





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


UpperCamelCase__           =			["gpt2"]
UpperCamelCase__           =			"gpt2"

if is_tf_available():

					class 							__SCREAMING_SNAKE_CASE       (  tf.Module							):






												def __init__(    self	,			__lowerCAmelCase	):
																			super().__init__()
																			UpperCamelCase__					  =      tokenizer
																			UpperCamelCase__					  =      AutoConfig.from_pretrained(__lowerCAmelCase	)
																			UpperCamelCase__					  =      TFGPTaLMHeadModel.from_config(__lowerCAmelCase	)







												@tf.function(input_signature=(tf.TensorSpec((None,)	,			tf.string	,			name="""text"""	),)	)
												def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
																			UpperCamelCase__					  =      self.tokenizer(__lowerCAmelCase	)
																			UpperCamelCase__					  =      tokenized["""input_ids"""].to_tensor()

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

																			UpperCamelCase__					  =      self.model(input_ids=__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	)["""logits"""]

																			return outputs

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






							def 					_lowerCamelCase		(    self	):
														super().setUp()

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

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






							def 					_lowerCamelCase		(    self	):
														for tokenizer, tf_tokenizer in zip(self.tokenizers	,			self.tf_tokenizers	):
																					for test_inputs in self.test_sentences:
																												UpperCamelCase__					  =      tokenizer([test_inputs]	,			return_tensors="""tf"""	)
																												UpperCamelCase__					  =      tf_tokenizer([test_inputs]	)

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

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






							@slow
							def 					_lowerCamelCase		(    self	):
														for tf_tokenizer in self.tf_tokenizers:
																					UpperCamelCase__					  =      tf.function(__lowerCAmelCase	)
																					for test_inputs in self.test_sentences:
																												UpperCamelCase__					  =      tf.constant(__lowerCAmelCase	)
																												UpperCamelCase__					  =      compiled_tokenizer(__lowerCAmelCase	)
																												UpperCamelCase__					  =      tf_tokenizer(__lowerCAmelCase	)

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






							@slow
							def 					_lowerCamelCase		(    self	):
														for tf_tokenizer in self.tf_tokenizers:
																					UpperCamelCase__					  =      ModelToSave(tokenizer=__lowerCAmelCase	)
																					UpperCamelCase__					  =      tf.convert_to_tensor([self.test_sentences[0]]	)
																					UpperCamelCase__					  =      model.serving(__lowerCAmelCase	)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												UpperCamelCase__					  =      Path(__lowerCAmelCase	) / """saved.model"""
																												tf.saved_model.save(__lowerCAmelCase	,			__lowerCAmelCase	,			signatures={"""serving_default""": model.serving}	)
																												UpperCamelCase__					  =      tf.saved_model.load(__lowerCAmelCase	)
																					UpperCamelCase__					  =      loaded_model.signatures["""serving_default"""](__lowerCAmelCase	)["""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 					_lowerCamelCase		(    self	):
														for tf_tokenizer in self.tf_tokenizers:
																					UpperCamelCase__					  =      tf.convert_to_tensor([self.test_sentences[0]]	)
																					UpperCamelCase__					  =      tf_tokenizer(__lowerCAmelCase	)  # Build model with some sample inputs

																					UpperCamelCase__					  =      tf_tokenizer.get_config()
																					UpperCamelCase__					  =      TFGPTaTokenizer.from_config(__lowerCAmelCase	)
																					UpperCamelCase__					  =      model_from_config(__lowerCAmelCase	)

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







							@slow
							def 					_lowerCamelCase		(    self	):
														for tf_tokenizer in self.tf_tokenizers:
																					# for the test to run
																					UpperCamelCase__					  =      123123

