





import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class        _a    :

      """simple docstring"""


      def __init__(		self:			int	,    __lowerCamelCase:			Union[str, Any]=2	,    __lowerCamelCase:			List[Any]=3	,    __lowerCamelCase:			str=64	,    __lowerCamelCase:			Any=None					):
          '''simple docstring'''


          UpperCamelCase__:	List[str]					=     np.random.default_rng(UpperCamelCase__					)
          UpperCamelCase__:	Union[str, Any]					=     length
          UpperCamelCase__:	Dict					=     rng.normal(size=(length,)					).astype(np.floataa					)
          UpperCamelCase__:	Optional[int]					=     a * self.x + b + rng.normal(scale=0.1	,    size=(length,)					).astype(np.floataa					)






      def __len__(		self:			Any					):
          '''simple docstring'''


          return self.length






      def __getitem__(		self:			int	,    __lowerCamelCase:			str					):
          '''simple docstring'''


          return {"x": self.x[i], "y": self.y[i]}




class        _a    (    torch.nn.Module):

      """simple docstring"""


      def __init__(		self:			str	,    __lowerCamelCase:			List[Any]=0	,    __lowerCamelCase:			Optional[int]=0	,    __lowerCamelCase:			int=False					):
          '''simple docstring'''


          super().__init__()
          UpperCamelCase__:	List[str]					=     torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
          UpperCamelCase__:	Any					=     torch.nn.Parameter(torch.tensor([2, 3]					).float()					)
          UpperCamelCase__:	str					=     True






      def 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			List[str]=None					):
          '''simple docstring'''


          if self.first_batch:
              print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"					)
              UpperCamelCase__:	Optional[int]					=     False
          return x * self.a[0] + self.b[0]




class        _a    (    torch.nn.Module):

      """simple docstring"""


      def __init__(		self:			Any	,    __lowerCamelCase:			Optional[Any]=0	,    __lowerCamelCase:			Tuple=0	,    __lowerCamelCase:			Any=False					):
          '''simple docstring'''


          super().__init__()
          UpperCamelCase__:	List[str]					=     torch.nn.Parameter(torch.tensor(UpperCamelCase__					).float()					)
          UpperCamelCase__:	str					=     torch.nn.Parameter(torch.tensor(UpperCamelCase__					).float()					)
          UpperCamelCase__:	List[str]					=     True






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			Union[str, Any]=None					):
          '''simple docstring'''


          if self.first_batch:
              print(F"Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}"					)
              UpperCamelCase__:	int					=     False
          return x * self.a + self.b



def 				lowerCAmelCase_ (							A_    ,A_ = 16):
    from datasets import load_dataset
    from transformers import AutoTokenizer

    UpperCamelCase__:	Tuple					=     AutoTokenizer.from_pretrained("bert-base-cased")
    UpperCamelCase__:	Dict					=     {"train": "tests/test_samples/MRPC/train.csv", "validation": "tests/test_samples/MRPC/dev.csv"}
    UpperCamelCase__:	List[str]					=     load_dataset("csv"    ,data_files=UpperCAmelCase__)
    UpperCamelCase__:	Optional[Any]					=     datasets["train"].unique("label")

    UpperCamelCase__:	Dict					=     {v: i for i, v in enumerate(UpperCAmelCase__)}

    def tokenize_function(A_):
        # max_length=None => use the model max length (it's actually the default)
        UpperCamelCase__:	Optional[Any]					=     tokenizer(
            examples["sentence1"]    ,examples["sentence2"]    ,truncation=UpperCAmelCase__    ,max_length=UpperCAmelCase__    ,padding="max_length")
        if "label" in examples:
            UpperCamelCase__:	Optional[int]					=     [label_to_id[l] for l in examples["label"]]
        return outputs

    # Apply the method we just defined to all the examples in all the splits of the dataset
    UpperCamelCase__:	int					=     datasets.map(
        UpperCAmelCase__    ,batched=UpperCAmelCase__    ,remove_columns=["sentence1", "sentence2", "label"]    ,)

    def collate_fn(A_):
        # On TPU it's best to pad everything to the same length or training will be very slow.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(UpperCAmelCase__    ,padding="max_length"    ,max_length=1_28    ,return_tensors="pt")
        return tokenizer.pad(UpperCAmelCase__    ,padding="longest"    ,return_tensors="pt")

