

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
   from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import GLPNImageProcessor






class _UpperCamelCase							(							_lowerCamelCase				):
   '''simple docstring'''







   def 							__UpperCamelCase	(     self							:	Union[str, Any]		)							->    Tuple:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[Any]			       =    self.config_class(**self.inputs_dict		)
    self.parent.assertTrue(hasattr(__UpperCamelCase , "hidden_sizes"		)		)
    self.parent.assertTrue(hasattr(__UpperCamelCase , "num_attention_heads"		)		)
    self.parent.assertTrue(hasattr(__UpperCamelCase , "num_encoder_blocks"		)		)






class _UpperCamelCase							:
   '''simple docstring'''







   def __init__(     self							:	List[str] , a							:	Any , a							:	Tuple=13 , a							:	Optional[Any]=64 , a							:	Tuple=3 , a							:	Union[str, Any]=4 , a							:	List[str]=[2, 2, 2, 2] , a							:	str=[8, 4, 2, 1] , a							:	Dict=[16, 32, 64, 128] , a							:	Tuple=[1, 4, 8, 16] , a							:	Optional[Any]=[1, 2, 4, 8] , a							:	List[str]=True , a							:	Union[str, Any]=True , a							:	Optional[int]="gelu" , a							:	List[Any]=0.1 , a							:	int=0.1 , a							:	Union[str, Any]=0.02 , a							:	str=16 , a							:	int=3 , a							:	int=None , )							->    str:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    parent
    SCREAMING_SNAKE_CASE				: Dict			       =    batch_size
    SCREAMING_SNAKE_CASE				: int			       =    image_size
    SCREAMING_SNAKE_CASE				: str			       =    num_channels
    SCREAMING_SNAKE_CASE				: Dict			       =    num_encoder_blocks
    SCREAMING_SNAKE_CASE				: List[str]			       =    sr_ratios
    SCREAMING_SNAKE_CASE				: str			       =    depths
    SCREAMING_SNAKE_CASE				: str			       =    hidden_sizes
    SCREAMING_SNAKE_CASE				: Dict			       =    downsampling_rates
    SCREAMING_SNAKE_CASE				: str			       =    num_attention_heads
    SCREAMING_SNAKE_CASE				: List[str]			       =    is_training
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    use_labels
    SCREAMING_SNAKE_CASE				: Any			       =    hidden_act
    SCREAMING_SNAKE_CASE				: int			       =    hidden_dropout_prob
    SCREAMING_SNAKE_CASE				: Tuple			       =    attention_probs_dropout_prob
    SCREAMING_SNAKE_CASE				: Dict			       =    initializer_range
    SCREAMING_SNAKE_CASE				: Tuple			       =    decoder_hidden_size
    SCREAMING_SNAKE_CASE				: Any			       =    num_labels
    SCREAMING_SNAKE_CASE				: Any			       =    scope
   def 							__UpperCamelCase	(     self							:	int		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Tuple			       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    None
    if self.use_labels:
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels		)

    SCREAMING_SNAKE_CASE				: List[str]			       =    self.get_config()
    return config, pixel_values, labels
   def 							__UpperCamelCase	(     self							:	Any		)							->    str:

    """simple docstring"""

    return GLPNConfig(
        image_size=self.image_size , num_channels=self.num_channels , num_encoder_blocks=self.num_encoder_blocks , depths=self.depths , hidden_sizes=self.hidden_sizes , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , initializer_range=self.initializer_range , decoder_hidden_size=self.decoder_hidden_size , )
   def 							__UpperCamelCase	(     self							:	Tuple , a							:	Optional[Any] , a							:	List[str] , a							:	Any		)							->    Union[str, Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Optional[int]			       =    GLPNModel(config=__UpperCamelCase		)
    model.to(__UpperCamelCase		)
    model.eval()
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model(__UpperCamelCase		)
    SCREAMING_SNAKE_CASE				: Any			       =    self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)		)
   def 							__UpperCamelCase	(     self							:	int , a							:	Union[str, Any] , a							:	Dict , a							:	Optional[int]		)							->    Union[str, Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Dict			       =    self.num_labels
    SCREAMING_SNAKE_CASE				: Any			       =    GLPNForDepthEstimation(__UpperCamelCase		)
    model.to(__UpperCamelCase		)
    model.eval()
    SCREAMING_SNAKE_CASE				: int			       =    model(__UpperCamelCase		)
    self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size)		)
    SCREAMING_SNAKE_CASE				: Dict			       =    model(__UpperCamelCase , labels=__UpperCamelCase		)
    self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size)		)






   def 							__UpperCamelCase	(     self							:	Dict		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: int			       =    self.prepare_config_and_inputs()
    SCREAMING_SNAKE_CASE				: Dict			       =    config_and_inputs
    SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    {'pixel_values': pixel_values}
    return config, inputs_dict






