
"""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							   =				["gpt2"]
_lowercase							   =				"gpt2"

if is_tf_available():







		class 				lowerCAmelCase_				(      tf.Module    ):



								'''simple docstring'''


								def __init__( self     :      List[str]  ,A_     :      List[str]      )     ->   Optional[Any]:
									super().__init__()
									A						   =   tokenizer
									A						   =   AutoConfig.from_pretrained(_UpperCamelCase      )
									A						   =   TFGPTaLMHeadModel.from_config(_UpperCamelCase      )




								@tf.function(input_signature=(tf.TensorSpec((None,)  ,tf.string  ,name='text'      ),)      )
								def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      Tuple      )     ->   List[Any]:
									A						   =   self.tokenizer(_UpperCamelCase      )
									A						   =   tokenized["""input_ids"""].to_tensor()

									A						   =   tf.cast(input_ids_dense > 0  ,tf.intaa      )
									# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])

									A						   =   self.model(input_ids=_UpperCamelCase  ,attention_mask=_UpperCamelCase      )["""logits"""]

									return outputs







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



						'''simple docstring'''


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Dict:
							super().setUp()

							A						   =   [GPTaTokenizer.from_pretrained(_UpperCamelCase      ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
							A						   =   [TFGPTaTokenizer.from_pretrained(_UpperCamelCase      ) for checkpoint in TOKENIZER_CHECKPOINTS]
							assert len(self.tokenizers      ) == len(self.tf_tokenizers      )

							A						   =   [
							    """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						   =   list(zip(self.test_sentences  ,self.test_sentences[::-1]      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Optional[Any]:


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

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

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


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Tuple:


							for tf_tokenizer in self.tf_tokenizers:
								A						   =   tf.function(_UpperCamelCase      )
								for test_inputs in self.test_sentences:
									A						   =   tf.constant(_UpperCamelCase      )
									A						   =   compiled_tokenizer(_UpperCamelCase      )
									A						   =   tf_tokenizer(_UpperCamelCase      )

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


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   List[Any]:


							for tf_tokenizer in self.tf_tokenizers:
								A						   =   ModelToSave(tokenizer=_UpperCamelCase      )
								A						   =   tf.convert_to_tensor([self.test_sentences[0]]      )
								A						   =   model.serving(_UpperCamelCase      )  # Build model with some sample inputs
								with TemporaryDirectory() as tempdir:
									A						   =   Path(_UpperCamelCase      ) / """saved.model"""
									tf.saved_model.save(_UpperCamelCase  ,_UpperCamelCase  ,signatures={'serving_default': model.serving}      )
									A						   =   tf.saved_model.load(_UpperCamelCase      )
								A						   =   loaded_model.signatures["""serving_default"""](_UpperCamelCase      )["""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 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   Dict:


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

								A						   =   tf_tokenizer.get_config()
								A						   =   TFGPTaTokenizer.from_config(_UpperCamelCase      )
								A						   =   model_from_config(_UpperCamelCase      )

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




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict      )     ->   Optional[Any]:


							for tf_tokenizer in self.tf_tokenizers:
								# for the test to run
								A						   =   12_3123

								for max_length in [3, 5, 1024]:
									A						   =   tf.convert_to_tensor([self.test_sentences[0]]      )
									A						   =   tf_tokenizer(_UpperCamelCase  ,max_length=_UpperCamelCase      )

									A						   =   out["""input_ids"""].numpy().shape[1]

									assert out_length == max_length

"""simple docstring"""


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_lowercase							   =				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
_lowercase							   =				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

_lowercase							   =				'''|'''.join(sys.argv[1:])
_lowercase							   =				re.compile(rF"""^({joined_dirs}).*?\.py$""")

_lowercase							   =				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')