
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    A (      lowercase				)     ->						Any:
		'''simple docstring'''

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


@require_flax
class        lowercase      :



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      Optional[Any]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				pass



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  np.abs((a - b) ).max()
				self.assertLessEqual(A_       ,   A_       ,   F'''Difference between torch and flax is {diff} (>= {tol}).''' )



		def __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_=None       ,   **A_ )						->      List[Any]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_=None       ,   **A_ )						->      Dict:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_=None       ,   **A_ )						->      List[Any]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_=None       ,   **A_ )						->      Dict:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      List[str]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      int:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_       ,   A_ )						->      List[Any]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				self.check_model_from_pretrained_configs(**A_ )



		def __UpperCamelCase				(				self )						->      Dict:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				self.check_vision_text_dual_encoder_from_pretrained(**A_ )



		def __UpperCamelCase				(				self )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				self.check_save_load(**A_ )



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				UpperCamelCase						=  self.prepare_config_and_inputs()
				self.check_vision_text_output_attention(**A_ )



		@is_pt_flax_cross_test
		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				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 __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				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        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):



		def __UpperCamelCase				(				self )						->      Optional[Any]:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      List[str]:
				"""simple docstring"""
				UpperCamelCase						=  FlaxViTModel(A_ )
				UpperCamelCase						=  FlaxBertModel(A_ )
				return vision_model, text_model



		def __UpperCamelCase				(				self )						->      List[str]:
				"""simple docstring"""
				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        lowercase      (	_SCREAMING_SNAKE_CASE						,     unittest.TestCase     ):



		def __UpperCamelCase				(				self )						->      Tuple:
				"""simple docstring"""
				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 __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      int:
				"""simple docstring"""
				UpperCamelCase						=  FlaxCLIPVisionModel(A_ )
				UpperCamelCase						=  FlaxBertModel(A_ )
				return vision_model, text_model



		def __UpperCamelCase				(				self )						->      Optional[int]:
				"""simple docstring"""
				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        lowercase      (	unittest.TestCase     ):



		@slow
		def __UpperCamelCase				(				self )						->      str:
				"""simple docstring"""
				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([[1.228_4727, 0.310_4122]] )

				self.assertTrue(np.allclose(outputs.logits_per_image       ,   A_       ,   atol=1e-3 ) )


from random import shuffle

import tensorflow as tf
from numpy import array


def    A (      lowercase					, lowercase				)     ->						Optional[Any]:
		'''simple docstring'''

		UpperCamelCase						=  int(lowercase				)
		assert noofclusters < len(lowercase				)

		# Find out the dimensionality
		UpperCamelCase						=  len(vectors[0]				)

		# Will help select random centroids from among the available vectors
		UpperCamelCase						=  list(range(len(lowercase				)				)				)
		shuffle(lowercase				)

		# GRAPH OF COMPUTATION
		# We initialize a new graph and set it as the default during each run
		# of this algorithm. This ensures that as this function is called
		# multiple times, the default graph doesn't keep getting crowded with
		# unused ops and Variables from previous function calls.

		UpperCamelCase						=  tf.Graph()

		with graph.as_default():
				# SESSION OF COMPUTATION

				UpperCamelCase						=  tf.Session()

				##CONSTRUCTING THE ELEMENTS OF COMPUTATION

				##First lets ensure we have a Variable vector for each centroid,
				##initialized to one of the vectors from the available data points
				UpperCamelCase						=  [
				    tf.Variable(vectors[vector_indices[i]]				) for i in range(lowercase				)
				]
				##These nodes will assign the centroid Variables the appropriate
				##values
				UpperCamelCase						=  tf.placeholder('float64'					, [dim]				)
				UpperCamelCase						=  []
				for centroid in centroids:
						cent_assigns.append(tf.assign(lowercase					, lowercase				)				)

				##Variables for cluster assignments of individual vectors(initialized
				##to 0 at first)
				UpperCamelCase						=  [tf.Variable(0				) for i in range(len(lowercase				)				)]
				##These nodes will assign an assignment Variable the appropriate
				##value
				UpperCamelCase						=  tf.placeholder('int32'				)
				UpperCamelCase						=  []
				for assignment in assignments:
						cluster_assigns.append(tf.assign(lowercase					, lowercase				)				)

				##Now lets construct the node that will compute the mean
				# The placeholder for the input
				UpperCamelCase						=  tf.placeholder('float'					, [None, dim]				)
				# The Node/op takes the input and computes a mean along the 0th
				# dimension, i.e. the list of input vectors
				UpperCamelCase						=  tf.reduce_mean(lowercase					, 0				)

				##Node for computing Euclidean distances
				# Placeholders for input
				UpperCamelCase						=  tf.placeholder('float'					, [dim]				)
				UpperCamelCase						=  tf.placeholder('float'					, [dim]				)
				UpperCamelCase						=  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(lowercase					, lowercase				)					, 2				)				)				)

				##This node will figure out which cluster to assign a vector to,
				##based on Euclidean distances of the vector from the centroids.
				# Placeholder for input
				UpperCamelCase						=  tf.placeholder('float'					, [noofclusters]				)
				UpperCamelCase						=  tf.argmin(lowercase					, 0				)

				##INITIALIZING STATE VARIABLES

				##This will help initialization of all Variables defined with respect
				##to the graph. The Variable-initializer should be defined after
				##all the Variables have been constructed, so that each of them
				##will be included in the initialization.
				UpperCamelCase						=  tf.initialize_all_variables()

				# Initialize all variables
				sess.run(lowercase				)

				##CLUSTERING ITERATIONS

				# Now perform the Expectation-Maximization steps of K-Means clustering
				# iterations. To keep things simple, we will only do a set number of
				# iterations, instead of using a Stopping Criterion.
				UpperCamelCase						=  100
				for _ in range(lowercase				):
						##EXPECTATION STEP
						##Based on the centroid locations till last iteration, compute
						##the _expected_ centroid assignments.
						# Iterate over each vector
						for vector_n in range(len(lowercase				)				):
								UpperCamelCase						=  vectors[vector_n]
								# Compute Euclidean distance between this vector and each
								# centroid. Remember that this list cannot be named
								#'centroid_distances', since that is the input to the
								# cluster assignment node.
								UpperCamelCase						=  [
								    sess.run(lowercase					, feed_dict={va: vect, va: sess.run(lowercase				)}				)
								    for centroid in centroids
								]
								# Now use the cluster assignment node, with the distances
								# as the input
								UpperCamelCase						=  sess.run(
								    lowercase					, feed_dict={centroid_distances: distances}				)
								# Now assign the value to the appropriate state variable
								sess.run(
								    cluster_assigns[vector_n]					, feed_dict={assignment_value: assignment}				)

						##MAXIMIZATION STEP
						# Based on the expected state computed from the Expectation Step,
						# compute the locations of the centroids so as to maximize the
						# overall objective of minimizing within-cluster Sum-of-Squares
						for cluster_n in range(lowercase				):
								# Collect all the vectors assigned to this cluster
								UpperCamelCase						=  [
								    vectors[i]
								    for i in range(len(lowercase				)				)
								    if sess.run(assignments[i]				) == cluster_n
								]
								# Compute new centroid location
								UpperCamelCase						=  sess.run(
								    lowercase					, feed_dict={mean_input: array(lowercase				)}				)
								# Assign value to appropriate variable
								sess.run(
								    cent_assigns[cluster_n]					, feed_dict={centroid_value: new_location}				)

        # Return centroids and assignments
				UpperCamelCase						=  sess.run(lowercase				)
				UpperCamelCase						=  sess.run(lowercase				)
				return centroids, assignments
