





"""simple docstring"""






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch

    from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer



class 	lowerCAmelCase					:






       def __init__(   self    ,  a__    ,  a__=13    ,  a__=7    ,  a__=True    ,  a__=True    ,  a__=False    ,  a__=True    ,  a__=99    ,  a__=32    ,  a__=5    ,  a__=4    ,  a__=37    ,  a__="gelu"    ,  a__=0.1    ,  a__=0.1    ,  a__=5_12    ,  a__=16    ,  a__=2    ,  a__=0.02    ,  a__=3    ,  a__=4    ,  a__=None    ,  ):
              _UpperCAmelCase     =     parent
              _UpperCAmelCase     =     batch_size
              _UpperCAmelCase     =     seq_length
              _UpperCAmelCase     =     is_training
              _UpperCAmelCase     =     use_input_mask
              _UpperCAmelCase     =     use_token_type_ids
              _UpperCAmelCase     =     use_labels
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_hidden_layers
              _UpperCAmelCase     =     num_attention_heads
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     hidden_act
              _UpperCAmelCase     =     hidden_dropout_prob
              _UpperCAmelCase     =     attention_probs_dropout_prob
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     type_vocab_size
              _UpperCAmelCase     =     type_sequence_label_size
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     num_labels
              _UpperCAmelCase     =     num_choices
              _UpperCAmelCase     =     scope






       def 							__A (   self     ):
              _UpperCAmelCase     =     ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size     )

              _UpperCAmelCase     =     None
              if self.use_input_mask:
                     _UpperCAmelCase     =     random_attention_mask([self.batch_size, self.seq_length]     )

              _UpperCAmelCase     =     None
              if self.use_token_type_ids:
                     _UpperCAmelCase     =     ids_tensor([self.batch_size, self.seq_length]    ,  self.type_vocab_size     )

              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     None
              _UpperCAmelCase     =     None
              if self.use_labels:
                     _UpperCAmelCase     =     ids_tensor([self.batch_size]    ,  self.type_sequence_label_size     )
                     _UpperCAmelCase     =     ids_tensor([self.batch_size, self.seq_length]    ,  self.num_labels     )
                     _UpperCAmelCase     =     ids_tensor([self.batch_size]    ,  self.num_choices     )

              _UpperCAmelCase     =     self.get_config()

              return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels






       def 							__A (   self     ):
              return LlamaConfig(
                  vocab_size=self.vocab_size    ,  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    ,  max_position_embeddings=self.max_position_embeddings    ,  type_vocab_size=self.type_vocab_size    ,  is_decoder=a__    ,  initializer_range=self.initializer_range    ,  )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     LlamaModel(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__     )
              _UpperCAmelCase     =     model(a__     )
              self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     LlamaModel(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  )
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  )
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__     )
              self.parent.assertEqual(result.last_hidden_state.shape    ,  (self.batch_size, self.seq_length, self.hidden_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     LlamaForCausalLM(config=a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.parent.assertEqual(result.logits.shape    ,  (self.batch_size, self.seq_length, self.vocab_size)     )






       def 							__A (   self    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  a__    ,  ):
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     True
              _UpperCAmelCase     =     LlamaForCausalLM(config=a__     )
              model.to(a__     )
              model.eval()

              # first forward pass
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  use_cache=a__    ,  )
              _UpperCAmelCase     =     outputs.past_key_values

              # create hypothetical multiple next token and extent to next_input_ids
              _UpperCAmelCase     =     ids_tensor((self.batch_size, 3)    ,  config.vocab_size     )
              _UpperCAmelCase     =     ids_tensor((self.batch_size, 3)    ,  vocab_size=2     )

              # append to next input_ids and
              _UpperCAmelCase     =     torch.cat([input_ids, next_tokens]    ,  dim=-1     )
              _UpperCAmelCase     =     torch.cat([input_mask, next_mask]    ,  dim=-1     )