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

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

																												assert out_length == max_length











from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a							):
							snake_case  :   int      =							"""pixel_values"""
							snake_case  :   List[Any]      =							False
							snake_case  :   str      =							TimmBackboneConfig






							def __init__(    self	,			__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(self	,			"""timm"""	)
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      config

														if config.backbone is None:
																					raise ValueError("""backbone is not set in the config. Please set it to a timm model name."""	)

														if config.backbone not in timm.list_models():
																					raise ValueError(f"""backbone {config.backbone} is not supported by timm."""	)

														if hasattr(__lowerCAmelCase	,			"""out_features"""	) and config.out_features is not None:
																					raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead."""	)

														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""use_pretrained_backbone"""	,			__lowerCAmelCase	)
														if pretrained is None:
																					raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False."""	)

														# We just take the final layer by default. This matches the default for the transformers models.
														UpperCamelCase__					  =      config.out_indices if getattr(__lowerCAmelCase	,			"""out_indices"""	,			__lowerCAmelCase	) is not None else (-1,)

														UpperCamelCase__					  =      timm.create_model(
														    config.backbone	,			pretrained=__lowerCAmelCase	,			features_only=config.features_only	,			in_chans=config.num_channels	,			out_indices=__lowerCAmelCase	,			**__lowerCAmelCase	,			)
														# These are used to control the output of the model when called. If output_hidden_states is True, then
														# return_layers is modified to include all layers.
														UpperCamelCase__					  =      self._backbone.return_layers
														UpperCamelCase__					  =      {layer["""module"""]: str(__lowerCAmelCase	) for i, layer in enumerate(self._backbone.feature_info.info	)}
														super()._init_backbone(__lowerCAmelCase	)






							@classmethod
							def 					_lowerCamelCase		(    cls	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														requires_backends(cls	,			["""vision""", """timm"""]	)
														from ...models.timm_backbone import TimmBackboneConfig

														UpperCamelCase__					  =      kwargs.pop("""config"""	,			TimmBackboneConfig()	)

														UpperCamelCase__					  =      kwargs.pop("""use_timm_backbone"""	,			__lowerCAmelCase	)
														if not use_timm:
																					raise ValueError("""use_timm_backbone must be True for timm backbones"""	)

														UpperCamelCase__					  =      kwargs.pop("""num_channels"""	,			config.num_channels	)
														UpperCamelCase__					  =      kwargs.pop("""features_only"""	,			config.features_only	)
														UpperCamelCase__					  =      kwargs.pop("""use_pretrained_backbone"""	,			config.use_pretrained_backbone	)
														UpperCamelCase__					  =      kwargs.pop("""out_indices"""	,			config.out_indices	)
														UpperCamelCase__					  =      TimmBackboneConfig(
														    backbone=__lowerCAmelCase	,			num_channels=__lowerCAmelCase	,			features_only=__lowerCAmelCase	,			use_pretrained_backbone=__lowerCAmelCase	,			out_indices=__lowerCAmelCase	,			)
														return super()._from_config(__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														pass







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			__lowerCAmelCase=None	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict
														UpperCamelCase__					  =      (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														UpperCamelCase__					  =      output_attentions if output_attentions is not None else self.config.output_attentions

														if output_attentions:
																					raise ValueError("""Cannot output attentions for timm backbones at the moment"""	)

														if output_hidden_states:
																					# We modify the return layers to include all the stages of the backbone
																					UpperCamelCase__					  =      self._all_layers
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      self._return_layers
																					UpperCamelCase__					  =      tuple(hidden_states[i] for i in self.out_indices	)
														else:
																					UpperCamelCase__					  =      self._backbone(__lowerCAmelCase	,			**__lowerCAmelCase	)
																					UpperCamelCase__					  =      None

														UpperCamelCase__					  =      tuple(__lowerCAmelCase	)
														UpperCamelCase__					  =      tuple(__lowerCAmelCase	) if hidden_states is not None else None

														if not return_dict:
																					UpperCamelCase__					  =      (feature_maps,)
																					if output_hidden_states:
																												UpperCamelCase__					  =      output + (hidden_states,)
																					return output

														return BackboneOutput(feature_maps=__lowerCAmelCase	,			hidden_states=__lowerCAmelCase	,			attentions=__lowerCAmelCase	)




