





'''simple docstring'''




import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
    from transformers import (
        FlaxBertModel,
        FlaxCLIPVisionModel,
        FlaxVisionTextDualEncoderModel,
        FlaxViTModel,
        VisionTextDualEncoderConfig,
        VisionTextDualEncoderProcessor,
    )
    from transformers.modeling_flax_pytorch_utils import (
        convert_pytorch_state_dict_to_flax,
        load_flax_weights_in_pytorch_model,
    )


if is_torch_available():
    import torch

    from transformers import VisionTextDualEncoderModel

if is_vision_available():
    from PIL import Image







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Optional[int]:
 if isinstance(_lowerCAmelCase       ,    collections.abc.Iterable    ):
  return x
 return (x, x)






@require_flax
class UpperCAmelCase							:



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     pass



    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self					):
     pass



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   np.abs((a - b)					).max()
     self.assertLessEqual(__A     ,			__A     ,			f'Difference between torch and flax is {diff} (>= {tol}).'					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   VisionTextDualEncoderConfig.from_vision_text_configs(__A     ,			__A					)

     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel(__A					)

     __UpperCAmelCase					=   model(input_ids=__A     ,			pixel_values=__A     ,			attention_mask=__A					)

     self.assertEqual(output['text_embeds'].shape     ,			(input_ids.shape[0], config.projection_dim)					)
     self.assertEqual(output['image_embeds'].shape     ,			(pixel_values.shape[0], config.projection_dim)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_vision_text_model(__A     ,			__A					)
     __UpperCAmelCase					=   {'vision_model': vision_model, 'text_model': text_model}
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__A					)

     __UpperCAmelCase					=   model(input_ids=__A     ,			pixel_values=__A     ,			attention_mask=__A					)

     self.assertEqual(output['text_embeds'].shape     ,			(input_ids.shape[0], model.config.projection_dim)					)
     self.assertEqual(output['image_embeds'].shape     ,			(pixel_values.shape[0], model.config.projection_dim)					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_vision_text_model(__A     ,			__A					)
     __UpperCAmelCase					=   {'vision_model': vision_model, 'text_model': text_model}
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__A					)

     __UpperCAmelCase					=   model(input_ids=__A     ,			pixel_values=__A     ,			attention_mask=__A					)
     __UpperCAmelCase					=   output[0]

     with tempfile.TemporaryDirectory() as tmpdirname:
      model.save_pretrained(__A					)
      __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_pretrained(__A					)

      __UpperCAmelCase					=   model(input_ids=__A     ,			pixel_values=__A     ,			attention_mask=__A					)
      __UpperCAmelCase					=   after_output[0]
      __UpperCAmelCase					=   np.amax(np.abs(out_a - out_a					)					)
      self.assertLessEqual(__A     ,			1E-3					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_vision_text_model(__A     ,			__A					)
     __UpperCAmelCase					=   {'vision_model': vision_model, 'text_model': text_model}
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__A					)

     __UpperCAmelCase					=   model(
         input_ids=__A     ,			pixel_values=__A     ,			attention_mask=__A     ,			output_attentions=__A					)

     __UpperCAmelCase					=   output.vision_model_output.attentions
     self.assertEqual(len(__A					)     ,			vision_config.num_hidden_layers					)

     # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
     __UpperCAmelCase					=   to_atuple(vision_model.config.image_size					)
     __UpperCAmelCase					=   to_atuple(vision_model.config.patch_size					)
     __UpperCAmelCase					=   (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
     __UpperCAmelCase					=   num_patches + 1
     self.assertEqual(vision_attentions[0].shape[-3:]     ,			(vision_config.num_attention_heads, seq_len, seq_len)					)

     __UpperCAmelCase					=   output.text_model_output.attentions
     self.assertEqual(len(__A					)     ,			text_config.num_hidden_layers					)

     self.assertEqual(
         text_attentions[0].shape[-3:]     ,			(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])     ,			)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     pt_model.to(__A					)
     pt_model.eval()