              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  output_hidden_states=a__    ,  )['hidden_states'][0]
              _UpperCAmelCase     =     model(
                  a__    ,  attention_mask=a__    ,  encoder_hidden_states=a__    ,  encoder_attention_mask=a__    ,  past_key_values=a__    ,  output_hidden_states=a__    ,  )['hidden_states'][0]

              # select random slice
              _UpperCAmelCase     =     ids_tensor((1,)    ,  output_from_past.shape[-1]     ).item()
              _UpperCAmelCase     =     output_from_no_past[:, -3:, random_slice_idx].detach()
              _UpperCAmelCase     =     output_from_past[:, :, random_slice_idx].detach()

              self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]     )

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(a__    ,  a__    ,  atol=1E-3     )     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.prepare_config_and_inputs()
              (
                  (
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							(
                  _UpperCAmelCase
              )    ,							
              )     =     config_and_inputs
              _UpperCAmelCase     =     {'input_ids': input_ids, 'attention_mask': input_mask}
              return config, inputs_dict



@require_torch
class 	lowerCAmelCase					(      snake_case			,	snake_case			,	snake_case			,	unittest.TestCase							):
       lowerCAmelCase__						 =  (LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
       lowerCAmelCase__						 =  (LlamaForCausalLM,) if is_torch_available() else ()
       lowerCAmelCase__						 =  (
           {
               """feature-extraction""": LlamaModel,
               """text-classification""": LlamaForSequenceClassification,
               """text-generation""": LlamaForCausalLM,
               """zero-shot""": LlamaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__						 =  False
       lowerCAmelCase__						 =  False






       def 							__A (   self     ):
              _UpperCAmelCase     =     LlamaModelTester(self     )
              _UpperCAmelCase     =     ConfigTester(self    ,  config_class=a__    ,  hidden_size=37     )






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






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs()
              for type in ["absolute", "relative_key", "relative_key_query"]:
                     _UpperCAmelCase     =     type
                     self.model_tester.create_and_check_model(*a__     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     input_dict['input_ids']
              _UpperCAmelCase     =     input_ids.ne(1     ).to(a__     )
              _UpperCAmelCase     =     ids_tensor([self.model_tester.batch_size]    ,  self.model_tester.type_sequence_label_size     )
              _UpperCAmelCase     =     LlamaForSequenceClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.assertEqual(result.logits.shape    ,  (self.model_tester.batch_size, self.model_tester.num_labels)     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     'single_label_classification'
              _UpperCAmelCase     =     input_dict['input_ids']
              _UpperCAmelCase     =     input_ids.ne(1     ).to(a__     )
              _UpperCAmelCase     =     ids_tensor([self.model_tester.batch_size]    ,  self.model_tester.type_sequence_label_size     )
              _UpperCAmelCase     =     LlamaForSequenceClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.assertEqual(result.logits.shape    ,  (self.model_tester.batch_size, self.model_tester.num_labels)     )






       def 							__A (   self     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     3
              _UpperCAmelCase     =     'multi_label_classification'
              _UpperCAmelCase     =     input_dict['input_ids']
              _UpperCAmelCase     =     input_ids.ne(1     ).to(a__     )
              _UpperCAmelCase     =     ids_tensor(
                  [self.model_tester.batch_size, config.num_labels]    ,  self.model_tester.type_sequence_label_size     ).to(torch.float     )
              _UpperCAmelCase     =     LlamaForSequenceClassification(a__     )
              model.to(a__     )
              model.eval()
              _UpperCAmelCase     =     model(a__    ,  attention_mask=a__    ,  labels=a__     )
              self.assertEqual(result.logits.shape    ,  (self.model_tester.batch_size, self.model_tester.num_labels)     )






       @unittest.skip('LLaMA buffers include complex numbers, which breaks this test'     )
       def 							__A (   self     ):
              pass