    # Instantiate dataloaders.
    UpperCamelCase__:	Dict					=     DataLoader(tokenized_datasets["train"]    ,shuffle=UpperCAmelCase__    ,collate_fn=UpperCAmelCase__    ,batch_size=2)
    UpperCamelCase__:	int					=     DataLoader(tokenized_datasets["validation"]    ,shuffle=UpperCAmelCase__    ,collate_fn=UpperCAmelCase__    ,batch_size=1)

    return train_dataloader, eval_dataloader




'''simple docstring'''







from __future__ import annotations

import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask


if is_tf_available():
      import tensorflow as tf

      from transformers import TFBlipTextModel
      from transformers.models.blip.modeling_tf_blip import TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST


class        A__   :



    def __init__(       self		,					UpperCamelCase__		,					UpperCamelCase__=12		,					UpperCamelCase__=7		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=True		,					UpperCamelCase__=99		,					UpperCamelCase__=32		,					UpperCamelCase__=32		,					UpperCamelCase__=2		,					UpperCamelCase__=4		,					UpperCamelCase__=37		,					UpperCamelCase__=0.1		,					UpperCamelCase__=0.1		,					UpperCamelCase__=512		,					UpperCamelCase__=0.02		,					UpperCamelCase__=0		,					UpperCamelCase__=None		,					)    ->   Union[str, Any]:





           '''simple docstring'''
           A_						       =   parent
           A_						       =   batch_size
           A_						       =   seq_length
           A_						       =   is_training
           A_						       =   use_input_mask
           A_						       =   use_labels
           A_						       =   vocab_size
           A_						       =   hidden_size
           A_						       =   projection_dim
           A_						       =   num_hidden_layers
           A_						       =   num_attention_heads
           A_						       =   intermediate_size
           A_						       =   dropout
           A_						       =   attention_dropout
           A_						       =   max_position_embeddings
           A_						       =   initializer_range
           A_						       =   scope
           A_						       =   bos_token_id



    def        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size							)

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

           if input_mask is not None:
                  A_						       =   input_mask.numpy()
                  A_       ,      A_						       =   input_mask.shape
                  A_						       =   np.random.randint(1		,					seq_length - 1		,					size=(batch_size,)							)
                  for batch_idx, start_index in enumerate(UpperCamelCase__							):
                         A_						       =   1
                         A_						       =   0

           A_						       =   self.get_config()

           return config, input_ids, tf.convert_to_tensor(UpperCamelCase__							)



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





           '''simple docstring'''
           return BlipTextConfig(
               vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					projection_dim=self.projection_dim		,					num_hidden_layers=self.num_hidden_layers		,					num_attention_heads=self.num_attention_heads		,					intermediate_size=self.intermediate_size		,					dropout=self.dropout		,					attention_dropout=self.attention_dropout		,					max_position_embeddings=self.max_position_embeddings		,					initializer_range=self.initializer_range		,					bos_token_id=self.bos_token_id		,					)



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   int:





           '''simple docstring'''
           A_						       =   TFBlipTextModel(config=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					attention_mask=UpperCamelCase__		,					training=UpperCamelCase__							)
           A_						       =   model(UpperCamelCase__		,					training=UpperCamelCase__							)
           self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size)							)
           self.parent.assertEqual(result.pooler_output.shape		,					(self.batch_size, self.hidden_size)							)







    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   self.prepare_config_and_inputs()
           A_       ,      A_       ,      A_						       =   config_and_inputs
           A_						       =   {"""input_ids""": input_ids, """attention_mask""": input_mask}
           return config, inputs_dict


@require_tf
class        A__   (   _snake_case    ,    unittest.TestCase      ):
    lowercase						       =		(TFBlipTextModel,) if is_tf_available() else ()
    lowercase						       =		False
    lowercase						       =		False
    lowercase						       =		False



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





           '''simple docstring'''
           A_						       =   BlipTextModelTester(self							)
           A_						       =   ConfigTester(self		,					config_class=UpperCamelCase__		,					hidden_size=37							)



    def        snake_case_      (       self							)    ->   Tuple:





           '''simple docstring'''
           self.config_tester.run_common_tests()



    def        snake_case_      (       self							)    ->   str:





           '''simple docstring'''
           A_						       =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCamelCase__							)



    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



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





           '''simple docstring'''
           pass



    @unittest.skip(reason="""Blip does not use inputs_embeds"""							)
    def        snake_case_      (       self							)    ->   Union[str, Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           pass



    @unittest.skip(reason="""BlipTextModel has no base class and is not available in MODEL_MAPPING"""							)
    def        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           pass



    @slow
    def        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           for model_name in TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A_						       =   TFBlipTextModel.from_pretrained(UpperCamelCase__							)
                  self.assertIsNotNone(UpperCamelCase__							)







    def        snake_case_      (       self		,					UpperCamelCase__=True							)    ->   str:





           '''simple docstring'''
           super().test_pt_tf_model_equivalence(allow_missing_keys=UpperCamelCase__							)
