



import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
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 torch import nn

   from transformers import RegNetForImageClassification, RegNetModel
   from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor



class      _UpperCAmelCase      :




     def __init__(					self			,    a__			,    a__=3			,    a__=3_2			,    a__=3			,    a__=1_0			,    a__=[1_0, 2_0, 3_0, 4_0]			,    a__=[1, 1, 2, 1]			,    a__=True			,    a__=True			,    a__="relu"			,    a__=3			,    a__=None			,    ):
         A__					=       parent
         A__					=       batch_size
         A__					=       image_size
         A__					=       num_channels
         A__					=       embeddings_size
         A__					=       hidden_sizes
         A__					=       depths
         A__					=       is_training
         A__					=       use_labels
         A__					=       hidden_act
         A__					=       num_labels
         A__					=       scope
         A__					=       len(a__)




     def 		snake_case_		(					self):
         A__					=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

         A__					=       None
         if self.use_labels:
             A__					=       ids_tensor([self.batch_size]			,    self.num_labels)

         A__					=       self.get_config()

         return config, pixel_values, labels




     def 		snake_case_		(					self):
         return RegNetConfig(
             num_channels=self.num_channels			,    embeddings_size=self.embeddings_size			,    hidden_sizes=self.hidden_sizes			,    depths=self.depths			,    hidden_act=self.hidden_act			,    num_labels=self.num_labels			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       RegNetModel(config=a__)
         model.to(a__)
         model.eval()
         A__					=       model(a__)
         # expected last hidden states: B, C, H // 32, W // 32
         self.parent.assertEqual(
             result.last_hidden_state.shape			,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)			,    )




     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       self.num_labels
         A__					=       RegNetForImageClassification(a__)
         model.to(a__)
         model.eval()
         A__					=       model(a__			,    labels=a__)
         self.parent.assertEqual(result.logits.shape			,    (self.batch_size, self.num_labels))




     def 		snake_case_		(					self):
         A__					=       self.prepare_config_and_inputs()
         A__			,						A__			,						A__					=       config_and_inputs
         A__					=       {'''pixel_values''': pixel_values}
         return config, inputs_dict







@require_torch
class      _UpperCAmelCase      (							A__      ,   A__      ,   unittest.TestCase  ):
     UpperCamelCase__		     =					(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
     UpperCamelCase__		     =					(
         {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False
     UpperCamelCase__		     =					False




     def 		snake_case_		(					self):
         A__					=       RegNetModelTester(self)
         A__					=       ConfigTester(self			,    config_class=a__			,    has_text_modality=a__)




     def 		snake_case_		(					self):
         self.create_and_test_config_common_properties()
         self.config_tester.create_and_test_config_to_json_string()
         self.config_tester.create_and_test_config_to_json_file()
         self.config_tester.create_and_test_config_from_and_save_pretrained()
         self.config_tester.create_and_test_config_with_num_labels()
         self.config_tester.check_config_can_be_init_without_params()
         self.config_tester.check_config_arguments_init()




     def 		snake_case_		(					self):
         return




     @unittest.skip(reason='''RegNet does not use inputs_embeds''')
     def 		snake_case_		(					self):
         pass




     @unittest.skip(reason='''RegNet does not support input and output embeddings''')
     def 		snake_case_		(					self):
         pass




     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__					=       ['''pixel_values''']
             self.assertListEqual(arg_names[:1]			,    a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*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(config=a__)
             for name, module in model.named_modules():
                 if isinstance(a__			,    (nn.BatchNormad, nn.GroupNorm)):
                     self.assertTrue(
                         torch.all(module.weight == 1)			,    msg=F"Parameter {name} of model {model_class} seems not properly initialized"			,    )
                     self.assertTrue(
                         torch.all(module.bias == 0)			,    msg=F"Parameter {name} of model {model_class} seems not properly initialized"			,    )




     def 		snake_case_		(					self):
         def check_hidden_states_output(a__			,    a__			,    a__):
             A__					=       model_class(a__)
             model.to(a__)
             model.eval()

             with torch.no_grad():
                 A__					=       model(**self._prepare_for_class(a__			,    a__))

             A__					=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

             A__					=       self.model_tester.num_stages
             self.assertEqual(len(a__)			,    expected_num_stages + 1)

