

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
			import torch

			from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class _UpperCamelCase							(							unittest.TestCase				):
			'''simple docstring'''







			@slow
			def 							__UpperCamelCase	(     self							:	int		)							->    Any:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE				: Optional[int]			       =    CamembertModel.from_pretrained("camembert-base"		)
				model.to(a		)

				SCREAMING_SNAKE_CASE				: int			       =    torch.tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]] , device=a , dtype=torch.long , )  # J'aime le camembert !
				with torch.no_grad():
					SCREAMING_SNAKE_CASE				: int			       =    model(a		)["last_hidden_state"]
				SCREAMING_SNAKE_CASE				: Tuple			       =    torch.Size((1, 10, 768)		)
				self.assertEqual(output.shape , a		)
				# compare the actual values for a slice.
				SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    torch.tensor(
				    [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , device=a , dtype=torch.float , )
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(torch.allclose(output[:, :3, :3] , a , atol=1e-4		)		)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_         =      {
    'configuration_codegen': ['CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CodeGenConfig', 'CodeGenOnnxConfig'],
    'tokenization_codegen': ['CodeGenTokenizer'],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      ['CodeGenTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_         =      [
			    'CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'CodeGenForCausalLM',
			    'CodeGenModel',
			    'CodeGenPreTrainedModel',
			]

if TYPE_CHECKING:
			from .configuration_codegen import CODEGEN_PRETRAINED_CONFIG_ARCHIVE_MAP, CodeGenConfig, CodeGenOnnxConfig
			from .tokenization_codegen import CodeGenTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_codegen_fast import CodeGenTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_codegen import (
						    CODEGEN_PRETRAINED_MODEL_ARCHIVE_LIST,
						    CodeGenForCausalLM,
						    CodeGenModel,
						    CodeGenPreTrainedModel,
						)

else:
			import sys

			a_         =      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)