@require_torch
class _UpperCamelCase							(							_lowerCamelCase							,		_lowerCamelCase							,		unittest.TestCase				):
   '''simple docstring'''







   lowerCamelCase__          =(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   lowerCamelCase__          =(
       {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
   )

   lowerCamelCase__          =False
   lowerCamelCase__          =False
   lowerCamelCase__          =False
   def 							__UpperCamelCase	(     self							:	Tuple		)							->    str:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[Any]			       =    GLPNModelTester(self		)
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    GLPNConfigTester(self , config_class=__UpperCamelCase		)
   def 							__UpperCamelCase	(     self							:	List[Any]		)							->    List[str]:

    """simple docstring"""

    self.config_tester.run_common_tests()
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    Optional[int]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__UpperCamelCase		)
   def 							__UpperCamelCase	(     self							:	Dict		)							->    Tuple:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: str			       =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase		)
   @unittest.skip("GLPN does not use inputs_embeds"		)
   def 							__UpperCamelCase	(     self							:	Dict		)							->    Any:

    """simple docstring"""

    pass
   @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"		)
   def 							__UpperCamelCase	(     self							:	int		)							->    Any:

    """simple docstring"""

    pass
   def 							__UpperCamelCase	(     self							:	List[Any]		)							->    Union[str, Any]:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE				: Dict			       =    model_class(__UpperCamelCase		)
     SCREAMING_SNAKE_CASE				: str			       =    inspect.signature(model.forward		)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     SCREAMING_SNAKE_CASE				: List[Any]			       =    [*signature.parameters.keys()]

     SCREAMING_SNAKE_CASE				: Dict			       =    ['pixel_values']
     self.assertListEqual(arg_names[:1] , __UpperCamelCase		)
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    Any:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: Any			       =    self.model_tester.prepare_config_and_inputs_for_common()
    SCREAMING_SNAKE_CASE				: List[Any]			       =    True

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE				: int			       =    True
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    False
     SCREAMING_SNAKE_CASE				: int			       =    True
     SCREAMING_SNAKE_CASE				: Tuple			       =    model_class(__UpperCamelCase		)
     model.to(__UpperCamelCase		)
     model.eval()
     with torch.no_grad():
      SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase		)		)
     SCREAMING_SNAKE_CASE				: List[Any]			       =    outputs.attentions

     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    sum(self.model_tester.depths		)
     self.assertEqual(len(__UpperCamelCase		) , __UpperCamelCase		)

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    True
     SCREAMING_SNAKE_CASE				: Optional[Any]			       =    model_class(__UpperCamelCase		)
     model.to(__UpperCamelCase		)
     model.eval()
     with torch.no_grad():
      SCREAMING_SNAKE_CASE				: List[Any]			       =    model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase		)		)
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    outputs.attentions

     self.assertEqual(len(__UpperCamelCase		) , __UpperCamelCase		)

     # verify the first attentions (first block, first layer)
     SCREAMING_SNAKE_CASE				: Any			       =    (self.model_tester.image_size // 4) ** 2
     SCREAMING_SNAKE_CASE				: Any			       =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
     self.assertListEqual(
         list(attentions[0].shape[-3:]		) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )

     # verify the last attentions (last block, last layer)
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    (self.model_tester.image_size // 32) ** 2
     SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
     self.assertListEqual(
         list(attentions[-1].shape[-3:]		) , [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] , )
     SCREAMING_SNAKE_CASE				: List[str]			       =    len(__UpperCamelCase		)

     # Check attention is always last and order is fine
     SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    True
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    True
     SCREAMING_SNAKE_CASE				: List[str]			       =    model_class(__UpperCamelCase		)
     model.to(__UpperCamelCase		)
     model.eval()
     with torch.no_grad():
      SCREAMING_SNAKE_CASE				: Tuple			       =    model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase		)		)

     self.assertEqual(out_len + 1 , len(__UpperCamelCase		)		)

     SCREAMING_SNAKE_CASE				: Tuple			       =    outputs.attentions

     self.assertEqual(len(__UpperCamelCase		) , __UpperCamelCase		)
     # verify the first attentions (first block, first layer)
     SCREAMING_SNAKE_CASE				: List[str]			       =    (self.model_tester.image_size // 4) ** 2
     SCREAMING_SNAKE_CASE				: str			       =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
     self.assertListEqual(
         list(self_attentions[0].shape[-3:]		) , [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] , )
   def 							__UpperCamelCase	(     self							:	List[str]		)							->    Tuple:

    """simple docstring"""

    def check_hidden_states_output(a							:	Union[str, Any] , a							:	Dict , a							:	Optional[Any]		):
     SCREAMING_SNAKE_CASE				: List[Any]			       =    model_class(__UpperCamelCase		)
     model.to(__UpperCamelCase		)
     model.eval()

     with torch.no_grad():
      SCREAMING_SNAKE_CASE				: List[str]			       =    model(**self._prepare_for_class(__UpperCamelCase , __UpperCamelCase		)		)

     SCREAMING_SNAKE_CASE				: List[Any]			       =    outputs.hidden_states

     SCREAMING_SNAKE_CASE				: Any			       =    self.model_tester.num_encoder_blocks
     self.assertEqual(len(__UpperCamelCase		) , __UpperCamelCase		)

     # verify the first hidden states (first block)
     self.assertListEqual(
         list(hidden_states[0].shape[-3:]		) , [
             self.model_tester.hidden_sizes[0],
             self.model_tester.image_size // 4,
             self.model_tester.image_size // 4,
         ] , )

    SCREAMING_SNAKE_CASE				: Tuple			       =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
     SCREAMING_SNAKE_CASE				: Optional[int]			       =    True
     check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase		)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     SCREAMING_SNAKE_CASE				: List[Any]			       =    True

     check_hidden_states_output(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase		)
   def 							__UpperCamelCase	(     self							:	int		)							->    str:

    """simple docstring"""

    if not self.model_tester.is_training:
     return

    SCREAMING_SNAKE_CASE				: int			       =    self.model_tester.prepare_config_and_inputs_for_common()
    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    True

    for model_class in self.all_model_classes:
     if model_class in get_values(__UpperCamelCase		):
      continue
     # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
     # this can then be incorporated into _prepare_for_class in test_modeling_common.py
     if model_class.__name__ == "GLPNForDepthEstimation":
      SCREAMING_SNAKE_CASE				: Optional[int]			       =    inputs_dict['pixel_values'].shape
      SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.zeros(
          [self.model_tester.batch_size, height, width] , device=__UpperCamelCase		).long()
     SCREAMING_SNAKE_CASE				: Dict			       =    model_class(__UpperCamelCase		)
     model.to(__UpperCamelCase		)
     model.train()
     SCREAMING_SNAKE_CASE				: List[Any]			       =    self._prepare_for_class(__UpperCamelCase , __UpperCamelCase , return_labels=__UpperCamelCase		)
     SCREAMING_SNAKE_CASE				: Union[str, Any]			       =    model(**__UpperCamelCase		).loss
     loss.backward()






   @slow
   def 							__UpperCamelCase	(     self							:	Tuple		)							->    Optional[int]:

    """simple docstring"""

    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     SCREAMING_SNAKE_CASE				: List[str]			       =    GLPNModel.from_pretrained(__UpperCamelCase		)
     self.assertIsNotNone(__UpperCamelCase		)

def 	lowerCamelCase__	(   ):
 SCREAMING_SNAKE_CASE				: Any			       =    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
 return image






@require_torch
@require_vision
@slow
class _UpperCamelCase							(							unittest.TestCase				):
   '''simple docstring'''







   @slow
   def 							__UpperCamelCase	(     self							:	Dict		)							->    str:

    """simple docstring"""

    SCREAMING_SNAKE_CASE				: List[Any]			       =    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		)
    SCREAMING_SNAKE_CASE				: int			       =    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]		).to(__UpperCamelCase		)

    SCREAMING_SNAKE_CASE				: Optional[Any]			       =    prepare_img()
    SCREAMING_SNAKE_CASE				: Any			       =    image_processor(images=__UpperCamelCase , return_tensors="pt"		).to(__UpperCamelCase		)

    # forward pass
    with torch.no_grad():
     SCREAMING_SNAKE_CASE				: Any			       =    model(**__UpperCamelCase		)

    # verify the predicted depth
    SCREAMING_SNAKE_CASE				: Optional[int]			       =    torch.Size([1, 480, 640]		)
    self.assertEqual(outputs.predicted_depth.shape , __UpperCamelCase		)

    SCREAMING_SNAKE_CASE				: List[str]			       =    torch.tensor(
        [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		).to(__UpperCamelCase		)

    self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] , __UpperCamelCase , 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__)