     # prepare inputs
     __UpperCAmelCase					=   inputs_dict
     __UpperCAmelCase					=   {k: torch.tensor(v.tolist()					) for k, v in flax_inputs.items()}

     with torch.no_grad():
      __UpperCAmelCase					=   pt_model(**__A					).to_tuple()

     __UpperCAmelCase					=   fx_model(**__A					).to_tuple()
     self.assertEqual(len(__A					)     ,			len(__A					)     ,			'Output lengths differ between Flax and PyTorch'					)
     for fx_output, pt_output in zip(fx_outputs[:4]     ,			pt_outputs[:4]					):
      self.assert_almost_equals(__A     ,			pt_output.numpy()     ,			4E-2					)

     # PT -> Flax
     with tempfile.TemporaryDirectory() as tmpdirname:
      pt_model.save_pretrained(__A					)
      __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_pretrained(__A     ,			from_pt=__A					)

     __UpperCAmelCase					=   fx_model_loaded(**__A					).to_tuple()
     self.assertEqual(len(__A					)     ,			len(__A					)     ,			'Output lengths differ between Flax and PyTorch'					)
     for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]     ,			pt_outputs[:4]					):
      self.assert_almost_equals(__A     ,			pt_output.numpy()     ,			4E-2					)

     # Flax -> PT
     with tempfile.TemporaryDirectory() as tmpdirname:
      fx_model.save_pretrained(__A					)
      __UpperCAmelCase					=   VisionTextDualEncoderModel.from_pretrained(__A     ,			from_flax=__A					)

     pt_model_loaded.to(__A					)
     pt_model_loaded.eval()

     with torch.no_grad():
      __UpperCAmelCase					=   pt_model_loaded(**__A					).to_tuple()

     self.assertEqual(len(__A					)     ,			len(__A					)     ,			'Output lengths differ between Flax and PyTorch'					)
     for fx_output, pt_output_loaded in zip(fx_outputs[:4]     ,			pt_outputs_loaded[:4]					):
      self.assert_almost_equals(__A     ,			pt_output_loaded.numpy()     ,			4E-2					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   VisionTextDualEncoderConfig.from_vision_text_configs(__A     ,			__A					)

     __UpperCAmelCase					=   VisionTextDualEncoderModel(__A					)
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel(__A					)

     __UpperCAmelCase					=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()     ,			__A					)
     __UpperCAmelCase					=   fx_state

     self.check_pt_flax_equivalence(__A     ,			__A     ,			__A					)



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A					):
     __UpperCAmelCase					=   VisionTextDualEncoderConfig.from_vision_text_configs(__A     ,			__A					)

     __UpperCAmelCase					=   VisionTextDualEncoderModel(__A					)
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel(__A					)

     __UpperCAmelCase					=   load_flax_weights_in_pytorch_model(__A     ,			fx_model.params					)

     self.check_pt_flax_equivalence(__A     ,			__A     ,			__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     self.check_model_from_pretrained_configs(**__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     self.check_vision_text_dual_encoder_from_pretrained(**__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     self.check_save_load(**__A					)



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     self.check_vision_text_output_attention(**__A					)



    @is_pt_flax_cross_test
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   self.prepare_config_and_inputs()
     __UpperCAmelCase					=   config_inputs_dict.pop('vision_config'					)
     __UpperCAmelCase					=   config_inputs_dict.pop('text_config'					)

     __UpperCAmelCase					=   config_inputs_dict

     self.check_equivalence_pt_to_flax(__A     ,			__A     ,			__A					)
     self.check_equivalence_flax_to_pt(__A     ,			__A     ,			__A					)




    @slow
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.get_pretrained_model_and_inputs()

     __UpperCAmelCase					=   model_a(**__A					)
     __UpperCAmelCase					=   outputs[0]

     with tempfile.TemporaryDirectory() as tmp_dirname:
      model_a.save_pretrained(__A					)
      __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_pretrained(__A					)

