


"""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			(	_UpperCamelCase   ):

			'''simple docstring'''




			if isinstance(_UpperCamelCase					,				collections.abc.Iterable   ):
						return x
			return (x, x)
@require_flax
class 						_UpperCamelCase						:
							'''simple docstring'''







							def     snake_case       (  self					,     __a					,     __a			):
										pass

							def     snake_case       (  self			):
										pass

							def     snake_case       (  self			):
										pass

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    np.abs((a - b)			).max()
										self.assertLessEqual(__a					,     __a					,     f"Difference between torch and flax is {diff} (>= {tol})."			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										__lowerCAmelCase       					=    VisionTextDualEncoderConfig.from_vision_text_configs(__a					,     __a			)

										__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel(__a			)

										__lowerCAmelCase       					=    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     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.get_vision_text_model(__a					,     __a			)
										__lowerCAmelCase       					=    {"vision_model": vision_model, "text_model": text_model}
										__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__a			)

										__lowerCAmelCase       					=    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     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.get_vision_text_model(__a					,     __a			)
										__lowerCAmelCase       					=    {"vision_model": vision_model, "text_model": text_model}
										__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__a			)

										__lowerCAmelCase       					=    model(input_ids=__a					,     pixel_values=__a					,     attention_mask=__a			)
										__lowerCAmelCase       					=    output[0]

										with tempfile.TemporaryDirectory() as tmpdirname:
													model.save_pretrained(__a			)
													__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel.from_pretrained(__a			)

													__lowerCAmelCase       					=    model(input_ids=__a					,     pixel_values=__a					,     attention_mask=__a			)
													__lowerCAmelCase       					=    after_output[0]
													__lowerCAmelCase       					=    np.amax(np.abs(out_a - out_a			)			)
													self.assertLessEqual(__a					,     1e-3			)

							def     snake_case       (  self					,     __a					,     __a					,     __a					,     __a					,     __a=None					,     **__a			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.get_vision_text_model(__a					,     __a			)
										__lowerCAmelCase       					=    {"vision_model": vision_model, "text_model": text_model}
										__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**__a			)

										__lowerCAmelCase       					=    model(
										    input_ids=__a					,     pixel_values=__a					,     attention_mask=__a					,     output_attentions=__a			)

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

										__lowerCAmelCase       					=    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     snake_case       (  self					,     __a					,     __a					,     __a			):
										pt_model.to(__a			)
										pt_model.eval()

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

										with torch.no_grad():
													__lowerCAmelCase       					=    pt_model(**__a			).to_tuple()

										__lowerCAmelCase       					=    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			)
													__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel.from_pretrained(__a					,     from_pt=__a			)

										__lowerCAmelCase       					=    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			)
													__lowerCAmelCase       					=    VisionTextDualEncoderModel.from_pretrained(__a					,     from_flax=__a			)

										pt_model_loaded.to(__a			)
										pt_model_loaded.eval()

										with torch.no_grad():
													__lowerCAmelCase       					=    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     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    VisionTextDualEncoderConfig.from_vision_text_configs(__a					,     __a			)

										__lowerCAmelCase       					=    VisionTextDualEncoderModel(__a			)
										__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel(__a			)

										__lowerCAmelCase       					=    convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,     __a			)
										__lowerCAmelCase       					=    fx_state

										self.check_pt_flax_equivalence(__a					,     __a					,     __a			)

							def     snake_case       (  self					,     __a					,     __a					,     __a			):
										__lowerCAmelCase       					=    VisionTextDualEncoderConfig.from_vision_text_configs(__a					,     __a			)

										__lowerCAmelCase       					=    VisionTextDualEncoderModel(__a			)
										__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel(__a			)

										__lowerCAmelCase       					=    load_flax_weights_in_pytorch_model(__a					,     fx_model.params			)

										self.check_pt_flax_equivalence(__a					,     __a					,     __a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										self.check_model_from_pretrained_configs(**__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										self.check_vision_text_dual_encoder_from_pretrained(**__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										self.check_save_load(**__a			)