       @parameterized.expand([('linear',), ('dynamic',)]     )
       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase    ,							_UpperCAmelCase     =     self.model_tester.prepare_config_and_inputs_for_common()
              _UpperCAmelCase     =     ids_tensor([1, 10]    ,  config.vocab_size     )
              _UpperCAmelCase     =     ids_tensor([1, int(config.max_position_embeddings * 1.5     )]    ,  config.vocab_size     )

              set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
              _UpperCAmelCase     =     LlamaModel(a__     )
              original_model.to(a__     )
              original_model.eval()
              _UpperCAmelCase     =     original_model(a__     ).last_hidden_state
              _UpperCAmelCase     =     original_model(a__     ).last_hidden_state

              set_seed(42     )  # Fixed seed at init time so the two models get the same random weights
              _UpperCAmelCase     =     {'type': scaling_type, 'factor': 10.0}
              _UpperCAmelCase     =     LlamaModel(a__     )
              scaled_model.to(a__     )
              scaled_model.eval()
              _UpperCAmelCase     =     scaled_model(a__     ).last_hidden_state
              _UpperCAmelCase     =     scaled_model(a__     ).last_hidden_state

              # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
              # maximum sequence length, so the outputs for the short input should match.
              if scaling_type == "dynamic":
                     self.assertTrue(torch.allclose(a__    ,  a__    ,  atol=1E-5     )     )
              else:
                     self.assertFalse(torch.allclose(a__    ,  a__    ,  atol=1E-5     )     )

              # The output should be different for long inputs
              self.assertFalse(torch.allclose(a__    ,  a__    ,  atol=1E-5     )     )



@require_torch
class 	lowerCAmelCase					(      unittest.TestCase							):






       @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-7b-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor([input_ids]     )     )
              # Expected mean on dim = -1
              _UpperCAmelCase     =     torch.tensor([[-6.6_550, -4.1_227, -4.9_859, -3.2_406, 0.8_262, -3.0_033, 1.2_964, -3.3_699]]     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # slicing logits[0, 0, 0:30]
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-12.8_281, -7.4_453, -0.4_639, -8.0_625, -7.2_500, -8.0_000, -6.4_883, -7.7_695, -7.8_438, -7.0_312, -6.2_188, -7.1_328, -1.8_496, 1.9_961, -8.6_250, -6.7_227, -12.8_281, -6.9_492, -7.0_742, -7.7_852, -7.5_820, -7.9_062, -6.9_375, -7.9_805, -8.3_438, -8.1_562, -8.0_469, -7.6_250, -7.7_422, -7.3_398,]     )
              # fmt: on
              torch.testing.assert_close(out[0, 0, :30]    ,  a__    ,  atol=1E-5    ,  rtol=1E-5     )






       @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor(a__     )     )
              # Expected mean on dim = -1
              _UpperCAmelCase     =     torch.tensor([[-2.0_622, -1.2_794, -1.1_638, -0.9_788, -1.4_603, -1.0_238, -1.7_893, -1.4_411]]     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # slicing logits[0, 0, 0:30]
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-8.1_406, -8.0_547, 2.7_461, -1.2_344, -0.1_448, -1.8_262, -1.0_020, -1.8_154, -1.6_895, -1.8_516, -2.3_574, -0.9_277, 3.7_598, 6.5_742, -1.2_998, -0.1_177, -8.1_406, -2.9_688, -2.9_199, -3.1_699, -3.5_254, -2.3_555, -2.7_988, -3.4_141, -2.8_262, -4.5_195, -3.3_379, -3.3_164, -2.7_832, -3.0_273]     )
              # fmt: on
              torch.testing.assert_close(out[0, 0, :30]    ,  a__    ,  atol=1E-5    ,  rtol=1E-5     )