      __UpperCAmelCase					=   model_a(**__A					)
      __UpperCAmelCase					=   after_outputs[0]
      __UpperCAmelCase					=   np.amax(np.abs(out_a - out_a					)					)
      self.assertLessEqual(__A     ,			1E-5					)






@require_flax
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
         'hf-internal-testing/tiny-random-vit'     ,			'hf-internal-testing/tiny-bert'     ,			vision_from_pt=__A     ,			text_from_pt=__A     ,			)
     __UpperCAmelCase					=   13
     __UpperCAmelCase					=   floats_tensor(
         [
             batch_size,
             model.config.vision_config.num_channels,
             model.config.vision_config.image_size,
             model.config.vision_config.image_size,
         ]					)
     __UpperCAmelCase					=   ids_tensor([batch_size, 4]     ,			model.config.text_config.vocab_size					)
     __UpperCAmelCase					=   random_attention_mask([batch_size, 4]					)
     __UpperCAmelCase					=   {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

     return model, inputs



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   FlaxViTModel(__A					)
     __UpperCAmelCase					=   FlaxBertModel(__A					)
     return vision_model, text_model




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   FlaxViTModelTester(self					)
     __UpperCAmelCase					=   FlaxBertModelTester(self					)
     __UpperCAmelCase					=   vit_model_tester.prepare_config_and_inputs()
     __UpperCAmelCase					=   bert_model_tester.prepare_config_and_inputs()

     __UpperCAmelCase					,    __UpperCAmelCase					=   vision_config_and_inputs

     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   text_config_and_inputs

     # make sure that cross attention layers are added
     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": attention_mask,
         "input_ids": input_ids,
         "token_type_ids": token_type_ids,
     }






@require_torch
class UpperCAmelCase							(		UpperCAmelCase_					,			unittest.TestCase							):



    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
         'hf-internal-testing/tiny-random-clip'     ,			'hf-internal-testing/tiny-bert'     ,			vision_from_pt=__A     ,			text_from_pt=__A     ,			)
     __UpperCAmelCase					=   13
     __UpperCAmelCase					=   floats_tensor(
         [
             batch_size,
             model.config.vision_config.num_channels,
             model.config.vision_config.image_size,
             model.config.vision_config.image_size,
         ]					)
     __UpperCAmelCase					=   ids_tensor([batch_size, 4]     ,			model.config.text_config.vocab_size					)
     __UpperCAmelCase					=   random_attention_mask([batch_size, 4]					)
     __UpperCAmelCase					=   {'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

     return model, inputs



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					=   FlaxCLIPVisionModel(__A					)
     __UpperCAmelCase					=   FlaxBertModel(__A					)
     return vision_model, text_model




    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   FlaxCLIPVisionModelTester(self					)
     __UpperCAmelCase					=   FlaxBertModelTester(self					)
     __UpperCAmelCase					=   clip_model_tester.prepare_config_and_inputs()
     __UpperCAmelCase					=   bert_model_tester.prepare_config_and_inputs()

     __UpperCAmelCase					,    __UpperCAmelCase					=   vision_config_and_inputs

     __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					,    __UpperCAmelCase					=   text_config_and_inputs

     # make sure that cross attention layers are added
     return {
         "text_config": text_config,
         "vision_config": vision_config,
         "pixel_values": pixel_values,
         "attention_mask": attention_mask,
         "input_ids": input_ids,
         "token_type_ids": token_type_ids,
     }






@require_flax
@require_vision
class UpperCAmelCase							(		unittest.TestCase							):



    @slow
    def 					__lowerCamelCase						(  self					):
     __UpperCAmelCase					=   FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'     ,			logit_scale_init_value=1.0					)
     __UpperCAmelCase					=   VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'					)

     __UpperCAmelCase					=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
     __UpperCAmelCase					=   processor(
         text=['una foto di un gatto', 'una foto di un cane']     ,			images=__A     ,			padding=__A     ,			return_tensors='np'					)

