      {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	:Tuple		=      getattr(__lowerCamelCase					,    __lowerCamelCase      )

				UpperCamelCase	:Tuple		=      pt_model_class(__lowerCamelCase      ).eval()
				UpperCamelCase	:Any		=      model_class(__lowerCamelCase					,    dtype=jnp.floataa      )

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

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

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

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

				with tempfile.TemporaryDirectory() as tmpdirname:
					fx_model.save_pretrained(__lowerCamelCase      )
					UpperCamelCase	:Dict		=      pt_model_class.from_pretrained(__lowerCamelCase					,    from_flax=__lowerCamelCase      )

				with torch.no_grad():
					UpperCamelCase	:int		=      pt_model_loaded(**__lowerCamelCase      ).to_tuple()

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





	@slow
	def 		_A					(						self							:   int      ):
		for model_class_name in self.all_model_classes:
			UpperCamelCase	:Dict		=      model_class_name.from_pretrained("""EleutherAI/gpt-neo-125M"""      )
			UpperCamelCase	:Tuple		=      model(np.ones((1, 1)      )      )
			self.assertIsNotNone(__lowerCamelCase      )








# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_					:		int           = '''Update an existing config file with the latest defaults while maintaining the old configuration.'''

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str   )						->     Dict:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      args.config_file
	if config_file is None and Path(__magic_name__   ).exists():
		UpperCamelCase	:int		=      default_config_file
	elif not Path(__magic_name__   ).exists():
		raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""   )
	UpperCamelCase	:Dict		=      load_config_from_file(__magic_name__   )

	if config_file.endswith(""".json"""   ):
		config.to_json_file(__magic_name__   )
	else:
		config.to_yaml_file(__magic_name__   )
	return config_file

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Optional[Any]   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      parser.add_parser("""update"""      ,     parents=__magic_name__      ,     help=__magic_name__      ,     formatter_class=__magic_name__   )
	parser.add_argument(
	    """--config_file"""      ,     default=__magic_name__      ,     help=(
	        """The path to the config file to update. Will default to a file named default_config.yaml in the cache """
	        """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
	        """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
	        """with 'huggingface'."""
	    )      ,     )

	parser.set_defaults(func=__magic_name__   )
	return parser

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Union[str, Any]   )						->     Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      update_config(__magic_name__   )
	print(f"""Sucessfully updated the configuration file at {config_file}."""   )
