


import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import DecisionTransformerModel
  from transformers.models.decision_transformer.modeling_decision_transformer import (
      DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


class     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A=13  ,       __A=7  ,       __A=6  ,       __A=17  ,       __A=23  ,       __A=11  ,       __A=True  ,       ):
        __a					=  parent
        __a					=  batch_size
        __a					=  seq_length
        __a					=  act_dim
        __a					=  state_dim
        __a					=  hidden_size
        __a					=  max_length
        __a					=  is_training


     def 	snake_case_  (	self    ):
        __a					=  floats_tensor((self.batch_size, self.seq_length, self.state_dim)    )
        __a					=  floats_tensor((self.batch_size, self.seq_length, self.act_dim)    )
        __a					=  floats_tensor((self.batch_size, self.seq_length, 1)    )
        __a					=  floats_tensor((self.batch_size, self.seq_length, 1)    )
        __a					=  ids_tensor((self.batch_size, self.seq_length)  ,       vocab_size=1000    )
        __a					=  random_attention_mask((self.batch_size, self.seq_length)    )

        __a					=  self.get_config()

        return (
            config,
            states,
            actions,
            rewards,
            returns_to_go,
            timesteps,
            attention_mask,
        )


     def 	snake_case_  (	self    ):
        return DecisionTransformerConfig(
            batch_size=self.batch_size  ,       seq_length=self.seq_length  ,       act_dim=self.act_dim  ,       state_dim=self.state_dim  ,       hidden_size=self.hidden_size  ,       max_length=self.max_length  ,       )


     def 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        __a					=  DecisionTransformerModel(config=__A    )
        model.to(__A    )
        model.eval()
        __a					=  model(__A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    )

        self.parent.assertEqual(result.state_preds.shape  ,       states.shape    )
        self.parent.assertEqual(result.action_preds.shape  ,       actions.shape    )
        self.parent.assertEqual(result.return_preds.shape  ,       returns_to_go.shape    )
        self.parent.assertEqual(
            result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length * 3, self.hidden_size)    )  # seq length *3 as there are 3 modelities: states, returns and actions


     def 	snake_case_  (	self    ):
        __a					=  self.prepare_config_and_inputs()
        (
            (
            __a
        )	,    (
            __a
        )	,    (
            __a
        )	,    (
            __a
        )	,    (
            __a
        )	,    (
            __a
        )	,    (
            __a
        )	,    
        )					=  config_and_inputs
        __a					=  {
            """states""": states,
            """actions""": actions,
            """rewards""": rewards,
            """returns_to_go""": returns_to_go,
            """timesteps""": timesteps,
            """attention_mask""": attention_mask,
        }
        return config, inputs_dict







@require_torch
class     __UpperCAmelCase							(       __A ,					__A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      (DecisionTransformerModel,) if is_torch_available() else ()
     _lowerCamelCase   =      ()
     _lowerCamelCase   =      {"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

     # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
     _lowerCamelCase   =      False

     # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False


     def 	snake_case_  (	self    ):
        __a					=  DecisionTransformerModelTester(self    )
        __a					=  ConfigTester(self  ,       config_class=__A  ,       hidden_size=37    )


     def 	snake_case_  (	self    ):
        self.config_tester.run_common_tests()


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A    )


     @slow
     def 	snake_case_  (	self    ):
        for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __a					=  DecisionTransformerModel.from_pretrained(__A    )
           self.assertIsNotNone(__A    )


     def 	snake_case_  (	self    ):
        __a	,    __a					=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __a					=  model_class(__A    )
           __a					=  inspect.signature(model.forward    )
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           __a					=  [*signature.parameters.keys()]

           __a					=  [
               """states""",
               """actions""",
               """rewards""",
               """returns_to_go""",
               """timesteps""",
               """attention_mask""",
           ]

           self.assertListEqual(arg_names[: len(__A    )]  ,       __A    )







