

import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
 import jax

 from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel







class   __lowercase      (unittest.TestCase ):
 def __init__(							self      ,							A_      ,							A_=13      ,							A_=30      ,							A_=2      ,							A_=3      ,							A_=True      ,							A_=True      ,							A_=32      ,							A_=5      ,							A_=4      ,							A_=37      ,							A_="gelu"      ,							A_=0.1      ,							A_=0.1      ,							A_=10      ,							A_=0.02      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					parent
       __lowerCAmelCase					:						Optional[Any]					=					batch_size
       __lowerCAmelCase					:						Dict					=					image_size
       __lowerCAmelCase					:						Optional[Any]					=					patch_size
       __lowerCAmelCase					:						Dict					=					num_channels
       __lowerCAmelCase					:						Union[str, Any]					=					is_training
       __lowerCAmelCase					:						Optional[int]					=					use_labels
       __lowerCAmelCase					:						int					=					hidden_size
       __lowerCAmelCase					:						Tuple					=					num_hidden_layers
       __lowerCAmelCase					:						Any					=					num_attention_heads
       __lowerCAmelCase					:						Union[str, Any]					=					intermediate_size
       __lowerCAmelCase					:						Optional[Any]					=					hidden_act
       __lowerCAmelCase					:						Optional[Any]					=					hidden_dropout_prob
       __lowerCAmelCase					:						str					=					attention_probs_dropout_prob
       __lowerCAmelCase					:						str					=					type_sequence_label_size
       __lowerCAmelCase					:						List[str]					=					initializer_range

       # in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
       __lowerCAmelCase					:						Union[str, Any]					=					(image_size // patch_size) ** 2
       __lowerCAmelCase					:						Optional[int]					=					num_patches + 1
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

       __lowerCAmelCase					:						Any					=					ViTConfig(
           image_size=self.image_size      ,							patch_size=self.patch_size      ,							num_channels=self.num_channels      ,							hidden_size=self.hidden_size      ,							num_hidden_layers=self.num_hidden_layers      ,							num_attention_heads=self.num_attention_heads      ,							intermediate_size=self.intermediate_size      ,							hidden_act=self.hidden_act      ,							hidden_dropout_prob=self.hidden_dropout_prob      ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,							is_decoder=A_      ,							initializer_range=self.initializer_range      ,							)

       return config, pixel_values
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_					)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					FlaxViTModel(config=A_					)
       __lowerCAmelCase					:						Tuple					=					model(A_					)
       # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
       __lowerCAmelCase					:						Tuple					=					(self.image_size, self.image_size)
       __lowerCAmelCase					:						Optional[Any]					=					(self.patch_size, self.patch_size)
       __lowerCAmelCase					:						List[str]					=					(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
       self.parent.assertEqual(result.last_hidden_state.shape      ,							(self.batch_size, num_patches + 1, self.hidden_size)					)
 def 	UpperCamelCase__  (							self      ,							A_      ,							A_					)					->Dict:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.type_sequence_label_size
       __lowerCAmelCase					:						Optional[Any]					=					FlaxViTForImageClassification(config=A_					)
       __lowerCAmelCase					:						Any					=					model(A_					)
       self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.type_sequence_label_size)					)

       # test greyscale images
       __lowerCAmelCase					:						Dict					=					1
       __lowerCAmelCase					:						Union[str, Any]					=					FlaxViTForImageClassification(A_					)

       __lowerCAmelCase					:						str					=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
       __lowerCAmelCase					:						Dict					=					model(A_					)





 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						int					=					self.prepare_config_and_inputs()
       (
           (
           __lowerCAmelCase
       ),							(
           __lowerCAmelCase
       ),							
       )					:						Optional[Any]					=					config_and_inputs
       __lowerCAmelCase					:						Optional[int]					=					{'''pixel_values''': pixel_values}
       return config, inputs_dict







@require_flax
class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
 def 	UpperCamelCase__  (							self					)					->None:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					FlaxViTModelTester(self					)
       __lowerCAmelCase					:						Optional[Any]					=					ConfigTester(self      ,							config_class=A_      ,							has_text_modality=A_      ,							hidden_size=37					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       self.config_tester.run_common_tests()
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						List[Any]					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_					)
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase					:						int					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             __lowerCAmelCase					:						Union[str, Any]					=					model_class(A_					)
             __lowerCAmelCase					:						Tuple					=					inspect.signature(model.__call__					)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             __lowerCAmelCase					:						Optional[int]					=					[*signature.parameters.keys()]