							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										self.check_vision_text_output_attention(**__a			)

							@is_pt_flax_cross_test
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    self.prepare_config_and_inputs()
										__lowerCAmelCase       					=    config_inputs_dict.pop("vision_config"			)
										__lowerCAmelCase       					=    config_inputs_dict.pop("text_config"			)

										__lowerCAmelCase       					=    config_inputs_dict

										self.check_equivalence_pt_to_flax(__a					,     __a					,     __a			)
										self.check_equivalence_flax_to_pt(__a					,     __a					,     __a			)



							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase					,			__lowerCAmelCase       					=    self.get_pretrained_model_and_inputs()

										__lowerCAmelCase       					=    model_a(**__a			)
										__lowerCAmelCase       					=    outputs[0]

										with tempfile.TemporaryDirectory() as tmp_dirname:
													model_a.save_pretrained(__a			)
													__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel.from_pretrained(__a			)

													__lowerCAmelCase       					=    model_a(**__a			)
													__lowerCAmelCase       					=    after_outputs[0]
													__lowerCAmelCase       					=    np.amax(np.abs(out_a - out_a			)			)
													self.assertLessEqual(__a					,     1e-5			)
@require_flax
class 						_UpperCamelCase						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







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

										return model, inputs

							def     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    FlaxViTModel(__a			)
										__lowerCAmelCase       					=    FlaxBertModel(__a			)
										return vision_model, text_model



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    FlaxViTModelTester(self			)
										__lowerCAmelCase       					=    FlaxBertModelTester(self			)
										__lowerCAmelCase       					=    vit_model_tester.prepare_config_and_inputs()
										__lowerCAmelCase       					=    bert_model_tester.prepare_config_and_inputs()

										__lowerCAmelCase					,			__lowerCAmelCase       					=    vision_config_and_inputs

										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    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						(     lowerCAmelCase__ ,unittest.TestCase	):
							'''simple docstring'''







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

										return model, inputs

							def     snake_case       (  self					,     __a					,     __a			):
										__lowerCAmelCase       					=    FlaxCLIPVisionModel(__a			)
										__lowerCAmelCase       					=    FlaxBertModel(__a			)
										return vision_model, text_model



							def     snake_case       (  self			):
										__lowerCAmelCase       					=    FlaxCLIPVisionModelTester(self			)
										__lowerCAmelCase       					=    FlaxBertModelTester(self			)
										__lowerCAmelCase       					=    clip_model_tester.prepare_config_and_inputs()
										__lowerCAmelCase       					=    bert_model_tester.prepare_config_and_inputs()

										__lowerCAmelCase					,			__lowerCAmelCase       					=    vision_config_and_inputs

										__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    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	):
							'''simple docstring'''







							@slow
							def     snake_case       (  self			):
										__lowerCAmelCase       					=    FlaxVisionTextDualEncoderModel.from_pretrained("clip-italian/clip-italian"					,     logit_scale_init_value=1.0			)
										__lowerCAmelCase       					=    VisionTextDualEncoderProcessor.from_pretrained("clip-italian/clip-italian"			)

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

										__lowerCAmelCase       					=    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])					,     )

										__lowerCAmelCase       					=    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"""


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__lowerCAmelCase       					=    len(_UpperCamelCase   )
			print("The following activities are selected:"   )

			# The first activity is always selected
			__lowerCAmelCase       					=    0
			print(_UpperCamelCase					,				end=","   )

			# Consider rest of the activities
			for j in range(_UpperCamelCase   ):
						# If this activity has start time greater than
						# or equal to the finish time of previously
						# selected activity, then select it
						if start[j] >= finish[i]:
									print(_UpperCamelCase					,				end=","   )
									__lowerCAmelCase       					=    j


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	A  :     Dict    					=    [1, 3, 0, 5, 8, 5]
	A  :     int    					=    [2, 4, 6, 7, 9, 9]
	print_max_activities(start, finish)