@require_torch
class     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     @slow
     def 	snake_case_  (	self    ):
        __a					=  2  # number of steps of autoregressive prediction we will perform
        __a					=  10  # defined by the RL environment, may be normalized
        __a					=  DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""    )
        __a					=  model.to(__A    )
        __a					=  model.config
        torch.manual_seed(0    )
        __a					=  torch.randn(1  ,       1  ,       config.state_dim    ).to(device=__A  ,       dtype=torch.floataa    )  # env.reset()

        __a					=  torch.tensor(
            [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]  ,       device=__A    )

        __a					=  torch.tensor(__A  ,       device=__A  ,       dtype=torch.floataa    ).reshape(1  ,       1  ,       1    )
        __a					=  state
        __a					=  torch.zeros(1  ,       0  ,       config.act_dim  ,       device=__A  ,       dtype=torch.floataa    )
        __a					=  torch.zeros(1  ,       0  ,       device=__A  ,       dtype=torch.floataa    )
        __a					=  torch.tensor(0  ,       device=__A  ,       dtype=torch.long    ).reshape(1  ,       1    )

        for step in range(__A    ):
           __a					=  torch.cat([actions, torch.zeros(1  ,       1  ,       config.act_dim  ,       device=__A    )]  ,       dim=1    )
           __a					=  torch.cat([rewards, torch.zeros(1  ,       1  ,       device=__A    )]  ,       dim=1    )

           __a					=  torch.ones(1  ,       states.shape[1]    ).to(dtype=torch.long  ,       device=states.device    )

           with torch.no_grad():
              __a	,    __a	,    __a					=  model(
                  states=__A  ,       actions=__A  ,       rewards=__A  ,       returns_to_go=__A  ,       timesteps=__A  ,       attention_mask=__A  ,       return_dict=__A  ,       )

           self.assertEqual(action_pred.shape  ,       actions.shape    )
           self.assertTrue(torch.allclose(action_pred[0, -1]  ,       expected_outputs[step]  ,       atol=1E-4    )    )
           __a	,    __a	,    __a	,    __a					=  (  # env.step(action)
               torch.randn(1  ,       1  ,       config.state_dim    ).to(device=__A  ,       dtype=torch.floataa    ),
               1.0,
               False,
               {},
           )

           __a					=  action_pred[0, -1]
           __a					=  torch.cat([states, state]  ,       dim=1    )
           __a					=  returns_to_go[0, -1] - reward
           __a					=  torch.cat([returns_to_go, pred_return.reshape(1  ,       1  ,       1    )]  ,       dim=1    )
           __a					=  torch.cat(
               [timesteps, torch.ones((1, 1)  ,       device=__A  ,       dtype=torch.long    ) * (step + 1)]  ,       dim=1    )







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
import torch

from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool


if is_vision_available():
  from PIL import Image


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""
     _lowerCamelCase   =      (
         """This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image."""
         """It takes two arguments named `image` which should be the original image, and `label` which should be a text """
         """describing the elements what should be identified in the segmentation mask. The tool returns the mask."""
     )
     _lowerCamelCase   =      """CIDAS/clipseg-rd64-refined"""
     _lowerCamelCase   =      """image_segmenter"""
     _lowerCamelCase   =      CLIPSegForImageSegmentation

     _lowerCamelCase   =      ["""image""", """text"""]
     _lowerCamelCase   =      ["""image"""]


     def __init__(	self  ,       *__A  ,       **__A    ):
        requires_backends(self  ,       ["""vision"""]    )
        super().__init__(*__A  ,       **__A    )


     def 	snake_case_  (	self  ,       __A  ,       __A    ):
        return self.pre_processor(text=[label]  ,       images=[image]  ,       padding=__A  ,       return_tensors="""pt"""    )


     def 	snake_case_  (	self  ,       __A    ):
        with torch.no_grad():
           __a					=  self.model(**__A    ).logits
        return logits


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  outputs.cpu().detach().numpy()
        __a					=  0
        __a					=  1
        return Image.fromarray((array * 255).astype(np.uinta    )    )