             __lowerCAmelCase					:						str					=					['''pixel_values''']
             self.assertListEqual(arg_names[:1]      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase,							__lowerCAmelCase					:						str					=					self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__					):
                   __lowerCAmelCase					:						List[Any]					=					self._prepare_for_class(A_      ,							A_					)
                   __lowerCAmelCase					:						Optional[Any]					=					model_class(A_					)

                   @jax.jit
                   def model_jitted(A_      ,							**A_					):
                         return model(pixel_values=A_      ,							**A_					)

                   with self.subTest('''JIT Enabled'''					):
                         __lowerCAmelCase					:						List[Any]					=					model_jitted(**A_					).to_tuple()

                   with self.subTest('''JIT Disabled'''					):
                         with jax.disable_jit():
                               __lowerCAmelCase					:						Any					=					model_jitted(**A_					).to_tuple()

                   self.assertEqual(len(A_					)      ,							len(A_					)					)
                   for jitted_output, output in zip(A_      ,							A_					):
                         self.assertEqual(jitted_output.shape      ,							output.shape					)





 @slow
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       for model_class_name in self.all_model_classes:
             __lowerCAmelCase					:						Union[str, Any]					=					model_class_name.from_pretrained('''google/vit-base-patch16-224'''					)
             __lowerCAmelCase					:						List[str]					=					model(np.ones((1, 3, 224, 224)					)					)
             self.assertIsNotNone(A_					)





from ....configuration_utils import PretrainedConfig
from ....utils import logging


_UpperCamelCase 			=		logging.get_logger(__name__)

_UpperCamelCase 			=		{
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  """trajectory_transformer"""
 _UpperCamelCase			  =  ["""past_key_values"""]
 _UpperCamelCase			  =  {
     """hidden_size""": """n_embd""",
     """num_attention_heads""": """n_head""",
     """num_hidden_layers""": """n_layer""",
 }
 def __init__(							self      ,							A_=100      ,							A_=5      ,							A_=1      ,							A_=1      ,							A_=249      ,							A_=6      ,							A_=17      ,							A_=25      ,							A_=4      ,							A_=4      ,							A_=128      ,							A_=0.1      ,							A_=0.1      ,							A_=0.1      ,							A_=0.0_006      ,							A_=512      ,							A_=0.02      ,							A_=1e-12      ,							A_=1      ,							A_=True      ,							A_=1      ,							A_=5_0256      ,							A_=5_0256      ,							**A_      ,							)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					vocab_size
       __lowerCAmelCase					:						Tuple					=					action_weight
       __lowerCAmelCase					:						Tuple					=					reward_weight
       __lowerCAmelCase					:						Union[str, Any]					=					value_weight
       __lowerCAmelCase					:						List[str]					=					max_position_embeddings
       __lowerCAmelCase					:						str					=					block_size
       __lowerCAmelCase					:						Optional[Any]					=					action_dim
       __lowerCAmelCase					:						Union[str, Any]					=					observation_dim
       __lowerCAmelCase					:						Union[str, Any]					=					transition_dim
       __lowerCAmelCase					:						Dict					=					learning_rate
       __lowerCAmelCase					:						Any					=					n_layer
       __lowerCAmelCase					:						Any					=					n_head
       __lowerCAmelCase					:						Optional[int]					=					n_embd
       __lowerCAmelCase					:						str					=					embd_pdrop
       __lowerCAmelCase					:						Dict					=					attn_pdrop
       __lowerCAmelCase					:						Optional[int]					=					resid_pdrop
       __lowerCAmelCase					:						Union[str, Any]					=					initializer_range
       __lowerCAmelCase					:						Optional[int]					=					layer_norm_eps
       __lowerCAmelCase					:						Any					=					kaiming_initializer_range
       __lowerCAmelCase					:						List[str]					=					use_cache
       super().__init__(pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							**A_					)


