


'''simple docstring'''



import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow


if is_tf_available():
	import tensorflow as tf

if is_keras_nlp_available():
	from transformers.models.gpta import TFGPTaTokenizer


__lowercase	:						List[str]       =	['gpt2']
__lowercase	:						int       =	'gpt2'

if is_tf_available():





	class 							__UpperCamelCase				(		tf.Module					):




		def __init__(   self						,				__a							):

							'''simple docstring'''





							super().__init__()
							__a			:       Dict					    =  tokenizer
							__a			:       List[Any]					    =  AutoConfig.from_pretrained(__a							)
							__a			:       List[Any]					    =  TFGPTaLMHeadModel.from_config(__a							)





		@tf.function(input_signature=(tf.TensorSpec((None,)						,				tf.string						,				name='text'							),)							)
		def       __UpperCAmelCase							(   self						,				__a							):

							'''simple docstring'''





							__a			:       Optional[Any]					    =  self.tokenizer(__a							)
							__a			:       Optional[int]					    =  tokenized['input_ids'].to_tensor()

							__a			:       Union[str, Any]					    =  tf.cast(input_ids_dense > 0						,				tf.intaa							)
							# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

							__a			:       str					    =  self.model(input_ids=__a						,				attention_mask=__a							)['logits']

							return outputs





@require_tf
@require_keras_nlp
class 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						super().setUp()

						__a			:       List[str]					    =  [GPTaTokenizer.from_pretrained(__a							) for checkpoint in (TOKENIZER_CHECKPOINTS)]
						__a			:       Any					    =  [TFGPTaTokenizer.from_pretrained(__a							) for checkpoint in TOKENIZER_CHECKPOINTS]
						assert len(self.tokenizers							) == len(self.tf_tokenizers							)

						__a			:       int					    =  [
						    'This is a straightforward English test sentence.',
						    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
						    'Now we\'re going to add some Chinese: 一 二 三 一二三',
						    'And some much more rare Chinese: 齉 堃 齉堃',
						    'Je vais aussi écrire en français pour tester les accents',
						    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
						]
						__a			:       int					    =  list(zip(self.test_sentences						,				self.test_sentences[::-1]							)							)




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tokenizer, tf_tokenizer in zip(self.tokenizers						,				self.tf_tokenizers							):
											for test_inputs in self.test_sentences:
																__a			:       List[Any]					    =  tokenizer([test_inputs]						,				return_tensors='tf'							)
																__a			:       List[str]					    =  tf_tokenizer([test_inputs]							)

																for key in python_outputs.keys():
																					# convert them to numpy to avoid messing with ragged tensors
																					__a			:       Optional[Any]					    =  python_outputs[key].numpy()
																					__a			:       str					    =  tf_outputs[key].numpy()

																					self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape							)							)
																					self.assertTrue(tf.reduce_all(tf.cast(__a						,				tf.intaa							) == tf_outputs_values							)							)




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tf_tokenizer in self.tf_tokenizers:
											__a			:       int					    =  tf.function(__a							)
											for test_inputs in self.test_sentences:
																__a			:       Dict					    =  tf.constant(__a							)
																__a			:       List[str]					    =  compiled_tokenizer(__a							)
																__a			:       str					    =  tf_tokenizer(__a							)

																for key in eager_outputs.keys():
																					self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]							)							)




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tf_tokenizer in self.tf_tokenizers:
											__a			:       int					    =  ModelToSave(tokenizer=__a							)
											__a			:       List[Any]					    =  tf.convert_to_tensor([self.test_sentences[0]]							)
											__a			:       Optional[Any]					    =  model.serving(__a							)  # Build model with some sample inputs
											with TemporaryDirectory() as tempdir:
																__a			:       int					    =  Path(__a							) / 'saved.model'
																tf.saved_model.save(__a						,				__a						,				signatures={'serving_default': model.serving}							)
																__a			:       List[Any]					    =  tf.saved_model.load(__a							)
											__a			:       List[Any]					    =  loaded_model.signatures['serving_default'](__a							)['output_0']
											# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
											self.assertTrue(tf.reduce_all(out == loaded_output							)							)




	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tf_tokenizer in self.tf_tokenizers:
											__a			:       List[str]					    =  tf.convert_to_tensor([self.test_sentences[0]]							)
											__a			:       str					    =  tf_tokenizer(__a							)  # Build model with some sample inputs

											__a			:       Union[str, Any]					    =  tf_tokenizer.get_config()
											__a			:       List[Any]					    =  TFGPTaTokenizer.from_config(__a							)
											__a			:       Tuple					    =  model_from_config(__a							)

											for key in from_config_output.keys():
																self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]							)							)





	@slow
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						for tf_tokenizer in self.tf_tokenizers:
											# for the test to run
											__a			:       List[Any]					    =  12_3123

											for max_length in [3, 5, 1024]:
																__a			:       Union[str, Any]					    =  tf.convert_to_tensor([self.test_sentences[0]]							)
																__a			:       Dict					    =  tf_tokenizer(__a						,				max_length=__a							)

																__a			:       Dict					    =  out['input_ids'].numpy().shape[1]

																assert out_length == max_length







'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key						)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        UpperCAmelCase__			:		Optional[int]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        UpperCAmelCase__			:		List[Any]													=				pt_tensor.transpose(2  ,					3  ,					1  ,					0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    UpperCAmelCase__			:		Tuple													=				flax_model.init_weights(PRNGKey(lowerCAmelCase__						)						)

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