             # RegNet's feature maps are of shape (batch_size, num_channels, height, width)
             self.assertListEqual(
                 list(hidden_states[0].shape[-2:])			,    [self.model_tester.image_size // 2, self.model_tester.image_size // 2]			,    )

         A__			,						A__					=       self.model_tester.prepare_config_and_inputs_for_common()
         A__					=       ['''basic''', '''bottleneck''']
         for model_class in self.all_model_classes:
             for layer_type in layers_type:
                 A__					=       layer_type
                 A__					=       True
                 check_hidden_states_output(a__			,    a__			,    a__)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 A__					=       True

                 check_hidden_states_output(a__			,    a__			,    a__)




     def 		snake_case_		(					self):
         A__					=       self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__)




     @slow
     def 		snake_case_		(					self):
         for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             A__					=       RegNetModel.from_pretrained(a__)
             self.assertIsNotNone(a__)







def 		lowerCAmelCase__     (       )->	str:
    A__					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
    return image



@require_torch
@require_vision
class      _UpperCAmelCase      (							unittest.TestCase  ):




     @cached_property
     def 		snake_case_		(					self):
         return (
             AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
             if is_vision_available()
             else None
         )




     @slow
     def 		snake_case_		(					self):
         A__					=       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(a__)

         A__					=       self.default_image_processor
         A__					=       prepare_img()
         A__					=       image_processor(images=a__			,    return_tensors='''pt''').to(a__)

         # forward pass
         with torch.no_grad():
             A__					=       model(**a__)

         # verify the logits
         A__					=       torch.Size((1, 1_0_0_0))
         self.assertEqual(outputs.logits.shape			,    a__)

         A__					=       torch.tensor([-0.4_1_8_0, -1.5_0_5_1, -3.4_8_3_6]).to(a__)

         self.assertTrue(torch.allclose(outputs.logits[0, :3]			,    a__			,    atol=1e-4))





from typing import Optional

import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      _UpperCAmelCase      (							A__      ,   A__      ,   A__  ):
     UpperCamelCase__		     =					[r'''h\.\d+\.attn\.bias''', r'''h\.\d+\.attn\.masked_bias''']




     @register_to_config
     def __init__(					self			,    a__			,    a__			,    a__ = None			,    a__ = 5_0_2_5_7			,    a__ = 1_0_2_4			,    a__ = 7_6_8			,    a__ = 1_2			,    a__ = 1_2			,    a__ = None			,    a__ = "gelu_new"			,    a__ = 0.1			,    a__ = 0.1			,    a__ = 0.1			,    a__ = 1e-5			,    a__ = 0.0_2			,    a__ = True			,    a__ = True			,    a__ = False			,    a__ = False			,    ):
         super().__init__()

         A__					=       prefix_length

         if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
             raise ValueError(
                 F"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
                 F" `n_embd`: {n_embd} are not equal.")

         A__					=       prefix_inner_dim
         A__					=       prefix_hidden_dim

         A__					=       (
             nn.Linear(self.prefix_inner_dim			,    self.prefix_hidden_dim)
             if self.prefix_hidden_dim is not None
             else nn.Identity()
         )
         A__					=       (
             nn.Linear(self.prefix_hidden_dim			,    a__) if self.prefix_hidden_dim is not None else nn.Identity()
         )

         A__					=       GPTaConfig(
             vocab_size=a__			,    n_positions=a__			,    n_embd=a__			,    n_layer=a__			,    n_head=a__			,    n_inner=a__			,    activation_function=a__			,    resid_pdrop=a__			,    embd_pdrop=a__			,    attn_pdrop=a__			,    layer_norm_epsilon=a__			,    initializer_range=a__			,    scale_attn_weights=a__			,    use_cache=a__			,    scale_attn_by_inverse_layer_idx=a__			,    reorder_and_upcast_attn=a__			,    )
         A__					=       GPTaLMHeadModel(a__)




     def 		snake_case_		(					self			,    a__			,    a__			,    a__ = None			,    a__ = None			,    ):
         A__					=       self.transformer.transformer.wte(a__)
         A__					=       self.encode_prefix(a__)
         A__					=       self.decode_prefix(a__)
         A__					=       torch.cat((prefix_embeds, embedding_text)			,    dim=1)