     __UpperCAmelCase					=   model(**__A					)

     # verify the logits
     self.assertEqual(outputs.logits_per_image.shape     ,			(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])					)
     self.assertEqual(
         outputs.logits_per_text.shape     ,			(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])     ,			)

     __UpperCAmelCase					=   np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]]					)

     self.assertTrue(np.allclose(outputs.logits_per_image     ,			__A     ,			atol=1E-3					)					)












'''simple docstring'''




import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


_A:					int							=				logging.getLogger(__name__)






class UpperCAmelCase							:



    def __init__(  self					):
     __UpperCAmelCase					=   False



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A     ,			__A     ,			__A					):
     if not self.initialized:
      __UpperCAmelCase					=   RagRetriever(
          __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
      __UpperCAmelCase					=   True



    def 					__lowerCamelCase						(  self					):
     self.retriever.index.init_index()




    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     __UpperCAmelCase					,    __UpperCAmelCase					=   self.retriever._main_retrieve(__A     ,			__A					)
     return doc_ids, retrieved_doc_embeds






class UpperCAmelCase							(		UpperCAmelCase_							):



    def __init__(  self     ,			__A     ,			__A     ,			__A     ,			__A     ,			__A=None					):
     if index is not None and index.is_initialized() and len(__A					) > 0:
      raise ValueError(
          'When using Ray for distributed fine-tuning, '
          'you\'ll need to provide the paths instead, '
          'as the dataset and the index are loaded '
          'separately. More info in examples/rag/use_own_knowledge_dataset.py '					)
     super().__init__(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			index=__A     ,			init_retrieval=__A     ,			)
     __UpperCAmelCase					=   retrieval_workers
     if len(self.retrieval_workers					) > 0:
      ray.get(
          [
              worker.create_rag_retriever.remote(__A     ,			__A     ,			__A     ,			__A					)
              for worker in self.retrieval_workers
          ]					)



    def 					__lowerCamelCase						(  self					):
     logger.info('initializing retrieval'					)

     if len(self.retrieval_workers					) > 0:
      ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]					)
     else:
      # Non-distributed training. Load index into this same process.
      self.index.init_index()



    def 					__lowerCamelCase						(  self     ,			__A     ,			__A					):
     if len(self.retrieval_workers					) > 0:
      # Select a random retrieval actor.
      __UpperCAmelCase					=   self.retrieval_workers[random.randint(0     ,			len(self.retrieval_workers					) - 1					)]
      __UpperCAmelCase					,    __UpperCAmelCase					=   ray.get(random_worker.retrieve.remote(__A     ,			__A					)					)
     else:
      __UpperCAmelCase					,    __UpperCAmelCase					=   self._main_retrieve(__A     ,			__A					)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__A					)



    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A=None     ,			**__A					):
     return super(__A     ,			cls					).get_tokenizers(__A     ,			__A     ,			**__A					)




    @classmethod
    def 					__lowerCamelCase						(  cls     ,			__A     ,			__A     ,			__A=None     ,			**__A					):
     __UpperCAmelCase					=   kwargs.pop('config'     ,			__A					) or RagConfig.from_pretrained(__A     ,			**__A					)
     __UpperCAmelCase					=   RagTokenizer.from_pretrained(__A     ,			config=__A					)
     __UpperCAmelCase					=   rag_tokenizer.question_encoder
     __UpperCAmelCase					=   rag_tokenizer.generator
     if indexed_dataset is not None:
      __UpperCAmelCase					=   'custom'
      __UpperCAmelCase					=   CustomHFIndex(config.retrieval_vector_size     ,			__A					)
     else:
      __UpperCAmelCase					=   cls._build_index(__A					)
     return cls(
         __A     ,			question_encoder_tokenizer=__A     ,			generator_tokenizer=__A     ,			retrieval_workers=__A     ,			index=__A     ,			)





