

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 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    if isinstance(_SCREAMING_SNAKE_CASE ,   collections.abc.Iterable       ):
        return x
    return (x, x)



@require_flax
class       __snake_case  :

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						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):
      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderConfig.from_vision_text_configs(_A						,		_A)

      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel(_A)

      SCREAMING_SNAKE_CASE_									=						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):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_vision_text_model(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						{'vision_model': vision_model, 'text_model': text_model}
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

      SCREAMING_SNAKE_CASE_									=						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):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_vision_text_model(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						{'vision_model': vision_model, 'text_model': text_model}
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

      SCREAMING_SNAKE_CASE_									=						model(input_ids=_A						,		pixel_values=_A						,		attention_mask=_A)
      SCREAMING_SNAKE_CASE_									=						output[0]

      with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_pretrained(_A)

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_vision_text_model(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						{'vision_model': vision_model, 'text_model': text_model}
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

      SCREAMING_SNAKE_CASE_									=						model(
          input_ids=_A						,		pixel_values=_A						,		attention_mask=_A						,		output_attentions=_A)

      SCREAMING_SNAKE_CASE_									=						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)
      SCREAMING_SNAKE_CASE_									=						to_atuple(vision_model.config.image_size)
      SCREAMING_SNAKE_CASE_									=						to_atuple(vision_model.config.patch_size)
      SCREAMING_SNAKE_CASE_									=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      SCREAMING_SNAKE_CASE_									=						num_patches + 1
      self.assertEqual(vision_attentions[0].shape[-3:]						,		(vision_config.num_attention_heads, seq_len, seq_len))

      SCREAMING_SNAKE_CASE_									=						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
      SCREAMING_SNAKE_CASE_									=						inputs_dict
      SCREAMING_SNAKE_CASE_									=						{k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_									=						pt_model(**_A).to_tuple()

      SCREAMING_SNAKE_CASE_									=						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)
          SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_pretrained(_A						,		from_pt=_A)

      SCREAMING_SNAKE_CASE_									=						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)
          SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderModel.from_pretrained(_A						,		from_flax=_A)

      pt_model_loaded.to(_A)
      pt_model_loaded.eval()

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_									=						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):
      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderConfig.from_vision_text_configs(_A						,		_A)

      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel(_A)

      SCREAMING_SNAKE_CASE_									=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,		_A)
      SCREAMING_SNAKE_CASE_									=						fx_state

      self.check_pt_flax_equivalence(_A						,		_A						,		_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderConfig.from_vision_text_configs(_A						,		_A)

      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel(_A)

      SCREAMING_SNAKE_CASE_									=						load_flax_weights_in_pytorch_model(_A						,		fx_model.params)

      self.check_pt_flax_equivalence(_A						,		_A						,		_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_save_load(**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**_A)

  @is_pt_flax_cross_test
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_									=						config_inputs_dict.pop('vision_config')
      SCREAMING_SNAKE_CASE_									=						config_inputs_dict.pop('text_config')

      SCREAMING_SNAKE_CASE_									=						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):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_pretrained_model_and_inputs()

      SCREAMING_SNAKE_CASE_									=						model_a(**_A)
      SCREAMING_SNAKE_CASE_									=						outputs[0]

      with tempfile.TemporaryDirectory() as tmp_dirname:
          model_a.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_pretrained(_A)

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






@require_flax
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):

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

      return model, inputs

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						FlaxViTModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModel(_A)
      return vision_model, text_model


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxViTModelTester(self)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModelTester(self)
      SCREAMING_SNAKE_CASE_									=						vit_model_tester.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_									=						bert_model_tester.prepare_config_and_inputs()

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						vision_config_and_inputs

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						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       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):

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

      return model, inputs

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						FlaxCLIPVisionModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModel(_A)
      return vision_model, text_model


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxCLIPVisionModelTester(self)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModelTester(self)
      SCREAMING_SNAKE_CASE_									=						clip_model_tester.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_									=						bert_model_tester.prepare_config_and_inputs()

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						vision_config_and_inputs

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						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       __snake_case  (    unittest.TestCase       ):

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

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

      SCREAMING_SNAKE_CASE_									=						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])						,		)

      SCREAMING_SNAKE_CASE_									=						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))




from typing import List

import numpy as np


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{key: len(_SCREAMING_SNAKE_CASE       ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    if len(set(lists_lengths.values()       )       ) > 1:
        raise RuntimeError(
            (
                'Sharding is ambiguous for this dataset: '
                + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
                + '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()       )
                + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
                + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
            )       )
    SCREAMING_SNAKE_CASE_									=						max(lists_lengths.values() ,   default=0       )
    return max(1 ,   _SCREAMING_SNAKE_CASE       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		int ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						[]
    for group_idx in range(_SCREAMING_SNAKE_CASE       ):
        SCREAMING_SNAKE_CASE_									=						num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
        if num_shards_to_add == 0:
            break
        SCREAMING_SNAKE_CASE_									=						shards_indices_per_group[-1].stop if shards_indices_per_group else 0
        SCREAMING_SNAKE_CASE_									=						range(_SCREAMING_SNAKE_CASE ,   start + num_shards_to_add       )
        shards_indices_per_group.append(_SCREAMING_SNAKE_CASE       )
    return shards_indices_per_group


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		dict ,   _SCREAMING_SNAKE_CASE					:		int       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						_number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE       )
    if num_shards == 1:
        return [dict(_SCREAMING_SNAKE_CASE       )]
    else:
        SCREAMING_SNAKE_CASE_									=						_distribute_shards(num_shards=_SCREAMING_SNAKE_CASE ,   max_num_jobs=_SCREAMING_SNAKE_CASE       )
        return [
            {
                key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )
                else value
                for key, value in gen_kwargs.items()
            }
            for group_idx in range(len(_SCREAMING_SNAKE_CASE       )       )
        ]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		List[dict]       ):





    """simple docstring"""


    return {
        key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
        if isinstance(gen_kwargs_list[0][key] ,   _SCREAMING_SNAKE_CASE       )
        else gen_kwargs_list[0][key]
        for key in gen_kwargs_list[0]
    }





def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		np.random.Generator ,   _SCREAMING_SNAKE_CASE					:		dict       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						{len(_SCREAMING_SNAKE_CASE       ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       )}
    SCREAMING_SNAKE_CASE_									=						{}
    for size in list_sizes:
        SCREAMING_SNAKE_CASE_									=						list(range(_SCREAMING_SNAKE_CASE       )       )
        rng.shuffle(indices_per_size[size]       )
    # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
    SCREAMING_SNAKE_CASE_									=						dict(_SCREAMING_SNAKE_CASE       )
    for key, value in shuffled_kwargs.items():
        if isinstance(_SCREAMING_SNAKE_CASE ,   _SCREAMING_SNAKE_CASE       ):
            SCREAMING_SNAKE_CASE_									=						[value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE       )]]
    return shuffled_kwargs