         if labels is not None:
             A__					=       self.get_dummy_token(input_ids.shape[0]			,    input_ids.device)
             A__					=       torch.cat((dummy_token, input_ids)			,    dim=1)
         A__					=       self.transformer(inputs_embeds=a__			,    labels=a__			,    attention_mask=a__)
         if self.prefix_hidden_dim is not None:
             return out, hidden
         else:
             return out




     def 		snake_case_		(					self			,    a__			,    a__):
         return torch.zeros(a__			,    self.prefix_length			,    dtype=torch.intaa			,    device=a__)




     def 		snake_case_		(					self			,    a__):
         return self.encode_prefix(a__)




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__			,    a__			,    a__):
         A__					=       torch.split(a__			,    1			,    dim=0)
         A__					=       []
         A__					=       []
         for feature in features:
             A__					=       self.decode_prefix(feature.to(a__))  # back to the clip feature
             # Only support beam search for now
             A__			,						A__					=       self.generate_beam(
                 input_embeds=a__			,    device=a__			,    eos_token_id=a__)
             generated_tokens.append(output_tokens[0])
             generated_seq_lengths.append(seq_lengths[0])
         A__					=       torch.stack(a__)
         A__					=       torch.stack(a__)
         return generated_tokens, generated_seq_lengths




     @torch.no_grad()
     def 		snake_case_		(					self			,    a__=None			,    a__=None			,    a__=None			,    a__ = 5			,    a__ = 6_7			,    a__ = 1.0			,    a__ = None			,    ):
         A__					=       eos_token_id
         A__					=       None
         A__					=       None
         A__					=       torch.ones(a__			,    device=a__			,    dtype=torch.int)
         A__					=       torch.zeros(a__			,    device=a__			,    dtype=torch.bool)

         if input_embeds is not None:
             A__					=       input_embeds
         else:
             A__					=       self.transformer.transformer.wte(a__)

         for i in range(a__):
             A__					=       self.transformer(inputs_embeds=a__)
             A__					=       outputs.logits
             A__					=       logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
             A__					=       logits.softmax(-1).log()

             if scores is None:
                 A__			,						A__					=       logits.topk(a__			,    -1)
                 A__					=       generated.expand(a__			,    *generated.shape[1:])
                 A__			,						A__					=       next_tokens.permute(1			,    0), scores.squeeze(0)
                 if tokens is None:
                     A__					=       next_tokens
                 else:
                     A__					=       tokens.expand(a__			,    *tokens.shape[1:])
                     A__					=       torch.cat((tokens, next_tokens)			,    dim=1)
             else:
                 A__					=       -float(np.inf)
                 A__					=       0
                 A__					=       scores[:, None] + logits
                 seq_lengths[~is_stopped] += 1
                 A__					=       scores_sum / seq_lengths[:, None]
                 A__			,						A__					=       scores_sum_average.view(-1).topk(a__			,    -1)
                 A__					=       next_tokens // scores_sum.shape[1]
                 A__					=       seq_lengths[next_tokens_source]
                 A__					=       next_tokens % scores_sum.shape[1]
                 A__					=       next_tokens.unsqueeze(1)
                 A__					=       tokens[next_tokens_source]
                 A__					=       torch.cat((tokens, next_tokens)			,    dim=1)
                 A__					=       generated[next_tokens_source]
                 A__					=       scores_sum_average * seq_lengths
                 A__					=       is_stopped[next_tokens_source]

             A__					=       self.transformer.transformer.wte(next_tokens.squeeze()).view(generated.shape[0]			,    1			,    -1)
             A__					=       torch.cat((generated, next_token_embed)			,    dim=1)
             A__					=       is_stopped + next_tokens.eq(a__).squeeze()
             if is_stopped.all():
                 break

         A__					=       scores / seq_lengths
         A__					=       scores.argsort(descending=a__)
         # tokens tensors are already padded to max_seq_length
         A__					=       [tokens[i] for i in order]
         A__					=       torch.stack(a__			,    dim=0)
         A__					=       torch.tensor([seq_lengths[i] for i in order]			,    dtype=seq_lengths.dtype)
         return output_texts, seq_lengths