       @unittest.skip('Logits are not exactly the same, once we fix the instabalities somehow, will update!'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-13b-chat-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor(a__     )     )
              # Expected mean on dim = -1
              _UpperCAmelCase     =     torch.tensor([[-0.8_562, -1.8_520, -0.7_551, -0.4_162, -1.5_161, -1.2_038, -2.4_823, -2.3_254]]     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # slicing logits[0, 0, 0:30]
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-2.2_227, 4.8_828, 0.9_023, -0.4_578, -0.7_871, -0.1_033, -0.6_221, -0.5_786, -0.7_803, -1.0_674, -1.2_920, -0.1_570, 0.8_008, 2.0_723, -0.9_497, 0.2_771, -2.2_227, -0.7_612, -1.4_346, -1.2_061, -1.6_426, -0.3_000, -0.7_139, -1.1_934, -1.8_691, -1.6_973, -1.5_947, -1.2_705, -0.3_523, -0.5_513]     )
              # fmt: on
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )






       @unittest.skip(
           'Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     [1, 3_06, 46_58, 2_78, 65_93, 3_10, 28_34, 3_38]
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained('meta-llama/Llama-2-70b-hf'    ,  device_map='auto'     )
              _UpperCAmelCase     =     model(torch.tensor(a__     )     )

              _UpperCAmelCase     =     torch.tensor(
                  [[-4.2_327, -3.3_360, -4.6_665, -4.7_631, -1.8_180, -3.4_170, -1.4_211, -3.1_810]]    ,  dtype=torch.floataa     )
              torch.testing.assert_close(out.mean(-1     )    ,  a__    ,  atol=1E-2    ,  rtol=1E-2     )
              # fmt: off
              _UpperCAmelCase     =     torch.tensor([-9.4_922, -3.9_551, 1.7_998, -5.6_758, -5.1_055, -5.8_984, -4.8_320, -6.8_086, -6.5_391, -5.6_172, -5.5_820, -5.5_352, 1.7_881, 3.6_289, -6.5_117, -3.4_785, -9.5_000, -6.0_352, -6.8_125, -6.0_195, -6.6_836, -5.4_727, -6.2_812, -6.0_391, -7.3_398, -7.4_297, -7.4_844, -6.5_820, -5.8_789, -5.5_312]     )
              # fmt: on
              torch.testing.assert_close(out[0, 0, :30]    ,  a__    ,  atol=1E-5    ,  rtol=1E-5     )






       @unittest.skip('Model is curently gated'     )
       @slow
       def 							__A (   self     ):
              _UpperCAmelCase     =     'Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the "princi'
              _UpperCAmelCase     =     'Simply put, the theory of relativity states that '
              _UpperCAmelCase     =     LlamaTokenizer.from_pretrained('meta-llama/Llama-2-13b-chat-hf'     )
              _UpperCAmelCase     =     tokenizer.encode(a__    ,  return_tensors='pt'     )
              _UpperCAmelCase     =     LlamaForCausalLM.from_pretrained(
                  'meta-llama/Llama-2-13b-chat-hf'    ,  device_map='sequential'    ,  use_safetensors=a__     )

              # greedy generation outputs
              _UpperCAmelCase     =     model.generate(a__    ,  max_new_tokens=64    ,  top_p=a__    ,  temperature=1    ,  do_sample=a__     )
              _UpperCAmelCase     =     tokenizer.decode(generated_ids[0]    ,  skip_special_tokens=a__     )
              self.assertEqual(a__    ,  a__     )













"""simple docstring"""






# 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 	lowerCAmelCase					(      snake_case							):
       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 							__A (   self    ,  a__    ,  a__     ):
              return self.pre_processor(text=[label]    ,  images=[image]    ,  padding=a__    ,  return_tensors='pt'     )






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






       def 							__A (   self    ,  a__     ):
              _UpperCAmelCase     =     outputs.cpu().detach().numpy()
              _UpperCAmelCase     =     0
              _UpperCAmelCase     =     1
              return Image.fromarray((array * 2_55).astype(np.uinta     )     )






